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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
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# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
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__pypackages__/
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celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
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.spyderproject
.spyproject
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/site
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.mypy_cache/
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dmypy.json
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.pytype/
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cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea
*.pt
*.pth
*.ckpt
*.bin
*.safetensors
# Editor setting metadata
.idea/
.vscode/
detected_maps/
annotator/downloads/
# test results and expectations
web_tests/results/
web_tests/expectations/
tests/web_api/full_coverage/results/
tests/web_api/full_coverage/expectations/
*_diff.png
# Presets
presets/
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annotator/hand_refiner_portable

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Also add information on how to contact you by electronic and paper mail.
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# ControlNet for Stable Diffusion WebUI
The WebUI extension for ControlNet and other injection-based SD controls.
![image](https://github.com/Mikubill/sd-webui-controlnet/assets/20929282/51172d20-606b-4b9f-aba5-db2f2417cb0b)
This extension is for AUTOMATIC1111's [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), allows the Web UI to add [ControlNet](https://github.com/lllyasviel/ControlNet) to the original Stable Diffusion model to generate images. The addition is on-the-fly, the merging is not required.
# Installation
1. Open "Extensions" tab.
2. Open "Install from URL" tab in the tab.
3. Enter `https://github.com/Mikubill/sd-webui-controlnet.git` to "URL for extension's git repository".
4. Press "Install" button.
5. Wait for 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\sd-webui-controlnet. Use Installed tab to restart".
6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use these buttons to update ControlNet.)
7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer to achieve the same effect.)
8. Download models (see below).
9. After you put models in the correct folder, you may need to refresh to see the models. The refresh button is right to your "Model" dropdown.
# Download Models
Right now all the 14 models of ControlNet 1.1 are in the beta test.
Download the models from ControlNet 1.1: https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main
You need to download model files ending with ".pth" .
Put models in your "stable-diffusion-webui\extensions\sd-webui-controlnet\models". You only need to download "pth" files.
Do not right-click the filenames in HuggingFace website to download. Some users right-clicked those HuggingFace HTML websites and saved those HTML pages as PTH/YAML files. They are not downloading correct files. Instead, please click the small download arrow “↓” icon in HuggingFace to download.
# Download Models for SDXL
See instructions [here](https://github.com/Mikubill/sd-webui-controlnet/discussions/2039).
# Features in ControlNet 1.1
### Perfect Support for All ControlNet 1.0/1.1 and T2I Adapter Models.
Now we have perfect support all available models and preprocessors, including perfect support for T2I style adapter and ControlNet 1.1 Shuffle. (Make sure that your YAML file names and model file names are same, see also YAML files in "stable-diffusion-webui\extensions\sd-webui-controlnet\models".)
### Perfect Support for A1111 High-Res. Fix
Now if you turn on High-Res Fix in A1111, each controlnet will output two different control images: a small one and a large one. The small one is for your basic generating, and the big one is for your High-Res Fix generating. The two control images are computed by a smart algorithm called "super high-quality control image resampling". This is turned on by default, and you do not need to change any setting.
### Perfect Support for All A1111 Img2Img or Inpaint Settings and All Mask Types
Now ControlNet is extensively tested with A1111's different types of masks, including "Inpaint masked"/"Inpaint not masked", and "Whole picture"/"Only masked", and "Only masked padding"&"Mask blur". The resizing perfectly matches A1111's "Just resize"/"Crop and resize"/"Resize and fill". This means you can use ControlNet in nearly everywhere in your A1111 UI without difficulty!
### The New "Pixel-Perfect" Mode
Now if you turn on pixel-perfect mode, you do not need to set preprocessor (annotator) resolutions manually. The ControlNet will automatically compute the best annotator resolution for you so that each pixel perfectly matches Stable Diffusion.
### User-Friendly GUI and Preprocessor Preview
We reorganized some previously confusing UI like "canvas width/height for new canvas" and it is in the 📝 button now. Now the preview GUI is controlled by the "allow preview" option and the trigger button 💥. The preview image size is better than before, and you do not need to scroll up and down - your a1111 GUI will not be messed up anymore!
### Support for Almost All Upscaling Scripts
Now ControlNet 1.1 can support almost all Upscaling/Tile methods. ControlNet 1.1 support the script "Ultimate SD upscale" and almost all other tile-based extensions. Please do not confuse ["Ultimate SD upscale"](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111) with "SD upscale" - they are different scripts. Note that the most recommended upscaling method is ["Tiled VAE/Diffusion"](https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111) but we test as many methods/extensions as possible. Note that "SD upscale" is supported since 1.1.117, and if you use it, you need to leave all ControlNet images as blank (We do not recommend "SD upscale" since it is somewhat buggy and cannot be maintained - use the "Ultimate SD upscale" instead).
### More Control Modes (previously called Guess Mode)
We have fixed many bugs in previous 1.0’s Guess Mode and now it is called Control Mode
![image](https://user-images.githubusercontent.com/19834515/236641759-6c44ddf6-c7ad-4bda-92be-e90a52911d75.png)
Now you can control which aspect is more important (your prompt or your ControlNet)：
* "Balanced": ControlNet on both sides of CFG scale, same as turning off "Guess Mode" in ControlNet 1.0
* "My prompt is more important": ControlNet on both sides of CFG scale, with progressively reduced SD U-Net injections (layer_weight*=0.825**I, where 0<=I <13, and the 13 means ControlNet injected SD 13 times). In this way, you can make sure that your prompts are perfectly displayed in your generated images.
* "ControlNet is more important": ControlNet only on the Conditional Side of CFG scale (the cond in A1111's batch-cond-uncond). This means the ControlNet will be X times stronger if your cfg-scale is X. For example, if your cfg-scale is 7, then ControlNet is 7 times stronger. Note that here the X times stronger is different from "Control Weights" since your weights are not modified. This "stronger" effect usually has less artifact and give ControlNet more room to guess what is missing from your prompts (and in the previous 1.0, it is called "Guess Mode").
<table width="100%">
<tr>
<td width="25%" style="text-align: center">Input (depth+canny+hed)</td>
<td width="25%" style="text-align: center">"Balanced"</td>
<td width="25%" style="text-align: center">"My prompt is more important"</td>
<td width="25%" style="text-align: center">"ControlNet is more important"</td>
</tr>
<tr>
<td width="25%" style="text-align: center"><img src="samples/cm1.png"></td>
<td width="25%" style="text-align: center"><img src="samples/cm2.png"></td>
<td width="25%" style="text-align: center"><img src="samples/cm3.png"></td>
<td width="25%" style="text-align: center"><img src="samples/cm4.png"></td>
</tr>
</table>
### Reference-Only Control
Now we have a `reference-only` preprocessor that does not require any control models. It can guide the diffusion directly using images as references.
(Prompt "a dog running on grassland, best quality, ...")
![image](samples/ref.png)
This method is similar to inpaint-based reference but it does not make your image disordered.
Many professional A1111 users know a trick to diffuse image with references by inpaint. For example, if you have a 512x512 image of a dog, and want to generate another 512x512 image with the same dog, some users will connect the 512x512 dog image and a 512x512 blank image into a 1024x512 image, send to inpaint, and mask out the blank 512x512 part to diffuse a dog with similar appearance. However, that method is usually not very satisfying since images are connected and many distortions will appear.
This `reference-only` ControlNet can directly link the attention layers of your SD to any independent images, so that your SD will read arbitrary images for reference. You need at least ControlNet 1.1.153 to use it.
To use, just select `reference-only` as preprocessor and put an image. Your SD will just use the image as reference.
*Note that this method is as "non-opinioned" as possible. It only contains very basic connection codes, without any personal preferences, to connect the attention layers with your reference images. However, even if we tried best to not include any opinioned codes, we still need to write some subjective implementations to deal with weighting, cfg-scale, etc - tech report is on the way.*
More examples [here](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236).
# Technical Documents
See also the documents of ControlNet 1.1:
https://github.com/lllyasviel/ControlNet-v1-1-nightly#model-specification
# Default Setting
This is my setting. If you run into any problem, you can use this setting as a sanity check
![image](https://user-images.githubusercontent.com/19834515/235620638-17937171-8ac1-45bc-a3cb-3aebf605b4ef.png)
# Use Previous Models
### Use ControlNet 1.0 Models
https://huggingface.co/lllyasviel/ControlNet/tree/main/models
You can still use all previous models in the previous ControlNet 1.0. Now, the previous "depth" is now called "depth_midas", the previous "normal" is called "normal_midas", the previous "hed" is called "softedge_hed". And starting from 1.1, all line maps, edge maps, lineart maps, boundary maps will have black background and white lines.
### Use T2I-Adapter Models
(From TencentARC/T2I-Adapter)
To use T2I-Adapter models:
1. Download files from https://huggingface.co/TencentARC/T2I-Adapter/tree/main/models
2. Put them in "stable-diffusion-webui\extensions\sd-webui-controlnet\models".
3. Make sure that the file names of pth files and yaml files are consistent.
*Note that "CoAdapter" is not implemented yet.*
# Gallery
The below results are from ControlNet 1.0.
| Source | Input | Output |
|:-------------------------:|:-------------------------:|:-------------------------:|
| (no preprocessor) | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/bal-source.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/bal-gen.png?raw=true"> |
| (no preprocessor) | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/dog_rel.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/dog_rel.png?raw=true"> |
|<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro_input.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro_canny.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro-out.png?raw=true"> |
|<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_source.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_hed.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_gen.png?raw=true"> |
|<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-source.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-pose.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-gen.png?raw=true"> |
|<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-src.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-dep.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-out.png?raw=true"> |
The below examples are from T2I-Adapter.
From `t2iadapter_color_sd14v1.pth` :
| Source | Input | Output |
|:-------------------------:|:-------------------------:|:-------------------------:|
| <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947416-ec9e52a4-a1d0-48d8-bb81-736bf636145e.jpeg"> | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947435-1164e7d8-d857-42f9-ab10-2d4a4b25f33a.png"> | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947557-5520d5f8-88b4-474d-a576-5c9cd3acac3a.png"> |
From `t2iadapter_style_sd14v1.pth` :
| Source | Input | Output |
|:-------------------------:|:-------------------------:|:-------------------------:|
| <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947416-ec9e52a4-a1d0-48d8-bb81-736bf636145e.jpeg"> | (clip, non-image) | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222965711-7b884c9e-7095-45cb-a91c-e50d296ba3a2.png"> |
# Minimum Requirements
* (Windows) (NVIDIA: Ampere) 4gb - with `--xformers` enabled, and `Low VRAM` mode ticked in the UI, goes up to 768x832
# Multi-ControlNet
This option allows multiple ControlNet inputs for a single generation. To enable this option, change `Multi ControlNet: Max models amount (requires restart)` in the settings. Note that you will need to restart the WebUI for changes to take effect.
<table width="100%">
<tr>
<td width="25%" style="text-align: center">Source A</td>
<td width="25%" style="text-align: center">Source B</td>
<td width="25%" style="text-align: center">Output</td>
</tr>
<tr>
<td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448620-cd3ede92-8d3f-43d5-b771-32dd8417618f.png"></td>
<td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448619-beed9bdb-f6bb-41c2-a7df-aa3ef1f653c5.png"></td>
<td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448613-c99a9e04-0450-40fd-bc73-a9122cefaa2c.png"></td>
</tr>
</table>
# Control Weight/Start/End
Weight is the weight of the controlnet "influence". It's analogous to prompt attention/emphasis. E.g. (myprompt: 1.2). Technically, it's the factor by which to multiply the ControlNet outputs before merging them with original SD Unet.
Guidance Start/End is the percentage of total steps the controlnet applies (guidance strength = guidance end). It's analogous to prompt editing/shifting. E.g. \[myprompt::0.8\] (It applies from the beginning until 80% of total steps)
# Batch Mode
Put any unit into batch mode to activate batch mode for all units. Specify a batch directory for each unit, or use the new textbox in the img2img batch tab as a fallback. Although the textbox is located in the img2img batch tab, you can use it to generate images in the txt2img tab as well.
Note that this feature is only available in the gradio user interface. Call the APIs as many times as you want for custom batch scheduling.
# API and Script Access
This extension can accept txt2img or img2img tasks via API or external extension call. Note that you may need to enable `Allow other scripts to control this extension` in settings for external calls.
To use the API: start WebUI with argument `--api` and go to `http://webui-address/docs` for documents or checkout [examples](https://github.com/Mikubill/sd-webui-controlnet/blob/main/example/txt2img_example/api_txt2img.py).
To use external call: Checkout [Wiki](https://github.com/Mikubill/sd-webui-controlnet/wiki/API)
# Command Line Arguments
This extension adds these command line arguments to the webui:
```
--controlnet-dir <path to directory with controlnet models> ADD a controlnet models directory
--controlnet-annotator-models-path <path to directory with annotator model directories> SET the directory for annotator models
--no-half-controlnet load controlnet models in full precision
--controlnet-preprocessor-cache-size Cache size for controlnet preprocessor results
--controlnet-loglevel Log level for the controlnet extension
--controlnet-tracemalloc Enable malloc memory tracing
```
# MacOS Support
Tested with pytorch nightly: https://github.com/Mikubill/sd-webui-controlnet/pull/143#issuecomment-1435058285
To use this extension with mps and normal pytorch, currently you may need to start WebUI with `--no-half`.
# Archive of Deprecated Versions
The previous version (sd-webui-controlnet 1.0) is archived in
https://github.com/lllyasviel/webui-controlnet-v1-archived
Using this version is not a temporary stop of updates. You will stop all updates forever.
Please consider this version if you work with professional studios that requires 100% reproducing of all previous results pixel by pixel.
# Thanks
This implementation is inspired by kohya-ss/sd-webui-additional-networks

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MIT License
Copyright (c) 2021 Miaomiao Li
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
import fnmatch
import cv2
import sys
import numpy as np
from modules import devices
from einops import rearrange
from annotator.annotator_path import models_path
import torchvision
from torchvision.models import MobileNet_V2_Weights
from torchvision import transforms
COLOR_BACKGROUND = (255,255,0)
COLOR_HAIR = (0,0,255)
COLOR_EYE = (255,0,0)
COLOR_MOUTH = (255,255,255)
COLOR_FACE = (0,255,0)
COLOR_SKIN = (0,255,255)
COLOR_CLOTHES = (255,0,255)
PALETTE = [COLOR_BACKGROUND,COLOR_HAIR,COLOR_EYE,COLOR_MOUTH,COLOR_FACE,COLOR_SKIN,COLOR_CLOTHES]
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.NUM_SEG_CLASSES = 7 # Background, hair, face, eye, mouth, skin, clothes
mobilenet_v2 = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.IMAGENET1K_V1)
mob_blocks = mobilenet_v2.features
# Encoder
self.en_block0 = nn.Sequential( # in_ch=3 out_ch=16
mob_blocks[0],
mob_blocks[1]
)
self.en_block1 = nn.Sequential( # in_ch=16 out_ch=24
mob_blocks[2],
mob_blocks[3],
)
self.en_block2 = nn.Sequential( # in_ch=24 out_ch=32
mob_blocks[4],
mob_blocks[5],
mob_blocks[6],
)
self.en_block3 = nn.Sequential( # in_ch=32 out_ch=96
mob_blocks[7],
mob_blocks[8],
mob_blocks[9],
mob_blocks[10],
mob_blocks[11],
mob_blocks[12],
mob_blocks[13],
)
self.en_block4 = nn.Sequential( # in_ch=96 out_ch=160
mob_blocks[14],
mob_blocks[15],
mob_blocks[16],
)
# Decoder
self.de_block4 = nn.Sequential( # in_ch=160 out_ch=96
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(160, 96, kernel_size=3, padding=1),
nn.InstanceNorm2d(96),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block3 = nn.Sequential( # in_ch=96x2 out_ch=32
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(96*2, 32, kernel_size=3, padding=1),
nn.InstanceNorm2d(32),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block2 = nn.Sequential( # in_ch=32x2 out_ch=24
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(32*2, 24, kernel_size=3, padding=1),
nn.InstanceNorm2d(24),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block1 = nn.Sequential( # in_ch=24x2 out_ch=16
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(24*2, 16, kernel_size=3, padding=1),
nn.InstanceNorm2d(16),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block0 = nn.Sequential( # in_ch=16x2 out_ch=7
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(16*2, self.NUM_SEG_CLASSES, kernel_size=3, padding=1),
nn.Softmax2d()
)
def forward(self, x):
e0 = self.en_block0(x)
e1 = self.en_block1(e0)
e2 = self.en_block2(e1)
e3 = self.en_block3(e2)
e4 = self.en_block4(e3)
d4 = self.de_block4(e4)
d4 = F.interpolate(d4, size=e3.size()[2:], mode='bilinear', align_corners=True)
c4 = torch.cat((d4,e3),1)
d3 = self.de_block3(c4)
d3 = F.interpolate(d3, size=e2.size()[2:], mode='bilinear', align_corners=True)
c3 = torch.cat((d3,e2),1)
d2 = self.de_block2(c3)
d2 = F.interpolate(d2, size=e1.size()[2:], mode='bilinear', align_corners=True)
c2 =torch.cat((d2,e1),1)
d1 = self.de_block1(c2)
d1 = F.interpolate(d1, size=e0.size()[2:], mode='bilinear', align_corners=True)
c1 = torch.cat((d1,e0),1)
y = self.de_block0(c1)
return y
class AnimeFaceSegment:
model_dir = os.path.join(models_path, "anime_face_segment")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/UNet.pth"
modelpath = os.path.join(self.model_dir, "UNet.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
net = UNet()
ckpt = torch.load(modelpath, map_location=self.device)
for key in list(ckpt.keys()):
if 'module.' in key:
ckpt[key.replace('module.', '')] = ckpt[key]
del ckpt[key]
net.load_state_dict(ckpt)
net.eval()
self.model = net.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
transform = transforms.Compose([
transforms.Resize(512,interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),])
img = Image.fromarray(input_image)
with torch.no_grad():
img = transform(img).unsqueeze(dim=0).to(self.device)
seg = self.model(img).squeeze(dim=0)
seg = seg.cpu().detach().numpy()
img = rearrange(seg,'h w c -> w c h')
img = [[PALETTE[np.argmax(val)] for val in buf]for buf in img]
return np.array(img).astype(np.uint8)

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extensions-builtin/sd_forge_controlnet/annotator/annotator_path.py Normal file
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import os
from modules import shared
models_path = shared.opts.data.get('control_net_modules_path', None)
if not models_path:
models_path = getattr(shared.cmd_opts, 'controlnet_annotator_models_path', None)
if not models_path:
models_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'downloads')
if not os.path.isabs(models_path):
models_path = os.path.join(shared.data_path, models_path)
clip_vision_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision')
# clip vision is always inside controlnet "extensions\sd-webui-controlnet"
# and any problem can be solved by removing controlnet and reinstall
models_path = os.path.realpath(models_path)
os.makedirs(models_path, exist_ok=True)
print(f'ControlNet preprocessor location: {models_path}')
# Make sure that the default location is inside controlnet "extensions\sd-webui-controlnet"
# so that any problem can be solved by removing controlnet and reinstall
# if users do not change configs on their own (otherwise users will know what is wrong)

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extensions-builtin/sd_forge_controlnet/annotator/binary/__init__.py Normal file
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import cv2
def apply_binary(img, bin_threshold):
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if bin_threshold == 0 or bin_threshold == 255:
# Otsu's threshold
otsu_threshold, img_bin = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
print("Otsu threshold:", otsu_threshold)
else:
_, img_bin = cv2.threshold(img_gray, bin_threshold, 255, cv2.THRESH_BINARY_INV)
return cv2.cvtColor(img_bin, cv2.COLOR_GRAY2RGB)

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extensions-builtin/sd_forge_controlnet/annotator/canny/__init__.py Normal file
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import cv2
def apply_canny(img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)

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extensions-builtin/sd_forge_controlnet/annotator/clipvision/__init__.py Normal file
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import os
import cv2
import torch
from modules import devices
from modules.modelloader import load_file_from_url
from annotator.annotator_path import models_path
from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor
config_clip_g = {
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "gelu",
"hidden_size": 1664,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 8192,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 48,
"patch_size": 14,
"projection_dim": 1280,
"torch_dtype": "float32"
}
config_clip_h = {
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "gelu",
"hidden_size": 1280,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 32,
"patch_size": 14,
"projection_dim": 1024,
"torch_dtype": "float32"
}
config_clip_vitl = {
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "quick_gelu",
"hidden_size": 1024,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dim": 768,
"torch_dtype": "float32"
}
configs = {
'clip_g': config_clip_g,
'clip_h': config_clip_h,
'clip_vitl': config_clip_vitl,
}
downloads = {
'clip_vitl': 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin',
'clip_g': 'https://huggingface.co/lllyasviel/Annotators/resolve/main/clip_g.pth',
'clip_h': 'https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin'
}
clip_vision_h_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_h_uc.data')
clip_vision_h_uc = torch.load(clip_vision_h_uc, map_location=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))['uc']
clip_vision_vith_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_vith_uc.data')
clip_vision_vith_uc = torch.load(clip_vision_vith_uc, map_location=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))['uc']
class ClipVisionDetector:
def __init__(self, config, low_vram: bool):
assert config in downloads
self.download_link = downloads[config]
self.model_path = os.path.join(models_path, 'clip_vision')
self.file_name = config + '.pth'
self.config = configs[config]
self.device = (
torch.device("cpu") if low_vram else
devices.get_device_for("controlnet")
)
os.makedirs(self.model_path, exist_ok=True)
file_path = os.path.join(self.model_path, self.file_name)
if not os.path.exists(file_path):
load_file_from_url(url=self.download_link, model_dir=self.model_path, file_name=self.file_name)
config = CLIPVisionConfig(**self.config)
self.model = CLIPVisionModelWithProjection(config)
self.processor = CLIPImageProcessor(crop_size=224,
do_center_crop=True,
do_convert_rgb=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224)
sd = torch.load(file_path, map_location=self.device)
self.model.load_state_dict(sd, strict=False)
del sd
self.model.to(self.device)
self.model.eval()
def unload_model(self):
if self.model is not None:
self.model.to('meta')
def __call__(self, input_image):
with torch.no_grad():
input_image = cv2.resize(input_image, (224, 224), interpolation=cv2.INTER_AREA)
feat = self.processor(images=input_image, return_tensors="pt")
feat['pixel_values'] = feat['pixel_values'].to(self.device)
result = self.model(**feat, output_hidden_states=True)
result['hidden_states'] = [v.to(self.device) for v in result['hidden_states']]
result = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in result.items()}
return result

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extensions-builtin/sd_forge_controlnet/annotator/color/__init__.py Normal file
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import cv2
def cv2_resize_shortest_edge(image, size):
h, w = image.shape[:2]
if h < w:
new_h = size
new_w = int(round(w / h * size))
else:
new_w = size
new_h = int(round(h / w * size))
resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
return resized_image
def apply_color(img, res=512):
img = cv2_resize_shortest_edge(img, res)
h, w = img.shape[:2]
input_img_color = cv2.resize(img, (w//64, h//64), interpolation=cv2.INTER_CUBIC)
input_img_color = cv2.resize(input_img_color, (w, h), interpolation=cv2.INTER_NEAREST)
return input_img_color

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extensions-builtin/sd_forge_controlnet/annotator/densepose/__init__.py Normal file
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import torchvision # Fix issue Unknown builtin op: torchvision::nms
import cv2
import numpy as np
import torch
from einops import rearrange
from .densepose import DensePoseMaskedColormapResultsVisualizer, _extract_i_from_iuvarr, densepose_chart_predictor_output_to_result_with_confidences
from modules import devices
from annotator.annotator_path import models_path
import os
N_PART_LABELS = 24
result_visualizer = DensePoseMaskedColormapResultsVisualizer(
alpha=1,
data_extractor=_extract_i_from_iuvarr,
segm_extractor=_extract_i_from_iuvarr,
val_scale = 255.0 / N_PART_LABELS
)
remote_torchscript_path = "https://huggingface.co/LayerNorm/DensePose-TorchScript-with-hint-image/resolve/main/densepose_r50_fpn_dl.torchscript"
torchscript_model = None
model_dir = os.path.join(models_path, "densepose")
def apply_densepose(input_image, cmap="viridis"):
global torchscript_model
if torchscript_model is None:
model_path = os.path.join(model_dir, "densepose_r50_fpn_dl.torchscript")
if not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_torchscript_path, model_dir=model_dir)
torchscript_model = torch.jit.load(model_path, map_location="cpu").to(devices.get_device_for("controlnet")).eval()
H, W = input_image.shape[:2]
hint_image_canvas = np.zeros([H, W], dtype=np.uint8)
hint_image_canvas = np.tile(hint_image_canvas[:, :, np.newaxis], [1, 1, 3])
input_image = rearrange(torch.from_numpy(input_image).to(devices.get_device_for("controlnet")), 'h w c -> c h w')
pred_boxes, corase_segm, fine_segm, u, v = torchscript_model(input_image)
extractor = densepose_chart_predictor_output_to_result_with_confidences
densepose_results = [extractor(pred_boxes[i:i+1], corase_segm[i:i+1], fine_segm[i:i+1], u[i:i+1], v[i:i+1]) for i in range(len(pred_boxes))]
if cmap=="viridis":
result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_VIRIDIS
hint_image = result_visualizer.visualize(hint_image_canvas, densepose_results)
hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB)
hint_image[:, :, 0][hint_image[:, :, 0] == 0] = 68
hint_image[:, :, 1][hint_image[:, :, 1] == 0] = 1
hint_image[:, :, 2][hint_image[:, :, 2] == 0] = 84
else:
result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_PARULA
hint_image = result_visualizer.visualize(hint_image_canvas, densepose_results)
hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB)
return hint_image
def unload_model():
global torchscript_model
if torchscript_model is not None:
torchscript_model.cpu()

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from typing import Tuple
import math
import numpy as np
from enum import IntEnum
from typing import List, Tuple, Union
import torch
from torch.nn import functional as F
import logging
import cv2
Image = np.ndarray
Boxes = torch.Tensor
ImageSizeType = Tuple[int, int]
_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray]
IntTupleBox = Tuple[int, int, int, int]
class BoxMode(IntEnum):
"""
Enum of different ways to represent a box.
"""
XYXY_ABS = 0
"""
(x0, y0, x1, y1) in absolute floating points coordinates.
The coordinates in range [0, width or height].
"""
XYWH_ABS = 1
"""
(x0, y0, w, h) in absolute floating points coordinates.
"""
XYXY_REL = 2
"""
Not yet supported!
(x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.
"""
XYWH_REL = 3
"""
Not yet supported!
(x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.
"""
XYWHA_ABS = 4
"""
(xc, yc, w, h, a) in absolute floating points coordinates.
(xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.
"""
@staticmethod
def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType:
"""
Args:
box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5
from_mode, to_mode (BoxMode)
Returns:
The converted box of the same type.
"""
if from_mode == to_mode:
return box
original_type = type(box)
is_numpy = isinstance(box, np.ndarray)
single_box = isinstance(box, (list, tuple))
if single_box:
assert len(box) == 4 or len(box) == 5, (
"BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,"
" where k == 4 or 5"
)
arr = torch.tensor(box)[None, :]
else:
# avoid modifying the input box
if is_numpy:
arr = torch.from_numpy(np.asarray(box)).clone()
else:
arr = box.clone()
assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [
BoxMode.XYXY_REL,
BoxMode.XYWH_REL,
], "Relative mode not yet supported!"
if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:
assert (
arr.shape[-1] == 5
), "The last dimension of input shape must be 5 for XYWHA format"
original_dtype = arr.dtype
arr = arr.double()
w = arr[:, 2]
h = arr[:, 3]
a = arr[:, 4]
c = torch.abs(torch.cos(a * math.pi / 180.0))
s = torch.abs(torch.sin(a * math.pi / 180.0))
# This basically computes the horizontal bounding rectangle of the rotated box
new_w = c * w + s * h
new_h = c * h + s * w
# convert center to top-left corner
arr[:, 0] -= new_w / 2.0
arr[:, 1] -= new_h / 2.0
# bottom-right corner
arr[:, 2] = arr[:, 0] + new_w
arr[:, 3] = arr[:, 1] + new_h
arr = arr[:, :4].to(dtype=original_dtype)
elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:
original_dtype = arr.dtype
arr = arr.double()
arr[:, 0] += arr[:, 2] / 2.0
arr[:, 1] += arr[:, 3] / 2.0
angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)
arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)
else:
if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:
arr[:, 2] += arr[:, 0]
arr[:, 3] += arr[:, 1]
elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:
arr[:, 2] -= arr[:, 0]
arr[:, 3] -= arr[:, 1]
else:
raise NotImplementedError(
"Conversion from BoxMode {} to {} is not supported yet".format(
from_mode, to_mode
)
)
if single_box:
return original_type(arr.flatten().tolist())
if is_numpy:
return arr.numpy()
else:
return arr
class MatrixVisualizer:
"""
Base visualizer for matrix data
"""
def __init__(
self,
inplace=True,
cmap=cv2.COLORMAP_PARULA,
val_scale=1.0,
alpha=0.7,
interp_method_matrix=cv2.INTER_LINEAR,
interp_method_mask=cv2.INTER_NEAREST,
):
self.inplace = inplace
self.cmap = cmap
self.val_scale = val_scale
self.alpha = alpha
self.interp_method_matrix = interp_method_matrix
self.interp_method_mask = interp_method_mask
def visualize(self, image_bgr, mask, matrix, bbox_xywh):
self._check_image(image_bgr)
self._check_mask_matrix(mask, matrix)
if self.inplace:
image_target_bgr = image_bgr
else:
image_target_bgr = image_bgr * 0
x, y, w, h = [int(v) for v in bbox_xywh]
if w <= 0 or h <= 0:
return image_bgr
mask, matrix = self._resize(mask, matrix, w, h)
mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3])
matrix_scaled = matrix.astype(np.float32) * self.val_scale
_EPSILON = 1e-6
if np.any(matrix_scaled > 255 + _EPSILON):
logger = logging.getLogger(__name__)
logger.warning(
f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]"
)
matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8)
matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap)
matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg]
image_target_bgr[y : y + h, x : x + w, :] = (
image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha
)
return image_target_bgr.astype(np.uint8)
def _resize(self, mask, matrix, w, h):
if (w != mask.shape[1]) or (h != mask.shape[0]):
mask = cv2.resize(mask, (w, h), self.interp_method_mask)
if (w != matrix.shape[1]) or (h != matrix.shape[0]):
matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix)
return mask, matrix
def _check_image(self, image_rgb):
assert len(image_rgb.shape) == 3
assert image_rgb.shape[2] == 3
assert image_rgb.dtype == np.uint8
def _check_mask_matrix(self, mask, matrix):
assert len(matrix.shape) == 2
assert len(mask.shape) == 2
assert mask.dtype == np.uint8
class DensePoseResultsVisualizer:
def visualize(
self,
image_bgr: Image,
results,
) -> Image:
context = self.create_visualization_context(image_bgr)
for i, result in enumerate(results):
boxes_xywh, labels, uv = result
iuv_array = torch.cat(
(labels[None].type(torch.float32), uv * 255.0)
).type(torch.uint8)
self.visualize_iuv_arr(context, iuv_array.cpu().numpy(), boxes_xywh)
image_bgr = self.context_to_image_bgr(context)
return image_bgr
def create_visualization_context(self, image_bgr: Image):
return image_bgr
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None:
pass
def context_to_image_bgr(self, context):
return context
def get_image_bgr_from_context(self, context):
return context
class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer):
def __init__(
self,
data_extractor,
segm_extractor,
inplace=True,
cmap=cv2.COLORMAP_PARULA,
alpha=0.7,
val_scale=1.0,
**kwargs,
):
self.mask_visualizer = MatrixVisualizer(
inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha
)
self.data_extractor = data_extractor
self.segm_extractor = segm_extractor
def context_to_image_bgr(self, context):
return context
def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None:
image_bgr = self.get_image_bgr_from_context(context)
matrix = self.data_extractor(iuv_arr)
segm = self.segm_extractor(iuv_arr)
mask = np.zeros(matrix.shape, dtype=np.uint8)
mask[segm > 0] = 1
image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh)
def _extract_i_from_iuvarr(iuv_arr):
return iuv_arr[0, :, :]
def _extract_u_from_iuvarr(iuv_arr):
return iuv_arr[1, :, :]
def _extract_v_from_iuvarr(iuv_arr):
return iuv_arr[2, :, :]
def make_int_box(box: torch.Tensor) -> IntTupleBox:
int_box = [0, 0, 0, 0]
int_box[0], int_box[1], int_box[2], int_box[3] = tuple(box.long().tolist())
return int_box[0], int_box[1], int_box[2], int_box[3]
def densepose_chart_predictor_output_to_result_with_confidences(
boxes: Boxes,
coarse_segm,
fine_segm,
u, v
):
boxes_xyxy_abs = boxes.clone()
boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
box_xywh = make_int_box(boxes_xywh_abs[0])
labels = resample_fine_and_coarse_segm_tensors_to_bbox(fine_segm, coarse_segm, box_xywh).squeeze(0)
uv = resample_uv_tensors_to_bbox(u, v, labels, box_xywh)
confidences = []
return box_xywh, labels, uv
def resample_fine_and_coarse_segm_tensors_to_bbox(
fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox
):
"""
Resample fine and coarse segmentation tensors to the given
bounding box and derive labels for each pixel of the bounding box
Args:
fine_segm: float tensor of shape [1, C, Hout, Wout]
coarse_segm: float tensor of shape [1, K, Hout, Wout]
box_xywh_abs (tuple of 4 int): bounding box given by its upper-left
corner coordinates, width (W) and height (H)
Return:
Labels for each pixel of the bounding box, a long tensor of size [1, H, W]
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
# coarse segmentation
coarse_segm_bbox = F.interpolate(
coarse_segm,
(h, w),
mode="bilinear",
align_corners=False,
).argmax(dim=1)
# combined coarse and fine segmentation
labels = (
F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1)
* (coarse_segm_bbox > 0).long()
)
return labels
def resample_uv_tensors_to_bbox(
u: torch.Tensor,
v: torch.Tensor,
labels: torch.Tensor,
box_xywh_abs: IntTupleBox,
) -> torch.Tensor:
"""
Resamples U and V coordinate estimates for the given bounding box
Args:
u (tensor [1, C, H, W] of float): U coordinates
v (tensor [1, C, H, W] of float): V coordinates
labels (tensor [H, W] of long): labels obtained by resampling segmentation
outputs for the given bounding box
box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
Return:
Resampled U and V coordinates - a tensor [2, H, W] of float
"""
x, y, w, h = box_xywh_abs
w = max(int(w), 1)
h = max(int(h), 1)
u_bbox = F.interpolate(u, (h, w), mode="bilinear", align_corners=False)
v_bbox = F.interpolate(v, (h, w), mode="bilinear", align_corners=False)
uv = torch.zeros([2, h, w], dtype=torch.float32, device=u.device)
for part_id in range(1, u_bbox.size(1)):
uv[0][labels == part_id] = u_bbox[0, part_id][labels == part_id]
uv[1][labels == part_id] = v_bbox[0, part_id][labels == part_id]
return uv

