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Update app.py
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app.py
CHANGED
@@ -55,9 +55,9 @@ class Generator(nn.Module):
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model2 += [ResidualBlock(in_features)]
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self.model2 = nn.Sequential(*model2)
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#
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model3 = []
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out_features = in_features
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for _ in range(2):
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model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
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norm_layer(out_features),
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@@ -87,9 +87,13 @@ model1 = Generator(3, 1, 3)
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model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
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model1.eval()
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def predict(input_img):
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input_img = Image.open(input_img)
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@@ -99,7 +103,7 @@ def predict(input_img):
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drawing = 0
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with torch.no_grad():
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drawing =
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drawing = transforms.ToPILImage()(drawing)
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return drawing
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model2 += [ResidualBlock(in_features)]
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self.model2 = nn.Sequential(*model2)
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# More downsampling
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model3 = []
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out_features = in_features*3
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for _ in range(2):
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model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
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norm_layer(out_features),
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model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
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model1.eval()
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model3 = Generator(3, 1, 3)
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model3.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
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model3.eval()
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# model2 = Generator(3, 1, 3)
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# model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
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# model2.eval()
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def predict(input_img):
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input_img = Image.open(input_img)
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drawing = 0
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with torch.no_grad():
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drawing = model3(input_img)[0].detach()
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drawing = transforms.ToPILImage()(drawing)
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return drawing
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