Commit @66fa69f192cd7b7466e2b6b160a5ae9503b1bc41

윤영준 2023-06-21

testing update

@66fa69f192cd7b7466e2b6b160a5ae9503b1bc41

12b83f0

model/discriminator.py (deleted)

--- model/discriminator.py

...	...	@@ -1,1 +0,0 @@
	1	-from torch import nn(파일 끝에 줄바꿈 문자 없음)

12b83f0

66fa69f

model/generator.py

--- model/generator.py

+++ model/generator.py


 class ConvLSTM(nn.Module):
     def __init__(self, ch, kernel_size=3):
         super(ConvLSTM, self).__init__()
-        self.padding = (kernel_size-1)/2
+        self.padding = (len(kernel_size)-1)/2
         self.conv_i = nn.Conv2d(in_channels=ch, out_channels=ch, kernel_size=kernel_size, stride=1, padding=1,
                                 bias=False)
         self.conv_f = nn.Conv2d(in_channels=ch, out_channels=ch, kernel_size=kernel_size, stride=1, padding=1,

         )
         self.generator_blocks = nn.ModuleList()
         for repetition in range(repetition):
-            self.conv_hidden.append(
+            self.generator_blocks.append(
                 self.generator_block
             )
 

         fc_out = torch.clamp(fc_out, min=1e-7, max=1 - 1e-7)
 
         return fc_out, attention_map, fc2
+
+if __name__ == "__main__":
+    from torchinfo import summary
+    generator = Generator(3)
+    batch_size = 10
+    summary(generator, input_size=(batch_size, 3, 1920,1080))

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...	...	@@ -63,7 +63,7 @@
63	63	class ConvLSTM(nn.Module):
64	64	def __init__(self, ch, kernel_size=3):
65	65	super(ConvLSTM, self).__init__()
66		- self.padding = (kernel_size-1)/2
	66	+ self.padding = (len(kernel_size)-1)/2
67	67	self.conv_i = nn.Conv2d(in_channels=ch, out_channels=ch, kernel_size=kernel_size, stride=1, padding=1,
68	68	bias=False)
69	69	self.conv_f = nn.Conv2d(in_channels=ch, out_channels=ch, kernel_size=kernel_size, stride=1, padding=1,
...	...	@@ -181,7 +181,7 @@
181	181	)
182	182	self.generator_blocks = nn.ModuleList()
183	183	for repetition in range(repetition):
184		- self.conv_hidden.append(
	184	+ self.generator_blocks.append(
185	185	self.generator_block
186	186	)
187	187
...	...	@@ -248,3 +248,9 @@
248	248	fc_out = torch.clamp(fc_out, min=1e-7, max=1 - 1e-7)
249	249
250	250	return fc_out, attention_map, fc2
	251	+
	252	+if __name__ == "__main__":
	253	+ from torchinfo import summary
	254	+ generator = Generator(3)
	255	+ batch_size = 10
	256	+ summary(generator, input_size=(batch_size, 3, 1920,1080))

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