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| import functools | |
| import torch | |
| import torch.nn as nn | |
| def count_params(model): | |
| total_params = sum(p.numel() for p in model.parameters()) | |
| return total_params | |
| class ActNorm(nn.Module): | |
| def __init__( | |
| self, num_features, logdet=False, affine=True, allow_reverse_init=False | |
| ): | |
| assert affine | |
| super().__init__() | |
| self.logdet = logdet | |
| self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) | |
| self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) | |
| self.allow_reverse_init = allow_reverse_init | |
| self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8)) | |
| def initialize(self, input): | |
| with torch.no_grad(): | |
| flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) | |
| mean = ( | |
| flatten.mean(1) | |
| .unsqueeze(1) | |
| .unsqueeze(2) | |
| .unsqueeze(3) | |
| .permute(1, 0, 2, 3) | |
| ) | |
| std = ( | |
| flatten.std(1) | |
| .unsqueeze(1) | |
| .unsqueeze(2) | |
| .unsqueeze(3) | |
| .permute(1, 0, 2, 3) | |
| ) | |
| self.loc.data.copy_(-mean) | |
| self.scale.data.copy_(1 / (std + 1e-6)) | |
| def forward(self, input, reverse=False): | |
| if reverse: | |
| return self.reverse(input) | |
| if len(input.shape) == 2: | |
| input = input[:, :, None, None] | |
| squeeze = True | |
| else: | |
| squeeze = False | |
| _, _, height, width = input.shape | |
| if self.training and self.initialized.item() == 0: | |
| self.initialize(input) | |
| self.initialized.fill_(1) | |
| h = self.scale * (input + self.loc) | |
| if squeeze: | |
| h = h.squeeze(-1).squeeze(-1) | |
| if self.logdet: | |
| log_abs = torch.log(torch.abs(self.scale)) | |
| logdet = height * width * torch.sum(log_abs) | |
| logdet = logdet * torch.ones(input.shape[0]).to(input) | |
| return h, logdet | |
| return h | |
| def reverse(self, output): | |
| if self.training and self.initialized.item() == 0: | |
| if not self.allow_reverse_init: | |
| raise RuntimeError( | |
| "Initializing ActNorm in reverse direction is " | |
| "disabled by default. Use allow_reverse_init=True to enable." | |
| ) | |
| else: | |
| self.initialize(output) | |
| self.initialized.fill_(1) | |
| if len(output.shape) == 2: | |
| output = output[:, :, None, None] | |
| squeeze = True | |
| else: | |
| squeeze = False | |
| h = output / self.scale - self.loc | |
| if squeeze: | |
| h = h.squeeze(-1).squeeze(-1) | |
| return h | |
| class AbstractEncoder(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def encode(self, *args, **kwargs): | |
| raise NotImplementedError | |
| class Labelator(AbstractEncoder): | |
| """Net2Net Interface for Class-Conditional Model""" | |
| def __init__(self, n_classes, quantize_interface=True): | |
| super().__init__() | |
| self.n_classes = n_classes | |
| self.quantize_interface = quantize_interface | |
| def encode(self, c): | |
| c = c[:, None] | |
| if self.quantize_interface: | |
| return c, None, [None, None, c.long()] | |
| return c | |
| class SOSProvider(AbstractEncoder): | |
| # for unconditional training | |
| def __init__(self, sos_token, quantize_interface=True): | |
| super().__init__() | |
| self.sos_token = sos_token | |
| self.quantize_interface = quantize_interface | |
| def encode(self, x): | |
| # get batch size from data and replicate sos_token | |
| c = torch.ones(x.shape[0], 1) * self.sos_token | |
| c = c.long().to(x.device) | |
| if self.quantize_interface: | |
| return c, None, [None, None, c] | |
| return c | |
| 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) | |