Added my good ole pattern loss. God I love that thing, conv transpose pattern instantly wiped from vae

toolkit/llvae.py

@@ -5,13 +5,14 @@ import itertools
 
 
 class LosslessLatentDecoder(nn.Module):
-    def __init__(self, in_channels, latent_depth):
+    def __init__(self, in_channels, latent_depth, dtype=torch.float32):
         super(LosslessLatentDecoder, self).__init__()
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.latent_depth = latent_depth
         self.in_channels = in_channels
         self.out_channels = int(in_channels // (latent_depth * latent_depth))
         numpy_kernel = self.build_kernel(in_channels, latent_depth)
-        self.kernel = torch.from_numpy(numpy_kernel).float()
+        self.kernel = torch.from_numpy(numpy_kernel).to(device=device, dtype=dtype)
 
     def build_kernel(self, in_channels, latent_depth):
         # my old code from tensorflow.
@@ -39,13 +40,15 @@ class LosslessLatentDecoder(nn.Module):
 
 
 class LosslessLatentEncoder(nn.Module):
-    def __init__(self, in_channels, latent_depth):
+    def __init__(self, in_channels, latent_depth, dtype=torch.float32):
         super(LosslessLatentEncoder, self).__init__()
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.latent_depth = latent_depth
         self.in_channels = in_channels
         self.out_channels = int(in_channels * (latent_depth * latent_depth))
         numpy_kernel = self.build_kernel(in_channels, latent_depth)
-        self.kernel = torch.from_numpy(numpy_kernel).float()
+        self.kernel = torch.from_numpy(numpy_kernel).to(device=device, dtype=dtype)
+
 
     def build_kernel(self, in_channels, latent_depth):
         # my old code from tensorflow.
@@ -72,13 +75,13 @@ class LosslessLatentEncoder(nn.Module):
 
 
 class LosslessLatentVAE(nn.Module):
-    def __init__(self, in_channels, latent_depth):
+    def __init__(self, in_channels, latent_depth, dtype=torch.float32):
         super(LosslessLatentVAE, self).__init__()
         self.latent_depth = latent_depth
         self.in_channels = in_channels
-        self.encoder = LosslessLatentEncoder(in_channels, latent_depth)
+        self.encoder = LosslessLatentEncoder(in_channels, latent_depth, dtype=dtype)
         encoder_out_channels = self.encoder.out_channels
-        self.decoder = LosslessLatentDecoder(encoder_out_channels, latent_depth)
+        self.decoder = LosslessLatentDecoder(encoder_out_channels, latent_depth, dtype=dtype)
 
     def forward(self, x):
         latent = self.latent_encoder(x)

toolkit/losses.py

@@ -1,4 +1,5 @@
 import torch
+from .llvae import LosslessLatentEncoder
 
 
 def total_variation(image):
@@ -45,3 +46,40 @@ def get_gradient_penalty(critic, real, fake, device):
         gradient_penalty = ((gradient_norm - 1) ** 2).mean()
         return gradient_penalty
 
+
+class PatternLoss(torch.nn.Module):
+    def __init__(self, pattern_size=4, dtype=torch.float32):
+        super().__init__()
+        self.pattern_size = pattern_size
+        self.llvae_encoder = LosslessLatentEncoder(3, pattern_size, dtype=dtype)
+
+    def forward(self, pred, target):
+        pred_latents = self.llvae_encoder(pred)
+        target_latents = self.llvae_encoder(target)
+
+        matrix_pixels = self.pattern_size * self.pattern_size
+
+        color_chans = pred_latents.shape[1] // 3
+        # pytorch
+        r_chans, g_chans, b_chans = torch.split(pred_latents, [color_chans, color_chans, color_chans], 1)
+        r_chans_target, g_chans_target, b_chans_target = torch.split(target_latents, [color_chans, color_chans, color_chans], 1)
+
+        def separated_chan_loss(latent_chan):
+            nonlocal matrix_pixels
+            chan_mean = torch.mean(latent_chan, dim=[1, 2, 3])
+            chan_splits = torch.split(latent_chan, [1 for i in range(matrix_pixels)], 1)
+            chan_loss = None
+            for chan in chan_splits:
+                this_mean = torch.mean(chan, dim=[1, 2, 3])
+                this_chan_loss = torch.abs(this_mean - chan_mean)
+                if chan_loss is None:
+                    chan_loss = this_chan_loss
+                else:
+                    chan_loss = chan_loss + this_chan_loss
+            chan_loss = chan_loss * (1 / matrix_pixels)
+            return chan_loss
+
+        r_chan_loss = torch.abs(separated_chan_loss(r_chans) - separated_chan_loss(r_chans_target))
+        g_chan_loss = torch.abs(separated_chan_loss(g_chans) - separated_chan_loss(g_chans_target))
+        b_chan_loss = torch.abs(separated_chan_loss(b_chans) - separated_chan_loss(b_chans_target))
+        return (r_chan_loss + g_chan_loss + b_chan_loss) * 0.3333

toolkit/optimizer.py

@@ -6,13 +6,16 @@ def get_optimizer(
         optimizer_type='adam',
         learning_rate=1e-6
 ):
-    if optimizer_type == 'dadaptation':
+    lower_type = optimizer_type.lower()
+    if lower_type == 'dadaptation':
         # dadaptation optimizer does not use standard learning rate. 1 is the default value
         import dadaptation
         print("Using DAdaptAdam optimizer")
         optimizer = dadaptation.DAdaptAdam(params, lr=1.0)
-    elif optimizer_type == 'adam':
+    elif lower_type == 'adam':
         optimizer = torch.optim.Adam(params, lr=float(learning_rate))
+    elif lower_type == 'adamw':
+        optimizer = torch.optim.AdamW(params, lr=float(learning_rate))
     else:
         raise ValueError(f'Unknown optimizer type {optimizer_type}')
     return optimizer