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import numpy as np |
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import torch |
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import torch.nn as nn |
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import gradio as gr |
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from PIL import Image |
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import torchvision.transforms as transforms |
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norm_layer = nn.InstanceNorm2d |
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class ResidualBlock(nn.Module): |
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def __init__(self, in_features): |
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super(ResidualBlock, self).__init__() |
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conv_block = [ nn.ReflectionPad2d(1), |
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nn.Conv2d(in_features, in_features, 3), |
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norm_layer(in_features), |
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nn.ReLU(inplace=True), |
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nn.ReflectionPad2d(1), |
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nn.Conv2d(in_features, in_features, 3), |
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norm_layer(in_features) |
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] |
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self.conv_block = nn.Sequential(*conv_block) |
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def forward(self, x): |
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return x + self.conv_block(x) |
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class Generator(nn.Module): |
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def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True): |
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super(Generator, self).__init__() |
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model0 = [ nn.ReflectionPad2d(3), |
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nn.Conv2d(input_nc, 64, 7), |
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norm_layer(64), |
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nn.ReLU(inplace=True) ] |
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self.model0 = nn.Sequential(*model0) |
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model1 = [] |
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in_features = 64 |
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out_features = in_features*2 |
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for _ in range(2): |
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model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), |
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norm_layer(out_features), |
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nn.ReLU(inplace=True) ] |
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in_features = out_features |
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out_features = in_features*2 |
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self.model1 = nn.Sequential(*model1) |
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model2 = [] |
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for _ in range(n_residual_blocks): |
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model2 += [ResidualBlock(in_features)] |
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self.model2 = nn.Sequential(*model2) |
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model3 = [] |
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out_features = in_features//2 |
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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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nn.ReLU(inplace=True) ] |
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in_features = out_features |
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out_features = in_features//2 |
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self.model3 = nn.Sequential(*model3) |
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model4 = [ nn.ReflectionPad2d(3), |
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nn.Conv2d(64, output_nc, 7)] |
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if sigmoid: |
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model4 += [nn.Sigmoid()] |
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self.model4 = nn.Sequential(*model4) |
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def forward(self, x, cond=None): |
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out = self.model0(x) |
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out = self.model1(out) |
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out = self.model2(out) |
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out = self.model3(out) |
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out = self.model4(out) |
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return out |
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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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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, ver): |
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input_img = Image.open(input_img) |
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transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()]) |
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input_img = transform(input_img) |
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input_img = torch.unsqueeze(input_img, 0) |
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drawing = 0 |
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with torch.no_grad(): |
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if ver == 'style 2': |
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drawing = model2(input_img)[0].detach() |
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else: |
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drawing = model1(input_img)[0].detach() |
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drawing = transforms.ToPILImage()(drawing) |
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return drawing |
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title="informative-drawings" |
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description="Gradio Demo for line drawing generation. " |
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examples=[['cat.png', 'style 1'], ['bridge.png', 'style 1'], ['lizard.png', 'style 2'],] |
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iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'), |
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gr.inputs.Radio(['style 1','style 2'], type="value", default='style 1', label='version')], |
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gr.outputs.Image(type="pil"), title=title,description=description,examples=examples) |
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iface.launch() |