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extensions-builtin/sd_forge_controlnet/annotator/depth_anything.py Normal file
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import os
import torch
import cv2
import numpy as np
import torch.nn.functional as F
from torchvision.transforms import Compose
from depth_anything.dpt import DPT_DINOv2
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from .util import load_model
from .annotator_path import models_path
transform = Compose(
[
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
]
)
class DepthAnythingDetector:
"""https://github.com/LiheYoung/Depth-Anything"""
model_dir = os.path.join(models_path, "depth_anything")
def __init__(self, device: torch.device):
self.device = device
self.model = (
DPT_DINOv2(
encoder="vitl",
features=256,
out_channels=[256, 512, 1024, 1024],
localhub=False,
)
.to(device)
.eval()
)
remote_url = os.environ.get(
"CONTROLNET_DEPTH_ANYTHING_MODEL_URL",
"https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth",
)
model_path = load_model(
"depth_anything_vitl14.pth", remote_url=remote_url, model_dir=self.model_dir
)
self.model.load_state_dict(torch.load(model_path))
def __call__(self, image: np.ndarray, colored: bool = True) -> np.ndarray:
self.model.to(self.device)
h, w = image.shape[:2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
image = transform({"image": image})["image"]
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
@torch.no_grad()
def predict_depth(model, image):
return model(image)
depth = predict_depth(self.model, image)
depth = F.interpolate(
depth[None], (h, w), mode="bilinear", align_corners=False
)[0, 0]
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
depth = depth.cpu().numpy().astype(np.uint8)
if colored:
return cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
else:
return depth
def unload_model(self):
self.model.to("cpu")

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# This is an improved version and model of HED edge detection with Apache License, Version 2.0.
# Please use this implementation in your products
# This implementation may produce slightly different results from Saining Xie's official implementations,
# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
# Different from official models and other implementations, this is an RGB-input model (rather than BGR)
# and in this way it works better for gradio's RGB protocol
import os
import cv2
import torch
import numpy as np
from einops import rearrange
import os
from modules import devices
from annotator.annotator_path import models_path
from annotator.util import safe_step, nms
class DoubleConvBlock(torch.nn.Module):
def __init__(self, input_channel, output_channel, layer_number):
super().__init__()
self.convs = torch.nn.Sequential()
self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
for i in range(1, layer_number):
self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0)
def __call__(self, x, down_sampling=False):
h = x
if down_sampling:
h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
for conv in self.convs:
h = conv(h)
h = torch.nn.functional.relu(h)
return h, self.projection(h)
class ControlNetHED_Apache2(torch.nn.Module):
def __init__(self):
super().__init__()
self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)
def __call__(self, x):
h = x - self.norm
h, projection1 = self.block1(h)
h, projection2 = self.block2(h, down_sampling=True)
h, projection3 = self.block3(h, down_sampling=True)
h, projection4 = self.block4(h, down_sampling=True)
h, projection5 = self.block5(h, down_sampling=True)
return projection1, projection2, projection3, projection4, projection5
netNetwork = None
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
modeldir = os.path.join(models_path, "hed")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
def apply_hed(input_image, is_safe=False):
global netNetwork
if netNetwork is None:
modelpath = os.path.join(modeldir, "ControlNetHED.pth")
old_modelpath = os.path.join(old_modeldir, "ControlNetHED.pth")
if os.path.exists(old_modelpath):
modelpath = old_modelpath
elif not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=modeldir)
netNetwork = ControlNetHED_Apache2().to(devices.get_device_for("controlnet"))
netNetwork.load_state_dict(torch.load(modelpath, map_location='cpu'))
netNetwork.to(devices.get_device_for("controlnet")).float().eval()
assert input_image.ndim == 3
H, W, C = input_image.shape
with torch.no_grad():
image_hed = torch.from_numpy(input_image.copy()).float().to(devices.get_device_for("controlnet"))
image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
edges = netNetwork(image_hed)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
if is_safe:
edge = safe_step(edge)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
return edge
def unload_hed_model():
global netNetwork
if netNetwork is not None:
netNetwork.cpu()

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import numpy as np
import cv2
import torch
import os
from modules import devices
from annotator.annotator_path import models_path
import mmcv
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model
from mmpose.apis import init_pose_model, process_mmdet_results, vis_pose_result
def preprocessing(image, device):
# Resize
scale = 640 / max(image.shape[:2])
image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
raw_image = image.astype(np.uint8)
# Subtract mean values
image = image.astype(np.float32)
image -= np.array(
[
float(104.008),
float(116.669),
float(122.675),
]
)
# Convert to torch.Tensor and add "batch" axis
image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0)
image = image.to(device)
return image, raw_image
def imshow_keypoints(img,
pose_result,
skeleton=None,
kpt_score_thr=0.1,
pose_kpt_color=None,
pose_link_color=None,
radius=4,
thickness=1):
"""Draw keypoints and links on an image.
Args:
img (ndarry): The image to draw poses on.
pose_result (list[kpts]): The poses to draw. Each element kpts is
a set of K keypoints as an Kx3 numpy.ndarray, where each
keypoint is represented as x, y, score.
kpt_score_thr (float, optional): Minimum score of keypoints
to be shown. Default: 0.3.
pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None,
the keypoint will not be drawn.
pose_link_color (np.array[Mx3]): Color of M links. If None, the
links will not be drawn.
thickness (int): Thickness of lines.
"""
img_h, img_w, _ = img.shape
img = np.zeros(img.shape)
for idx, kpts in enumerate(pose_result):
if idx > 1:
continue
kpts = kpts['keypoints']
# print(kpts)
kpts = np.array(kpts, copy=False)
# draw each point on image
if pose_kpt_color is not None:
assert len(pose_kpt_color) == len(kpts)
for kid, kpt in enumerate(kpts):
x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None:
# skip the point that should not be drawn
continue
color = tuple(int(c) for c in pose_kpt_color[kid])
cv2.circle(img, (int(x_coord), int(y_coord)),
radius, color, -1)
# draw links
if skeleton is not None and pose_link_color is not None:
assert len(pose_link_color) == len(skeleton)
for sk_id, sk in enumerate(skeleton):
pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1]))
pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1]))
if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0
or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr
or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None):
# skip the link that should not be drawn
continue
color = tuple(int(c) for c in pose_link_color[sk_id])
cv2.line(img, pos1, pos2, color, thickness=thickness)
return img
human_det, pose_model = None, None
det_model_path = "https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth"
pose_model_path = "https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth"
modeldir = os.path.join(models_path, "keypose")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
det_config = 'faster_rcnn_r50_fpn_coco.py'
pose_config = 'hrnet_w48_coco_256x192.py'
det_checkpoint = 'faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
pose_checkpoint = 'hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
det_cat_id = 1
bbox_thr = 0.2
skeleton = [
[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8],
[7, 9], [8, 10],
[1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]
]
pose_kpt_color = [
[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255],
[0, 255, 0],
[255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0],
[255, 128, 0],
[0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]
]
pose_link_color = [
[0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0],
[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0],
[255, 128, 0],
[0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255],
[51, 153, 255],
[51, 153, 255], [51, 153, 255], [51, 153, 255]
]
def find_download_model(checkpoint, remote_path):
modelpath = os.path.join(modeldir, checkpoint)
old_modelpath = os.path.join(old_modeldir, checkpoint)
if os.path.exists(old_modelpath):
modelpath = old_modelpath
elif not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_path, model_dir=modeldir)
return modelpath
def apply_keypose(input_image):
global human_det, pose_model
if netNetwork is None:
det_model_local = find_download_model(det_checkpoint, det_model_path)
hrnet_model_local = find_download_model(pose_checkpoint, pose_model_path)
det_config_mmcv = mmcv.Config.fromfile(det_config)
pose_config_mmcv = mmcv.Config.fromfile(pose_config)
human_det = init_detector(det_config_mmcv, det_model_local, device=devices.get_device_for("controlnet"))
pose_model = init_pose_model(pose_config_mmcv, hrnet_model_local, device=devices.get_device_for("controlnet"))
assert input_image.ndim == 3
input_image = input_image.copy()
with torch.no_grad():
image = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet"))
image = image / 255.0
mmdet_results = inference_detector(human_det, image)
# keep the person class bounding boxes.
person_results = process_mmdet_results(mmdet_results, det_cat_id)
return_heatmap = False
dataset = pose_model.cfg.data['test']['type']
# e.g. use ('backbone', ) to return backbone feature
output_layer_names = None
pose_results, _ = inference_top_down_pose_model(
pose_model,
image,
person_results,
bbox_thr=bbox_thr,
format='xyxy',
dataset=dataset,
dataset_info=None,
return_heatmap=return_heatmap,
outputs=output_layer_names
)
im_keypose_out = imshow_keypoints(
image,
pose_results,
skeleton=skeleton,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
radius=2,
thickness=2
)
im_keypose_out = im_keypose_out.astype(np.uint8)
# image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
# edge = netNetwork(image_hed)[0]
# edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
return im_keypose_out
def unload_hed_model():
global netNetwork
if netNetwork is not None:
netNetwork.cpu()

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extensions-builtin/sd_forge_controlnet/annotator/keypose/faster_rcnn_r50_fpn_coco.py Normal file
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checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
total_epochs = 12
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
))
dataset_type = 'CocoDataset'
data_root = 'data/coco'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=f'{data_root}/annotations/instances_train2017.json',
img_prefix=f'{data_root}/train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=f'{data_root}/annotations/instances_val2017.json',
img_prefix=f'{data_root}/val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=f'{data_root}/annotations/instances_val2017.json',
img_prefix=f'{data_root}/val2017/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')

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# _base_ = [
# '../../../../_base_/default_runtime.py',
# '../../../../_base_/datasets/coco.py'
# ]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=False,
det_bbox_thr=0.0,
bbox_file='data/coco/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownGetBboxCenterScale', padding=1.25),
dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownGetBboxCenterScale', padding=1.25),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/coco'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
img_prefix=f'{data_root}/train2017/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/val2017/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/val2017/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)

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# https://github.com/advimman/lama
import yaml
import torch
from omegaconf import OmegaConf
import numpy as np
from einops import rearrange
import os
from modules import devices
from annotator.annotator_path import models_path
from annotator.lama.saicinpainting.training.trainers import load_checkpoint
class LamaInpainting:
model_dir = os.path.join(models_path, "lama")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth"
modelpath = os.path.join(self.model_dir, "ControlNetLama.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml')
cfg = yaml.safe_load(open(config_path, 'rt'))
cfg = OmegaConf.create(cfg)
cfg.training_model.predict_only = True
cfg.visualizer.kind = 'noop'
self.model = load_checkpoint(cfg, os.path.abspath(modelpath), strict=False, map_location='cpu')
self.model = self.model.to(self.device)
self.model.eval()
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
color = np.ascontiguousarray(input_image[:, :, 0:3]).astype(np.float32) / 255.0
mask = np.ascontiguousarray(input_image[:, :, 3:4]).astype(np.float32) / 255.0
with torch.no_grad():
color = torch.from_numpy(color).float().to(self.device)
mask = torch.from_numpy(mask).float().to(self.device)
mask = (mask > 0.5).float()
color = color * (1 - mask)
image_feed = torch.cat([color, mask], dim=2)
image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
result = self.model(image_feed)[0]
result = rearrange(result, 'c h w -> h w c')
result = result * mask + color * (1 - mask)
result *= 255.0
return result.detach().cpu().numpy().clip(0, 255).astype(np.uint8)

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run_title: b18_ffc075_batch8x15
training_model:
kind: default
visualize_each_iters: 1000
concat_mask: true
store_discr_outputs_for_vis: true
losses:
l1:
weight_missing: 0
weight_known: 10
perceptual:
weight: 0
adversarial:
kind: r1
weight: 10
gp_coef: 0.001
mask_as_fake_target: true
allow_scale_mask: true
feature_matching:
weight: 100
resnet_pl:
weight: 30
weights_path: ${env:TORCH_HOME}
optimizers:
generator:
kind: adam
lr: 0.001
discriminator:
kind: adam
lr: 0.0001
visualizer:
key_order:
- image
- predicted_image
- discr_output_fake
- discr_output_real
- inpainted
rescale_keys:
- discr_output_fake
- discr_output_real
kind: directory
outdir: /group-volume/User-Driven-Content-Generation/r.suvorov/inpainting/experiments/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/samples
location:
data_root_dir: /group-volume/User-Driven-Content-Generation/datasets/inpainting_data_root_large
out_root_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/experiments
tb_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/tb_logs
data:
batch_size: 15
val_batch_size: 2
num_workers: 3
train:
indir: ${location.data_root_dir}/train
out_size: 256
mask_gen_kwargs:
irregular_proba: 1
irregular_kwargs:
max_angle: 4
max_len: 200
max_width: 100
max_times: 5
min_times: 1
box_proba: 1
box_kwargs:
margin: 10
bbox_min_size: 30
bbox_max_size: 150
max_times: 3
min_times: 1
segm_proba: 0
segm_kwargs:
confidence_threshold: 0.5
max_object_area: 0.5
min_mask_area: 0.07
downsample_levels: 6
num_variants_per_mask: 1
rigidness_mode: 1
max_foreground_coverage: 0.3
max_foreground_intersection: 0.7
max_mask_intersection: 0.1
max_hidden_area: 0.1
max_scale_change: 0.25
horizontal_flip: true
max_vertical_shift: 0.2
position_shuffle: true
transform_variant: distortions
dataloader_kwargs:
batch_size: ${data.batch_size}
shuffle: true
num_workers: ${data.num_workers}
val:
indir: ${location.data_root_dir}/val
img_suffix: .png
dataloader_kwargs:
batch_size: ${data.val_batch_size}
shuffle: false
num_workers: ${data.num_workers}
visual_test:
indir: ${location.data_root_dir}/korean_test
img_suffix: _input.png
pad_out_to_modulo: 32
dataloader_kwargs:
batch_size: 1
shuffle: false
num_workers: ${data.num_workers}
generator:
kind: ffc_resnet
input_nc: 4
output_nc: 3
ngf: 64
n_downsampling: 3
n_blocks: 18
add_out_act: sigmoid
init_conv_kwargs:
ratio_gin: 0
ratio_gout: 0
enable_lfu: false
downsample_conv_kwargs:
ratio_gin: ${generator.init_conv_kwargs.ratio_gout}
ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}
enable_lfu: false
resnet_conv_kwargs:
ratio_gin: 0.75
ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}
enable_lfu: false
discriminator:
kind: pix2pixhd_nlayer
input_nc: 3
ndf: 64
n_layers: 4
evaluator:
kind: default
inpainted_key: inpainted
integral_kind: ssim_fid100_f1
trainer:
kwargs:
gpus: -1
accelerator: ddp
max_epochs: 200
gradient_clip_val: 1
log_gpu_memory: None
limit_train_batches: 25000
val_check_interval: ${trainer.kwargs.limit_train_batches}
log_every_n_steps: 1000
precision: 32
terminate_on_nan: false
check_val_every_n_epoch: 1
num_sanity_val_steps: 8
limit_val_batches: 1000
replace_sampler_ddp: false
checkpoint_kwargs:
verbose: true
save_top_k: 5
save_last: true
period: 1
monitor: val_ssim_fid100_f1_total_mean
mode: max

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import math
import random
import hashlib
import logging
from enum import Enum
import cv2
import numpy as np
# from annotator.lama.saicinpainting.evaluation.masks.mask import SegmentationMask
from annotator.lama.saicinpainting.utils import LinearRamp
LOGGER = logging.getLogger(__name__)
class DrawMethod(Enum):
LINE = 'line'
CIRCLE = 'circle'
SQUARE = 'square'
def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,
draw_method=DrawMethod.LINE):
draw_method = DrawMethod(draw_method)
height, width = shape
mask = np.zeros((height, width), np.float32)
times = np.random.randint(min_times, max_times + 1)
for i in range(times):
start_x = np.random.randint(width)
start_y = np.random.randint(height)
for j in range(1 + np.random.randint(5)):
angle = 0.01 + np.random.randint(max_angle)
if i % 2 == 0:
angle = 2 * 3.1415926 - angle
length = 10 + np.random.randint(max_len)
brush_w = 5 + np.random.randint(max_width)
end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
if draw_method == DrawMethod.LINE:
cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
elif draw_method == DrawMethod.CIRCLE:
cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)
elif draw_method == DrawMethod.SQUARE:
radius = brush_w // 2
mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1
start_x, start_y = end_x, end_y
return mask[None, ...]
class RandomIrregularMaskGenerator:
def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,
draw_method=DrawMethod.LINE):
self.max_angle = max_angle
self.max_len = max_len
self.max_width = max_width
self.min_times = min_times
self.max_times = max_times
self.draw_method = draw_method
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
def __call__(self, img, iter_i=None, raw_image=None):
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
cur_max_len = int(max(1, self.max_len * coef))
cur_max_width = int(max(1, self.max_width * coef))
cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)
return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,
max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,
draw_method=self.draw_method)
def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):
height, width = shape
mask = np.zeros((height, width), np.float32)
bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
times = np.random.randint(min_times, max_times + 1)
for i in range(times):
box_width = np.random.randint(bbox_min_size, bbox_max_size)
box_height = np.random.randint(bbox_min_size, bbox_max_size)
start_x = np.random.randint(margin, width - margin - box_width + 1)
start_y = np.random.randint(margin, height - margin - box_height + 1)
mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1
return mask[None, ...]
class RandomRectangleMaskGenerator:
def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):
self.margin = margin
self.bbox_min_size = bbox_min_size
self.bbox_max_size = bbox_max_size
self.min_times = min_times
self.max_times = max_times
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
def __call__(self, img, iter_i=None, raw_image=None):
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)
cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)
return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,
bbox_max_size=cur_bbox_max_size, min_times=self.min_times,
max_times=cur_max_times)
class RandomSegmentationMaskGenerator:
def __init__(self, **kwargs):
self.impl = None # will be instantiated in first call (effectively in subprocess)
self.kwargs = kwargs
def __call__(self, img, iter_i=None, raw_image=None):
if self.impl is None:
self.impl = SegmentationMask(**self.kwargs)
masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))
masks = [m for m in masks if len(np.unique(m)) > 1]
return np.random.choice(masks)
def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):
height, width = shape
mask = np.zeros((height, width), np.float32)
step_x = np.random.randint(min_step, max_step + 1)
width_x = np.random.randint(min_width, min(step_x, max_width + 1))
offset_x = np.random.randint(0, step_x)
step_y = np.random.randint(min_step, max_step + 1)
width_y = np.random.randint(min_width, min(step_y, max_width + 1))
offset_y = np.random.randint(0, step_y)
for dy in range(width_y):
mask[offset_y + dy::step_y] = 1
for dx in range(width_x):
mask[:, offset_x + dx::step_x] = 1
return mask[None, ...]
class RandomSuperresMaskGenerator:
def __init__(self, **kwargs):
self.kwargs = kwargs
def __call__(self, img, iter_i=None):
return make_random_superres_mask(img.shape[1:], **self.kwargs)
class DumbAreaMaskGenerator:
min_ratio = 0.1
max_ratio = 0.35
default_ratio = 0.225
def __init__(self, is_training):
#Parameters:
# is_training(bool): If true - random rectangular mask, if false - central square mask
self.is_training = is_training
def _random_vector(self, dimension):
if self.is_training:
lower_limit = math.sqrt(self.min_ratio)
upper_limit = math.sqrt(self.max_ratio)
mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)
u = random.randint(0, dimension-mask_side-1)
v = u+mask_side
else:
margin = (math.sqrt(self.default_ratio) / 2) * dimension
u = round(dimension/2 - margin)
v = round(dimension/2 + margin)
return u, v
def __call__(self, img, iter_i=None, raw_image=None):
c, height, width = img.shape
mask = np.zeros((height, width), np.float32)
x1, x2 = self._random_vector(width)
y1, y2 = self._random_vector(height)
mask[x1:x2, y1:y2] = 1
return mask[None, ...]
class OutpaintingMaskGenerator:
def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5,
right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):
"""
is_fixed_randomness - get identical paddings for the same image if args are the same
"""
self.min_padding_percent = min_padding_percent
self.max_padding_percent = max_padding_percent
self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]
self.is_fixed_randomness = is_fixed_randomness
assert self.min_padding_percent <= self.max_padding_percent
assert self.max_padding_percent > 0
assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]"
assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}"
assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}"
if len([x for x in self.probs if x > 0]) == 1:
LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side")
def apply_padding(self, mask, coord):
mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),
int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1
return mask
def get_padding(self, size):
n1 = int(self.min_padding_percent*size)
n2 = int(self.max_padding_percent*size)
return self.rnd.randint(n1, n2) / size
@staticmethod
def _img2rs(img):
arr = np.ascontiguousarray(img.astype(np.uint8))
str_hash = hashlib.sha1(arr).hexdigest()
res = hash(str_hash)%(2**32)
return res
def __call__(self, img, iter_i=None, raw_image=None):
c, self.img_h, self.img_w = img.shape
mask = np.zeros((self.img_h, self.img_w), np.float32)
at_least_one_mask_applied = False
if self.is_fixed_randomness:
assert raw_image is not None, f"Cant calculate hash on raw_image=None"
rs = self._img2rs(raw_image)
self.rnd = np.random.RandomState(rs)
else:
self.rnd = np.random
coords = [[
(0,0),
(1,self.get_padding(size=self.img_h))
],
[
(0,0),
(self.get_padding(size=self.img_w),1)
],
[
(0,1-self.get_padding(size=self.img_h)),
(1,1)
],
[
(1-self.get_padding(size=self.img_w),0),
(1,1)
]]
for pp, coord in zip(self.probs, coords):
if self.rnd.random() < pp:
at_least_one_mask_applied = True
mask = self.apply_padding(mask=mask, coord=coord)
if not at_least_one_mask_applied:
idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))
mask = self.apply_padding(mask=mask, coord=coords[idx])
return mask[None, ...]
class MixedMaskGenerator:
def __init__(self, irregular_proba=1/3, irregular_kwargs=None,
box_proba=1/3, box_kwargs=None,
segm_proba=1/3, segm_kwargs=None,
squares_proba=0, squares_kwargs=None,
superres_proba=0, superres_kwargs=None,
outpainting_proba=0, outpainting_kwargs=None,
invert_proba=0):
self.probas = []
self.gens = []
if irregular_proba > 0:
self.probas.append(irregular_proba)
if irregular_kwargs is None:
irregular_kwargs = {}
else:
irregular_kwargs = dict(irregular_kwargs)
irregular_kwargs['draw_method'] = DrawMethod.LINE
self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))
if box_proba > 0:
self.probas.append(box_proba)
if box_kwargs is None:
box_kwargs = {}
self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))
if segm_proba > 0:
self.probas.append(segm_proba)
if segm_kwargs is None:
segm_kwargs = {}
self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))
if squares_proba > 0:
self.probas.append(squares_proba)
if squares_kwargs is None:
squares_kwargs = {}
else:
squares_kwargs = dict(squares_kwargs)
squares_kwargs['draw_method'] = DrawMethod.SQUARE
self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))
if superres_proba > 0:
self.probas.append(superres_proba)
if superres_kwargs is None:
superres_kwargs = {}
self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))
if outpainting_proba > 0:
self.probas.append(outpainting_proba)
if outpainting_kwargs is None:
outpainting_kwargs = {}
self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))
self.probas = np.array(self.probas, dtype='float32')
self.probas /= self.probas.sum()
self.invert_proba = invert_proba
def __call__(self, img, iter_i=None, raw_image=None):
kind = np.random.choice(len(self.probas), p=self.probas)
gen = self.gens[kind]
result = gen(img, iter_i=iter_i, raw_image=raw_image)
if self.invert_proba > 0 and random.random() < self.invert_proba:
result = 1 - result
return result
def get_mask_generator(kind, kwargs):
if kind is None:
kind = "mixed"
if kwargs is None:
kwargs = {}
if kind == "mixed":
cl = MixedMaskGenerator
elif kind == "outpainting":
cl = OutpaintingMaskGenerator
elif kind == "dumb":
cl = DumbAreaMaskGenerator
else:
raise NotImplementedError(f"No such generator kind = {kind}")
return cl(**kwargs)

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/__init__.py Normal file
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from typing import Tuple, Dict, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class BaseAdversarialLoss:
def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
generator: nn.Module, discriminator: nn.Module):
"""
Prepare for generator step
:param real_batch: Tensor, a batch of real samples
:param fake_batch: Tensor, a batch of samples produced by generator
:param generator:
:param discriminator:
:return: None
"""
def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
generator: nn.Module, discriminator: nn.Module):
"""
Prepare for discriminator step
:param real_batch: Tensor, a batch of real samples
:param fake_batch: Tensor, a batch of samples produced by generator
:param generator:
:param discriminator:
:return: None
"""
def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
mask: Optional[torch.Tensor] = None) \
-> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Calculate generator loss
:param real_batch: Tensor, a batch of real samples
:param fake_batch: Tensor, a batch of samples produced by generator
:param discr_real_pred: Tensor, discriminator output for real_batch
:param discr_fake_pred: Tensor, discriminator output for fake_batch
:param mask: Tensor, actual mask, which was at input of generator when making fake_batch
:return: total generator loss along with some values that might be interesting to log
"""
raise NotImplemented()
def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
mask: Optional[torch.Tensor] = None) \
-> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Calculate discriminator loss and call .backward() on it
:param real_batch: Tensor, a batch of real samples
:param fake_batch: Tensor, a batch of samples produced by generator
:param discr_real_pred: Tensor, discriminator output for real_batch
:param discr_fake_pred: Tensor, discriminator output for fake_batch
:param mask: Tensor, actual mask, which was at input of generator when making fake_batch
:return: total discriminator loss along with some values that might be interesting to log
"""
raise NotImplemented()
def interpolate_mask(self, mask, shape):
assert mask is not None
assert self.allow_scale_mask or shape == mask.shape[-2:]
if shape != mask.shape[-2:] and self.allow_scale_mask:
if self.mask_scale_mode == 'maxpool':
mask = F.adaptive_max_pool2d(mask, shape)
else:
mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
return mask
def make_r1_gp(discr_real_pred, real_batch):
if torch.is_grad_enabled():
grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
else:
grad_penalty = 0
real_batch.requires_grad = False
return grad_penalty
class NonSaturatingWithR1(BaseAdversarialLoss):
def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
use_unmasked_for_gen=True, use_unmasked_for_discr=True):
self.gp_coef = gp_coef
self.weight = weight
# use for discr => use for gen;
# otherwise we teach only the discr to pay attention to very small difference
assert use_unmasked_for_gen or (not use_unmasked_for_discr)
# mask as target => use unmasked for discr:
# if we don't care about unmasked regions at all
# then it doesn't matter if the value of mask_as_fake_target is true or false
assert use_unmasked_for_discr or (not mask_as_fake_target)
self.use_unmasked_for_gen = use_unmasked_for_gen
self.use_unmasked_for_discr = use_unmasked_for_discr
self.mask_as_fake_target = mask_as_fake_target
self.allow_scale_mask = allow_scale_mask
self.mask_scale_mode = mask_scale_mode
self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
mask=None) \
-> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
fake_loss = F.softplus(-discr_fake_pred)
if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
not self.use_unmasked_for_gen: # == if masked region should be treated differently
mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
if not self.use_unmasked_for_gen:
fake_loss = fake_loss * mask
else:
pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
fake_loss = fake_loss * pixel_weights
return fake_loss.mean() * self.weight, dict()
def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
generator: nn.Module, discriminator: nn.Module):
real_batch.requires_grad = True
def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
mask=None) \
-> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
real_loss = F.softplus(-discr_real_pred)
grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
fake_loss = F.softplus(discr_fake_pred)
if not self.use_unmasked_for_discr or self.mask_as_fake_target:
# == if masked region should be treated differently
mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
# use_unmasked_for_discr=False only makes sense for fakes;
# for reals there is no difference beetween two regions
fake_loss = fake_loss * mask
if self.mask_as_fake_target:
fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
sum_discr_loss = real_loss + grad_penalty + fake_loss
metrics = dict(discr_real_out=discr_real_pred.mean(),
discr_fake_out=discr_fake_pred.mean(),
discr_real_gp=grad_penalty)
return sum_discr_loss.mean(), metrics
class BCELoss(BaseAdversarialLoss):
def __init__(self, weight):
self.weight = weight
self.bce_loss = nn.BCEWithLogitsLoss()
def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
return fake_loss, dict()
def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
generator: nn.Module, discriminator: nn.Module):
real_batch.requires_grad = True
def discriminator_loss(self,
mask: torch.Tensor,
discr_real_pred: torch.Tensor,
discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) + self.bce_loss(discr_fake_pred, mask)) / 2
metrics = dict(discr_real_out=discr_real_pred.mean(),
discr_fake_out=discr_fake_pred.mean(),
discr_real_gp=0)
return sum_discr_loss, metrics
def make_discrim_loss(kind, **kwargs):
if kind == 'r1':
return NonSaturatingWithR1(**kwargs)
elif kind == 'bce':
return BCELoss(**kwargs)
raise ValueError(f'Unknown adversarial loss kind {kind}')

