Source code for imaginaire.discriminators.funit

# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out
import warnings
import torch
from torch import nn
from imaginaire.layers import Conv2dBlock, Res2dBlock
[docs]class Discriminator(nn.Module): r"""Discriminator in the improved FUNIT baseline in the COCO-FUNIT paper. Args: dis_cfg (obj): Discriminator definition part of the yaml config file. data_cfg (obj): Data definition part of the yaml config file. """ def __init__(self, dis_cfg, data_cfg): super().__init__() self.model = ResDiscriminator(**vars(dis_cfg))
[docs] def forward(self, data, net_G_output, recon=True): r"""Improved FUNIT discriminator forward function. Args: data (dict): Training data at the current iteration. net_G_output (dict): Fake data generated at the current iteration. recon (bool): If ``True``, also classifies reconstructed images. """ source_labels = data['labels_content'] target_labels = data['labels_style'] fake_out_trans, fake_features_trans = \ self.model(net_G_output['images_trans'], target_labels) output = dict(fake_out_trans=fake_out_trans, fake_features_trans=fake_features_trans) real_out_style, real_features_style = \ self.model(data['images_style'], target_labels) output.update(dict(real_out_style=real_out_style, real_features_style=real_features_style)) if recon: fake_out_recon, fake_features_recon = \ self.model(net_G_output['images_recon'], source_labels) output.update(dict(fake_out_recon=fake_out_recon, fake_features_recon=fake_features_recon)) return output
[docs]class ResDiscriminator(nn.Module): r"""Residual discriminator architecture used in the FUNIT paper.""" def __init__(self, image_channels=3, num_classes=119, num_filters=64, max_num_filters=1024, num_layers=6, padding_mode='reflect', weight_norm_type='', **kwargs): super().__init__() for key in kwargs: if key != 'type': warnings.warn( "Discriminator argument {} is not used".format(key)) conv_params = dict(padding_mode=padding_mode, activation_norm_type='none', weight_norm_type=weight_norm_type, bias=[True, True, True], nonlinearity='leakyrelu', order='NACNAC') first_kernel_size = 7 first_padding = (first_kernel_size - 1) // 2 model = [Conv2dBlock(image_channels, num_filters, first_kernel_size, 1, first_padding, padding_mode=padding_mode, weight_norm_type=weight_norm_type)] for i in range(num_layers): num_filters_prev = num_filters num_filters = min(num_filters * 2, max_num_filters) model += [Res2dBlock(num_filters_prev, num_filters_prev, **conv_params), Res2dBlock(num_filters_prev, num_filters, **conv_params)] if i != num_layers - 1: model += [nn.ReflectionPad2d(1), nn.AvgPool2d(3, stride=2)] self.model = nn.Sequential(*model) self.classifier = Conv2dBlock(num_filters, 1, 1, 1, 0, nonlinearity='leakyrelu', weight_norm_type=weight_norm_type, order='NACNAC') self.embedder = nn.Embedding(num_classes, num_filters)
[docs] def forward(self, images, labels=None): r"""Forward function of the projection discriminator. Args: images (image tensor): Images inputted to the discriminator. labels (long int tensor): Class labels of the images. """ assert (images.size(0) == labels.size(0)) features = self.model(images) outputs = self.classifier(features) features_1x1 = features.mean(3).mean(2) if labels is None: return features_1x1 embeddings = self.embedder(labels) outputs += torch.sum(embeddings * features_1x1, dim=1, keepdim=True).view(images.size(0), 1, 1, 1) return outputs, features_1x1