129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
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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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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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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# Initial convolution block
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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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# Downsampling
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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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# Residual blocks
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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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# Upsampling
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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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# Output layer
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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 == '风格1':
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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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examples=[['cat.png', '风格1'], ['bridge.png', '风格1'], ['lizard.png', '风格2'],]
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iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
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gr.inputs.Radio(['风格1','风格2'], type="value", default='风格1')],
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gr.outputs.Image(type="pil"), examples=examples)
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iface.launch(server_name = "0.0.0.0")
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