152
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/constants.py Normal file
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weights = {"ade20k":
[6.34517766497462,
9.328358208955224,
11.389521640091116,
16.10305958132045,
20.833333333333332,
22.22222222222222,
25.125628140703515,
43.29004329004329,
50.5050505050505,
54.6448087431694,
55.24861878453038,
60.24096385542168,
62.5,
66.2251655629139,
84.74576271186442,
90.90909090909092,
91.74311926605505,
96.15384615384616,
96.15384615384616,
97.08737864077669,
102.04081632653062,
135.13513513513513,
149.2537313432836,
153.84615384615384,
163.93442622950818,
166.66666666666666,
188.67924528301887,
192.30769230769232,
217.3913043478261,
227.27272727272725,
227.27272727272725,
227.27272727272725,
303.03030303030306,
322.5806451612903,
333.3333333333333,
370.3703703703703,
384.61538461538464,
416.6666666666667,
416.6666666666667,
434.7826086956522,
434.7826086956522,
454.5454545454545,
454.5454545454545,
500.0,
526.3157894736842,
526.3157894736842,
555.5555555555555,
555.5555555555555,
555.5555555555555,
555.5555555555555,
555.5555555555555,
555.5555555555555,
555.5555555555555,
588.2352941176471,
588.2352941176471,
588.2352941176471,
588.2352941176471,
588.2352941176471,
666.6666666666666,
666.6666666666666,
666.6666666666666,
666.6666666666666,
714.2857142857143,
714.2857142857143,
714.2857142857143,
714.2857142857143,
714.2857142857143,
769.2307692307693,
769.2307692307693,
769.2307692307693,
833.3333333333334,
833.3333333333334,
833.3333333333334,
833.3333333333334,
909.090909090909,
1000.0,
1111.111111111111,
1111.111111111111,
1111.111111111111,
1111.111111111111,
1111.111111111111,
1250.0,
1250.0,
1250.0,
1250.0,
1250.0,
1428.5714285714287,
1428.5714285714287,
1428.5714285714287,
1428.5714285714287,
1428.5714285714287,
1428.5714285714287,
1428.5714285714287,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
1666.6666666666667,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2000.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
2500.0,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
3333.3333333333335,
5000.0,
5000.0,
5000.0]
}

126
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/distance_weighting.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from annotator.lama.saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN
def dummy_distance_weighter(real_img, pred_img, mask):
return mask
def get_gauss_kernel(kernel_size, width_factor=1):
coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),
torch.arange(kernel_size)),
dim=0).float()
diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)
diff /= diff.sum()
return diff
class BlurMask(nn.Module):
def __init__(self, kernel_size=5, width_factor=1):
super().__init__()
self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)
self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))
def forward(self, real_img, pred_img, mask):
with torch.no_grad():
result = self.filter(mask) * mask
return result
class EmulatedEDTMask(nn.Module):
def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):
super().__init__()
self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',
bias=False)
self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))
self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)
self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))
def forward(self, real_img, pred_img, mask):
with torch.no_grad():
known_mask = 1 - mask
dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()
result = self.blur_filter(1 - dilated_known_mask) * mask
return result
class PropagatePerceptualSim(nn.Module):
def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):
super().__init__()
vgg = torchvision.models.vgg19(pretrained=True).features
vgg_avg_pooling = []
for weights in vgg.parameters():
weights.requires_grad = False
cur_level_i = 0
for module in vgg.modules():
if module.__class__.__name__ == 'Sequential':
continue
elif module.__class__.__name__ == 'MaxPool2d':
vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
else:
vgg_avg_pooling.append(module)
if module.__class__.__name__ == 'ReLU':
cur_level_i += 1
if cur_level_i == level:
break
self.features = nn.Sequential(*vgg_avg_pooling)
self.max_iters = max_iters
self.temperature = temperature
self.do_erode = erode_mask_size > 0
if self.do_erode:
self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)
self.erode_mask.weight.data.fill_(1)
def forward(self, real_img, pred_img, mask):
with torch.no_grad():
real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)
real_feats = self.features(real_img)
vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)
/ self.temperature)
horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)
/ self.temperature)
mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)
if self.do_erode:
mask_scaled = (self.erode_mask(mask_scaled) > 1).float()
cur_knowness = 1 - mask_scaled
for iter_i in range(self.max_iters):
new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')
new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')
new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')
new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')
new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,
new_left_knowness, new_right_knowness],
dim=0).max(0).values
cur_knowness = torch.max(cur_knowness, new_knowness)
cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')
result = torch.min(mask, 1 - cur_knowness)
return result
def make_mask_distance_weighter(kind='none', **kwargs):
if kind == 'none':
return dummy_distance_weighter
if kind == 'blur':
return BlurMask(**kwargs)
if kind == 'edt':
return EmulatedEDTMask(**kwargs)
if kind == 'pps':
return PropagatePerceptualSim(**kwargs)
raise ValueError(f'Unknown mask distance weighter kind {kind}')

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/feature_matching.py Normal file
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from typing import List
import torch
import torch.nn.functional as F
def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
pixel_weights = mask * weight_missing + (1 - mask) * weight_known
return (pixel_weights * per_pixel_l2).mean()
def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
pixel_weights = mask * weight_missing + (1 - mask) * weight_known
return (pixel_weights * per_pixel_l1).mean()
def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
if mask is None:
res = torch.stack([F.mse_loss(fake_feat, target_feat)
for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
else:
res = 0
norm = 0
for fake_feat, target_feat in zip(fake_features, target_features):
cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
error_weights = 1 - cur_mask
cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
res = res + cur_val
norm += 1
res = res / norm
return res

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/perceptual.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
# from models.ade20k import ModelBuilder
from annotator.lama.saicinpainting.utils import check_and_warn_input_range
IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
class PerceptualLoss(nn.Module):
def __init__(self, normalize_inputs=True):
super(PerceptualLoss, self).__init__()
self.normalize_inputs = normalize_inputs
self.mean_ = IMAGENET_MEAN
self.std_ = IMAGENET_STD
vgg = torchvision.models.vgg19(pretrained=True).features
vgg_avg_pooling = []
for weights in vgg.parameters():
weights.requires_grad = False
for module in vgg.modules():
if module.__class__.__name__ == 'Sequential':
continue
elif module.__class__.__name__ == 'MaxPool2d':
vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
else:
vgg_avg_pooling.append(module)
self.vgg = nn.Sequential(*vgg_avg_pooling)
def do_normalize_inputs(self, x):
return (x - self.mean_.to(x.device)) / self.std_.to(x.device)
def partial_losses(self, input, target, mask=None):
check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses')
# we expect input and target to be in [0, 1] range
losses = []
if self.normalize_inputs:
features_input = self.do_normalize_inputs(input)
features_target = self.do_normalize_inputs(target)
else:
features_input = input
features_target = target
for layer in self.vgg[:30]:
features_input = layer(features_input)
features_target = layer(features_target)
if layer.__class__.__name__ == 'ReLU':
loss = F.mse_loss(features_input, features_target, reduction='none')
if mask is not None:
cur_mask = F.interpolate(mask, size=features_input.shape[-2:],
mode='bilinear', align_corners=False)
loss = loss * (1 - cur_mask)
loss = loss.mean(dim=tuple(range(1, len(loss.shape))))
losses.append(loss)
return losses
def forward(self, input, target, mask=None):
losses = self.partial_losses(input, target, mask=mask)
return torch.stack(losses).sum(dim=0)
def get_global_features(self, input):
check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features')
if self.normalize_inputs:
features_input = self.do_normalize_inputs(input)
else:
features_input = input
features_input = self.vgg(features_input)
return features_input
class ResNetPL(nn.Module):
def __init__(self, weight=1,
weights_path=None, arch_encoder='resnet50dilated', segmentation=True):
super().__init__()
self.impl = ModelBuilder.get_encoder(weights_path=weights_path,
arch_encoder=arch_encoder,
arch_decoder='ppm_deepsup',
fc_dim=2048,
segmentation=segmentation)
self.impl.eval()
for w in self.impl.parameters():
w.requires_grad_(False)
self.weight = weight
def forward(self, pred, target):
pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred)
target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target)
pred_feats = self.impl(pred, return_feature_maps=True)
target_feats = self.impl(target, return_feature_maps=True)
result = torch.stack([F.mse_loss(cur_pred, cur_target)
for cur_pred, cur_target
in zip(pred_feats, target_feats)]).sum() * self.weight
return result

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/segmentation.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
from .constants import weights as constant_weights
class CrossEntropy2d(nn.Module):
def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs):
"""
weight (Tensor, optional): a manual rescaling weight given to each class.
If given, has to be a Tensor of size "nclasses"
"""
super(CrossEntropy2d, self).__init__()
self.reduction = reduction
self.ignore_label = ignore_label
self.weights = weights
if self.weights is not None:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.weights = torch.FloatTensor(constant_weights[weights]).to(device)
def forward(self, predict, target):
"""
Args:
predict:(n, c, h, w)
target:(n, 1, h, w)
"""
target = target.long()
assert not target.requires_grad
assert predict.dim() == 4, "{0}".format(predict.size())
assert target.dim() == 4, "{0}".format(target.size())
assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
assert target.size(1) == 1, "{0}".format(target.size(1))
assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
target = target.squeeze(1)
n, c, h, w = predict.size()
target_mask = (target >= 0) * (target != self.ignore_label)
target = target[target_mask]
predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)
return loss

155
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/losses/style_loss.py Normal file
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import torch
import torch.nn as nn
import torchvision.models as models
class PerceptualLoss(nn.Module):
r"""
Perceptual loss, VGG-based
https://arxiv.org/abs/1603.08155
https://github.com/dxyang/StyleTransfer/blob/master/utils.py
"""
def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
super(PerceptualLoss, self).__init__()
self.add_module('vgg', VGG19())
self.criterion = torch.nn.L1Loss()
self.weights = weights
def __call__(self, x, y):
# Compute features
x_vgg, y_vgg = self.vgg(x), self.vgg(y)
content_loss = 0.0
content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
return content_loss
class VGG19(torch.nn.Module):
def __init__(self):
super(VGG19, self).__init__()
features = models.vgg19(pretrained=True).features
self.relu1_1 = torch.nn.Sequential()
self.relu1_2 = torch.nn.Sequential()
self.relu2_1 = torch.nn.Sequential()
self.relu2_2 = torch.nn.Sequential()
self.relu3_1 = torch.nn.Sequential()
self.relu3_2 = torch.nn.Sequential()
self.relu3_3 = torch.nn.Sequential()
self.relu3_4 = torch.nn.Sequential()
self.relu4_1 = torch.nn.Sequential()
self.relu4_2 = torch.nn.Sequential()
self.relu4_3 = torch.nn.Sequential()
self.relu4_4 = torch.nn.Sequential()
self.relu5_1 = torch.nn.Sequential()
self.relu5_2 = torch.nn.Sequential()
self.relu5_3 = torch.nn.Sequential()
self.relu5_4 = torch.nn.Sequential()
for x in range(2):
self.relu1_1.add_module(str(x), features[x])
for x in range(2, 4):
self.relu1_2.add_module(str(x), features[x])
for x in range(4, 7):
self.relu2_1.add_module(str(x), features[x])
for x in range(7, 9):
self.relu2_2.add_module(str(x), features[x])
for x in range(9, 12):
self.relu3_1.add_module(str(x), features[x])
for x in range(12, 14):
self.relu3_2.add_module(str(x), features[x])
for x in range(14, 16):
self.relu3_2.add_module(str(x), features[x])
for x in range(16, 18):
self.relu3_4.add_module(str(x), features[x])
for x in range(18, 21):
self.relu4_1.add_module(str(x), features[x])
for x in range(21, 23):
self.relu4_2.add_module(str(x), features[x])
for x in range(23, 25):
self.relu4_3.add_module(str(x), features[x])
for x in range(25, 27):
self.relu4_4.add_module(str(x), features[x])
for x in range(27, 30):
self.relu5_1.add_module(str(x), features[x])
for x in range(30, 32):
self.relu5_2.add_module(str(x), features[x])
for x in range(32, 34):
self.relu5_3.add_module(str(x), features[x])
for x in range(34, 36):
self.relu5_4.add_module(str(x), features[x])
# don't need the gradients, just want the features
for param in self.parameters():
param.requires_grad = False
def forward(self, x):
relu1_1 = self.relu1_1(x)
relu1_2 = self.relu1_2(relu1_1)
relu2_1 = self.relu2_1(relu1_2)
relu2_2 = self.relu2_2(relu2_1)
relu3_1 = self.relu3_1(relu2_2)
relu3_2 = self.relu3_2(relu3_1)
relu3_3 = self.relu3_3(relu3_2)
relu3_4 = self.relu3_4(relu3_3)
relu4_1 = self.relu4_1(relu3_4)
relu4_2 = self.relu4_2(relu4_1)
relu4_3 = self.relu4_3(relu4_2)
relu4_4 = self.relu4_4(relu4_3)
relu5_1 = self.relu5_1(relu4_4)
relu5_2 = self.relu5_2(relu5_1)
relu5_3 = self.relu5_3(relu5_2)
relu5_4 = self.relu5_4(relu5_3)
out = {
'relu1_1': relu1_1,
'relu1_2': relu1_2,
'relu2_1': relu2_1,
'relu2_2': relu2_2,
'relu3_1': relu3_1,
'relu3_2': relu3_2,
'relu3_3': relu3_3,
'relu3_4': relu3_4,
'relu4_1': relu4_1,
'relu4_2': relu4_2,
'relu4_3': relu4_3,
'relu4_4': relu4_4,
'relu5_1': relu5_1,
'relu5_2': relu5_2,
'relu5_3': relu5_3,
'relu5_4': relu5_4,
}
return out

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/__init__.py Normal file
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import logging
from annotator.lama.saicinpainting.training.modules.ffc import FFCResNetGenerator
from annotator.lama.saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
NLayerDiscriminator, MultidilatedNLayerDiscriminator
def make_generator(config, kind, **kwargs):
logging.info(f'Make generator {kind}')
if kind == 'pix2pixhd_multidilated':
return MultiDilatedGlobalGenerator(**kwargs)
if kind == 'pix2pixhd_global':
return GlobalGenerator(**kwargs)
if kind == 'ffc_resnet':
return FFCResNetGenerator(**kwargs)
raise ValueError(f'Unknown generator kind {kind}')
def make_discriminator(kind, **kwargs):
logging.info(f'Make discriminator {kind}')
if kind == 'pix2pixhd_nlayer_multidilated':
return MultidilatedNLayerDiscriminator(**kwargs)
if kind == 'pix2pixhd_nlayer':
return NLayerDiscriminator(**kwargs)
raise ValueError(f'Unknown discriminator kind {kind}')

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/base.py Normal file
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import abc
from typing import Tuple, List
import torch
import torch.nn as nn
from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
class BaseDiscriminator(nn.Module):
@abc.abstractmethod
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""
Predict scores and get intermediate activations. Useful for feature matching loss
:return tuple (scores, list of intermediate activations)
"""
raise NotImplemented()
def get_conv_block_ctor(kind='default'):
if not isinstance(kind, str):
return kind
if kind == 'default':
return nn.Conv2d
if kind == 'depthwise':
return DepthWiseSeperableConv
if kind == 'multidilated':
return MultidilatedConv
raise ValueError(f'Unknown convolutional block kind {kind}')
def get_norm_layer(kind='bn'):
if not isinstance(kind, str):
return kind
if kind == 'bn':
return nn.BatchNorm2d
if kind == 'in':
return nn.InstanceNorm2d
raise ValueError(f'Unknown norm block kind {kind}')
def get_activation(kind='tanh'):
if kind == 'tanh':
return nn.Tanh()
if kind == 'sigmoid':
return nn.Sigmoid()
if kind is False:
return nn.Identity()
raise ValueError(f'Unknown activation kind {kind}')
class SimpleMultiStepGenerator(nn.Module):
def __init__(self, steps: List[nn.Module]):
super().__init__()
self.steps = nn.ModuleList(steps)
def forward(self, x):
cur_in = x
outs = []
for step in self.steps:
cur_out = step(cur_in)
outs.append(cur_out)
cur_in = torch.cat((cur_in, cur_out), dim=1)
return torch.cat(outs[::-1], dim=1)
def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
if kind == 'convtranspose':
return [nn.ConvTranspose2d(min(max_features, ngf * mult),
min(max_features, int(ngf * mult / 2)),
kernel_size=3, stride=2, padding=1, output_padding=1),
norm_layer(min(max_features, int(ngf * mult / 2))), activation]
elif kind == 'bilinear':
return [nn.Upsample(scale_factor=2, mode='bilinear'),
DepthWiseSeperableConv(min(max_features, ngf * mult),
min(max_features, int(ngf * mult / 2)),
kernel_size=3, stride=1, padding=1),
norm_layer(min(max_features, int(ngf * mult / 2))), activation]
else:
raise Exception(f"Invalid deconv kind: {kind}")

17
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/depthwise_sep_conv.py Normal file
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import torch
import torch.nn as nn
class DepthWiseSeperableConv(nn.Module):
def __init__(self, in_dim, out_dim, *args, **kwargs):
super().__init__()
if 'groups' in kwargs:
# ignoring groups for Depthwise Sep Conv
del kwargs['groups']
self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs)
self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1)
def forward(self, x):
out = self.depthwise(x)
out = self.pointwise(out)
return out

47
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/fake_fakes.py Normal file
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import torch
from kornia import SamplePadding
from kornia.augmentation import RandomAffine, CenterCrop
class FakeFakesGenerator:
def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
self.grad_aug = RandomAffine(degrees=360,
translate=0.2,
padding_mode=SamplePadding.REFLECTION,
keepdim=False,
p=1)
self.img_aug = RandomAffine(degrees=img_aug_degree,
translate=img_aug_translate,
padding_mode=SamplePadding.REFLECTION,
keepdim=True,
p=1)
self.aug_proba = aug_proba
def __call__(self, input_images, masks):
blend_masks = self._fill_masks_with_gradient(masks)
blend_target = self._make_blend_target(input_images)
result = input_images * (1 - blend_masks) + blend_target * blend_masks
return result, blend_masks
def _make_blend_target(self, input_images):
batch_size = input_images.shape[0]
permuted = input_images[torch.randperm(batch_size)]
augmented = self.img_aug(input_images)
is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
result = augmented * is_aug + permuted * (1 - is_aug)
return result
def _fill_masks_with_gradient(self, masks):
batch_size, _, height, width = masks.shape
grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
.view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
grad = self.grad_aug(grad)
grad = CenterCrop((height, width))(grad)
grad *= masks
grad_for_min = grad + (1 - masks) * 10
grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
grad.clamp_(min=0, max=1)
return grad

485
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/ffc.py Normal file
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# Fast Fourier Convolution NeurIPS 2020
# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.lama.saicinpainting.training.modules.base import get_activation, BaseDiscriminator
from annotator.lama.saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper
from annotator.lama.saicinpainting.training.modules.squeeze_excitation import SELayer
from annotator.lama.saicinpainting.utils import get_shape
class FFCSE_block(nn.Module):
def __init__(self, channels, ratio_g):
super(FFCSE_block, self).__init__()
in_cg = int(channels * ratio_g)
in_cl = channels - in_cg
r = 16
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.conv1 = nn.Conv2d(channels, channels // r,
kernel_size=1, bias=True)
self.relu1 = nn.ReLU(inplace=True)
self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
channels // r, in_cl, kernel_size=1, bias=True)
self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
channels // r, in_cg, kernel_size=1, bias=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = x if type(x) is tuple else (x, 0)
id_l, id_g = x
x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
x = self.avgpool(x)
x = self.relu1(self.conv1(x))
x_l = 0 if self.conv_a2l is None else id_l * \
self.sigmoid(self.conv_a2l(x))
x_g = 0 if self.conv_a2g is None else id_g * \
self.sigmoid(self.conv_a2g(x))
return x_l, x_g
class FourierUnit(nn.Module):
def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
# bn_layer not used
super(FourierUnit, self).__init__()
self.groups = groups
self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
out_channels=out_channels * 2,
kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
self.bn = torch.nn.BatchNorm2d(out_channels * 2)
self.relu = torch.nn.ReLU(inplace=True)
# squeeze and excitation block
self.use_se = use_se
if use_se:
if se_kwargs is None:
se_kwargs = {}
self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
self.spatial_scale_factor = spatial_scale_factor
self.spatial_scale_mode = spatial_scale_mode
self.spectral_pos_encoding = spectral_pos_encoding
self.ffc3d = ffc3d
self.fft_norm = fft_norm
def forward(self, x):
batch = x.shape[0]
if self.spatial_scale_factor is not None:
orig_size = x.shape[-2:]
x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
r_size = x.size()
# (batch, c, h, w/2+1, 2)
fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
ffted = ffted.view((batch, -1,) + ffted.size()[3:])
if self.spectral_pos_encoding:
height, width = ffted.shape[-2:]
coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
if self.use_se:
ffted = self.se(ffted)
ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1)
ffted = self.relu(self.bn(ffted))
ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
ffted = torch.complex(ffted[..., 0], ffted[..., 1])
ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
if self.spatial_scale_factor is not None:
output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
return output
class SeparableFourierUnit(nn.Module):
def __init__(self, in_channels, out_channels, groups=1, kernel_size=3):
# bn_layer not used
super(SeparableFourierUnit, self).__init__()
self.groups = groups
row_out_channels = out_channels // 2
col_out_channels = out_channels - row_out_channels
self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
out_channels=row_out_channels * 2,
kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
stride=1, padding=(kernel_size // 2, 0),
padding_mode='reflect',
groups=self.groups, bias=False)
self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
out_channels=col_out_channels * 2,
kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
stride=1, padding=(kernel_size // 2, 0),
padding_mode='reflect',
groups=self.groups, bias=False)
self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2)
self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2)
self.relu = torch.nn.ReLU(inplace=True)
def process_branch(self, x, conv, bn):
batch = x.shape[0]
r_size = x.size()
# (batch, c, h, w/2+1, 2)
ffted = torch.fft.rfft(x, norm="ortho")
ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
ffted = ffted.view((batch, -1,) + ffted.size()[3:])
ffted = self.relu(bn(conv(ffted)))
ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
ffted = torch.complex(ffted[..., 0], ffted[..., 1])
output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho")
return output
def forward(self, x):
rowwise = self.process_branch(x, self.row_conv, self.row_bn)
colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2)
out = torch.cat((rowwise, colwise), dim=1)
return out
class SpectralTransform(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs):
# bn_layer not used
super(SpectralTransform, self).__init__()
self.enable_lfu = enable_lfu
if stride == 2:
self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
else:
self.downsample = nn.Identity()
self.stride = stride
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels //
2, kernel_size=1, groups=groups, bias=False),
nn.BatchNorm2d(out_channels // 2),
nn.ReLU(inplace=True)
)
fu_class = SeparableFourierUnit if separable_fu else FourierUnit
self.fu = fu_class(
out_channels // 2, out_channels // 2, groups, **fu_kwargs)
if self.enable_lfu:
self.lfu = fu_class(
out_channels // 2, out_channels // 2, groups)
self.conv2 = torch.nn.Conv2d(
out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
def forward(self, x):
x = self.downsample(x)
x = self.conv1(x)
output = self.fu(x)
if self.enable_lfu:
n, c, h, w = x.shape
split_no = 2
split_s = h // split_no
xs = torch.cat(torch.split(
x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
xs = torch.cat(torch.split(xs, split_s, dim=-1),
dim=1).contiguous()
xs = self.lfu(xs)
xs = xs.repeat(1, 1, split_no, split_no).contiguous()
else:
xs = 0
output = self.conv2(x + output + xs)
return output
class FFC(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
ratio_gin, ratio_gout, stride=1, padding=0,
dilation=1, groups=1, bias=False, enable_lfu=True,
padding_type='reflect', gated=False, **spectral_kwargs):
super(FFC, self).__init__()
assert stride == 1 or stride == 2, "Stride should be 1 or 2."
self.stride = stride
in_cg = int(in_channels * ratio_gin)
in_cl = in_channels - in_cg
out_cg = int(out_channels * ratio_gout)
out_cl = out_channels - out_cg
#groups_g = 1 if groups == 1 else int(groups * ratio_gout)
#groups_l = 1 if groups == 1 else groups - groups_g
self.ratio_gin = ratio_gin
self.ratio_gout = ratio_gout
self.global_in_num = in_cg
module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
self.convl2l = module(in_cl, out_cl, kernel_size,
stride, padding, dilation, groups, bias, padding_mode=padding_type)
module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
self.convl2g = module(in_cl, out_cg, kernel_size,
stride, padding, dilation, groups, bias, padding_mode=padding_type)
module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
self.convg2l = module(in_cg, out_cl, kernel_size,
stride, padding, dilation, groups, bias, padding_mode=padding_type)
module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
self.convg2g = module(
in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
self.gated = gated
module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
self.gate = module(in_channels, 2, 1)
def forward(self, x):
x_l, x_g = x if type(x) is tuple else (x, 0)
out_xl, out_xg = 0, 0
if self.gated:
total_input_parts = [x_l]
if torch.is_tensor(x_g):
total_input_parts.append(x_g)
total_input = torch.cat(total_input_parts, dim=1)
gates = torch.sigmoid(self.gate(total_input))
g2l_gate, l2g_gate = gates.chunk(2, dim=1)
else:
g2l_gate, l2g_gate = 1, 1
if self.ratio_gout != 1:
out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
if self.ratio_gout != 0:
out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
return out_xl, out_xg
class FFC_BN_ACT(nn.Module):
def __init__(self, in_channels, out_channels,
kernel_size, ratio_gin, ratio_gout,
stride=1, padding=0, dilation=1, groups=1, bias=False,
norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,
padding_type='reflect',
enable_lfu=True, **kwargs):
super(FFC_BN_ACT, self).__init__()
self.ffc = FFC(in_channels, out_channels, kernel_size,
ratio_gin, ratio_gout, stride, padding, dilation,
groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
lnorm = nn.Identity if ratio_gout == 1 else norm_layer
gnorm = nn.Identity if ratio_gout == 0 else norm_layer
global_channels = int(out_channels * ratio_gout)
self.bn_l = lnorm(out_channels - global_channels)
self.bn_g = gnorm(global_channels)
lact = nn.Identity if ratio_gout == 1 else activation_layer
gact = nn.Identity if ratio_gout == 0 else activation_layer
self.act_l = lact(inplace=True)
self.act_g = gact(inplace=True)
def forward(self, x):
x_l, x_g = self.ffc(x)
x_l = self.act_l(self.bn_l(x_l))
x_g = self.act_g(self.bn_g(x_g))
return x_l, x_g
class FFCResnetBlock(nn.Module):
def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
spatial_transform_kwargs=None, inline=False, **conv_kwargs):
super().__init__()
self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
norm_layer=norm_layer,
activation_layer=activation_layer,
padding_type=padding_type,
**conv_kwargs)
self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
norm_layer=norm_layer,
activation_layer=activation_layer,
padding_type=padding_type,
**conv_kwargs)
if spatial_transform_kwargs is not None:
self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)
self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)
self.inline = inline
def forward(self, x):
if self.inline:
x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
else:
x_l, x_g = x if type(x) is tuple else (x, 0)
id_l, id_g = x_l, x_g
x_l, x_g = self.conv1((x_l, x_g))
x_l, x_g = self.conv2((x_l, x_g))
x_l, x_g = id_l + x_l, id_g + x_g
out = x_l, x_g
if self.inline:
out = torch.cat(out, dim=1)
return out
class ConcatTupleLayer(nn.Module):
def forward(self, x):
assert isinstance(x, tuple)
x_l, x_g = x
assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
if not torch.is_tensor(x_g):
return x_l
return torch.cat(x, dim=1)
class FFCResNetGenerator(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', activation_layer=nn.ReLU,
up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),
init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},
spatial_transform_layers=None, spatial_transform_kwargs={},
add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):
assert (n_blocks >= 0)
super().__init__()
model = [nn.ReflectionPad2d(3),
FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,
activation_layer=activation_layer, **init_conv_kwargs)]
### downsample
for i in range(n_downsampling):
mult = 2 ** i
if i == n_downsampling - 1:
cur_conv_kwargs = dict(downsample_conv_kwargs)
cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)
else:
cur_conv_kwargs = downsample_conv_kwargs
model += [FFC_BN_ACT(min(max_features, ngf * mult),
min(max_features, ngf * mult * 2),
kernel_size=3, stride=2, padding=1,
norm_layer=norm_layer,
activation_layer=activation_layer,
**cur_conv_kwargs)]
mult = 2 ** n_downsampling
feats_num_bottleneck = min(max_features, ngf * mult)
### resnet blocks
for i in range(n_blocks):
cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,
norm_layer=norm_layer, **resnet_conv_kwargs)
if spatial_transform_layers is not None and i in spatial_transform_layers:
cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)
model += [cur_resblock]
model += [ConcatTupleLayer()]
### upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
min(max_features, int(ngf * mult / 2)),
kernel_size=3, stride=2, padding=1, output_padding=1),
up_norm_layer(min(max_features, int(ngf * mult / 2))),
up_activation]
if out_ffc:
model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,
norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]
model += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
model.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class FFCNLayerDiscriminator(BaseDiscriminator):
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,
init_conv_kwargs={}, conv_kwargs={}):
super().__init__()
self.n_layers = n_layers
def _act_ctor(inplace=True):
return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)
kw = 3
padw = int(np.ceil((kw-1.0)/2))
sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,
activation_layer=_act_ctor, **init_conv_kwargs)]]
nf = ndf
for n in range(1, n_layers):
nf_prev = nf
nf = min(nf * 2, max_features)
cur_model = [
FFC_BN_ACT(nf_prev, nf,
kernel_size=kw, stride=2, padding=padw,
norm_layer=norm_layer,
activation_layer=_act_ctor,
**conv_kwargs)
]
sequence.append(cur_model)
nf_prev = nf
nf = min(nf * 2, 512)
cur_model = [
FFC_BN_ACT(nf_prev, nf,
kernel_size=kw, stride=1, padding=padw,
norm_layer=norm_layer,
activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),
**conv_kwargs),
ConcatTupleLayer()
]
sequence.append(cur_model)
sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
for n in range(len(sequence)):
setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
def get_all_activations(self, x):
res = [x]
for n in range(self.n_layers + 2):
model = getattr(self, 'model' + str(n))
res.append(model(res[-1]))
return res[1:]
def forward(self, x):
act = self.get_all_activations(x)
feats = []
for out in act[:-1]:
if isinstance(out, tuple):
if torch.is_tensor(out[1]):
out = torch.cat(out, dim=1)
else:
out = out[0]
feats.append(out)
return act[-1], feats

