import functools import torch.nn as nn from ..util import ActNorm def weights_init(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find("BatchNorm") != -1: nn.init.normal_(m.weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0) class NLayerDiscriminator(nn.Module): """Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py """ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): """Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number of filters in the last conv layer n_layers (int) -- the number of conv layers in the discriminator norm_layer -- normalization layer """ super(NLayerDiscriminator, self).__init__() if not use_actnorm: norm_layer = nn.BatchNorm2d else: norm_layer = ActNorm if ( type(norm_layer) == functools.partial ): # no need to use bias as BatchNorm2d has affine parameters use_bias = norm_layer.func != nn.BatchNorm2d else: use_bias = norm_layer != nn.BatchNorm2d kw = 4 padw = 1 sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True), ] nf_mult = 1 nf_mult_prev = 1 for n in range(1, n_layers): # gradually increase the number of filters nf_mult_prev = nf_mult nf_mult = min(2**n, 8) sequence += [ nn.Conv2d( ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias, ), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), ] nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) sequence += [ nn.Conv2d( ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias, ), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), ] sequence += [ nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) ] # output 1 channel prediction map self.main = nn.Sequential(*sequence) def forward(self, input): """Standard forward.""" return self.main(input)