mirror of
https://github.com/huchenlei/Depth-Anything.git
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92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
# MIT License
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# Copyright (c) 2022 Intelligent Systems Lab Org
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# File author: Shariq Farooq Bhat
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import torch
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import torch.nn as nn
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class PatchTransformerEncoder(nn.Module):
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def __init__(self, in_channels, patch_size=10, embedding_dim=128, num_heads=4, use_class_token=False):
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"""ViT-like transformer block
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Args:
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in_channels (int): Input channels
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patch_size (int, optional): patch size. Defaults to 10.
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embedding_dim (int, optional): Embedding dimension in transformer model. Defaults to 128.
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num_heads (int, optional): number of attention heads. Defaults to 4.
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use_class_token (bool, optional): Whether to use extra token at the start for global accumulation (called as "class token"). Defaults to False.
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"""
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super(PatchTransformerEncoder, self).__init__()
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self.use_class_token = use_class_token
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encoder_layers = nn.TransformerEncoderLayer(
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embedding_dim, num_heads, dim_feedforward=1024)
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self.transformer_encoder = nn.TransformerEncoder(
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encoder_layers, num_layers=4) # takes shape S,N,E
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self.embedding_convPxP = nn.Conv2d(in_channels, embedding_dim,
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kernel_size=patch_size, stride=patch_size, padding=0)
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def positional_encoding_1d(self, sequence_length, batch_size, embedding_dim, device='cpu'):
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"""Generate positional encodings
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Args:
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sequence_length (int): Sequence length
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embedding_dim (int): Embedding dimension
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Returns:
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torch.Tensor SBE: Positional encodings
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"""
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position = torch.arange(
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0, sequence_length, dtype=torch.float32, device=device).unsqueeze(1)
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index = torch.arange(
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0, embedding_dim, 2, dtype=torch.float32, device=device).unsqueeze(0)
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div_term = torch.exp(index * (-torch.log(torch.tensor(10000.0, device=device)) / embedding_dim))
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pos_encoding = position * div_term
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pos_encoding = torch.cat([torch.sin(pos_encoding), torch.cos(pos_encoding)], dim=1)
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pos_encoding = pos_encoding.unsqueeze(1).repeat(1, batch_size, 1)
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return pos_encoding
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def forward(self, x):
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"""Forward pass
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Args:
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x (torch.Tensor - NCHW): Input feature tensor
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Returns:
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torch.Tensor - SNE: Transformer output embeddings. S - sequence length (=HW/patch_size^2), N - batch size, E - embedding dim
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"""
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embeddings = self.embedding_convPxP(x).flatten(
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2) # .shape = n,c,s = n, embedding_dim, s
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if self.use_class_token:
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# extra special token at start ?
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embeddings = nn.functional.pad(embeddings, (1, 0))
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# change to S,N,E format required by transformer
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embeddings = embeddings.permute(2, 0, 1)
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S, N, E = embeddings.shape
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embeddings = embeddings + self.positional_encoding_1d(S, N, E, device=embeddings.device)
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x = self.transformer_encoder(embeddings) # .shape = S, N, E
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return x
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