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/multidilated_conv.py Normal file
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import torch
import torch.nn as nn
import random
from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
class MultidilatedConv(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True,
shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs):
super().__init__()
convs = []
self.equal_dim = equal_dim
assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode
if comb_mode in ('cat_out', 'cat_both'):
self.cat_out = True
if equal_dim:
assert out_dim % dilation_num == 0
out_dims = [out_dim // dilation_num] * dilation_num
self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], [])
else:
out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
out_dims.append(out_dim - sum(out_dims))
index = []
starts = [0] + out_dims[:-1]
lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)]
for i in range(out_dims[-1]):
for j in range(dilation_num):
index += list(range(starts[j], starts[j] + lengths[j]))
starts[j] += lengths[j]
self.index = index
assert(len(index) == out_dim)
self.out_dims = out_dims
else:
self.cat_out = False
self.out_dims = [out_dim] * dilation_num
if comb_mode in ('cat_in', 'cat_both'):
if equal_dim:
assert in_dim % dilation_num == 0
in_dims = [in_dim // dilation_num] * dilation_num
else:
in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
in_dims.append(in_dim - sum(in_dims))
self.in_dims = in_dims
self.cat_in = True
else:
self.cat_in = False
self.in_dims = [in_dim] * dilation_num
conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d
dilation = min_dilation
for i in range(dilation_num):
if isinstance(padding, int):
cur_padding = padding * dilation
else:
cur_padding = padding[i]
convs.append(conv_type(
self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs
))
if i > 0 and shared_weights:
convs[-1].weight = convs[0].weight
convs[-1].bias = convs[0].bias
dilation *= 2
self.convs = nn.ModuleList(convs)
self.shuffle_in_channels = shuffle_in_channels
if self.shuffle_in_channels:
# shuffle list as shuffling of tensors is nondeterministic
in_channels_permute = list(range(in_dim))
random.shuffle(in_channels_permute)
# save as buffer so it is saved and loaded with checkpoint
self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute))
def forward(self, x):
if self.shuffle_in_channels:
x = x[:, self.in_channels_permute]
outs = []
if self.cat_in:
if self.equal_dim:
x = x.chunk(len(self.convs), dim=1)
else:
new_x = []
start = 0
for dim in self.in_dims:
new_x.append(x[:, start:start+dim])
start += dim
x = new_x
for i, conv in enumerate(self.convs):
if self.cat_in:
input = x[i]
else:
input = x
outs.append(conv(input))
if self.cat_out:
out = torch.cat(outs, dim=1)[:, self.index]
else:
out = sum(outs)
return out

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from typing import List, Tuple, Union, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.lama.saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
from annotator.lama.saicinpainting.training.modules.pix2pixhd import ResnetBlock
class ResNetHead(nn.Module):
def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
assert (n_blocks >= 0)
super(ResNetHead, self).__init__()
conv_layer = get_conv_block_ctor(conv_kind)
model = [nn.ReflectionPad2d(3),
conv_layer(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
activation]
### downsample
for i in range(n_downsampling):
mult = 2 ** i
model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
norm_layer(ngf * mult * 2),
activation]
mult = 2 ** n_downsampling
### resnet blocks
for i in range(n_blocks):
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=conv_kind)]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class ResNetTail(nn.Module):
def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
add_in_proj=None):
assert (n_blocks >= 0)
super(ResNetTail, self).__init__()
mult = 2 ** n_downsampling
model = []
if add_in_proj is not None:
model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
### resnet blocks
for i in range(n_blocks):
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=conv_kind)]
### upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
output_padding=1),
up_norm_layer(int(ngf * mult / 2)),
up_activation]
self.model = nn.Sequential(*model)
out_layers = []
for _ in range(out_extra_layers_n):
out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
up_norm_layer(ngf),
up_activation]
out_layers += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.out_proj = nn.Sequential(*out_layers)
def forward(self, input, return_last_act=False):
features = self.model(input)
out = self.out_proj(features)
if return_last_act:
return out, features
else:
return out
class MultiscaleResNet(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
out_cumulative=False, return_only_hr=False):
super().__init__()
self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
conv_kind=conv_kind, activation=activation)
for i in range(n_scales)])
tail_in_feats = ngf * (2 ** n_downsampling) + ngf
self.tails = nn.ModuleList([ResNetTail(output_nc,
ngf=ngf, n_downsampling=n_downsampling,
n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
up_activation=up_activation, add_out_act=add_out_act,
out_extra_layers_n=out_extra_layers_n,
add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
for i in range(n_scales)])
self.out_cumulative = out_cumulative
self.return_only_hr = return_only_hr
@property
def num_scales(self):
return len(self.heads)
def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-> Union[torch.Tensor, List[torch.Tensor]]:
"""
:param ms_inputs: List of inputs of different resolutions from HR to LR
:param smallest_scales_num: int or None, number of smallest scales to take at input
:return: Depending on return_only_hr:
True: Only the most HR output
False: List of outputs of different resolutions from HR to LR
"""
if smallest_scales_num is None:
assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
smallest_scales_num = len(self.heads)
else:
assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
cur_heads = self.heads[-smallest_scales_num:]
ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
all_outputs = []
prev_tail_features = None
for i in range(len(ms_features)):
scale_i = -i - 1
cur_tail_input = ms_features[-i - 1]
if prev_tail_features is not None:
if prev_tail_features.shape != cur_tail_input.shape:
prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
mode='bilinear', align_corners=False)
cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
prev_tail_features = cur_tail_feats
all_outputs.append(cur_out)
if self.out_cumulative:
all_outputs_cum = [all_outputs[0]]
for i in range(1, len(ms_features)):
cur_out = all_outputs[i]
cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
mode='bilinear', align_corners=False)
all_outputs_cum.append(cur_out_cum)
all_outputs = all_outputs_cum
if self.return_only_hr:
return all_outputs[-1]
else:
return all_outputs[::-1]
class MultiscaleDiscriminatorSimple(nn.Module):
def __init__(self, ms_impl):
super().__init__()
self.ms_impl = nn.ModuleList(ms_impl)
@property
def num_scales(self):
return len(self.ms_impl)
def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
"""
:param ms_inputs: List of inputs of different resolutions from HR to LR
:param smallest_scales_num: int or None, number of smallest scales to take at input
:return: List of pairs (prediction, features) for different resolutions from HR to LR
"""
if smallest_scales_num is None:
assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
smallest_scales_num = len(self.heads)
else:
assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
(len(self.ms_impl), len(ms_inputs), smallest_scales_num)
return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
class SingleToMultiScaleInputMixin:
def forward(self, x: torch.Tensor) -> List:
orig_height, orig_width = x.shape[2:]
factors = [2 ** i for i in range(self.num_scales)]
ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
for f in factors]
return super().forward(ms_inputs)
class GeneratorMultiToSingleOutputMixin:
def forward(self, x):
return super().forward(x)[0]
class DiscriminatorMultiToSingleOutputMixin:
def forward(self, x):
out_feat_tuples = super().forward(x)
return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
class DiscriminatorMultiToSingleOutputStackedMixin:
def __init__(self, *args, return_feats_only_levels=None, **kwargs):
super().__init__(*args, **kwargs)
self.return_feats_only_levels = return_feats_only_levels
def forward(self, x):
out_feat_tuples = super().forward(x)
outs = [out for out, _ in out_feat_tuples]
scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
mode='bilinear', align_corners=False)
for cur_out in outs[1:]]
out = torch.cat(scaled_outs, dim=1)
if self.return_feats_only_levels is not None:
feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
else:
feat_lists = [flist for _, flist in out_feat_tuples]
feats = [f for flist in feat_lists for f in flist]
return out, feats
class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
pass
class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
pass

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/pix2pixhd.py Normal file
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# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
import collections
from functools import partial
import functools
import logging
from collections import defaultdict
import numpy as np
import torch.nn as nn
from annotator.lama.saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
from annotator.lama.saicinpainting.training.modules.ffc import FFCResnetBlock
from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
class DotDict(defaultdict):
# https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
"""dot.notation access to dictionary attributes"""
__getattr__ = defaultdict.get
__setattr__ = defaultdict.__setitem__
__delattr__ = defaultdict.__delitem__
class Identity(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x
class ResnetBlock(nn.Module):
def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
dilation=1, in_dim=None, groups=1, second_dilation=None):
super(ResnetBlock, self).__init__()
self.in_dim = in_dim
self.dim = dim
if second_dilation is None:
second_dilation = dilation
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
second_dilation=second_dilation)
if self.in_dim is not None:
self.input_conv = nn.Conv2d(in_dim, dim, 1)
self.out_channnels = dim
def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
dilation=1, in_dim=None, groups=1, second_dilation=1):
conv_layer = get_conv_block_ctor(conv_kind)
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(dilation)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(dilation)]
elif padding_type == 'zero':
p = dilation
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
if in_dim is None:
in_dim = dim
conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),
norm_layer(dim),
activation]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(second_dilation)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(second_dilation)]
elif padding_type == 'zero':
p = second_dilation
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),
norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
x_before = x
if self.in_dim is not None:
x = self.input_conv(x)
out = x + self.conv_block(x_before)
return out
class ResnetBlock5x5(nn.Module):
def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
dilation=1, in_dim=None, groups=1, second_dilation=None):
super(ResnetBlock5x5, self).__init__()
self.in_dim = in_dim
self.dim = dim
if second_dilation is None:
second_dilation = dilation
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
second_dilation=second_dilation)
if self.in_dim is not None:
self.input_conv = nn.Conv2d(in_dim, dim, 1)
self.out_channnels = dim
def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
dilation=1, in_dim=None, groups=1, second_dilation=1):
conv_layer = get_conv_block_ctor(conv_kind)
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(dilation * 2)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(dilation * 2)]
elif padding_type == 'zero':
p = dilation * 2
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
if in_dim is None:
in_dim = dim
conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
norm_layer(dim),
activation]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
elif padding_type == 'zero':
p = second_dilation * 2
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
x_before = x
if self.in_dim is not None:
x = self.input_conv(x)
out = x + self.conv_block(x_before)
return out
class MultidilatedResnetBlock(nn.Module):
def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
super().__init__()
self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)
def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
conv_block = []
conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
norm_layer(dim),
activation]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
out = x + self.conv_block(x)
return out
class MultiDilatedGlobalGenerator(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
n_blocks=3, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default',
deconv_kind='convtranspose', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
add_out_act=True, max_features=1024, multidilation_kwargs={},
ffc_positions=None, ffc_kwargs={}):
assert (n_blocks >= 0)
super().__init__()
conv_layer = get_conv_block_ctor(conv_kind)
resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
norm_layer = get_norm_layer(norm_layer)
if affine is not None:
norm_layer = partial(norm_layer, affine=affine)
up_norm_layer = get_norm_layer(up_norm_layer)
if affine is not None:
up_norm_layer = partial(up_norm_layer, affine=affine)
model = [nn.ReflectionPad2d(3),
conv_layer(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
activation]
identity = Identity()
### downsample
for i in range(n_downsampling):
mult = 2 ** i
model += [conv_layer(min(max_features, ngf * mult),
min(max_features, ngf * mult * 2),
kernel_size=3, stride=2, padding=1),
norm_layer(min(max_features, ngf * mult * 2)),
activation]
mult = 2 ** n_downsampling
feats_num_bottleneck = min(max_features, ngf * mult)
### resnet blocks
for i in range(n_blocks):
if ffc_positions is not None and i in ffc_positions:
model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
inline=True, **ffc_kwargs)]
model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
conv_layer=resnet_conv_layer, activation=activation,
norm_layer=norm_layer)]
### upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
model += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
model.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class ConfigGlobalGenerator(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
n_blocks=3, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default',
deconv_kind='convtranspose', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
add_out_act=True, max_features=1024,
manual_block_spec=[],
resnet_block_kind='multidilatedresnetblock',
resnet_conv_kind='multidilated',
resnet_dilation=1,
multidilation_kwargs={}):
assert (n_blocks >= 0)
super().__init__()
conv_layer = get_conv_block_ctor(conv_kind)
resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
norm_layer = get_norm_layer(norm_layer)
if affine is not None:
norm_layer = partial(norm_layer, affine=affine)
up_norm_layer = get_norm_layer(up_norm_layer)
if affine is not None:
up_norm_layer = partial(up_norm_layer, affine=affine)
model = [nn.ReflectionPad2d(3),
conv_layer(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
activation]
identity = Identity()
### downsample
for i in range(n_downsampling):
mult = 2 ** i
model += [conv_layer(min(max_features, ngf * mult),
min(max_features, ngf * mult * 2),
kernel_size=3, stride=2, padding=1),
norm_layer(min(max_features, ngf * mult * 2)),
activation]
mult = 2 ** n_downsampling
feats_num_bottleneck = min(max_features, ngf * mult)
if len(manual_block_spec) == 0:
manual_block_spec = [
DotDict(lambda : None, {
'n_blocks': n_blocks,
'use_default': True})
]
### resnet blocks
for block_spec in manual_block_spec:
def make_and_add_blocks(model, block_spec):
block_spec = DotDict(lambda : None, block_spec)
if not block_spec.use_default:
resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
resnet_conv_kind = block_spec.resnet_conv_kind
resnet_block_kind = block_spec.resnet_block_kind
if block_spec.resnet_dilation is not None:
resnet_dilation = block_spec.resnet_dilation
for i in range(block_spec.n_blocks):
if resnet_block_kind == "multidilatedresnetblock":
model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
conv_layer=resnet_conv_layer, activation=activation,
norm_layer=norm_layer)]
if resnet_block_kind == "resnetblock":
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=resnet_conv_kind)]
if resnet_block_kind == "resnetblock5x5":
model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=resnet_conv_kind)]
if resnet_block_kind == "resnetblockdwdil":
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
make_and_add_blocks(model, block_spec)
### upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
model += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
model.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
blocks = []
for i in range(dilated_blocks_n):
if dilation_block_kind == 'simple':
blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
elif dilation_block_kind == 'multi':
blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
else:
raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
return blocks
class GlobalGenerator(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, affine=None,
up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
dilated_blocks_n_middle=0,
add_out_act=True,
max_features=1024, is_resblock_depthwise=False,
ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
dilation_block_kind='simple', multidilation_kwargs={}):
assert (n_blocks >= 0)
super().__init__()
conv_layer = get_conv_block_ctor(conv_kind)
norm_layer = get_norm_layer(norm_layer)
if affine is not None:
norm_layer = partial(norm_layer, affine=affine)
up_norm_layer = get_norm_layer(up_norm_layer)
if affine is not None:
up_norm_layer = partial(up_norm_layer, affine=affine)
if ffc_positions is not None:
ffc_positions = collections.Counter(ffc_positions)
model = [nn.ReflectionPad2d(3),
conv_layer(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
activation]
identity = Identity()
### downsample
for i in range(n_downsampling):
mult = 2 ** i
model += [conv_layer(min(max_features, ngf * mult),
min(max_features, ngf * mult * 2),
kernel_size=3, stride=2, padding=1),
norm_layer(min(max_features, ngf * mult * 2)),
activation]
mult = 2 ** n_downsampling
feats_num_bottleneck = min(max_features, ngf * mult)
dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
activation=activation, norm_layer=norm_layer)
if dilation_block_kind == 'simple':
dilated_block_kwargs['conv_kind'] = conv_kind
elif dilation_block_kind == 'multi':
dilated_block_kwargs['conv_layer'] = functools.partial(
get_conv_block_ctor('multidilated'), **multidilation_kwargs)
# dilated blocks at the start of the bottleneck sausage
if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)
# resnet blocks
for i in range(n_blocks):
# dilated blocks at the middle of the bottleneck sausage
if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
if ffc_positions is not None and i in ffc_positions:
for _ in range(ffc_positions[i]): # same position can occur more than once
model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
inline=True, **ffc_kwargs)]
if is_resblock_depthwise:
resblock_groups = feats_num_bottleneck
else:
resblock_groups = 1
model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
dilation=dilation, second_dilation=second_dilation)]
# dilated blocks at the end of the bottleneck sausage
if dilated_blocks_n is not None and dilated_blocks_n > 0:
model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)
# upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
min(max_features, int(ngf * mult / 2)),
kernel_size=3, stride=2, padding=1, output_padding=1),
up_norm_layer(min(max_features, int(ngf * mult / 2))),
up_activation]
model += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
model.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class GlobalGeneratorGated(GlobalGenerator):
def __init__(self, *args, **kwargs):
real_kwargs=dict(
conv_kind='gated_bn_relu',
activation=nn.Identity(),
norm_layer=nn.Identity
)
real_kwargs.update(kwargs)
super().__init__(*args, **real_kwargs)
class GlobalGeneratorFromSuperChannels(nn.Module):
def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
super().__init__()
self.n_downsampling = n_downsampling
norm_layer = get_norm_layer(norm_layer)
if type(norm_layer) == functools.partial:
use_bias = (norm_layer.func == nn.InstanceNorm2d)
else:
use_bias = (norm_layer == nn.InstanceNorm2d)
channels = self.convert_super_channels(super_channels)
self.channels = channels
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
norm_layer(channels[0]),
nn.ReLU(True)]
for i in range(n_downsampling): # add downsampling layers
mult = 2 ** i
model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
norm_layer(channels[1+i]),
nn.ReLU(True)]
mult = 2 ** n_downsampling
n_blocks1 = n_blocks // 3
n_blocks2 = n_blocks1
n_blocks3 = n_blocks - n_blocks1 - n_blocks2
for i in range(n_blocks1):
c = n_downsampling
dim = channels[c]
model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]
for i in range(n_blocks2):
c = n_downsampling+1
dim = channels[c]
kwargs = {}
if i == 0:
kwargs = {"in_dim": channels[c-1]}
model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
for i in range(n_blocks3):
c = n_downsampling+2
dim = channels[c]
kwargs = {}
if i == 0:
kwargs = {"in_dim": channels[c-1]}
model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
for i in range(n_downsampling): # add upsampling layers
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
channels[n_downsampling+3+i+1],
kernel_size=3, stride=2,
padding=1, output_padding=1,
bias=use_bias),
norm_layer(channels[n_downsampling+3+i+1]),
nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]
if add_out_act:
model.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.model = nn.Sequential(*model)
def convert_super_channels(self, super_channels):
n_downsampling = self.n_downsampling
result = []
cnt = 0
if n_downsampling == 2:
N1 = 10
elif n_downsampling == 3:
N1 = 13
else:
raise NotImplementedError
for i in range(0, N1):
if i in [1,4,7,10]:
channel = super_channels[cnt] * (2 ** cnt)
config = {'channel': channel}
result.append(channel)
logging.info(f"Downsample channels {result[-1]}")
cnt += 1
for i in range(3):
for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
if len(super_channels) == 6:
channel = super_channels[3] * 4
else:
channel = super_channels[i + 3] * 4
config = {'channel': channel}
if counter == 0:
result.append(channel)
logging.info(f"Bottleneck channels {result[-1]}")
cnt = 2
for i in range(N1+9, N1+21):
if i in [22, 25,28]:
cnt -= 1
if len(super_channels) == 6:
channel = super_channels[5 - cnt] * (2 ** cnt)
else:
channel = super_channels[7 - cnt] * (2 ** cnt)
result.append(int(channel))
logging.info(f"Upsample channels {result[-1]}")
return result
def forward(self, input):
return self.model(input)
# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(BaseDiscriminator):
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
super().__init__()
self.n_layers = n_layers
kw = 4
padw = int(np.ceil((kw-1.0)/2))
sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)]]
nf = ndf
for n in range(1, n_layers):
nf_prev = nf
nf = min(nf * 2, 512)
cur_model = []
cur_model += [
nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
norm_layer(nf),
nn.LeakyReLU(0.2, True)
]
sequence.append(cur_model)
nf_prev = nf
nf = min(nf * 2, 512)
cur_model = []
cur_model += [
nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
norm_layer(nf),
nn.LeakyReLU(0.2, True)
]
sequence.append(cur_model)
sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
for n in range(len(sequence)):
setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
def get_all_activations(self, x):
res = [x]
for n in range(self.n_layers + 2):
model = getattr(self, 'model' + str(n))
res.append(model(res[-1]))
return res[1:]
def forward(self, x):
act = self.get_all_activations(x)
return act[-1], act[:-1]
class MultidilatedNLayerDiscriminator(BaseDiscriminator):
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
super().__init__()
self.n_layers = n_layers
kw = 4
padw = int(np.ceil((kw-1.0)/2))
sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)]]
nf = ndf
for n in range(1, n_layers):
nf_prev = nf
nf = min(nf * 2, 512)
cur_model = []
cur_model += [
MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
norm_layer(nf),
nn.LeakyReLU(0.2, True)
]
sequence.append(cur_model)
nf_prev = nf
nf = min(nf * 2, 512)
cur_model = []
cur_model += [
nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
norm_layer(nf),
nn.LeakyReLU(0.2, True)
]
sequence.append(cur_model)
sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
for n in range(len(sequence)):
setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
def get_all_activations(self, x):
res = [x]
for n in range(self.n_layers + 2):
model = getattr(self, 'model' + str(n))
res.append(model(res[-1]))
return res[1:]
def forward(self, x):
act = self.get_all_activations(x)
return act[-1], act[:-1]
class NLayerDiscriminatorAsGen(NLayerDiscriminator):
def forward(self, x):
return super().forward(x)[0]

49
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/spatial_transform.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia.geometry.transform import rotate
class LearnableSpatialTransformWrapper(nn.Module):
def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
super().__init__()
self.impl = impl
self.angle = torch.rand(1) * angle_init_range
if train_angle:
self.angle = nn.Parameter(self.angle, requires_grad=True)
self.pad_coef = pad_coef
def forward(self, x):
if torch.is_tensor(x):
return self.inverse_transform(self.impl(self.transform(x)), x)
elif isinstance(x, tuple):
x_trans = tuple(self.transform(elem) for elem in x)
y_trans = self.impl(x_trans)
return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x))
else:
raise ValueError(f'Unexpected input type {type(x)}')
def transform(self, x):
height, width = x.shape[2:]
pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect')
x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded))
return x_padded_rotated
def inverse_transform(self, y_padded_rotated, orig_x):
height, width = orig_x.shape[2:]
pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated))
y_height, y_width = y_padded.shape[2:]
y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
return y
if __name__ == '__main__':
layer = LearnableSpatialTransformWrapper(nn.Identity())
x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float()
y = layer(x)
assert x.shape == y.shape
assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1])
print('all ok')

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/modules/squeeze_excitation.py Normal file
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import torch.nn as nn
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
res = x * y.expand_as(x)
return res

29
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/trainers/__init__.py Normal file
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import logging
import torch
from annotator.lama.saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule
def get_training_model_class(kind):
if kind == 'default':
return DefaultInpaintingTrainingModule
raise ValueError(f'Unknown trainer module {kind}')
def make_training_model(config):
kind = config.training_model.kind
kwargs = dict(config.training_model)
kwargs.pop('kind')
kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp'
logging.info(f'Make training model {kind}')
cls = get_training_model_class(kind)
return cls(config, **kwargs)
def load_checkpoint(train_config, path, map_location='cuda', strict=True):
model = make_training_model(train_config).generator
state = torch.load(path, map_location=map_location)
model.load_state_dict(state, strict=strict)
return model

293
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/trainers/base.py Normal file
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import copy
import logging
from typing import Dict, Tuple
import pandas as pd
import pytorch_lightning as ptl
import torch
import torch.nn as nn
import torch.nn.functional as F
# from torch.utils.data import DistributedSampler
# from annotator.lama.saicinpainting.evaluation import make_evaluator
# from annotator.lama.saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
# from annotator.lama.saicinpainting.training.losses.adversarial import make_discrim_loss
# from annotator.lama.saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
from annotator.lama.saicinpainting.training.modules import make_generator #, make_discriminator
# from annotator.lama.saicinpainting.training.visualizers import make_visualizer
from annotator.lama.saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \
get_has_ddp_rank
LOGGER = logging.getLogger(__name__)
def make_optimizer(parameters, kind='adamw', **kwargs):
if kind == 'adam':
optimizer_class = torch.optim.Adam
elif kind == 'adamw':
optimizer_class = torch.optim.AdamW
else:
raise ValueError(f'Unknown optimizer kind {kind}')
return optimizer_class(parameters, **kwargs)
def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999):
with torch.no_grad():
res_params = dict(result.named_parameters())
new_params = dict(new_iterate_model.named_parameters())
for k in res_params.keys():
res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay)
def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'):
batch_size, _, height, width = base_tensor.shape
cur_height, cur_width = height, width
result = []
align_corners = False if scale_mode in ('bilinear', 'bicubic') else None
for _ in range(scales):
cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device)
cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners)
result.append(cur_sample_scaled)
cur_height //= 2
cur_width //= 2
return torch.cat(result, dim=1)
class BaseInpaintingTrainingModule(ptl.LightningModule):
def __init__(self, config, use_ddp, *args, predict_only=False, visualize_each_iters=100,
average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000,
average_generator_period=10, store_discr_outputs_for_vis=False,
**kwargs):
super().__init__(*args, **kwargs)
LOGGER.info('BaseInpaintingTrainingModule init called')
self.config = config
self.generator = make_generator(config, **self.config.generator)
self.use_ddp = use_ddp
if not get_has_ddp_rank():
LOGGER.info(f'Generator\n{self.generator}')
# if not predict_only:
# self.save_hyperparameters(self.config)
# self.discriminator = make_discriminator(**self.config.discriminator)
# self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial)
# self.visualizer = make_visualizer(**self.config.visualizer)
# self.val_evaluator = make_evaluator(**self.config.evaluator)
# self.test_evaluator = make_evaluator(**self.config.evaluator)
#
# if not get_has_ddp_rank():
# LOGGER.info(f'Discriminator\n{self.discriminator}')
#
# extra_val = self.config.data.get('extra_val', ())
# if extra_val:
# self.extra_val_titles = list(extra_val)
# self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator)
# for k in extra_val})
# else:
# self.extra_evaluators = {}
#
# self.average_generator = average_generator
# self.generator_avg_beta = generator_avg_beta
# self.average_generator_start_step = average_generator_start_step
# self.average_generator_period = average_generator_period
# self.generator_average = None
# self.last_generator_averaging_step = -1
# self.store_discr_outputs_for_vis = store_discr_outputs_for_vis
#
# if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0:
# self.loss_l1 = nn.L1Loss(reduction='none')
#
# if self.config.losses.get("mse", {"weight": 0})['weight'] > 0:
# self.loss_mse = nn.MSELoss(reduction='none')
#
# if self.config.losses.perceptual.weight > 0:
# self.loss_pl = PerceptualLoss()
#
# # if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0:
# # self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl)
# # else:
# # self.loss_resnet_pl = None
#
# self.loss_resnet_pl = None
self.visualize_each_iters = visualize_each_iters
LOGGER.info('BaseInpaintingTrainingModule init done')
def configure_optimizers(self):
discriminator_params = list(self.discriminator.parameters())
return [
dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)),
dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)),
]
def train_dataloader(self):
kwargs = dict(self.config.data.train)
if self.use_ddp:
kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
rank=self.trainer.global_rank,
shuffle=True)
dataloader = make_default_train_dataloader(**self.config.data.train)
return dataloader
def val_dataloader(self):
res = [make_default_val_dataloader(**self.config.data.val)]
if self.config.data.visual_test is not None:
res = res + [make_default_val_dataloader(**self.config.data.visual_test)]
else:
res = res + res
extra_val = self.config.data.get('extra_val', ())
if extra_val:
res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles]
return res
def training_step(self, batch, batch_idx, optimizer_idx=None):
self._is_training_step = True
return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx)
def validation_step(self, batch, batch_idx, dataloader_idx):
extra_val_key = None
if dataloader_idx == 0:
mode = 'val'
elif dataloader_idx == 1:
mode = 'test'
else:
mode = 'extra_val'
extra_val_key = self.extra_val_titles[dataloader_idx - 2]
self._is_training_step = False
return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)
def training_step_end(self, batch_parts_outputs):
if self.training and self.average_generator \
and self.global_step >= self.average_generator_start_step \
and self.global_step >= self.last_generator_averaging_step + self.average_generator_period:
if self.generator_average is None:
self.generator_average = copy.deepcopy(self.generator)
else:
update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta)
self.last_generator_averaging_step = self.global_step
full_loss = (batch_parts_outputs['loss'].mean()
if torch.is_tensor(batch_parts_outputs['loss']) # loss is not tensor when no discriminator used
else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True))
log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()}
self.log_dict(log_info, on_step=True, on_epoch=False)
return full_loss
def validation_epoch_end(self, outputs):
outputs = [step_out for out_group in outputs for step_out in out_group]
averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs)
self.log_dict({k: v.mean() for k, v in averaged_logs.items()})
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# standard validation
val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s]
val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states)
val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0)
val_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, '
f'total {self.global_step} iterations:\n{val_evaluator_res_df}')
for k, v in flatten_dict(val_evaluator_res).items():
self.log(f'val_{k}', v)
# standard visual test
test_evaluator_states = [s['test_evaluator_state'] for s in outputs
if 'test_evaluator_state' in s]
test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states)
test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0)
test_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, '
f'total {self.global_step} iterations:\n{test_evaluator_res_df}')
for k, v in flatten_dict(test_evaluator_res).items():
self.log(f'test_{k}', v)
# extra validations
if self.extra_evaluators:
for cur_eval_title, cur_evaluator in self.extra_evaluators.items():
cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state'
cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s]
cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states)
cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0)
cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, '
f'total {self.global_step} iterations:\n{cur_evaluator_res_df}')
for k, v in flatten_dict(cur_evaluator_res).items():
self.log(f'extra_val_{cur_eval_title}_{k}', v)
def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None):
if optimizer_idx == 0: # step for generator
set_requires_grad(self.generator, True)
set_requires_grad(self.discriminator, False)
elif optimizer_idx == 1: # step for discriminator
set_requires_grad(self.generator, False)
set_requires_grad(self.discriminator, True)
batch = self(batch)
total_loss = 0
metrics = {}
if optimizer_idx is None or optimizer_idx == 0: # step for generator
total_loss, metrics = self.generator_loss(batch)
elif optimizer_idx is None or optimizer_idx == 1: # step for discriminator
if self.config.losses.adversarial.weight > 0:
total_loss, metrics = self.discriminator_loss(batch)
if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'):
if self.config.losses.adversarial.weight > 0:
if self.store_discr_outputs_for_vis:
with torch.no_grad():
self.store_discr_outputs(batch)
vis_suffix = f'_{mode}'
if mode == 'extra_val':
vis_suffix += f'_{extra_val_key}'
self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix)
metrics_prefix = f'{mode}_'
if mode == 'extra_val':
metrics_prefix += f'{extra_val_key}_'
result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix))
if mode == 'val':
result['val_evaluator_state'] = self.val_evaluator.process_batch(batch)
elif mode == 'test':
result['test_evaluator_state'] = self.test_evaluator.process_batch(batch)
elif mode == 'extra_val':
result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch)
return result
def get_current_generator(self, no_average=False):
if not no_average and not self.training and self.average_generator and self.generator_average is not None:
return self.generator_average
return self.generator
def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys"""
raise NotImplementedError()
def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
raise NotImplementedError()
def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
raise NotImplementedError()
def store_discr_outputs(self, batch):
out_size = batch['image'].shape[2:]
discr_real_out, _ = self.discriminator(batch['image'])
discr_fake_out, _ = self.discriminator(batch['predicted_image'])
batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest')
batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest')
batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake']
def get_ddp_rank(self):
return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None

