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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 == 'Simple Lines': | |
| drawing = model2(input_img)[0].detach() | |
| else: | |
| drawing = model1(input_img)[0].detach() | |
| drawing = transforms.ToPILImage()(drawing) | |
| return drawing | |
| title="informative-drawings" | |
| description="Image to Line Drawing" | |
| # article = "<p style='text-align: center'></p>" | |
| examples=[ | |
| ['01.png', 'Simple Lines'], ['02.png', 'Simple Lines'], ['03.png', 'Simple Lines'], | |
| ['04.png', 'Simple Lines'], ['05.png', 'Simple Lines'], ['06.png', 'Simple Lines'], | |
| ['01.png', 'Complex Lines'], ['02.png', 'Complex Lines'], ['03.png', 'Complex Lines'], | |
| ['04.png', 'Complex Lines'], ['05.png', 'Complex Lines'], ['06.png', 'Complex Lines'] | |
| ] | |
| iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'), | |
| gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')], | |
| gr.outputs.Image(type="pil"), title=title,description=description,examples=examples) | |
| iface.launch() |