CycleGAN pretrained

cyclegan.py

import torch
import torch.nn as nn


class ResnetBlock(nn.Module):

    def __init__(self, dim):
        super(ResnetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim)

    def build_conv_block(self, dim):
        conv_block = []

        conv_block += [nn.ReflectionPad2d(1)]

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=0, bias=True),
                       nn.InstanceNorm2d(dim),
                       nn.ReLU(True)]

        conv_block += [nn.ReflectionPad2d(1)]

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=0, bias=True),
                       nn.InstanceNorm2d(dim)]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        out = x + self.conv_block(x)
        return out


class ResnetGenerator(nn.Module):

    def __init__(self, input_nc, output_nc, ngf=64, n_blocks=6):

        assert(n_blocks >= 0)
        super(ResnetGenerator, self).__init__()

        self.input_nc = input_nc
        self.output_nc = output_nc
        self.ngf = ngf

        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=True),
                 nn.InstanceNorm2d(ngf),
                 nn.ReLU(True)]

        n_downsampling = 2
        for i in range(n_downsampling):
            mult = 2**i
            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,
                                stride=2, padding=1, bias=True),
                      nn.InstanceNorm2d(ngf * mult * 2),
                      nn.ReLU(True)]

        mult = 2**n_downsampling
        for i in range(n_blocks):
            model += [ResnetBlock(ngf * mult)]

        for i in range(n_downsampling):
            mult = 2**(n_downsampling - i)
            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                         kernel_size=3, stride=2,
                                         padding=1, output_padding=1,
                                         bias=True),
                      nn.InstanceNorm2d(int(ngf * mult / 2)),
                      nn.ReLU(True)]

        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        model += [nn.Tanh()]

        self.model = nn.Sequential(*model)

    def forward(self, input):
        return self.model(input)


if __name__ == '__main__':

    from PIL import Image
    from torchvision import transforms

    import sys
    model_path = sys.argv[1]
    image_path = sys.argv[2]

    input_nc = 3
    output_nc = 3
    ngf = 64
    n_blocks = 9

    netG = ResnetGenerator(input_nc, output_nc, ngf, n_blocks=n_blocks)
    netG.load_state_dict(torch.load(model_path))
    netG.eval()

    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.ToTensor(),
        ])

    img = Image.open(image_path)
    img_t = preprocess(img)
    input = torch.autograd.Variable(torch.unsqueeze(img_t, 0))
    
    out = netG(input)

    out_t = (out.data.squeeze() + 1.0) / 2.0
    out_img = transforms.ToPILImage()(out_t)

    out_img.show()