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import logging
import torch
import torch.nn.functional as F
from omegaconf import OmegaConf
# from annotator.lama.saicinpainting.training.data.datasets import make_constant_area_crop_params
from annotator.lama.saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter
from annotator.lama.saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss
# from annotator.lama.saicinpainting.training.modules.fake_fakes import FakeFakesGenerator
from annotator.lama.saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise
from annotator.lama.saicinpainting.utils import add_prefix_to_keys, get_ramp
LOGGER = logging.getLogger(__name__)
def make_constant_area_crop_batch(batch, **kwargs):
crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2],
img_width=batch['image'].shape[3],
**kwargs)
batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width]
batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width]
return batch
class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule):
def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image',
add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None,
distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False,
fake_fakes_proba=0, fake_fakes_generator_kwargs=None,
**kwargs):
super().__init__(*args, **kwargs)
self.concat_mask = concat_mask
self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None
self.image_to_discriminator = image_to_discriminator
self.add_noise_kwargs = add_noise_kwargs
self.noise_fill_hole = noise_fill_hole
self.const_area_crop_kwargs = const_area_crop_kwargs
self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \
if distance_weighter_kwargs is not None else None
self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr
self.fake_fakes_proba = fake_fakes_proba
if self.fake_fakes_proba > 1e-3:
self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {}))
def forward(self, batch):
if self.training and self.rescale_size_getter is not None:
cur_size = self.rescale_size_getter(self.global_step)
batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False)
batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest')
if self.training and self.const_area_crop_kwargs is not None:
batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs)
img = batch['image']
mask = batch['mask']
masked_img = img * (1 - mask)
if self.add_noise_kwargs is not None:
noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs)
if self.noise_fill_hole:
masked_img = masked_img + mask * noise[:, :masked_img.shape[1]]
masked_img = torch.cat([masked_img, noise], dim=1)
if self.concat_mask:
masked_img = torch.cat([masked_img, mask], dim=1)
batch['predicted_image'] = self.generator(masked_img)
batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image']
if self.fake_fakes_proba > 1e-3:
if self.training and torch.rand(1).item() < self.fake_fakes_proba:
batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask)
batch['use_fake_fakes'] = True
else:
batch['fake_fakes'] = torch.zeros_like(img)
batch['fake_fakes_masks'] = torch.zeros_like(mask)
batch['use_fake_fakes'] = False
batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \
if self.refine_mask_for_losses is not None and self.training \
else mask
return batch
def generator_loss(self, batch):
img = batch['image']
predicted_img = batch[self.image_to_discriminator]
original_mask = batch['mask']
supervised_mask = batch['mask_for_losses']
# L1
l1_value = masked_l1_loss(predicted_img, img, supervised_mask,
self.config.losses.l1.weight_known,
self.config.losses.l1.weight_missing)
total_loss = l1_value
metrics = dict(gen_l1=l1_value)
# vgg-based perceptual loss
if self.config.losses.perceptual.weight > 0:
pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight
total_loss = total_loss + pl_value
metrics['gen_pl'] = pl_value
# discriminator
# adversarial_loss calls backward by itself
mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask
self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img,
generator=self.generator, discriminator=self.discriminator)
discr_real_pred, discr_real_features = self.discriminator(img)
discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img,
fake_batch=predicted_img,
discr_real_pred=discr_real_pred,
discr_fake_pred=discr_fake_pred,
mask=mask_for_discr)
total_loss = total_loss + adv_gen_loss
metrics['gen_adv'] = adv_gen_loss
metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
# feature matching
if self.config.losses.feature_matching.weight > 0:
need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False)
mask_for_fm = supervised_mask if need_mask_in_fm else None
fm_value = feature_matching_loss(discr_fake_features, discr_real_features,
mask=mask_for_fm) * self.config.losses.feature_matching.weight
total_loss = total_loss + fm_value
metrics['gen_fm'] = fm_value
if self.loss_resnet_pl is not None:
resnet_pl_value = self.loss_resnet_pl(predicted_img, img)
total_loss = total_loss + resnet_pl_value
metrics['gen_resnet_pl'] = resnet_pl_value
return total_loss, metrics
def discriminator_loss(self, batch):
total_loss = 0
metrics = {}
predicted_img = batch[self.image_to_discriminator].detach()
self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img,
generator=self.generator, discriminator=self.discriminator)
discr_real_pred, discr_real_features = self.discriminator(batch['image'])
discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'],
fake_batch=predicted_img,
discr_real_pred=discr_real_pred,
discr_fake_pred=discr_fake_pred,
mask=batch['mask'])
total_loss = total_loss + adv_discr_loss
metrics['discr_adv'] = adv_discr_loss
metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
if batch.get('use_fake_fakes', False):
fake_fakes = batch['fake_fakes']
self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes,
generator=self.generator, discriminator=self.discriminator)
discr_fake_fakes_pred, _ = self.discriminator(fake_fakes)
fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss(
real_batch=batch['image'],
fake_batch=fake_fakes,
discr_real_pred=discr_real_pred,
discr_fake_pred=discr_fake_fakes_pred,
mask=batch['mask']
)
total_loss = total_loss + fake_fakes_adv_discr_loss
metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss
metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_'))
return total_loss, metrics

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/visualizers/__init__.py Normal file
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import logging
from annotator.lama.saicinpainting.training.visualizers.directory import DirectoryVisualizer
from annotator.lama.saicinpainting.training.visualizers.noop import NoopVisualizer
def make_visualizer(kind, **kwargs):
logging.info(f'Make visualizer {kind}')
if kind == 'directory':
return DirectoryVisualizer(**kwargs)
if kind == 'noop':
return NoopVisualizer()
raise ValueError(f'Unknown visualizer kind {kind}')

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/visualizers/base.py Normal file
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import abc
from typing import Dict, List
import numpy as np
import torch
from skimage import color
from skimage.segmentation import mark_boundaries
from . import colors
COLORS, _ = colors.generate_colors(151) # 151 - max classes for semantic segmentation
class BaseVisualizer:
@abc.abstractmethod
def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
"""
Take a batch, make an image from it and visualize
"""
raise NotImplementedError()
def visualize_mask_and_images(images_dict: Dict[str, np.ndarray], keys: List[str],
last_without_mask=True, rescale_keys=None, mask_only_first=None,
black_mask=False) -> np.ndarray:
mask = images_dict['mask'] > 0.5
result = []
for i, k in enumerate(keys):
img = images_dict[k]
img = np.transpose(img, (1, 2, 0))
if rescale_keys is not None and k in rescale_keys:
img = img - img.min()
img /= img.max() + 1e-5
if len(img.shape) == 2:
img = np.expand_dims(img, 2)
if img.shape[2] == 1:
img = np.repeat(img, 3, axis=2)
elif (img.shape[2] > 3):
img_classes = img.argmax(2)
img = color.label2rgb(img_classes, colors=COLORS)
if mask_only_first:
need_mark_boundaries = i == 0
else:
need_mark_boundaries = i < len(keys) - 1 or not last_without_mask
if need_mark_boundaries:
if black_mask:
img = img * (1 - mask[0][..., None])
img = mark_boundaries(img,
mask[0],
color=(1., 0., 0.),
outline_color=(1., 1., 1.),
mode='thick')
result.append(img)
return np.concatenate(result, axis=1)
def visualize_mask_and_images_batch(batch: Dict[str, torch.Tensor], keys: List[str], max_items=10,
last_without_mask=True, rescale_keys=None) -> np.ndarray:
batch = {k: tens.detach().cpu().numpy() for k, tens in batch.items()
if k in keys or k == 'mask'}
batch_size = next(iter(batch.values())).shape[0]
items_to_vis = min(batch_size, max_items)
result = []
for i in range(items_to_vis):
cur_dct = {k: tens[i] for k, tens in batch.items()}
result.append(visualize_mask_and_images(cur_dct, keys, last_without_mask=last_without_mask,
rescale_keys=rescale_keys))
return np.concatenate(result, axis=0)

76
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/visualizers/colors.py Normal file
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import random
import colorsys
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False):
# https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
"""
Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
:param nlabels: Number of labels (size of colormap)
:param type: 'bright' for strong colors, 'soft' for pastel colors
:param first_color_black: Option to use first color as black, True or False
:param last_color_black: Option to use last color as black, True or False
:param verbose: Prints the number of labels and shows the colormap. True or False
:return: colormap for matplotlib
"""
if type not in ('bright', 'soft'):
print ('Please choose "bright" or "soft" for type')
return
if verbose:
print('Number of labels: ' + str(nlabels))
# Generate color map for bright colors, based on hsv
if type == 'bright':
randHSVcolors = [(np.random.uniform(low=0.0, high=1),
np.random.uniform(low=0.2, high=1),
np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]
# Convert HSV list to RGB
randRGBcolors = []
for HSVcolor in randHSVcolors:
randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
if first_color_black:
randRGBcolors[0] = [0, 0, 0]
if last_color_black:
randRGBcolors[-1] = [0, 0, 0]
random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
# Generate soft pastel colors, by limiting the RGB spectrum
if type == 'soft':
low = 0.6
high = 0.95
randRGBcolors = [(np.random.uniform(low=low, high=high),
np.random.uniform(low=low, high=high),
np.random.uniform(low=low, high=high)) for i in range(nlabels)]
if first_color_black:
randRGBcolors[0] = [0, 0, 0]
if last_color_black:
randRGBcolors[-1] = [0, 0, 0]
random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
# Display colorbar
if verbose:
from matplotlib import colors, colorbar
from matplotlib import pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
bounds = np.linspace(0, nlabels, nlabels + 1)
norm = colors.BoundaryNorm(bounds, nlabels)
cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,
boundaries=bounds, format='%1i', orientation=u'horizontal')
return randRGBcolors, random_colormap

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extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/visualizers/directory.py Normal file
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import os
import cv2
import numpy as np
from annotator.lama.saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch
from annotator.lama.saicinpainting.utils import check_and_warn_input_range
class DirectoryVisualizer(BaseVisualizer):
DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ')
def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10,
last_without_mask=True, rescale_keys=None):
self.outdir = outdir
os.makedirs(self.outdir, exist_ok=True)
self.key_order = key_order
self.max_items_in_batch = max_items_in_batch
self.last_without_mask = last_without_mask
self.rescale_keys = rescale_keys
def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image')
vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch,
last_without_mask=self.last_without_mask,
rescale_keys=self.rescale_keys)
vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')
curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}')
os.makedirs(curoutdir, exist_ok=True)
rank_suffix = f'_r{rank}' if rank is not None else ''
out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg')
vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)
cv2.imwrite(out_fname, vis_img)

9
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/training/visualizers/noop.py Normal file
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from annotator.lama.saicinpainting.training.visualizers.base import BaseVisualizer
class NoopVisualizer(BaseVisualizer):
def __init__(self, *args, **kwargs):
pass
def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
pass

174
extensions-builtin/sd_forge_controlnet/annotator/lama/saicinpainting/utils.py Normal file
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import bisect
import functools
import logging
import numbers
import os
import signal
import sys
import traceback
import warnings
import torch
from pytorch_lightning import seed_everything
LOGGER = logging.getLogger(__name__)
def check_and_warn_input_range(tensor, min_value, max_value, name):
actual_min = tensor.min()
actual_max = tensor.max()
if actual_min < min_value or actual_max > max_value:
warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}")
def sum_dict_with_prefix(target, cur_dict, prefix, default=0):
for k, v in cur_dict.items():
target_key = prefix + k
target[target_key] = target.get(target_key, default) + v
def average_dicts(dict_list):
result = {}
norm = 1e-3
for dct in dict_list:
sum_dict_with_prefix(result, dct, '')
norm += 1
for k in list(result):
result[k] /= norm
return result
def add_prefix_to_keys(dct, prefix):
return {prefix + k: v for k, v in dct.items()}
def set_requires_grad(module, value):
for param in module.parameters():
param.requires_grad = value
def flatten_dict(dct):
result = {}
for k, v in dct.items():
if isinstance(k, tuple):
k = '_'.join(k)
if isinstance(v, dict):
for sub_k, sub_v in flatten_dict(v).items():
result[f'{k}_{sub_k}'] = sub_v
else:
result[k] = v
return result
class LinearRamp:
def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0):
self.start_value = start_value
self.end_value = end_value
self.start_iter = start_iter
self.end_iter = end_iter
def __call__(self, i):
if i < self.start_iter:
return self.start_value
if i >= self.end_iter:
return self.end_value
part = (i - self.start_iter) / (self.end_iter - self.start_iter)
return self.start_value * (1 - part) + self.end_value * part
class LadderRamp:
def __init__(self, start_iters, values):
self.start_iters = start_iters
self.values = values
assert len(values) == len(start_iters) + 1, (len(values), len(start_iters))
def __call__(self, i):
segment_i = bisect.bisect_right(self.start_iters, i)
return self.values[segment_i]
def get_ramp(kind='ladder', **kwargs):
if kind == 'linear':
return LinearRamp(**kwargs)
if kind == 'ladder':
return LadderRamp(**kwargs)
raise ValueError(f'Unexpected ramp kind: {kind}')
def print_traceback_handler(sig, frame):
LOGGER.warning(f'Received signal {sig}')
bt = ''.join(traceback.format_stack())
LOGGER.warning(f'Requested stack trace:\n{bt}')
def register_debug_signal_handlers(sig=None, handler=print_traceback_handler):
LOGGER.warning(f'Setting signal {sig} handler {handler}')
signal.signal(sig, handler)
def handle_deterministic_config(config):
seed = dict(config).get('seed', None)
if seed is None:
return False
seed_everything(seed)
return True
def get_shape(t):
if torch.is_tensor(t):
return tuple(t.shape)
elif isinstance(t, dict):
return {n: get_shape(q) for n, q in t.items()}
elif isinstance(t, (list, tuple)):
return [get_shape(q) for q in t]
elif isinstance(t, numbers.Number):
return type(t)
else:
raise ValueError('unexpected type {}'.format(type(t)))
def get_has_ddp_rank():
master_port = os.environ.get('MASTER_PORT', None)
node_rank = os.environ.get('NODE_RANK', None)
local_rank = os.environ.get('LOCAL_RANK', None)
world_size = os.environ.get('WORLD_SIZE', None)
has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None
return has_rank
def handle_ddp_subprocess():
def main_decorator(main_func):
@functools.wraps(main_func)
def new_main(*args, **kwargs):
# Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE
parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
has_parent = parent_cwd is not None
has_rank = get_has_ddp_rank()
assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
if has_parent:
# we are in the worker
sys.argv.extend([
f'hydra.run.dir={parent_cwd}',
# 'hydra/hydra_logging=disabled',
# 'hydra/job_logging=disabled'
])
# do nothing if this is a top-level process
# TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization
main_func(*args, **kwargs)
return new_main
return main_decorator
def handle_ddp_parent_process():
parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
has_parent = parent_cwd is not None
has_rank = get_has_ddp_rank()
assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
if parent_cwd is None:
os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd()
return has_parent

113
extensions-builtin/sd_forge_controlnet/annotator/leres/__init__.py Normal file
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import cv2
import numpy as np
import torch
import os
from modules import devices, shared
from annotator.annotator_path import models_path
from torchvision.transforms import transforms
# AdelaiDepth/LeReS imports
from .leres.depthmap import estimateleres, estimateboost
from .leres.multi_depth_model_woauxi import RelDepthModel
from .leres.net_tools import strip_prefix_if_present
# pix2pix/merge net imports
from .pix2pix.options.test_options import TestOptions
from .pix2pix.models.pix2pix4depth_model import Pix2Pix4DepthModel
base_model_path = os.path.join(models_path, "leres")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
remote_model_path_leres = "https://huggingface.co/lllyasviel/Annotators/resolve/main/res101.pth"
remote_model_path_pix2pix = "https://huggingface.co/lllyasviel/Annotators/resolve/main/latest_net_G.pth"
model = None
pix2pixmodel = None
def unload_leres_model():
global model, pix2pixmodel
if model is not None:
model = model.cpu()
if pix2pixmodel is not None:
pix2pixmodel = pix2pixmodel.unload_network('G')
def apply_leres(input_image, thr_a, thr_b, boost=False):
global model, pix2pixmodel
if model is None:
model_path = os.path.join(base_model_path, "res101.pth")
old_model_path = os.path.join(old_modeldir, "res101.pth")
if os.path.exists(old_model_path):
model_path = old_model_path
elif not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path_leres, model_dir=base_model_path)
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
model = RelDepthModel(backbone='resnext101')
model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
del checkpoint
if boost and pix2pixmodel is None:
pix2pixmodel_path = os.path.join(base_model_path, "latest_net_G.pth")
if not os.path.exists(pix2pixmodel_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path_pix2pix, model_dir=base_model_path)
opt = TestOptions().parse()
if not torch.cuda.is_available():
opt.gpu_ids = [] # cpu mode
pix2pixmodel = Pix2Pix4DepthModel(opt)
pix2pixmodel.save_dir = base_model_path
pix2pixmodel.load_networks('latest')
pix2pixmodel.eval()
if devices.get_device_for("controlnet").type != 'mps':
model = model.to(devices.get_device_for("controlnet"))
assert input_image.ndim == 3
height, width, dim = input_image.shape
with torch.no_grad():
if boost:
depth = estimateboost(input_image, model, 0, pix2pixmodel, max(width, height))
else:
depth = estimateleres(input_image, model, width, height)
numbytes=2
depth_min = depth.min()
depth_max = depth.max()
max_val = (2**(8*numbytes))-1
# check output before normalizing and mapping to 16 bit
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape)
# single channel, 16 bit image
depth_image = out.astype("uint16")
# convert to uint8
depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0))
# remove near
if thr_a != 0:
thr_a = ((thr_a/100)*255)
depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1]
# invert image
depth_image = cv2.bitwise_not(depth_image)
# remove bg
if thr_b != 0:
thr_b = ((thr_b/100)*255)
depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1]
return depth_image

23
extensions-builtin/sd_forge_controlnet/annotator/leres/leres/LICENSE Normal file
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https://github.com/thygate/stable-diffusion-webui-depthmap-script
MIT License
Copyright (c) 2023 Bob Thiry
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

199
extensions-builtin/sd_forge_controlnet/annotator/leres/leres/Resnet.py Normal file
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import torch.nn as nn
import torch.nn as NN
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = NN.BatchNorm2d(planes) #NN.BatchNorm2d
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = NN.BatchNorm2d(planes * self.expansion) #NN.BatchNorm2d
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = NN.BatchNorm2d(64) #NN.BatchNorm2d
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
#self.avgpool = nn.AvgPool2d(7, stride=1)
#self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
NN.BatchNorm2d(planes * block.expansion), #NN.BatchNorm2d
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
features = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
features.append(x)
x = self.layer2(x)
features.append(x)
x = self.layer3(x)
features.append(x)
x = self.layer4(x)
features.append(x)
return features
def resnet18(pretrained=True, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
return model
def resnet34(pretrained=True, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
return model
def resnet50(pretrained=True, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
return model
def resnet101(pretrained=True, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model
def resnet152(pretrained=True, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
return model

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#!/usr/bin/env python
# coding: utf-8
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
__all__ = ['resnext101_32x8d']
model_urls = {
'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
#self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
#self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def _forward_impl(self, x):
# See note [TorchScript super()]
features = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
features.append(x)
x = self.layer2(x)
features.append(x)
x = self.layer3(x)
features.append(x)
x = self.layer4(x)
features.append(x)
#x = self.avgpool(x)
#x = torch.flatten(x, 1)
#x = self.fc(x)
return features
def forward(self, x):
return self._forward_impl(x)
def resnext101_32x8d(pretrained=True, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
return model

