Create app.py

app.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import gradio as gr
+from PIL import Image
+import torchvision.transforms as transforms
+
+norm_layer = nn.InstanceNorm2d
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        conv_block = [  nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features),
+                        nn.ReLU(inplace=True),
+                        nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features)
+                        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        return x + self.conv_block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
+        super(Generator, self).__init__()
+
+        # Initial convolution block
+        model0 = [   nn.ReflectionPad2d(3),
+                    nn.Conv2d(input_nc, 64, 7),
+                    norm_layer(64),
+                    nn.ReLU(inplace=True) ]
+        self.model0 = nn.Sequential(*model0)
+
+        # Downsampling
+        model1 = []
+        in_features = 64
+        out_features = in_features*2
+        for _ in range(2):
+            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features*2
+        self.model1 = nn.Sequential(*model1)
+
+        model2 = []
+        # Residual blocks
+        for _ in range(n_residual_blocks):
+            model2 += [ResidualBlock(in_features)]
+        self.model2 = nn.Sequential(*model2)
+
+        # Upsampling
+        model3 = []
+        out_features = in_features//2
+        for _ in range(2):
+            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features//2
+        self.model3 = nn.Sequential(*model3)
+
+        # Output layer
+        model4 = [  nn.ReflectionPad2d(3),
+                        nn.Conv2d(64, output_nc, 7)]
+        if sigmoid:
+            model4 += [nn.Sigmoid()]
+
+        self.model4 = nn.Sequential(*model4)
+
+    def forward(self, x, cond=None):
+        out = self.model0(x)
+        out = self.model1(out)
+        out = self.model2(out)
+        out = self.model3(out)
+        out = self.model4(out)
+
+        return out
+
+model1 = Generator(3, 1, 3)
+model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
+model1.eval()
+
+model2 = Generator(3, 1, 3)
+model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
+model2.eval()
+
+def predict(input_img, ver):
+    input_img = Image.open(input_img)
+    transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
+    input_img = transform(input_img)
+    input_img = torch.unsqueeze(input_img, 0)
+
+    drawing = 0
+    with torch.no_grad():
+        if ver == 'style 2':
+            drawing = model2(input_img)[0].detach()
+        else:
+            drawing = model1(input_img)[0].detach()
+    
+    drawing = transforms.ToPILImage()(drawing)
+    return drawing
+
+title="informative-drawings"
+description="Gradio Demo for line drawing generation. "
+# article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
+examples=[['cat.png', 'style 1'], ['bridge.png', 'style 1'], ['lizard.png', 'style 2'],]
+
+
+iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
+    gr.inputs.Radio(['style 1','style 2'], type="value", default='style 1', label='version')],
+    gr.outputs.Image(type="pil"), title=title,description=description,examples=examples)
+
+iface.launch()