Update image sizing for dataset
parent
4686c7e83e
commit
42d312aa60
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@ -27,12 +27,12 @@ resource.setrlimit(resource.RLIMIT_AS, (memory_limit_gb * 1024**3, hard))
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# --------
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# --------
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# CONSTANTS
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# CONSTANTS
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# --------
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# --------
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IMG_H = 160 # On better gpu use 256 and adam optimizer
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IMG_H = 256 # On better gpu use 256 and adam optimizer
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IMG_W = IMG_H * 2
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IMG_W = IMG_H
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DATASET_PATHS = [
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DATASET_PATHS = [
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"../datasets/train",
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"../datasets/train/google/",
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]
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]
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LINE="\n----------------------------------------\n"
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LINE = "\n----------------------------------------\n"
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# configuring device
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# configuring device
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if torch.cuda.is_available():
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if torch.cuda.is_available():
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@ -134,7 +134,7 @@ class Encoder(nn.Module):
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kernel_size=2,
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kernel_size=2,
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stride=2,
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stride=2,
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act_fn=nn.LeakyReLU(),
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act_fn=nn.LeakyReLU(),
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debug=False,
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debug=True,
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):
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):
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super().__init__()
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super().__init__()
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self.debug = debug
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self.debug = debug
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@ -193,11 +193,16 @@ class Encoder(nn.Module):
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def forward(self, x):
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def forward(self, x):
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x = x.view(-1, 1, IMG_H, IMG_W)
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x = x.view(-1, 1, IMG_H, IMG_W)
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# Print also the function name
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# for layer in self.conv:
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# for layer in self.net:
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# x = layer(x)
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# x = layer(x)
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# if self.debug:
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# if self.debug:
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# print(layer.__class__.__name__, "output shape:\t", x.shape)
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# print(layer.__class__.__name__, "output shape:\t", x.shape)
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# for layer in self.linear:
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# x = layer(x)
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# if self.debug:
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# print(layer.__class__.__name__, "output shape:\t", x.shape)
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# encoded_latent_image = x
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encoded_latent_image = self.conv(x)
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encoded_latent_image = self.conv(x)
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encoded_latent_image = self.linear(encoded_latent_image)
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encoded_latent_image = self.linear(encoded_latent_image)
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return encoded_latent_image
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return encoded_latent_image
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@ -277,7 +282,7 @@ class Decoder(nn.Module):
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def forward(self, x):
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def forward(self, x):
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output = self.linear(x)
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output = self.linear(x)
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output = output.view(len(output), self.out_channels * 8, self.v, self.u)
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output = output.view(len(output), self.out_channels * 8, 8, 8)
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# for layer in self.conv:
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# for layer in self.conv:
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# output = layer(output)
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# output = layer(output)
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# if self.debug:
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# if self.debug:
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@ -404,7 +409,9 @@ class ConvolutionalAutoencoder:
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for i, img in enumerate(imgs):
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for i, img in enumerate(imgs):
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pil_img = TF.to_pil_image(img)
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pil_img = TF.to_pil_image(img)
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pil_img.save(f"visualizations/epoch_{epoch+1}/img_{plt_ix}_{i}.png")
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pil_img.save(
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f"visualizations/epoch_{epoch+1}/img_{plt_ix}_{i}.png"
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)
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plt_ix += 1
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plt_ix += 1
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@ -518,6 +525,7 @@ def preprocess_data():
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return training_data, validation_data, test_data
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return training_data, validation_data, test_data
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def print_dataset_info(training_set, validation_set, test_set):
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def print_dataset_info(training_set, validation_set, test_set):
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print(LINE)
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print(LINE)
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print("Training set size: ", len(training_set))
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print("Training set size: ", len(training_set))
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