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# Author: thygate
# https://github.com/thygate/stable-diffusion-webui-depthmap-script
from modules import devices
from modules.shared import opts
from torchvision.transforms import transforms
from operator import getitem
import torch, gc
import cv2
import numpy as np
import skimage.measure
whole_size_threshold = 1600 # R_max from the paper
pix2pixsize = 1024
def scale_torch(img):
"""
Scale the image and output it in torch.tensor.
:param img: input rgb is in shape [H, W, C], input depth/disp is in shape [H, W]
:param scale: the scale factor. float
:return: img. [C, H, W]
"""
if len(img.shape) == 2:
img = img[np.newaxis, :, :]
if img.shape[2] == 3:
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406) , (0.229, 0.224, 0.225) )])
img = transform(img.astype(np.float32))
else:
img = img.astype(np.float32)
img = torch.from_numpy(img)
return img
def estimateleres(img, model, w, h):
# leres transform input
rgb_c = img[:, :, ::-1].copy()
A_resize = cv2.resize(rgb_c, (w, h))
img_torch = scale_torch(A_resize)[None, :, :, :]
# compute
with torch.no_grad():
img_torch = img_torch.to(devices.get_device_for("controlnet"))
prediction = model.depth_model(img_torch)
prediction = prediction.squeeze().cpu().numpy()
prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
return prediction
def generatemask(size):
# Generates a Guassian mask
mask = np.zeros(size, dtype=np.float32)
sigma = int(size[0]/16)
k_size = int(2 * np.ceil(2 * int(size[0]/16)) + 1)
mask[int(0.15*size[0]):size[0] - int(0.15*size[0]), int(0.15*size[1]): size[1] - int(0.15*size[1])] = 1
mask = cv2.GaussianBlur(mask, (int(k_size), int(k_size)), sigma)
mask = (mask - mask.min()) / (mask.max() - mask.min())
mask = mask.astype(np.float32)
return mask
def resizewithpool(img, size):
i_size = img.shape[0]
n = int(np.floor(i_size/size))
out = skimage.measure.block_reduce(img, (n, n), np.max)
return out
def rgb2gray(rgb):
# Converts rgb to gray
return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
def calculateprocessingres(img, basesize, confidence=0.1, scale_threshold=3, whole_size_threshold=3000):
# Returns the R_x resolution described in section 5 of the main paper.
# Parameters:
# img :input rgb image
# basesize : size the dilation kernel which is equal to receptive field of the network.
# confidence: value of x in R_x; allowed percentage of pixels that are not getting any contextual cue.
# scale_threshold: maximum allowed upscaling on the input image ; it has been set to 3.
# whole_size_threshold: maximum allowed resolution. (R_max from section 6 of the main paper)
# Returns:
# outputsize_scale*speed_scale :The computed R_x resolution
# patch_scale: K parameter from section 6 of the paper
# speed scale parameter is to process every image in a smaller size to accelerate the R_x resolution search
speed_scale = 32
image_dim = int(min(img.shape[0:2]))
gray = rgb2gray(img)
grad = np.abs(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)) + np.abs(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3))
grad = cv2.resize(grad, (image_dim, image_dim), cv2.INTER_AREA)
# thresholding the gradient map to generate the edge-map as a proxy of the contextual cues
m = grad.min()
M = grad.max()
middle = m + (0.4 * (M - m))
grad[grad < middle] = 0
grad[grad >= middle] = 1
# dilation kernel with size of the receptive field
kernel = np.ones((int(basesize/speed_scale), int(basesize/speed_scale)), float)
# dilation kernel with size of the a quarter of receptive field used to compute k
# as described in section 6 of main paper
kernel2 = np.ones((int(basesize / (4*speed_scale)), int(basesize / (4*speed_scale))), float)
# Output resolution limit set by the whole_size_threshold and scale_threshold.
threshold = min(whole_size_threshold, scale_threshold * max(img.shape[:2]))
outputsize_scale = basesize / speed_scale
for p_size in range(int(basesize/speed_scale), int(threshold/speed_scale), int(basesize / (2*speed_scale))):
grad_resized = resizewithpool(grad, p_size)
grad_resized = cv2.resize(grad_resized, (p_size, p_size), cv2.INTER_NEAREST)
grad_resized[grad_resized >= 0.5] = 1
grad_resized[grad_resized < 0.5] = 0
dilated = cv2.dilate(grad_resized, kernel, iterations=1)
meanvalue = (1-dilated).mean()
if meanvalue > confidence:
break
else:
outputsize_scale = p_size
grad_region = cv2.dilate(grad_resized, kernel2, iterations=1)
patch_scale = grad_region.mean()
return int(outputsize_scale*speed_scale), patch_scale
# Generate a double-input depth estimation
def doubleestimate(img, size1, size2, pix2pixsize, model, net_type, pix2pixmodel):
# Generate the low resolution estimation
estimate1 = singleestimate(img, size1, model, net_type)
# Resize to the inference size of merge network.
estimate1 = cv2.resize(estimate1, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
# Generate the high resolution estimation
estimate2 = singleestimate(img, size2, model, net_type)
# Resize to the inference size of merge network.
estimate2 = cv2.resize(estimate2, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
# Inference on the merge model
pix2pixmodel.set_input(estimate1, estimate2)
pix2pixmodel.test()
visuals = pix2pixmodel.get_current_visuals()
prediction_mapped = visuals['fake_B']
prediction_mapped = (prediction_mapped+1)/2
prediction_mapped = (prediction_mapped - torch.min(prediction_mapped)) / (
torch.max(prediction_mapped) - torch.min(prediction_mapped))
prediction_mapped = prediction_mapped.squeeze().cpu().numpy()
return prediction_mapped
# Generate a single-input depth estimation
def singleestimate(img, msize, model, net_type):
# if net_type == 0:
return estimateleres(img, model, msize, msize)
# else:
# return estimatemidasBoost(img, model, msize, msize)
def applyGridpatch(blsize, stride, img, box):
# Extract a simple grid patch.
counter1 = 0
patch_bound_list = {}
for k in range(blsize, img.shape[1] - blsize, stride):
for j in range(blsize, img.shape[0] - blsize, stride):
patch_bound_list[str(counter1)] = {}
patchbounds = [j - blsize, k - blsize, j - blsize + 2 * blsize, k - blsize + 2 * blsize]
patch_bound = [box[0] + patchbounds[1], box[1] + patchbounds[0], patchbounds[3] - patchbounds[1],
patchbounds[2] - patchbounds[0]]
patch_bound_list[str(counter1)]['rect'] = patch_bound
patch_bound_list[str(counter1)]['size'] = patch_bound[2]
counter1 = counter1 + 1
return patch_bound_list
# Generating local patches to perform the local refinement described in section 6 of the main paper.
def generatepatchs(img, base_size):
# Compute the gradients as a proxy of the contextual cues.
img_gray = rgb2gray(img)
whole_grad = np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 0, 1, ksize=3)) +\
np.abs(cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3))
threshold = whole_grad[whole_grad > 0].mean()
whole_grad[whole_grad < threshold] = 0
# We use the integral image to speed-up the evaluation of the amount of gradients for each patch.
gf = whole_grad.sum()/len(whole_grad.reshape(-1))
grad_integral_image = cv2.integral(whole_grad)
# Variables are selected such that the initial patch size would be the receptive field size
# and the stride is set to 1/3 of the receptive field size.
blsize = int(round(base_size/2))
stride = int(round(blsize*0.75))
# Get initial Grid
patch_bound_list = applyGridpatch(blsize, stride, img, [0, 0, 0, 0])
# Refine initial Grid of patches by discarding the flat (in terms of gradients of the rgb image) ones. Refine
# each patch size to ensure that there will be enough depth cues for the network to generate a consistent depth map.
print("Selecting patches ...")
patch_bound_list = adaptiveselection(grad_integral_image, patch_bound_list, gf)
# Sort the patch list to make sure the merging operation will be done with the correct order: starting from biggest
# patch
patchset = sorted(patch_bound_list.items(), key=lambda x: getitem(x[1], 'size'), reverse=True)
return patchset
def getGF_fromintegral(integralimage, rect):
# Computes the gradient density of a given patch from the gradient integral image.
x1 = rect[1]
x2 = rect[1]+rect[3]
y1 = rect[0]
y2 = rect[0]+rect[2]
value = integralimage[x2, y2]-integralimage[x1, y2]-integralimage[x2, y1]+integralimage[x1, y1]
return value
# Adaptively select patches
def adaptiveselection(integral_grad, patch_bound_list, gf):
patchlist = {}
count = 0
height, width = integral_grad.shape
search_step = int(32/factor)
# Go through all patches
for c in range(len(patch_bound_list)):
# Get patch
bbox = patch_bound_list[str(c)]['rect']
# Compute the amount of gradients present in the patch from the integral image.
cgf = getGF_fromintegral(integral_grad, bbox)/(bbox[2]*bbox[3])
# Check if patching is beneficial by comparing the gradient density of the patch to
# the gradient density of the whole image
if cgf >= gf:
bbox_test = bbox.copy()
patchlist[str(count)] = {}
# Enlarge each patch until the gradient density of the patch is equal
# to the whole image gradient density
while True:
bbox_test[0] = bbox_test[0] - int(search_step/2)
bbox_test[1] = bbox_test[1] - int(search_step/2)
bbox_test[2] = bbox_test[2] + search_step
bbox_test[3] = bbox_test[3] + search_step
# Check if we are still within the image
if bbox_test[0] < 0 or bbox_test[1] < 0 or bbox_test[1] + bbox_test[3] >= height \
or bbox_test[0] + bbox_test[2] >= width:
break
# Compare gradient density
cgf = getGF_fromintegral(integral_grad, bbox_test)/(bbox_test[2]*bbox_test[3])
if cgf < gf:
break
bbox = bbox_test.copy()
# Add patch to selected patches
patchlist[str(count)]['rect'] = bbox
patchlist[str(count)]['size'] = bbox[2]
count = count + 1
# Return selected patches
return patchlist
def impatch(image, rect):
# Extract the given patch pixels from a given image.
w1 = rect[0]
h1 = rect[1]
w2 = w1 + rect[2]
h2 = h1 + rect[3]
image_patch = image[h1:h2, w1:w2]
return image_patch
class ImageandPatchs:
def __init__(self, root_dir, name, patchsinfo, rgb_image, scale=1):
self.root_dir = root_dir
self.patchsinfo = patchsinfo
self.name = name
self.patchs = patchsinfo
self.scale = scale
self.rgb_image = cv2.resize(rgb_image, (round(rgb_image.shape[1]*scale), round(rgb_image.shape[0]*scale)),
interpolation=cv2.INTER_CUBIC)
self.do_have_estimate = False
self.estimation_updated_image = None
self.estimation_base_image = None
def __len__(self):
return len(self.patchs)
def set_base_estimate(self, est):
self.estimation_base_image = est
if self.estimation_updated_image is not None:
self.do_have_estimate = True
def set_updated_estimate(self, est):
self.estimation_updated_image = est
if self.estimation_base_image is not None:
self.do_have_estimate = True
def __getitem__(self, index):
patch_id = int(self.patchs[index][0])
rect = np.array(self.patchs[index][1]['rect'])
msize = self.patchs[index][1]['size']
## applying scale to rect:
rect = np.round(rect * self.scale)
rect = rect.astype('int')
msize = round(msize * self.scale)
patch_rgb = impatch(self.rgb_image, rect)
if self.do_have_estimate:
patch_whole_estimate_base = impatch(self.estimation_base_image, rect)
patch_whole_estimate_updated = impatch(self.estimation_updated_image, rect)
return {'patch_rgb': patch_rgb, 'patch_whole_estimate_base': patch_whole_estimate_base,
'patch_whole_estimate_updated': patch_whole_estimate_updated, 'rect': rect,
'size': msize, 'id': patch_id}
else:
return {'patch_rgb': patch_rgb, 'rect': rect, 'size': msize, 'id': patch_id}
def print_options(self, opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
# save to the disk
"""
expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')
"""
def parse(self):
"""Parse our options, create checkpoints directory suffix, and set up gpu device."""
opt = self.gather_options()
opt.isTrain = self.isTrain # train or test
# process opt.suffix
if opt.suffix:
suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
opt.name = opt.name + suffix
#self.print_options(opt)
# set gpu ids
str_ids = opt.gpu_ids.split(',')
opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
opt.gpu_ids.append(id)
#if len(opt.gpu_ids) > 0:
# torch.cuda.set_device(opt.gpu_ids[0])
self.opt = opt
return self.opt
def estimateboost(img, model, model_type, pix2pixmodel, max_res=512):
global whole_size_threshold
# get settings
if hasattr(opts, 'depthmap_script_boost_rmax'):
whole_size_threshold = opts.depthmap_script_boost_rmax
if model_type == 0: #leres
net_receptive_field_size = 448
patch_netsize = 2 * net_receptive_field_size
elif model_type == 1: #dpt_beit_large_512
net_receptive_field_size = 512
patch_netsize = 2 * net_receptive_field_size
else: #other midas
net_receptive_field_size = 384
patch_netsize = 2 * net_receptive_field_size
gc.collect()
devices.torch_gc()
# Generate mask used to smoothly blend the local pathc estimations to the base estimate.
# It is arbitrarily large to avoid artifacts during rescaling for each crop.
mask_org = generatemask((3000, 3000))
mask = mask_org.copy()
# Value x of R_x defined in the section 5 of the main paper.
r_threshold_value = 0.2
#if R0:
# r_threshold_value = 0
input_resolution = img.shape
scale_threshold = 3 # Allows up-scaling with a scale up to 3
# Find the best input resolution R-x. The resolution search described in section 5-double estimation of the main paper and section B of the
# supplementary material.
whole_image_optimal_size, patch_scale = calculateprocessingres(img, net_receptive_field_size, r_threshold_value, scale_threshold, whole_size_threshold)
# print('wholeImage being processed in :', whole_image_optimal_size)
# Generate the base estimate using the double estimation.
whole_estimate = doubleestimate(img, net_receptive_field_size, whole_image_optimal_size, pix2pixsize, model, model_type, pix2pixmodel)
# Compute the multiplier described in section 6 of the main paper to make sure our initial patch can select
# small high-density regions of the image.
global factor
factor = max(min(1, 4 * patch_scale * whole_image_optimal_size / whole_size_threshold), 0.2)
# print('Adjust factor is:', 1/factor)
# Check if Local boosting is beneficial.
if max_res < whole_image_optimal_size:
# print("No Local boosting. Specified Max Res is smaller than R20, Returning doubleestimate result")
return cv2.resize(whole_estimate, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC)
# Compute the default target resolution.
if img.shape[0] > img.shape[1]:
a = 2 * whole_image_optimal_size
b = round(2 * whole_image_optimal_size * img.shape[1] / img.shape[0])
else:
a = round(2 * whole_image_optimal_size * img.shape[0] / img.shape[1])
b = 2 * whole_image_optimal_size
b = int(round(b / factor))
a = int(round(a / factor))
"""
# recompute a, b and saturate to max res.
if max(a,b) > max_res:
print('Default Res is higher than max-res: Reducing final resolution')
if img.shape[0] > img.shape[1]:
a = max_res
b = round(max_res * img.shape[1] / img.shape[0])
else:
a = round(max_res * img.shape[0] / img.shape[1])
b = max_res
b = int(b)
a = int(a)
"""
img = cv2.resize(img, (b, a), interpolation=cv2.INTER_CUBIC)
# Extract selected patches for local refinement
base_size = net_receptive_field_size * 2
patchset = generatepatchs(img, base_size)
# print('Target resolution: ', img.shape)
# Computing a scale in case user prompted to generate the results as the same resolution of the input.
# Notice that our method output resolution is independent of the input resolution and this parameter will only
# enable a scaling operation during the local patch merge implementation to generate results with the same resolution
# as the input.
"""
if output_resolution == 1:
mergein_scale = input_resolution[0] / img.shape[0]
print('Dynamicly change merged-in resolution; scale:', mergein_scale)
else:
mergein_scale = 1
"""
# always rescale to input res for now
mergein_scale = input_resolution[0] / img.shape[0]
imageandpatchs = ImageandPatchs('', '', patchset, img, mergein_scale)
whole_estimate_resized = cv2.resize(whole_estimate, (round(img.shape[1]*mergein_scale),
round(img.shape[0]*mergein_scale)), interpolation=cv2.INTER_CUBIC)
imageandpatchs.set_base_estimate(whole_estimate_resized.copy())
imageandpatchs.set_updated_estimate(whole_estimate_resized.copy())
print('Resulting depthmap resolution will be :', whole_estimate_resized.shape[:2])
print('Patches to process: '+str(len(imageandpatchs)))
# Enumerate through all patches, generate their estimations and refining the base estimate.
for patch_ind in range(len(imageandpatchs)):
# Get patch information
patch = imageandpatchs[patch_ind] # patch object
patch_rgb = patch['patch_rgb'] # rgb patch
patch_whole_estimate_base = patch['patch_whole_estimate_base'] # corresponding patch from base
rect = patch['rect'] # patch size and location
patch_id = patch['id'] # patch ID
org_size = patch_whole_estimate_base.shape # the original size from the unscaled input
print('\t Processing patch', patch_ind, '/', len(imageandpatchs)-1, '|', rect)
# We apply double estimation for patches. The high resolution value is fixed to twice the receptive
# field size of the network for patches to accelerate the process.
patch_estimation = doubleestimate(patch_rgb, net_receptive_field_size, patch_netsize, pix2pixsize, model, model_type, pix2pixmodel)
patch_estimation = cv2.resize(patch_estimation, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
patch_whole_estimate_base = cv2.resize(patch_whole_estimate_base, (pix2pixsize, pix2pixsize), interpolation=cv2.INTER_CUBIC)
# Merging the patch estimation into the base estimate using our merge network:
# We feed the patch estimation and the same region from the updated base estimate to the merge network
# to generate the target estimate for the corresponding region.
pix2pixmodel.set_input(patch_whole_estimate_base, patch_estimation)
# Run merging network
pix2pixmodel.test()
visuals = pix2pixmodel.get_current_visuals()
prediction_mapped = visuals['fake_B']
prediction_mapped = (prediction_mapped+1)/2
prediction_mapped = prediction_mapped.squeeze().cpu().numpy()
mapped = prediction_mapped
# We use a simple linear polynomial to make sure the result of the merge network would match the values of
# base estimate
p_coef = np.polyfit(mapped.reshape(-1), patch_whole_estimate_base.reshape(-1), deg=1)
merged = np.polyval(p_coef, mapped.reshape(-1)).reshape(mapped.shape)
merged = cv2.resize(merged, (org_size[1],org_size[0]), interpolation=cv2.INTER_CUBIC)
# Get patch size and location
w1 = rect[0]
h1 = rect[1]
w2 = w1 + rect[2]
h2 = h1 + rect[3]
# To speed up the implementation, we only generate the Gaussian mask once with a sufficiently large size
# and resize it to our needed size while merging the patches.
if mask.shape != org_size:
mask = cv2.resize(mask_org, (org_size[1],org_size[0]), interpolation=cv2.INTER_LINEAR)
tobemergedto = imageandpatchs.estimation_updated_image
# Update the whole estimation:
# We use a simple Gaussian mask to blend the merged patch region with the base estimate to ensure seamless
# blending at the boundaries of the patch region.
tobemergedto[h1:h2, w1:w2] = np.multiply(tobemergedto[h1:h2, w1:w2], 1 - mask) + np.multiply(merged, mask)
imageandpatchs.set_updated_estimate(tobemergedto)
# output
return cv2.resize(imageandpatchs.estimation_updated_image, (input_resolution[1], input_resolution[0]), interpolation=cv2.INTER_CUBIC)

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from . import network_auxi as network
from .net_tools import get_func
import torch
import torch.nn as nn
from modules import devices
class RelDepthModel(nn.Module):
def __init__(self, backbone='resnet50'):
super(RelDepthModel, self).__init__()
if backbone == 'resnet50':
encoder = 'resnet50_stride32'
elif backbone == 'resnext101':
encoder = 'resnext101_stride32x8d'
self.depth_model = DepthModel(encoder)
def inference(self, rgb):
with torch.no_grad():
input = rgb.to(self.depth_model.device)
depth = self.depth_model(input)
#pred_depth_out = depth - depth.min() + 0.01
return depth #pred_depth_out
class DepthModel(nn.Module):
def __init__(self, encoder):
super(DepthModel, self).__init__()
backbone = network.__name__.split('.')[-1] + '.' + encoder
self.encoder_modules = get_func(backbone)()
self.decoder_modules = network.Decoder()
def forward(self, x):
lateral_out = self.encoder_modules(x)
out_logit = self.decoder_modules(lateral_out)
return out_logit

54
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import importlib
import torch
import os
from collections import OrderedDict
def get_func(func_name):
"""Helper to return a function object by name. func_name must identify a
function in this module or the path to a function relative to the base
'modeling' module.
"""
if func_name == '':
return None
try:
parts = func_name.split('.')
# Refers to a function in this module
if len(parts) == 1:
return globals()[parts[0]]
# Otherwise, assume we're referencing a module under modeling
module_name = 'annotator.leres.leres.' + '.'.join(parts[:-1])
module = importlib.import_module(module_name)
return getattr(module, parts[-1])
except Exception:
print('Failed to f1ind function: %s', func_name)
raise
def load_ckpt(args, depth_model, shift_model, focal_model):
"""
Load checkpoint.
"""
if os.path.isfile(args.load_ckpt):
print("loading checkpoint %s" % args.load_ckpt)
checkpoint = torch.load(args.load_ckpt)
if shift_model is not None:
shift_model.load_state_dict(strip_prefix_if_present(checkpoint['shift_model'], 'module.'),
strict=True)
if focal_model is not None:
focal_model.load_state_dict(strip_prefix_if_present(checkpoint['focal_model'], 'module.'),
strict=True)
depth_model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."),
strict=True)
del checkpoint
if torch.cuda.is_available():
torch.cuda.empty_cache()
def strip_prefix_if_present(state_dict, prefix):
keys = sorted(state_dict.keys())
if not all(key.startswith(prefix) for key in keys):
return state_dict
stripped_state_dict = OrderedDict()
for key, value in state_dict.items():
stripped_state_dict[key.replace(prefix, "")] = value
return stripped_state_dict

417
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import torch
import torch.nn as nn
import torch.nn.init as init
from . import Resnet, Resnext_torch
def resnet50_stride32():
return DepthNet(backbone='resnet', depth=50, upfactors=[2, 2, 2, 2])
def resnext101_stride32x8d():
return DepthNet(backbone='resnext101_32x8d', depth=101, upfactors=[2, 2, 2, 2])
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.inchannels = [256, 512, 1024, 2048]
self.midchannels = [256, 256, 256, 512]
self.upfactors = [2,2,2,2]
self.outchannels = 1
self.conv = FTB(inchannels=self.inchannels[3], midchannels=self.midchannels[3])
self.conv1 = nn.Conv2d(in_channels=self.midchannels[3], out_channels=self.midchannels[2], kernel_size=3, padding=1, stride=1, bias=True)
self.upsample = nn.Upsample(scale_factor=self.upfactors[3], mode='bilinear', align_corners=True)
self.ffm2 = FFM(inchannels=self.inchannels[2], midchannels=self.midchannels[2], outchannels = self.midchannels[2], upfactor=self.upfactors[2])
self.ffm1 = FFM(inchannels=self.inchannels[1], midchannels=self.midchannels[1], outchannels = self.midchannels[1], upfactor=self.upfactors[1])
self.ffm0 = FFM(inchannels=self.inchannels[0], midchannels=self.midchannels[0], outchannels = self.midchannels[0], upfactor=self.upfactors[0])
self.outconv = AO(inchannels=self.midchannels[0], outchannels=self.outchannels, upfactor=2)
self._init_params()
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): #NN.BatchNorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, features):
x_32x = self.conv(features[3]) # 1/32
x_32 = self.conv1(x_32x)
x_16 = self.upsample(x_32) # 1/16
x_8 = self.ffm2(features[2], x_16) # 1/8
x_4 = self.ffm1(features[1], x_8) # 1/4
x_2 = self.ffm0(features[0], x_4) # 1/2
#-----------------------------------------
x = self.outconv(x_2) # original size
return x
class DepthNet(nn.Module):
__factory = {
18: Resnet.resnet18,
34: Resnet.resnet34,
50: Resnet.resnet50,
101: Resnet.resnet101,
152: Resnet.resnet152
}
def __init__(self,
backbone='resnet',
depth=50,
upfactors=[2, 2, 2, 2]):
super(DepthNet, self).__init__()
self.backbone = backbone
self.depth = depth
self.pretrained = False
self.inchannels = [256, 512, 1024, 2048]
self.midchannels = [256, 256, 256, 512]
self.upfactors = upfactors
self.outchannels = 1
# Build model
if self.backbone == 'resnet':
if self.depth not in DepthNet.__factory:
raise KeyError("Unsupported depth:", self.depth)
self.encoder = DepthNet.__factory[depth](pretrained=self.pretrained)
elif self.backbone == 'resnext101_32x8d':
self.encoder = Resnext_torch.resnext101_32x8d(pretrained=self.pretrained)
else:
self.encoder = Resnext_torch.resnext101(pretrained=self.pretrained)
def forward(self, x):
x = self.encoder(x) # 1/32, 1/16, 1/8, 1/4
return x
class FTB(nn.Module):
def __init__(self, inchannels, midchannels=512):
super(FTB, self).__init__()
self.in1 = inchannels
self.mid = midchannels
self.conv1 = nn.Conv2d(in_channels=self.in1, out_channels=self.mid, kernel_size=3, padding=1, stride=1,
bias=True)
# NN.BatchNorm2d
self.conv_branch = nn.Sequential(nn.ReLU(inplace=True), \
nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
padding=1, stride=1, bias=True), \
nn.BatchNorm2d(num_features=self.mid), \
nn.ReLU(inplace=True), \
nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
padding=1, stride=1, bias=True))
self.relu = nn.ReLU(inplace=True)
self.init_params()
def forward(self, x):
x = self.conv1(x)
x = x + self.conv_branch(x)
x = self.relu(x)
return x
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
class ATA(nn.Module):
def __init__(self, inchannels, reduction=8):
super(ATA, self).__init__()
self.inchannels = inchannels
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(nn.Linear(self.inchannels * 2, self.inchannels // reduction),
nn.ReLU(inplace=True),
nn.Linear(self.inchannels // reduction, self.inchannels),
nn.Sigmoid())
self.init_params()
def forward(self, low_x, high_x):
n, c, _, _ = low_x.size()
x = torch.cat([low_x, high_x], 1)
x = self.avg_pool(x)
x = x.view(n, -1)
x = self.fc(x).view(n, c, 1, 1)
x = low_x * x + high_x
return x
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
# init.normal(m.weight, std=0.01)
init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
# init.normal_(m.weight, std=0.01)
init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
class FFM(nn.Module):
def __init__(self, inchannels, midchannels, outchannels, upfactor=2):
super(FFM, self).__init__()
self.inchannels = inchannels
self.midchannels = midchannels
self.outchannels = outchannels
self.upfactor = upfactor
self.ftb1 = FTB(inchannels=self.inchannels, midchannels=self.midchannels)
# self.ata = ATA(inchannels = self.midchannels)
self.ftb2 = FTB(inchannels=self.midchannels, midchannels=self.outchannels)
self.upsample = nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True)
self.init_params()
def forward(self, low_x, high_x):
x = self.ftb1(low_x)
x = x + high_x
x = self.ftb2(x)
x = self.upsample(x)
return x
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.Batchnorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
class AO(nn.Module):
# Adaptive output module
def __init__(self, inchannels, outchannels, upfactor=2):
super(AO, self).__init__()
self.inchannels = inchannels
self.outchannels = outchannels
self.upfactor = upfactor
self.adapt_conv = nn.Sequential(
nn.Conv2d(in_channels=self.inchannels, out_channels=self.inchannels // 2, kernel_size=3, padding=1,
stride=1, bias=True), \
nn.BatchNorm2d(num_features=self.inchannels // 2), \
nn.ReLU(inplace=True), \
nn.Conv2d(in_channels=self.inchannels // 2, out_channels=self.outchannels, kernel_size=3, padding=1,
stride=1, bias=True), \
nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True))
self.init_params()
def forward(self, x):
x = self.adapt_conv(x)
return x
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.Batchnorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
# ==============================================================================================================
class ResidualConv(nn.Module):
def __init__(self, inchannels):
super(ResidualConv, self).__init__()
# NN.BatchNorm2d
self.conv = nn.Sequential(
# nn.BatchNorm2d(num_features=inchannels),
nn.ReLU(inplace=False),
# nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1, stride=1, groups=inchannels,bias=True),
# nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=1, padding=0, stride=1, groups=1,bias=True)
nn.Conv2d(in_channels=inchannels, out_channels=inchannels / 2, kernel_size=3, padding=1, stride=1,
bias=False),
nn.BatchNorm2d(num_features=inchannels / 2),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=inchannels / 2, out_channels=inchannels, kernel_size=3, padding=1, stride=1,
bias=False)
)
self.init_params()
def forward(self, x):
x = self.conv(x) + x
return x
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
class FeatureFusion(nn.Module):
def __init__(self, inchannels, outchannels):
super(FeatureFusion, self).__init__()
self.conv = ResidualConv(inchannels=inchannels)
# NN.BatchNorm2d
self.up = nn.Sequential(ResidualConv(inchannels=inchannels),
nn.ConvTranspose2d(in_channels=inchannels, out_channels=outchannels, kernel_size=3,
stride=2, padding=1, output_padding=1),
nn.BatchNorm2d(num_features=outchannels),
nn.ReLU(inplace=True))
def forward(self, lowfeat, highfeat):
return self.up(highfeat + self.conv(lowfeat))
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
# init.kaiming_normal_(m.weight, mode='fan_out')
init.normal_(m.weight, std=0.01)
# init.xavier_normal_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.01)
if m.bias is not None:
init.constant_(m.bias, 0)
class SenceUnderstand(nn.Module):
def __init__(self, channels):
super(SenceUnderstand, self).__init__()
self.channels = channels
self.conv1 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(inplace=True))
self.pool = nn.AdaptiveAvgPool2d(8)
self.fc = nn.Sequential(nn.Linear(512 * 8 * 8, self.channels),
nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, padding=0),
nn.ReLU(inplace=True))
self.initial_params()
def forward(self, x):
n, c, h, w = x.size()
x = self.conv1(x)
x = self.pool(x)
x = x.view(n, -1)
x = self.fc(x)
x = x.view(n, self.channels, 1, 1)
x = self.conv2(x)
x = x.repeat(1, 1, h, w)
return x
def initial_params(self, dev=0.01):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# print torch.sum(m.weight)
m.weight.data.normal_(0, dev)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.ConvTranspose2d):
# print torch.sum(m.weight)
m.weight.data.normal_(0, dev)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, dev)
if __name__ == '__main__':
net = DepthNet(depth=50, pretrained=True)
print(net)
inputs = torch.ones(4,3,128,128)
out = net(inputs)
print(out.size())

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/LICENSE Normal file
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https://github.com/compphoto/BoostingMonocularDepth
Copyright 2021, Seyed Mahdi Hosseini Miangoleh, Sebastian Dille, Computational Photography Laboratory. All rights reserved.
This software is for academic use only. A redistribution of this
software, with or without modifications, has to be for academic
use only, while giving the appropriate credit to the original
authors of the software. The methods implemented as a part of
this software may be covered under patents or patent applications.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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"""This package contains modules related to objective functions, optimizations, and network architectures.
To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
You need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- <set_input>: unpack data from dataset and apply preprocessing.
-- <forward>: produce intermediate results.
-- <optimize_parameters>: calculate loss, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
In the function <__init__>, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
Now you can use the model class by specifying flag '--model dummy'.
See our template model class 'template_model.py' for more details.
"""
import importlib
from .base_model import BaseModel
def find_model_using_name(model_name):
"""Import the module "models/[model_name]_model.py".
In the file, the class called DatasetNameModel() will
be instantiated. It has to be a subclass of BaseModel,
and it is case-insensitive.
"""
model_filename = "annotator.leres.pix2pix.models." + model_name + "_model"
modellib = importlib.import_module(model_filename)
model = None
target_model_name = model_name.replace('_', '') + 'model'
for name, cls in modellib.__dict__.items():
if name.lower() == target_model_name.lower() \
and issubclass(cls, BaseModel):
model = cls
if model is None:
print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
exit(0)
return model
def get_option_setter(model_name):
"""Return the static method <modify_commandline_options> of the model class."""
model_class = find_model_using_name(model_name)
return model_class.modify_commandline_options
def create_model(opt):
"""Create a model given the option.
This function warps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from models import create_model
>>> model = create_model(opt)
"""
model = find_model_using_name(opt.model)
instance = model(opt)
print("model [%s] was created" % type(instance).__name__)
return instance

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import os
import torch, gc
from modules import devices
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- <set_input>: unpack data from dataset and apply preprocessing.
-- <forward>: produce intermediate results.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this function, you should first call <BaseModel.__init__(self, opt)>
Then, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
"""
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir
if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
torch.backends.cudnn.benchmark = True
self.loss_names = []
self.model_names = []
self.visual_names = []
self.optimizers = []
self.image_paths = []
self.metric = 0 # used for learning rate policy 'plateau'
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new model-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): includes the data itself and its metadata information.
"""
pass
@abstractmethod
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
pass
@abstractmethod
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
pass
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
if not self.isTrain or opt.continue_train:
load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch
self.load_networks(load_suffix)
self.print_networks(opt.verbose)
def eval(self):
"""Make models eval mode during test time"""
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
net.eval()
def test(self):
"""Forward function used in test time.
This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
It also calls <compute_visuals> to produce additional visualization results
"""
with torch.no_grad():
self.forward()
self.compute_visuals()
def compute_visuals(self):
"""Calculate additional output images for visdom and HTML visualization"""
pass
def get_image_paths(self):
""" Return image paths that are used to load current data"""
return self.image_paths
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
old_lr = self.optimizers[0].param_groups[0]['lr']
for scheduler in self.schedulers:
if self.opt.lr_policy == 'plateau':
scheduler.step(self.metric)
else:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
print('learning rate %.7f -> %.7f' % (old_lr, lr))
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)
return visual_ret
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number
return errors_ret
def save_networks(self, epoch):
"""Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
save_filename = '%s_net_%s.pth' % (epoch, name)
save_path = os.path.join(self.save_dir, save_filename)
net = getattr(self, 'net' + name)
if len(self.gpu_ids) > 0 and torch.cuda.is_available():
torch.save(net.module.cpu().state_dict(), save_path)
net.cuda(self.gpu_ids[0])
else:
torch.save(net.cpu().state_dict(), save_path)
def unload_network(self, name):
"""Unload network and gc.
"""
if isinstance(name, str):
net = getattr(self, 'net' + name)
del net
gc.collect()
devices.torch_gc()
return None
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'running_mean' or key == 'running_var'):
if getattr(module, key) is None:
state_dict.pop('.'.join(keys))
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'num_batches_tracked'):
state_dict.pop('.'.join(keys))
else:
self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
def load_networks(self, epoch):
"""Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
load_filename = '%s_net_%s.pth' % (epoch, name)
load_path = os.path.join(self.save_dir, load_filename)
net = getattr(self, 'net' + name)
if isinstance(net, torch.nn.DataParallel):
net = net.module
# print('Loading depth boost model from %s' % load_path)
# if you are using PyTorch newer than 0.4 (e.g., built from
# GitHub source), you can remove str() on self.device
state_dict = torch.load(load_path, map_location=str(self.device))
if hasattr(state_dict, '_metadata'):
del state_dict._metadata
# patch InstanceNorm checkpoints prior to 0.4
for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop
self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
net.load_state_dict(state_dict)
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print('---------- Networks initialized -------------')
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
num_params = 0
for param in net.parameters():
num_params += param.numel()
if verbose:
print(net)
print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
print('-----------------------------------------------')
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad

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import os
import torch
class BaseModelHG():
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
def set_input(self, input):
self.input = input
def forward(self):
pass
# used in test time, no backprop
def test(self):
pass
def get_image_paths(self):
pass
def optimize_parameters(self):
pass
def get_current_visuals(self):
return self.input
def get_current_errors(self):
return {}
def save(self, label):
pass
# helper saving function that can be used by subclasses
def save_network(self, network, network_label, epoch_label, gpu_ids):
save_filename = '_%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.cpu().state_dict(), save_path)
if len(gpu_ids) and torch.cuda.is_available():
network.cuda(device_id=gpu_ids[0])
# helper loading function that can be used by subclasses
def load_network(self, network, network_label, epoch_label):
save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
print(save_path)
model = torch.load(save_path)
return model
# network.load_state_dict(torch.load(save_path))
def update_learning_rate():
pass

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import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
class Identity(nn.Module):
def forward(self, x):
return x
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
def norm_layer(x): return Identity()
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
def get_scheduler(optimizer, opt):
"""Return a learning rate scheduler
Parameters:
optimizer -- the optimizer of the network
opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
See https://pytorch.org/docs/stable/optim.html for more details.
"""
if opt.lr_policy == 'linear':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
elif opt.lr_policy == 'plateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
elif opt.lr_policy == 'cosine':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
else:
return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
return scheduler
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
# print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function <init_func>
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert(torch.cuda.is_available())
net.to(gpu_ids[0])
net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
init_weights(net, init_type, init_gain=init_gain)
return net
def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Create a generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
ngf (int) -- the number of filters in the last conv layer
netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
norm (str) -- the name of normalization layers used in the network: batch | instance | none
use_dropout (bool) -- if use dropout layers.
init_type (str) -- the name of our initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a generator
Our current implementation provides two types of generators:
U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
The original U-Net paper: https://arxiv.org/abs/1505.04597
Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
The generator has been initialized by <init_net>. It uses RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'resnet_9blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
elif netG == 'resnet_6blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
elif netG == 'resnet_12blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=12)
elif netG == 'unet_128':
net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'unet_256':
net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'unet_672':
net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'unet_960':
net = UnetGenerator(input_nc, output_nc, 6, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
elif netG == 'unet_1024':
net = UnetGenerator(input_nc, output_nc, 10, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
else:
raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
return init_net(net, init_type, init_gain, gpu_ids)
def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Create a discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the first conv layer
netD (str) -- the architecture's name: basic | n_layers | pixel
n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
norm (str) -- the type of normalization layers used in the network.
init_type (str) -- the name of the initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a discriminator
Our current implementation provides three types of discriminators:
[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
It can classify whether 70×70 overlapping patches are real or fake.
Such a patch-level discriminator architecture has fewer parameters
than a full-image discriminator and can work on arbitrarily-sized images
in a fully convolutional fashion.
[n_layers]: With this mode, you can specify the number of conv layers in the discriminator
with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
It encourages greater color diversity but has no effect on spatial statistics.
The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == 'basic': # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
elif netD == 'n_layers': # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
elif netD == 'pixel': # classify if each pixel is real or fake
net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
else:
raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
return init_net(net, init_type, init_gain, gpu_ids)
##############################################################################
# Classes
##############################################################################
class GANLoss(nn.Module):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)
def get_target_tensor(self, prediction, target_is_real):
"""Create label tensors with the same size as the input.
Parameters:
prediction (tensor) - - tpyically the prediction from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
A label tensor filled with ground truth label, and with the size of the input
"""
if target_is_real:
target_tensor = self.real_label
else:
target_tensor = self.fake_label
return target_tensor.expand_as(prediction)
def __call__(self, prediction, target_is_real):
"""Calculate loss given Discriminator's output and grount truth labels.
Parameters:
prediction (tensor) - - tpyically the prediction output from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
the calculated loss.
"""
if self.gan_mode in ['lsgan', 'vanilla']:
target_tensor = self.get_target_tensor(prediction, target_is_real)
loss = self.loss(prediction, target_tensor)
elif self.gan_mode == 'wgangp':
if target_is_real:
loss = -prediction.mean()
else:
loss = prediction.mean()
return loss
def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
Arguments:
netD (network) -- discriminator network
real_data (tensor array) -- real images
fake_data (tensor array) -- generated images from the generator
device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
type (str) -- if we mix real and fake data or not [real | fake | mixed].
constant (float) -- the constant used in formula ( ||gradient||_2 - constant)^2
lambda_gp (float) -- weight for this loss
Returns the gradient penalty loss
"""
if lambda_gp > 0.0:
if type == 'real': # either use real images, fake images, or a linear interpolation of two.
interpolatesv = real_data
elif type == 'fake':
interpolatesv = fake_data
elif type == 'mixed':
alpha = torch.rand(real_data.shape[0], 1, device=device)
alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
else:
raise NotImplementedError('{} not implemented'.format(type))
interpolatesv.requires_grad_(True)
disc_interpolates = netD(interpolatesv)
gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
grad_outputs=torch.ones(disc_interpolates.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
return gradient_penalty, gradients
else:
return 0.0, None
class ResnetGenerator(nn.Module):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
"""Construct a Resnet-based generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers
n_blocks (int) -- the number of ResNet blocks
padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
"""
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling): # add downsampling layers
mult = 2 ** i
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True)]
mult = 2 ** n_downsampling
for i in range(n_blocks): # add ResNet blocks
model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
for i in range(n_downsampling): # add upsampling layers
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
kernel_size=3, stride=2,
padding=1, output_padding=1,
bias=use_bias),
norm_layer(int(ngf * mult / 2)),
nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, input):
"""Standard forward"""
return self.model(input)
class ResnetBlock(nn.Module):
"""Define a Resnet block"""
def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Initialize the Resnet block
A resnet block is a conv block with skip connections
We construct a conv block with build_conv_block function,
and implement skip connections in <forward> function.
Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
"""
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
use_bias (bool) -- if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
"""Forward function (with skip connections)"""
out = x + self.conv_block(x) # add skip connections
return out
class UnetGenerator(nn.Module):
"""Create a Unet-based generator"""
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer
for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer
def forward(self, input):
"""Standard forward"""
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def __init__(self, outer_nc, inner_nc, input_nc=None,
submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return torch.cat([x, self.model(x)], 1)
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.model(input)
class PixelDiscriminator(nn.Module):
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
"""Construct a 1x1 PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
"""
super(PixelDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.net = [
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
norm_layer(ndf * 2),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
self.net = nn.Sequential(*self.net)
def forward(self, input):
"""Standard forward."""
return self.net(input)

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import torch
from .base_model import BaseModel
from . import networks
class Pix2Pix4DepthModel(BaseModel):
""" This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
The model training requires '--dataset_mode aligned' dataset.
By default, it uses a '--netG unet256' U-Net generator,
a '--netD basic' discriminator (PatchGAN),
and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
For pix2pix, we do not use image buffer
The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
"""
# changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
parser.set_defaults(input_nc=2,output_nc=1,norm='none', netG='unet_1024', dataset_mode='depthmerge')
if is_train:
parser.set_defaults(pool_size=0, gan_mode='vanilla',)
parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss')
return parser
def __init__(self, opt):
"""Initialize the pix2pix class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
# self.loss_names = ['G_L1']
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
if self.isTrain:
self.visual_names = ['outer','inner', 'fake_B', 'real_B']
else:
self.visual_names = ['fake_B']
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
if self.isTrain:
self.model_names = ['G','D']
else: # during test time, only load G
self.model_names = ['G']
# define networks (both generator and discriminator)
self.netG = networks.define_G(opt.input_nc, opt.output_nc, 64, 'unet_1024', 'none',
False, 'normal', 0.02, self.gpu_ids)
if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
if self.isTrain:
# define loss functions
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
self.criterionL1 = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
def set_input_train(self, input):
self.outer = input['data_outer'].to(self.device)
self.outer = torch.nn.functional.interpolate(self.outer,(1024,1024),mode='bilinear',align_corners=False)
self.inner = input['data_inner'].to(self.device)
self.inner = torch.nn.functional.interpolate(self.inner,(1024,1024),mode='bilinear',align_corners=False)
self.image_paths = input['image_path']
if self.isTrain:
self.gtfake = input['data_gtfake'].to(self.device)
self.gtfake = torch.nn.functional.interpolate(self.gtfake, (1024, 1024), mode='bilinear', align_corners=False)
self.real_B = self.gtfake
self.real_A = torch.cat((self.outer, self.inner), 1)
def set_input(self, outer, inner):
inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0)
outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0)
inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner))
outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer))
inner = self.normalize(inner)
outer = self.normalize(outer)
self.real_A = torch.cat((outer, inner), 1).to(self.device)
def normalize(self, input):
input = input * 2
input = input - 1
return input
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
self.fake_B = self.netG(self.real_A) # G(A)
def backward_D(self):
"""Calculate GAN loss for the discriminator"""
# Fake; stop backprop to the generator by detaching fake_B
fake_AB = torch.cat((self.real_A, self.fake_B), 1) # we use conditional GANs; we need to feed both input and output to the discriminator
pred_fake = self.netD(fake_AB.detach())
self.loss_D_fake = self.criterionGAN(pred_fake, False)
# Real
real_AB = torch.cat((self.real_A, self.real_B), 1)
pred_real = self.netD(real_AB)
self.loss_D_real = self.criterionGAN(pred_real, True)
# combine loss and calculate gradients
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
self.loss_D.backward()
def backward_G(self):
"""Calculate GAN and L1 loss for the generator"""
# First, G(A) should fake the discriminator
fake_AB = torch.cat((self.real_A, self.fake_B), 1)
pred_fake = self.netD(fake_AB)
self.loss_G_GAN = self.criterionGAN(pred_fake, True)
# Second, G(A) = B
self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
# combine loss and calculate gradients
self.loss_G = self.loss_G_L1 + self.loss_G_GAN
self.loss_G.backward()
def optimize_parameters(self):
self.forward() # compute fake images: G(A)
# update D
self.set_requires_grad(self.netD, True) # enable backprop for D
self.optimizer_D.zero_grad() # set D's gradients to zero
self.backward_D() # calculate gradients for D
self.optimizer_D.step() # update D's weights
# update G
self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G
self.optimizer_G.zero_grad() # set G's gradients to zero
self.backward_G() # calculate graidents for G
self.optimizer_G.step() # udpate G's weights

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"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""

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import argparse
import os
from ...pix2pix.util import util
# import torch
from ...pix2pix import models
# import pix2pix.data
import numpy as np
class BaseOptions():
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
"""
def __init__(self):
"""Reset the class; indicates the class hasn't been initailized"""
self.initialized = False
def initialize(self, parser):
"""Define the common options that are used in both training and test."""
# basic parameters
parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--name', type=str, default='void', help='mahdi_unet_new, scaled_unet')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--checkpoints_dir', type=str, default='./pix2pix/checkpoints', help='models are saved here')
# model parameters
parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
parser.add_argument('--input_nc', type=int, default=2, help='# of input image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--output_nc', type=int, default=1, help='# of output image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
# dataset parameters
parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
parser.add_argument('--load_size', type=int, default=672, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=672, help='then crop to this size')
parser.add_argument('--max_dataset_size', type=int, default=10000, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
# additional parameters
parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
parser.add_argument('--data_dir', type=str, required=False,
help='input files directory images can be .png .jpg .tiff')
parser.add_argument('--output_dir', type=str, required=False,
help='result dir. result depth will be png. vides are JMPG as avi')
parser.add_argument('--savecrops', type=int, required=False)
parser.add_argument('--savewholeest', type=int, required=False)
parser.add_argument('--output_resolution', type=int, required=False,
help='0 for no restriction 1 for resize to input size')
parser.add_argument('--net_receptive_field_size', type=int, required=False)
parser.add_argument('--pix2pixsize', type=int, required=False)
parser.add_argument('--generatevideo', type=int, required=False)
parser.add_argument('--depthNet', type=int, required=False, help='0: midas 1:strurturedRL')
parser.add_argument('--R0', action='store_true')
parser.add_argument('--R20', action='store_true')
parser.add_argument('--Final', action='store_true')
parser.add_argument('--colorize_results', action='store_true')
parser.add_argument('--max_res', type=float, default=np.inf)
self.initialized = True
return parser
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
opt, _ = parser.parse_known_args()
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args() # parse again with new defaults
# modify dataset-related parser options
# dataset_name = opt.dataset_mode
# dataset_option_setter = pix2pix.data.get_option_setter(dataset_name)
# parser = dataset_option_setter(parser, self.isTrain)
# save and return the parser
self.parser = parser
#return parser.parse_args() #EVIL
return opt
def print_options(self, opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
# save to the disk
expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')
def parse(self):
"""Parse our options, create checkpoints directory suffix, and set up gpu device."""
opt = self.gather_options()
opt.isTrain = self.isTrain # train or test
# process opt.suffix
if opt.suffix:
suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
opt.name = opt.name + suffix
#self.print_options(opt)
# set gpu ids
str_ids = opt.gpu_ids.split(',')
opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
opt.gpu_ids.append(id)
#if len(opt.gpu_ids) > 0:
# torch.cuda.set_device(opt.gpu_ids[0])
self.opt = opt
return self.opt

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from .base_options import BaseOptions
class TestOptions(BaseOptions):
"""This class includes test options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
# Dropout and Batchnorm has different behavioir during training and test.
parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')
# rewrite devalue values
parser.set_defaults(model='pix2pix4depth')
# To avoid cropping, the load_size should be the same as crop_size
parser.set_defaults(load_size=parser.get_default('crop_size'))
self.isTrain = False
return parser

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"""This package includes a miscellaneous collection of useful helper functions."""

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/util/get_data.py Normal file
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from __future__ import print_function
import os
import tarfile
import requests
from warnings import warn
from zipfile import ZipFile
from bs4 import BeautifulSoup
from os.path import abspath, isdir, join, basename
class GetData(object):
"""A Python script for downloading CycleGAN or pix2pix datasets.
Parameters:
technique (str) -- One of: 'cyclegan' or 'pix2pix'.
verbose (bool) -- If True, print additional information.
Examples:
>>> from util.get_data import GetData
>>> gd = GetData(technique='cyclegan')
>>> new_data_path = gd.get(save_path='./datasets') # options will be displayed.
Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh'
and 'scripts/download_cyclegan_model.sh'.
"""
def __init__(self, technique='cyclegan', verbose=True):
url_dict = {
'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/',
'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
}
self.url = url_dict.get(technique.lower())
self._verbose = verbose
def _print(self, text):
if self._verbose:
print(text)
@staticmethod
def _get_options(r):
soup = BeautifulSoup(r.text, 'lxml')
options = [h.text for h in soup.find_all('a', href=True)
if h.text.endswith(('.zip', 'tar.gz'))]
return options
def _present_options(self):
r = requests.get(self.url)
options = self._get_options(r)
print('Options:\n')
for i, o in enumerate(options):
print("{0}: {1}".format(i, o))
choice = input("\nPlease enter the number of the "
"dataset above you wish to download:")
return options[int(choice)]
def _download_data(self, dataset_url, save_path):
if not isdir(save_path):
os.makedirs(save_path)
base = basename(dataset_url)
temp_save_path = join(save_path, base)
with open(temp_save_path, "wb") as f:
r = requests.get(dataset_url)
f.write(r.content)
if base.endswith('.tar.gz'):
obj = tarfile.open(temp_save_path)
elif base.endswith('.zip'):
obj = ZipFile(temp_save_path, 'r')
else:
raise ValueError("Unknown File Type: {0}.".format(base))
self._print("Unpacking Data...")
obj.extractall(save_path)
obj.close()
os.remove(temp_save_path)
def get(self, save_path, dataset=None):
"""
Download a dataset.
Parameters:
save_path (str) -- A directory to save the data to.
dataset (str) -- (optional). A specific dataset to download.
Note: this must include the file extension.
If None, options will be presented for you
to choose from.
Returns:
save_path_full (str) -- the absolute path to the downloaded data.
"""
if dataset is None:
selected_dataset = self._present_options()
else:
selected_dataset = dataset
save_path_full = join(save_path, selected_dataset.split('.')[0])
if isdir(save_path_full):
warn("\n'{0}' already exists. Voiding Download.".format(
save_path_full))
else:
self._print('Downloading Data...')
url = "{0}/{1}".format(self.url, selected_dataset)
self._download_data(url, save_path=save_path)
return abspath(save_path_full)

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/util/guidedfilter.py Normal file
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import numpy as np
class GuidedFilter():
def __init__(self, source, reference, r=64, eps= 0.05**2):
self.source = source;
self.reference = reference;
self.r = r
self.eps = eps
self.smooth = self.guidedfilter(self.source,self.reference,self.r,self.eps)
def boxfilter(self,img, r):
(rows, cols) = img.shape
imDst = np.zeros_like(img)
imCum = np.cumsum(img, 0)
imDst[0 : r+1, :] = imCum[r : 2*r+1, :]
imDst[r+1 : rows-r, :] = imCum[2*r+1 : rows, :] - imCum[0 : rows-2*r-1, :]
imDst[rows-r: rows, :] = np.tile(imCum[rows-1, :], [r, 1]) - imCum[rows-2*r-1 : rows-r-1, :]
imCum = np.cumsum(imDst, 1)
imDst[:, 0 : r+1] = imCum[:, r : 2*r+1]
imDst[:, r+1 : cols-r] = imCum[:, 2*r+1 : cols] - imCum[:, 0 : cols-2*r-1]
imDst[:, cols-r: cols] = np.tile(imCum[:, cols-1], [r, 1]).T - imCum[:, cols-2*r-1 : cols-r-1]
return imDst
def guidedfilter(self,I, p, r, eps):
(rows, cols) = I.shape
N = self.boxfilter(np.ones([rows, cols]), r)
meanI = self.boxfilter(I, r) / N
meanP = self.boxfilter(p, r) / N
meanIp = self.boxfilter(I * p, r) / N
covIp = meanIp - meanI * meanP
meanII = self.boxfilter(I * I, r) / N
varI = meanII - meanI * meanI
a = covIp / (varI + eps)
b = meanP - a * meanI
meanA = self.boxfilter(a, r) / N
meanB = self.boxfilter(b, r) / N
q = meanA * I + meanB
return q

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import dominate
from dominate.tags import meta, h3, table, tr, td, p, a, img, br
import os
class HTML:
"""This HTML class allows us to save images and write texts into a single HTML file.
It consists of functions such as <add_header> (add a text header to the HTML file),
<add_images> (add a row of images to the HTML file), and <save> (save the HTML to the disk).
It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
"""
def __init__(self, web_dir, title, refresh=0):
"""Initialize the HTML classes
Parameters:
web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
title (str) -- the webpage name
refresh (int) -- how often the website refresh itself; if 0; no refreshing
"""
self.title = title
self.web_dir = web_dir
self.img_dir = os.path.join(self.web_dir, 'images')
if not os.path.exists(self.web_dir):
os.makedirs(self.web_dir)
if not os.path.exists(self.img_dir):
os.makedirs(self.img_dir)
self.doc = dominate.document(title=title)
if refresh > 0:
with self.doc.head:
meta(http_equiv="refresh", content=str(refresh))
def get_image_dir(self):
"""Return the directory that stores images"""
return self.img_dir
def add_header(self, text):
"""Insert a header to the HTML file
Parameters:
text (str) -- the header text
"""
with self.doc:
h3(text)
def add_images(self, ims, txts, links, width=400):
"""add images to the HTML file
Parameters:
ims (str list) -- a list of image paths
txts (str list) -- a list of image names shown on the website
links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page
"""
self.t = table(border=1, style="table-layout: fixed;") # Insert a table
self.doc.add(self.t)
with self.t:
with tr():
for im, txt, link in zip(ims, txts, links):
with td(style="word-wrap: break-word;", halign="center", valign="top"):
with p():
with a(href=os.path.join('images', link)):
img(style="width:%dpx" % width, src=os.path.join('images', im))
br()
p(txt)
def save(self):
"""save the current content to the HMTL file"""
html_file = '%s/index.html' % self.web_dir
f = open(html_file, 'wt')
f.write(self.doc.render())
f.close()
if __name__ == '__main__': # we show an example usage here.
html = HTML('web/', 'test_html')
html.add_header('hello world')
ims, txts, links = [], [], []
for n in range(4):
ims.append('image_%d.png' % n)
txts.append('text_%d' % n)
links.append('image_%d.png' % n)
html.add_images(ims, txts, links)
html.save()

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/util/image_pool.py Normal file
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import random
import torch
class ImagePool():
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_size):
"""Initialize the ImagePool class
Parameters:
pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
"""
self.pool_size = pool_size
if self.pool_size > 0: # create an empty pool
self.num_imgs = 0
self.images = []
def query(self, images):
"""Return an image from the pool.
Parameters:
images: the latest generated images from the generator
Returns images from the buffer.
By 50/100, the buffer will return input images.
By 50/100, the buffer will return images previously stored in the buffer,
and insert the current images to the buffer.
"""
if self.pool_size == 0: # if the buffer size is 0, do nothing
return images
return_images = []
for image in images:
image = torch.unsqueeze(image.data, 0)
if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer
self.num_imgs = self.num_imgs + 1
self.images.append(image)
return_images.append(image)
else:
p = random.uniform(0, 1)
if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
random_id = random.randint(0, self.pool_size - 1) # randint is inclusive
tmp = self.images[random_id].clone()
self.images[random_id] = image
return_images.append(tmp)
else: # by another 50% chance, the buffer will return the current image
return_images.append(image)
return_images = torch.cat(return_images, 0) # collect all the images and return
return return_images

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/util/util.py Normal file
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"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
def tensor2im(input_image, imtype=np.uint16):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = torch.squeeze(image_tensor).cpu().numpy() # convert it into a numpy array
image_numpy = (image_numpy + 1) / 2.0 * (2**16-1) #
else: # if it is a numpy array, do nothing
image_numpy = input_image
return image_numpy.astype(imtype)
def diagnose_network(net, name='network'):
"""Calculate and print the mean of average absolute(gradients)
Parameters:
net (torch network) -- Torch network
name (str) -- the name of the network
"""
mean = 0.0
count = 0
for param in net.parameters():
if param.grad is not None:
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if count > 0:
mean = mean / count
print(name)
print(mean)
def save_image(image_numpy, image_path, aspect_ratio=1.0):
"""Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
"""
image_pil = Image.fromarray(image_numpy)
image_pil = image_pil.convert('I;16')
# image_pil = Image.fromarray(image_numpy)
# h, w, _ = image_numpy.shape
#
# if aspect_ratio > 1.0:
# image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
# if aspect_ratio < 1.0:
# image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
image_pil.save(image_path)
def print_numpy(x, val=True, shp=False):
"""Print the mean, min, max, median, std, and size of a numpy array
Parameters:
val (bool) -- if print the values of the numpy array
shp (bool) -- if print the shape of the numpy array
"""
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
def mkdirs(paths):
"""create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
"""
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
"""create a single empty directory if it didn't exist
Parameters:
path (str) -- a single directory path
"""
if not os.path.exists(path):
os.makedirs(path)

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extensions-builtin/sd_forge_controlnet/annotator/leres/pix2pix/util/visualizer.py Normal file
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import numpy as np
import os
import sys
import ntpath
import time
from . import util, html
from subprocess import Popen, PIPE
import torch
if sys.version_info[0] == 2:
VisdomExceptionBase = Exception
else:
VisdomExceptionBase = ConnectionError
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
"""Save images to the disk.
Parameters:
webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
image_path (str) -- the string is used to create image paths
aspect_ratio (float) -- the aspect ratio of saved images
width (int) -- the images will be resized to width x width
This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
"""
image_dir = webpage.get_image_dir()
short_path = ntpath.basename(image_path[0])
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
im = util.tensor2im(im_data)
image_name = '%s_%s.png' % (name, label)
save_path = os.path.join(image_dir, image_name)
util.save_image(im, save_path, aspect_ratio=aspect_ratio)
ims.append(image_name)
txts.append(label)
links.append(image_name)
webpage.add_images(ims, txts, links, width=width)
class Visualizer():
"""This class includes several functions that can display/save images and print/save logging information.
It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
"""
def __init__(self, opt):
"""Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: connect to a visdom server
Step 3: create an HTML object for saveing HTML filters
Step 4: create a logging file to store training losses
"""
self.opt = opt # cache the option
self.display_id = opt.display_id
self.use_html = opt.isTrain and not opt.no_html
self.win_size = opt.display_winsize
self.name = opt.name
self.port = opt.display_port
self.saved = False
if self.use_html: # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
self.img_dir = os.path.join(self.web_dir, 'images')
print('create web directory %s...' % self.web_dir)
util.mkdirs([self.web_dir, self.img_dir])
# create a logging file to store training losses
self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(self.log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
def reset(self):
"""Reset the self.saved status"""
self.saved = False
def create_visdom_connections(self):
"""If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
print('Command: %s' % cmd)
Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
def display_current_results(self, visuals, epoch, save_result):
"""Display current results on visdom; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
epoch (int) - - the current epoch
save_result (bool) - - if save the current results to an HTML file
"""
if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved.
self.saved = True
# save images to the disk
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
util.save_image(image_numpy, img_path)
# update website
webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1)
for n in range(epoch, 0, -1):
webpage.add_header('epoch [%d]' % n)
ims, txts, links = [], [], []
for label, image_numpy in visuals.items():
# image_numpy = util.tensor2im(image)
img_path = 'epoch%.3d_%s.png' % (n, label)
ims.append(img_path)
txts.append(label)
links.append(img_path)
webpage.add_images(ims, txts, links, width=self.win_size)
webpage.save()
# def plot_current_losses(self, epoch, counter_ratio, losses):
# """display the current losses on visdom display: dictionary of error labels and values
#
# Parameters:
# epoch (int) -- current epoch
# counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
# losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
# """
# if not hasattr(self, 'plot_data'):
# self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
# self.plot_data['X'].append(epoch + counter_ratio)
# self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
# try:
# self.vis.line(
# X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
# Y=np.array(self.plot_data['Y']),
# opts={
# 'title': self.name + ' loss over time',
# 'legend': self.plot_data['legend'],
# 'xlabel': 'epoch',
# 'ylabel': 'loss'},
# win=self.display_id)
# except VisdomExceptionBase:
# self.create_visdom_connections()
# losses: same format as |losses| of plot_current_losses
def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
"""print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
"""
message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message

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extensions-builtin/sd_forge_controlnet/annotator/lineart/LICENSE Normal file
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MIT License
Copyright (c) 2022 Caroline Chan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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import os
import cv2
import torch
import numpy as np
import torch.nn as nn
from einops import rearrange
from modules import devices
from annotator.annotator_path import models_path
norm_layer = nn.InstanceNorm2d
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [ nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features)
]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class Generator(nn.Module):
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
super(Generator, self).__init__()
# Initial convolution block
model0 = [ nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, 64, 7),
norm_layer(64),
nn.ReLU(inplace=True) ]
self.model0 = nn.Sequential(*model0)
# Downsampling
model1 = []
in_features = 64
out_features = in_features*2
for _ in range(2):
model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True) ]
in_features = out_features
out_features = in_features*2
self.model1 = nn.Sequential(*model1)
model2 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# Upsampling
model3 = []
out_features = in_features//2
for _ in range(2):
model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True) ]
in_features = out_features
out_features = in_features//2
self.model3 = nn.Sequential(*model3)
# Output layer
model4 = [ nn.ReflectionPad2d(3),
nn.Conv2d(64, output_nc, 7)]
if sigmoid:
model4 += [nn.Sigmoid()]
self.model4 = nn.Sequential(*model4)
def forward(self, x, cond=None):
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
class LineartDetector:
model_dir = os.path.join(models_path, "lineart")
model_default = 'sk_model.pth'
model_coarse = 'sk_model2.pth'
def __init__(self, model_name):
self.model = None
self.model_name = model_name
self.device = devices.get_device_for("controlnet")
def load_model(self, name):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
model_path = os.path.join(self.model_dir, name)
if not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
model = Generator(3, 1, 3)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
self.model = model.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model(self.model_name)
self.model.to(self.device)
assert input_image.ndim == 3
image = input_image
with torch.no_grad():
image = torch.from_numpy(image).float().to(self.device)
image = image / 255.0
image = rearrange(image, 'h w c -> 1 c h w')
line = self.model(image)[0][0]
line = line.cpu().numpy()
line = (line * 255.0).clip(0, 255).astype(np.uint8)
return line

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extensions-builtin/sd_forge_controlnet/annotator/lineart_anime/LICENSE Normal file
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MIT License
Copyright (c) 2022 Caroline Chan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

161
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import numpy as np
import torch
import torch.nn as nn
import functools
import os
import cv2
from einops import rearrange
from modules import devices
from annotator.annotator_path import models_path
class UnetGenerator(nn.Module):
"""Create a Unet-based generator"""
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer
for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer
def forward(self, input):
"""Standard forward"""
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def __init__(self, outer_nc, inner_nc, input_nc=None,
submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
stride=2, padding=1, bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return torch.cat([x, self.model(x)], 1)
class LineartAnimeDetector:
model_dir = os.path.join(models_path, "lineart_anime")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/netG.pth"
modelpath = os.path.join(self.model_dir, "netG.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False)
ckpt = torch.load(modelpath)
for key in list(ckpt.keys()):
if 'module.' in key:
ckpt[key.replace('module.', '')] = ckpt[key]
del ckpt[key]
net.load_state_dict(ckpt)
net.eval()
self.model = net.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
H, W, C = input_image.shape
Hn = 256 * int(np.ceil(float(H) / 256.0))
Wn = 256 * int(np.ceil(float(W) / 256.0))
img = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC)
with torch.no_grad():
image_feed = torch.from_numpy(img).float().to(self.device)
image_feed = image_feed / 127.5 - 1.0
image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
line = self.model(image_feed)[0, 0] * 127.5 + 127.5
line = line.cpu().numpy()
line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC)
line = line.clip(0, 255).astype(np.uint8)
return line

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extensions-builtin/sd_forge_controlnet/annotator/manga_line/LICENSE Normal file
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MIT License
Copyright (c) 2021 Miaomiao Li
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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extensions-builtin/sd_forge_controlnet/annotator/manga_line/__init__.py Normal file
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import os
import torch
import torch.nn as nn
from PIL import Image
import fnmatch
import cv2
import sys
import numpy as np
from einops import rearrange
from modules import devices
from annotator.annotator_path import models_path
class _bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros')
)
def forward(self, x):
return self.model(x)
# the following are for debugs
print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
for i,layer in enumerate(self.model):
if i != 2:
x = layer(x)
else:
x = layer(x)
#x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
print(x[0])
return x
class _u_bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_u_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x):
return self.model(x)
class _shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample=1):
super(_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters or subsample != 1:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
)
def forward(self, x, y):
#print(x.size(), y.size(), self.process)
if self.process:
y0 = self.model(x)
#print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
return y0 + y
else:
#print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
return x + y
class _u_shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample):
super(_u_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x, y):
if self.process:
return self.model(x) + y
else:
return x + y
class basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(basic_block, self).__init__()
self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
x1 = self.conv1(x)
x2 = self.residual(x1)
return self.shortcut(x, x2)
class _u_basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(_u_basic_block, self).__init__()
self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
y = self.residual(self.conv1(x))
return self.shortcut(x, y)
class _residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
super(_residual_block, self).__init__()
layers = []
for i in range(repetitions):
init_subsample = 1
if i == repetitions - 1 and not is_first_layer:
init_subsample = 2
if i == 0:
l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample)
else:
l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class _upsampling_residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions):
super(_upsampling_residual_block, self).__init__()
layers = []
for i in range(repetitions):
l = None
if i == 0:
l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input)
else:
l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class res_skip(nn.Module):
def __init__(self):
super(res_skip, self).__init__()
self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input)
self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0)
self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1)
self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2)
self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3)
self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4)
self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1))
self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1)
self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1))
self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2)
self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1))
self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3)
self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1))
self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4)
self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7)
def forward(self, x):
x0 = self.block0(x)
x1 = self.block1(x0)
x2 = self.block2(x1)
x3 = self.block3(x2)
x4 = self.block4(x3)
x5 = self.block5(x4)
res1 = self.res1(x3, x5)
x6 = self.block6(res1)
res2 = self.res2(x2, x6)
x7 = self.block7(res2)
res3 = self.res3(x1, x7)
x8 = self.block8(res3)
res4 = self.res4(x0, x8)
x9 = self.block9(res4)
y = self.conv15(x9)
return y
class MangaLineExtration:
model_dir = os.path.join(models_path, "manga_line")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/erika.pth"
modelpath = os.path.join(self.model_dir, "erika.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
#norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
net = res_skip()
ckpt = torch.load(modelpath)
for key in list(ckpt.keys()):
if 'module.' in key:
ckpt[key.replace('module.', '')] = ckpt[key]
del ckpt[key]
net.load_state_dict(ckpt)
net.eval()
self.model = net.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
img = np.ascontiguousarray(img.copy()).copy()
with torch.no_grad():
image_feed = torch.from_numpy(img).float().to(self.device)
image_feed = rearrange(image_feed, 'h w -> 1 1 h w')
line = self.model(image_feed)
line = 255 - line.cpu().numpy()[0, 0]
return line.clip(0, 255).astype(np.uint8)

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extensions-builtin/sd_forge_controlnet/annotator/mediapipe_face/__init__.py Normal file
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from .mediapipe_face_common import generate_annotation
def apply_mediapipe_face(image, max_faces: int = 1, min_confidence: float = 0.5):
return generate_annotation(image, max_faces, min_confidence)

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extensions-builtin/sd_forge_controlnet/annotator/mediapipe_face/mediapipe_face_common.py Normal file
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from typing import Mapping
import mediapipe as mp
import numpy
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_detection = mp.solutions.face_detection # Only for counting faces.
mp_face_mesh = mp.solutions.face_mesh
mp_face_connections = mp.solutions.face_mesh_connections.FACEMESH_TESSELATION
mp_hand_connections = mp.solutions.hands_connections.HAND_CONNECTIONS
mp_body_connections = mp.solutions.pose_connections.POSE_CONNECTIONS
DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
PoseLandmark = mp.solutions.drawing_styles.PoseLandmark
min_face_size_pixels: int = 64
f_thick = 2
f_rad = 1
right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad)
head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
# mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
face_connection_spec = {}
for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
face_connection_spec[edge] = head_draw
for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
face_connection_spec[edge] = left_eye_draw
for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
face_connection_spec[edge] = left_eyebrow_draw
# for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
# face_connection_spec[edge] = left_iris_draw
for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
face_connection_spec[edge] = right_eye_draw
for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
face_connection_spec[edge] = right_eyebrow_draw
# for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
# face_connection_spec[edge] = right_iris_draw
for edge in mp_face_mesh.FACEMESH_LIPS:
face_connection_spec[edge] = mouth_draw
iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
def draw_pupils(image, landmark_list, drawing_spec, halfwidth: int = 2):
"""We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
landmarks. Until our PR is merged into mediapipe, we need this separate method."""
if len(image.shape) != 3:
raise ValueError("Input image must be H,W,C.")
image_rows, image_cols, image_channels = image.shape
if image_channels != 3: # BGR channels
raise ValueError('Input image must contain three channel bgr data.')
for idx, landmark in enumerate(landmark_list.landmark):
if (
(landmark.HasField('visibility') and landmark.visibility < 0.9) or
(landmark.HasField('presence') and landmark.presence < 0.5)
):
continue
if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
continue
image_x = int(image_cols*landmark.x)
image_y = int(image_rows*landmark.y)
draw_color = None
if isinstance(drawing_spec, Mapping):
if drawing_spec.get(idx) is None:
continue
else:
draw_color = drawing_spec[idx].color
elif isinstance(drawing_spec, DrawingSpec):
draw_color = drawing_spec.color
image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
def reverse_channels(image):
"""Given a numpy array in RGB form, convert to BGR. Will also convert from BGR to RGB."""
# im[:,:,::-1] is a neat hack to convert BGR to RGB by reversing the indexing order.
# im[:,:,::[2,1,0]] would also work but makes a copy of the data.
return image[:, :, ::-1]
def generate_annotation(
img_rgb,
max_faces: int,
min_confidence: float
):
"""
Find up to 'max_faces' inside the provided input image.
If min_face_size_pixels is provided and nonzero it will be used to filter faces that occupy less than this many
pixels in the image.
"""
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=max_faces,
refine_landmarks=True,
min_detection_confidence=min_confidence,
) as facemesh:
img_height, img_width, img_channels = img_rgb.shape
assert(img_channels == 3)
results = facemesh.process(img_rgb).multi_face_landmarks
if results is None:
print("No faces detected in controlnet image for Mediapipe face annotator.")
return numpy.zeros_like(img_rgb)
# Filter faces that are too small
filtered_landmarks = []
for lm in results:
landmarks = lm.landmark
face_rect = [
landmarks[0].x,
landmarks[0].y,
landmarks[0].x,
landmarks[0].y,
] # Left, up, right, down.
for i in range(len(landmarks)):
face_rect[0] = min(face_rect[0], landmarks[i].x)
face_rect[1] = min(face_rect[1], landmarks[i].y)
face_rect[2] = max(face_rect[2], landmarks[i].x)
face_rect[3] = max(face_rect[3], landmarks[i].y)
if min_face_size_pixels > 0:
face_width = abs(face_rect[2] - face_rect[0])
face_height = abs(face_rect[3] - face_rect[1])
face_width_pixels = face_width * img_width
face_height_pixels = face_height * img_height
face_size = min(face_width_pixels, face_height_pixels)
if face_size >= min_face_size_pixels:
filtered_landmarks.append(lm)
else:
filtered_landmarks.append(lm)
# Annotations are drawn in BGR for some reason, but we don't need to flip a zero-filled image at the start.
empty = numpy.zeros_like(img_rgb)
# Draw detected faces:
for face_landmarks in filtered_landmarks:
mp_drawing.draw_landmarks(
empty,
face_landmarks,
connections=face_connection_spec.keys(),
landmark_drawing_spec=None,
connection_drawing_spec=face_connection_spec
)
draw_pupils(empty, face_landmarks, iris_landmark_spec, 2)
# Flip BGR back to RGB.
empty = reverse_channels(empty).copy()
return empty

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extensions-builtin/sd_forge_controlnet/annotator/midas/LICENSE Normal file
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MIT License
Copyright (c) 2019 Intel ISL (Intel Intelligent Systems Lab)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

49
extensions-builtin/sd_forge_controlnet/annotator/midas/__init__.py Normal file
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import cv2
import numpy as np
import torch
from einops import rearrange
from .api import MiDaSInference
from modules import devices
model = None
def unload_midas_model():
global model
if model is not None:
model = model.cpu()
def apply_midas(input_image, a=np.pi * 2.0, bg_th=0.1):
global model
if model is None:
model = MiDaSInference(model_type="dpt_hybrid")
if devices.get_device_for("controlnet").type != 'mps':
model = model.to(devices.get_device_for("controlnet"))
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
image_depth = torch.from_numpy(image_depth).float()
if devices.get_device_for("controlnet").type != 'mps':
image_depth = image_depth.to(devices.get_device_for("controlnet"))
image_depth = image_depth / 127.5 - 1.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = model(image_depth)[0]
depth_pt = depth.clone()
depth_pt -= torch.min(depth_pt)
depth_pt /= torch.max(depth_pt)
depth_pt = depth_pt.cpu().numpy()
depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
depth_np = depth.cpu().numpy()
x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
z = np.ones_like(x) * a
x[depth_pt < bg_th] = 0
y[depth_pt < bg_th] = 0
normal = np.stack([x, y, z], axis=2)
normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1]
return depth_image, normal_image

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extensions-builtin/sd_forge_controlnet/annotator/midas/api.py Normal file
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# based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
import os
from annotator.annotator_path import models_path
from torchvision.transforms import Compose
from .midas.dpt_depth import DPTDepthModel
from .midas.midas_net import MidasNet
from .midas.midas_net_custom import MidasNet_small
from .midas.transforms import Resize, NormalizeImage, PrepareForNet
base_model_path = os.path.join(models_path, "midas")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt"
ISL_PATHS = {
"dpt_large": os.path.join(base_model_path, "dpt_large-midas-2f21e586.pt"),
"dpt_hybrid": os.path.join(base_model_path, "dpt_hybrid-midas-501f0c75.pt"),
"midas_v21": "",
"midas_v21_small": "",
}
OLD_ISL_PATHS = {
"dpt_large": os.path.join(old_modeldir, "dpt_large-midas-2f21e586.pt"),
"dpt_hybrid": os.path.join(old_modeldir, "dpt_hybrid-midas-501f0c75.pt"),
"midas_v21": "",
"midas_v21_small": "",
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def load_midas_transform(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load transform only
if model_type == "dpt_large": # DPT-Large
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
elif model_type == "midas_v21_small":
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
else:
assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return transform
def load_model(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load network
model_path = ISL_PATHS[model_type]
old_model_path = OLD_ISL_PATHS[model_type]
if model_type == "dpt_large": # DPT-Large
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
if os.path.exists(old_model_path):
model_path = old_model_path
elif not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=base_model_path)
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "midas_v21_small":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return model.eval(), transform
class MiDaSInference(nn.Module):
MODEL_TYPES_TORCH_HUB = [
"DPT_Large",
"DPT_Hybrid",
"MiDaS_small"
]
MODEL_TYPES_ISL = [
"dpt_large",
"dpt_hybrid",
"midas_v21",
"midas_v21_small",
]
def __init__(self, model_type):
super().__init__()
assert (model_type in self.MODEL_TYPES_ISL)
model, _ = load_model(model_type)
self.model = model
self.model.train = disabled_train
def forward(self, x):
with torch.no_grad():
prediction = self.model(x)
return prediction

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extensions-builtin/sd_forge_controlnet/annotator/midas/midas/__init__.py Normal file
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extensions-builtin/sd_forge_controlnet/annotator/midas/midas/base_model.py Normal file
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import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["model"]
self.load_state_dict(parameters)

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extensions-builtin/sd_forge_controlnet/annotator/midas/midas/blocks.py Normal file
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import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
if backbone == "vitl16_384":
pretrained = _make_pretrained_vitl16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[256, 512, 1024, 1024], features, groups=groups, expand=expand
) # ViT-L/16 - 85.0% Top1 (backbone)
elif backbone == "vitb_rn50_384":
pretrained = _make_pretrained_vitb_rn50_384(
use_pretrained,
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)
scratch = _make_scratch(
[256, 512, 768, 768], features, groups=groups, expand=expand
) # ViT-H/16 - 85.0% Top1 (backbone)
elif backbone == "vitb16_384":
pretrained = _make_pretrained_vitb16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[96, 192, 384, 768], features, groups=groups, expand=expand
) # ViT-B/16 - 84.6% Top1 (backbone)
elif backbone == "resnext101_wsl":
pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
elif backbone == "efficientnet_lite3":
pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
else:
print(f"Backbone '{backbone}' not implemented")
assert False
return pretrained, scratch
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
out_shape4 = out_shape
if expand==True:
out_shape1 = out_shape
out_shape2 = out_shape*2
out_shape3 = out_shape*4
out_shape4 = out_shape*8
scratch.layer1_rn = nn.Conv2d(
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer2_rn = nn.Conv2d(
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer3_rn = nn.Conv2d(
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer4_rn = nn.Conv2d(
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
return scratch
def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
efficientnet = torch.hub.load(
"rwightman/gen-efficientnet-pytorch",
"tf_efficientnet_lite3",
pretrained=use_pretrained,
exportable=exportable
)
return _make_efficientnet_backbone(efficientnet)
def _make_efficientnet_backbone(effnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
)
pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
return pretrained
def _make_resnet_backbone(resnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
)
pretrained.layer2 = resnet.layer2
pretrained.layer3 = resnet.layer3
pretrained.layer4 = resnet.layer4
return pretrained
def _make_pretrained_resnext101_wsl(use_pretrained):
resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
return _make_resnet_backbone(resnet)
class Interpolate(nn.Module):
"""Interpolation module.
"""
def __init__(self, scale_factor, mode, align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
)
return x
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.relu(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
return out + x
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.resConfUnit1 = ResidualConvUnit(features)
self.resConfUnit2 = ResidualConvUnit(features)
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
output += self.resConfUnit1(xs[1])
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=True
)
return output
class ResidualConvUnit_custom(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups=1
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
if self.bn==True:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn==True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn==True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
# return out + x
class FeatureFusionBlock_custom(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock_custom, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups=1
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
# output += res
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output

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import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class DPT(BaseModel):
def __init__(
self,
head,
features=256,
backbone="vitb_rn50_384",
readout="project",
channels_last=False,
use_bn=False,
):
super(DPT, self).__init__()
self.channels_last = channels_last
hooks = {
"vitb_rn50_384": [0, 1, 8, 11],
"vitb16_384": [2, 5, 8, 11],
"vitl16_384": [5, 11, 17, 23],
}
# Instantiate backbone and reassemble blocks
self.pretrained, self.scratch = _make_encoder(
backbone,
features,
False, # Set to true of you want to train from scratch, uses ImageNet weights
groups=1,
expand=False,
exportable=False,
hooks=hooks[backbone],
use_readout=readout,
)
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
self.scratch.output_conv = head
def forward(self, x):
if self.channels_last == True:
x.contiguous(memory_format=torch.channels_last)
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return out
class DPTDepthModel(DPT):
def __init__(self, path=None, non_negative=True, **kwargs):
features = kwargs["features"] if "features" in kwargs else 256
head = nn.Sequential(
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
super().__init__(head, **kwargs)
if path is not None:
self.load(path)
def forward(self, x):
return super().forward(x).squeeze(dim=1)

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extensions-builtin/sd_forge_controlnet/annotator/midas/midas/midas_net.py Normal file
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"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
class MidasNet(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=256, non_negative=True):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet, self).__init__()
use_pretrained = False if path is None else True
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureFusionBlock(features)
self.scratch.refinenet2 = FeatureFusionBlock(features)
self.scratch.refinenet1 = FeatureFusionBlock(features)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)

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"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
class MidasNet_small(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
blocks={'expand': True}):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet_small, self).__init__()
use_pretrained = False if path else True
self.channels_last = channels_last
self.blocks = blocks
self.backbone = backbone
self.groups = 1
features1=features
features2=features
features3=features
features4=features
self.expand = False
if "expand" in self.blocks and self.blocks['expand'] == True:
self.expand = True
features1=features
features2=features*2
features3=features*4
features4=features*8
self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
self.scratch.activation = nn.ReLU(False)
self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
self.scratch.activation,
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
if self.channels_last==True:
print("self.channels_last = ", self.channels_last)
x.contiguous(memory_format=torch.channels_last)
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)
def fuse_model(m):
prev_previous_type = nn.Identity()
prev_previous_name = ''
previous_type = nn.Identity()
previous_name = ''
for name, module in m.named_modules():
if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
# print("FUSED ", prev_previous_name, previous_name, name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
# print("FUSED ", prev_previous_name, previous_name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
# elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
# print("FUSED ", previous_name, name)
# torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
prev_previous_type = previous_type
prev_previous_name = previous_name
previous_type = type(module)
previous_name = name

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import numpy as np
import cv2
import math
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
Args:
sample (dict): sample
size (tuple): image size
Returns:
tuple: new size
"""
shape = list(sample["disparity"].shape)
if shape[0] >= size[0] and shape[1] >= size[1]:
return sample
scale = [0, 0]
scale[0] = size[0] / shape[0]
scale[1] = size[1] / shape[1]
scale = max(scale)
shape[0] = math.ceil(scale * shape[0])
shape[1] = math.ceil(scale * shape[1])
# resize
sample["image"] = cv2.resize(
sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
)
sample["disparity"] = cv2.resize(
sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
tuple(shape[::-1]),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return tuple(shape)
class Resize(object):
"""Resize sample to given size (width, height).
"""
def __init__(
self,
width,
height,
resize_target=True,
keep_aspect_ratio=False,
ensure_multiple_of=1,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_AREA,
):
"""Init.
Args:
width (int): desired output width
height (int): desired output height
resize_target (bool, optional):
True: Resize the full sample (image, mask, target).
False: Resize image only.
Defaults to True.
keep_aspect_ratio (bool, optional):
True: Keep the aspect ratio of the input sample.
Output sample might not have the given width and height, and
resize behaviour depends on the parameter 'resize_method'.
Defaults to False.
ensure_multiple_of (int, optional):
Output width and height is constrained to be multiple of this parameter.
Defaults to 1.
resize_method (str, optional):
"lower_bound": Output will be at least as large as the given size.
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
Defaults to "lower_bound".
"""
self.__width = width
self.__height = height
self.__resize_target = resize_target
self.__keep_aspect_ratio = keep_aspect_ratio
self.__multiple_of = ensure_multiple_of
self.__resize_method = resize_method
self.__image_interpolation_method = image_interpolation_method
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
if max_val is not None and y > max_val:
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
if y < min_val:
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
return y
def get_size(self, width, height):
# determine new height and width
scale_height = self.__height / height
scale_width = self.__width / width
if self.__keep_aspect_ratio:
if self.__resize_method == "lower_bound":
# scale such that output size is lower bound
if scale_width > scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "upper_bound":
# scale such that output size is upper bound
if scale_width < scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "minimal":
# scale as least as possbile
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
else:
raise ValueError(
f"resize_method {self.__resize_method} not implemented"
)
if self.__resize_method == "lower_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, min_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, min_val=self.__width
)
elif self.__resize_method == "upper_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, max_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, max_val=self.__width
)
elif self.__resize_method == "minimal":
new_height = self.constrain_to_multiple_of(scale_height * height)
new_width = self.constrain_to_multiple_of(scale_width * width)
else:
raise ValueError(f"resize_method {self.__resize_method} not implemented")
return (new_width, new_height)
def __call__(self, sample):
width, height = self.get_size(
sample["image"].shape[1], sample["image"].shape[0]
)
# resize sample
sample["image"] = cv2.resize(
sample["image"],
(width, height),
interpolation=self.__image_interpolation_method,
)
if self.__resize_target:
if "disparity" in sample:
sample["disparity"] = cv2.resize(
sample["disparity"],
(width, height),
interpolation=cv2.INTER_NEAREST,
)
if "depth" in sample:
sample["depth"] = cv2.resize(
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
(width, height),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return sample
class NormalizeImage(object):
"""Normlize image by given mean and std.
"""
def __init__(self, mean, std):
self.__mean = mean
self.__std = std
def __call__(self, sample):
sample["image"] = (sample["image"] - self.__mean) / self.__std
return sample
class PrepareForNet(object):
"""Prepare sample for usage as network input.
"""
def __init__(self):
pass
def __call__(self, sample):
image = np.transpose(sample["image"], (2, 0, 1))
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
if "mask" in sample:
sample["mask"] = sample["mask"].astype(np.float32)
sample["mask"] = np.ascontiguousarray(sample["mask"])
if "disparity" in sample:
disparity = sample["disparity"].astype(np.float32)
sample["disparity"] = np.ascontiguousarray(disparity)
if "depth" in sample:
depth = sample["depth"].astype(np.float32)
sample["depth"] = np.ascontiguousarray(depth)
return sample

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import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class AddReadout(nn.Module):
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.start_index = start_index
def forward(self, x):
if self.start_index == 2:
readout = (x[:, 0] + x[:, 1]) / 2
else:
readout = x[:, 0]
return x[:, self.start_index :] + readout.unsqueeze(1)
class ProjectReadout(nn.Module):
def __init__(self, in_features, start_index=1):
super(ProjectReadout, self).__init__()
self.start_index = start_index
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
def forward(self, x):
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
features = torch.cat((x[:, self.start_index :], readout), -1)
return self.project(features)
class Transpose(nn.Module):
def __init__(self, dim0, dim1):
super(Transpose, self).__init__()
self.dim0 = dim0
self.dim1 = dim1
def forward(self, x):
x = x.transpose(self.dim0, self.dim1)
return x
def forward_vit(pretrained, x):
b, c, h, w = x.shape
glob = pretrained.model.forward_flex(x)
layer_1 = pretrained.activations["1"]
layer_2 = pretrained.activations["2"]
layer_3 = pretrained.activations["3"]
layer_4 = pretrained.activations["4"]
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
unflatten = nn.Sequential(
nn.Unflatten(
2,
torch.Size(
[
h // pretrained.model.patch_size[1],
w // pretrained.model.patch_size[0],
]
),
)
)
if layer_1.ndim == 3:
layer_1 = unflatten(layer_1)
if layer_2.ndim == 3:
layer_2 = unflatten(layer_2)
if layer_3.ndim == 3:
layer_3 = unflatten(layer_3)
if layer_4.ndim == 3:
layer_4 = unflatten(layer_4)
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
return layer_1, layer_2, layer_3, layer_4
def _resize_pos_embed(self, posemb, gs_h, gs_w):
posemb_tok, posemb_grid = (
posemb[:, : self.start_index],
posemb[0, self.start_index :],
)
gs_old = int(math.sqrt(len(posemb_grid)))
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
def forward_flex(self, x):
b, c, h, w = x.shape
pos_embed = self._resize_pos_embed(
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
)
B = x.shape[0]
if hasattr(self.patch_embed, "backbone"):
x = self.patch_embed.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
if getattr(self, "dist_token", None) is not None:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
else:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
activations = {}
def get_activation(name):
def hook(model, input, output):
activations[name] = output
return hook
def get_readout_oper(vit_features, features, use_readout, start_index=1):
if use_readout == "ignore":
readout_oper = [Slice(start_index)] * len(features)
elif use_readout == "add":
readout_oper = [AddReadout(start_index)] * len(features)
elif use_readout == "project":
readout_oper = [
ProjectReadout(vit_features, start_index) for out_feat in features
]
else:
assert (
False
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
return readout_oper
def _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
size=[384, 384],
hooks=[2, 5, 8, 11],
vit_features=768,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
# 32, 48, 136, 384
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[256, 512, 1024, 1024],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model(
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
hooks=hooks,
use_readout=use_readout,
start_index=2,
)
def _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=[0, 1, 8, 11],
vit_features=768,
use_vit_only=False,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
if use_vit_only == True:
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
else:
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
get_activation("1")
)
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
get_activation("2")
)
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
if use_vit_only == True:
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
else:
pretrained.act_postprocess1 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess2 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitb_rn50_384(
pretrained, use_readout="ignore", hooks=None, use_vit_only=False
):
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
hooks = [0, 1, 8, 11] if hooks == None else hooks
return _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)

189
extensions-builtin/sd_forge_controlnet/annotator/midas/utils.py Normal file
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"""Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header.decode("ascii") == "PF":
color = True
elif header.decode("ascii") == "Pf":
color = False
else:
raise Exception("Not a PFM file: " + path)
dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
if dim_match:
width, height = list(map(int, dim_match.groups()))
else:
raise Exception("Malformed PFM header.")
scale = float(file.readline().decode("ascii").rstrip())
if scale < 0:
# little-endian
endian = "<"
scale = -scale
else:
# big-endian
endian = ">"
data = np.fromfile(file, endian + "f")
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
return data, scale
def write_pfm(path, image, scale=1):
"""Write pfm file.
Args:
path (str): pathto file
image (array): data
scale (int, optional): Scale. Defaults to 1.
"""
with open(path, "wb") as file:
color = None
if image.dtype.name != "float32":
raise Exception("Image dtype must be float32.")
image = np.flipud(image)
if len(image.shape) == 3 and image.shape[2] == 3: # color image
color = True
elif (
len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
): # greyscale
color = False
else:
raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
file.write("PF\n" if color else "Pf\n".encode())
file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
endian = image.dtype.byteorder
if endian == "<" or endian == "=" and sys.byteorder == "little":
scale = -scale
file.write("%f\n".encode() % scale)
image.tofile(file)
def read_image(path):
"""Read image and output RGB image (0-1).
Args:
path (str): path to file
Returns:
array: RGB image (0-1)
"""
img = cv2.imread(path)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
return img
def resize_image(img):
"""Resize image and make it fit for network.
Args:
img (array): image
Returns:
tensor: data ready for network
"""
height_orig = img.shape[0]
width_orig = img.shape[1]
if width_orig > height_orig:
scale = width_orig / 384
else:
scale = height_orig / 384
height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
img_resized = (
torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
)
img_resized = img_resized.unsqueeze(0)
return img_resized
def resize_depth(depth, width, height):
"""Resize depth map and bring to CPU (numpy).
Args:
depth (tensor): depth
width (int): image width
height (int): image height
Returns:
array: processed depth
"""
depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
depth_resized = cv2.resize(
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
)
return depth_resized
def write_depth(path, depth, bits=1):
"""Write depth map to pfm and png file.
Args:
path (str): filepath without extension
depth (array): depth
"""
write_pfm(path + ".pfm", depth.astype(np.float32))
depth_min = depth.min()
depth_max = depth.max()
max_val = (2**(8*bits))-1
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape, dtype=depth.type)
if bits == 1:
cv2.imwrite(path + ".png", out.astype("uint8"))
elif bits == 2:
cv2.imwrite(path + ".png", out.astype("uint16"))
return

201
extensions-builtin/sd_forge_controlnet/annotator/mlsd/LICENSE Normal file
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49
extensions-builtin/sd_forge_controlnet/annotator/mlsd/__init__.py Normal file
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import cv2
import numpy as np
import torch
import os
from einops import rearrange
from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
from .utils import pred_lines
from modules import devices
from annotator.annotator_path import models_path
mlsdmodel = None
remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
old_modeldir = os.path.dirname(os.path.realpath(__file__))
modeldir = os.path.join(models_path, "mlsd")
def unload_mlsd_model():
global mlsdmodel
if mlsdmodel is not None:
mlsdmodel = mlsdmodel.cpu()
def apply_mlsd(input_image, thr_v, thr_d):
global modelpath, mlsdmodel
if mlsdmodel is None:
modelpath = os.path.join(modeldir, "mlsd_large_512_fp32.pth")
old_modelpath = os.path.join(old_modeldir, "mlsd_large_512_fp32.pth")
if os.path.exists(old_modelpath):
modelpath = old_modelpath
elif not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=modeldir)
mlsdmodel = MobileV2_MLSD_Large()
mlsdmodel.load_state_dict(torch.load(modelpath), strict=True)
mlsdmodel = mlsdmodel.to(devices.get_device_for("controlnet")).eval()
model = mlsdmodel
assert input_image.ndim == 3
img = input_image
img_output = np.zeros_like(img)
try:
with torch.no_grad():
lines = pred_lines(img, model, [img.shape[0], img.shape[1]], thr_v, thr_d)
for line in lines:
x_start, y_start, x_end, y_end = [int(val) for val in line]
cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
except Exception as e:
pass
return img_output[:, :, 0]

292
extensions-builtin/sd_forge_controlnet/annotator/mlsd/models/mbv2_mlsd_large.py Normal file
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import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c2, out_c2, kernel_size=1),
nn.BatchNorm2d(out_c2),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c1, out_c1, kernel_size=1),
nn.BatchNorm2d(out_c1),
nn.ReLU(inplace=True)
)
self.upscale = upscale
def forward(self, a, b):
b = self.conv1(b)
a = self.conv2(a)
if self.upscale:
b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
return torch.cat((a, b), dim=1)
class BlockTypeB(nn.Module):
def __init__(self, in_c, out_c):
super(BlockTypeB, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
def forward(self, x):
x = self.conv1(x) + x
x = self.conv2(x)
return x
class BlockTypeC(nn.Module):
def __init__(self, in_c, out_c):
super(BlockTypeC, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
return x
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
self.channel_pad = out_planes - in_planes
self.stride = stride
#padding = (kernel_size - 1) // 2
# TFLite uses slightly different padding than PyTorch
if stride == 2:
padding = 0
else:
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_planes),
nn.ReLU6(inplace=True)
)
self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
def forward(self, x):
# TFLite uses different padding
if self.stride == 2:
x = F.pad(x, (0, 1, 0, 1), "constant", 0)
#print(x.shape)
for module in self:
if not isinstance(module, nn.MaxPool2d):
x = module(x)
return x
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, pretrained=True):
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
block: Module specifying inverted residual building block for mobilenet
"""
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
width_mult = 1.0
round_nearest = 8
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
#[6, 160, 3, 2],
#[6, 320, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features = [ConvBNReLU(4, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
self.features = nn.Sequential(*features)
self.fpn_selected = [1, 3, 6, 10, 13]
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
if pretrained:
self._load_pretrained_model()
def _forward_impl(self, x):
# This exists since TorchScript doesn't support inheritance, so the superclass method
# (this one) needs to have a name other than `forward` that can be accessed in a subclass
fpn_features = []
for i, f in enumerate(self.features):
if i > self.fpn_selected[-1]:
break
x = f(x)
if i in self.fpn_selected:
fpn_features.append(x)
c1, c2, c3, c4, c5 = fpn_features
return c1, c2, c3, c4, c5
def forward(self, x):
return self._forward_impl(x)
def _load_pretrained_model(self):
pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
model_dict = {}
state_dict = self.state_dict()
for k, v in pretrain_dict.items():
if k in state_dict:
model_dict[k] = v
state_dict.update(model_dict)
self.load_state_dict(state_dict)
class MobileV2_MLSD_Large(nn.Module):
def __init__(self):
super(MobileV2_MLSD_Large, self).__init__()
self.backbone = MobileNetV2(pretrained=False)
## A, B
self.block15 = BlockTypeA(in_c1= 64, in_c2= 96,
out_c1= 64, out_c2=64,
upscale=False)
self.block16 = BlockTypeB(128, 64)
## A, B
self.block17 = BlockTypeA(in_c1 = 32, in_c2 = 64,
out_c1= 64, out_c2= 64)
self.block18 = BlockTypeB(128, 64)
## A, B
self.block19 = BlockTypeA(in_c1=24, in_c2=64,
out_c1=64, out_c2=64)
self.block20 = BlockTypeB(128, 64)
## A, B, C
self.block21 = BlockTypeA(in_c1=16, in_c2=64,
out_c1=64, out_c2=64)
self.block22 = BlockTypeB(128, 64)
self.block23 = BlockTypeC(64, 16)
def forward(self, x):
c1, c2, c3, c4, c5 = self.backbone(x)
x = self.block15(c4, c5)
x = self.block16(x)
x = self.block17(c3, x)
x = self.block18(x)
x = self.block19(c2, x)
x = self.block20(x)
x = self.block21(c1, x)
x = self.block22(x)
x = self.block23(x)
x = x[:, 7:, :, :]
return x

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import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c2, out_c2, kernel_size=1),
nn.BatchNorm2d(out_c2),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c1, out_c1, kernel_size=1),
nn.BatchNorm2d(out_c1),
nn.ReLU(inplace=True)
)
self.upscale = upscale
def forward(self, a, b):
b = self.conv1(b)
a = self.conv2(a)
b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
return torch.cat((a, b), dim=1)
class BlockTypeB(nn.Module):
def __init__(self, in_c, out_c):
super(BlockTypeB, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
nn.BatchNorm2d(out_c),
nn.ReLU()
)
def forward(self, x):
x = self.conv1(x) + x
x = self.conv2(x)
return x
class BlockTypeC(nn.Module):
def __init__(self, in_c, out_c):
super(BlockTypeC, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
nn.BatchNorm2d(in_c),
nn.ReLU()
)
self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
return x
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNReLU(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
self.channel_pad = out_planes - in_planes
self.stride = stride
#padding = (kernel_size - 1) // 2
# TFLite uses slightly different padding than PyTorch
if stride == 2:
padding = 0
else:
padding = (kernel_size - 1) // 2
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
nn.BatchNorm2d(out_planes),
nn.ReLU6(inplace=True)
)
self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
def forward(self, x):
# TFLite uses different padding
if self.stride == 2:
x = F.pad(x, (0, 1, 0, 1), "constant", 0)
#print(x.shape)
for module in self:
if not isinstance(module, nn.MaxPool2d):
x = module(x)
return x
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, pretrained=True):
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
block: Module specifying inverted residual building block for mobilenet
"""
super(MobileNetV2, self).__init__()
block = InvertedResidual
input_channel = 32
last_channel = 1280
width_mult = 1.0
round_nearest = 8
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
#[6, 96, 3, 1],
#[6, 160, 3, 2],
#[6, 320, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features = [ConvBNReLU(4, input_channel, stride=2)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
input_channel = output_channel
self.features = nn.Sequential(*features)
self.fpn_selected = [3, 6, 10]
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
#if pretrained:
# self._load_pretrained_model()
def _forward_impl(self, x):
# This exists since TorchScript doesn't support inheritance, so the superclass method
# (this one) needs to have a name other than `forward` that can be accessed in a subclass
fpn_features = []
for i, f in enumerate(self.features):
if i > self.fpn_selected[-1]:
break
x = f(x)
if i in self.fpn_selected:
fpn_features.append(x)
c2, c3, c4 = fpn_features
return c2, c3, c4
def forward(self, x):
return self._forward_impl(x)
def _load_pretrained_model(self):
pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
model_dict = {}
state_dict = self.state_dict()
for k, v in pretrain_dict.items():
if k in state_dict:
model_dict[k] = v
state_dict.update(model_dict)
self.load_state_dict(state_dict)
class MobileV2_MLSD_Tiny(nn.Module):
def __init__(self):
super(MobileV2_MLSD_Tiny, self).__init__()
self.backbone = MobileNetV2(pretrained=True)
self.block12 = BlockTypeA(in_c1= 32, in_c2= 64,
out_c1= 64, out_c2=64)
self.block13 = BlockTypeB(128, 64)
self.block14 = BlockTypeA(in_c1 = 24, in_c2 = 64,
out_c1= 32, out_c2= 32)
self.block15 = BlockTypeB(64, 64)
self.block16 = BlockTypeC(64, 16)
def forward(self, x):
c2, c3, c4 = self.backbone(x)
x = self.block12(c3, c4)
x = self.block13(x)
x = self.block14(c2, x)
x = self.block15(x)
x = self.block16(x)
x = x[:, 7:, :, :]
#print(x.shape)
x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True)
return x

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