''' Copyright (C) 2019 NVIDIA Corporation. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu. BSD License. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ''' import torch import torch.nn as nn import functools import numpy as np import pytorch_lightning as pl ############################################################################### # Functions ############################################################################### def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find('BatchNorm2d') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def get_norm_layer(norm_type='instance'): if norm_type == 'batch': norm_layer = functools.partial(nn.BatchNorm2d, affine=True) elif norm_type == 'instance': norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) else: raise NotImplementedError('normalization layer [%s] is not found' % norm_type) return norm_layer def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, n_blocks_local=3, norm='instance', gpu_ids=[], last_op=nn.Tanh()): norm_layer = get_norm_layer(norm_type=norm) if netG == 'global': netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer, last_op=last_op) elif netG == 'local': netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, n_local_enhancers, n_blocks_local, norm_layer) elif netG == 'encoder': netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer) else: raise ('generator not implemented!') # print(netG) if len(gpu_ids) > 0: assert (torch.cuda.is_available()) device=torch.device(f"cuda:{gpu_ids[0]}") netG = netG.to(device) netG.apply(weights_init) return netG def print_network(net): if isinstance(net, list): net = net[0] num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Generator ############################################################################## class LocalEnhancer(pl.LightningModule): def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'): super(LocalEnhancer, self).__init__() self.n_local_enhancers = n_local_enhancers ###### global generator model ##### ngf_global = ngf * (2**n_local_enhancers) model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model model_global = [model_global[i] for i in range(len(model_global) - 3) ] # get rid of final convolution layers self.model = nn.Sequential(*model_global) ###### local enhancer layers ##### for n in range(1, n_local_enhancers + 1): # downsample ngf_global = ngf * (2**(n_local_enhancers - n)) model_downsample = [ nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), norm_layer(ngf_global), nn.ReLU(True), nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), norm_layer(ngf_global * 2), nn.ReLU(True) ] # residual blocks model_upsample = [] for i in range(n_blocks_local): model_upsample += [ ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer) ] # upsample model_upsample += [ nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), norm_layer(ngf_global), nn.ReLU(True) ] # final convolution if n == n_local_enhancers: model_upsample += [ nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh() ] setattr(self, 'model' + str(n) + '_1', nn.Sequential(*model_downsample)) setattr(self, 'model' + str(n) + '_2', nn.Sequential(*model_upsample)) self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False) def forward(self, input): # create input pyramid input_downsampled = [input] for i in range(self.n_local_enhancers): input_downsampled.append(self.downsample(input_downsampled[-1])) # output at coarest level output_prev = self.model(input_downsampled[-1]) # build up one layer at a time for n_local_enhancers in range(1, self.n_local_enhancers + 1): model_downsample = getattr(self, 'model' + str(n_local_enhancers) + '_1') model_upsample = getattr(self, 'model' + str(n_local_enhancers) + '_2') input_i = input_downsampled[self.n_local_enhancers - n_local_enhancers] output_prev = model_upsample( model_downsample(input_i) + output_prev) return output_prev class GlobalGenerator(pl.LightningModule): def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, padding_type='reflect', last_op=nn.Tanh()): assert (n_blocks >= 0) super(GlobalGenerator, self).__init__() activation = nn.ReLU(True) model = [ nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation ] # downsample for i in range(n_downsampling): mult = 2**i model += [ nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), norm_layer(ngf * mult * 2), activation ] # resnet blocks mult = 2**n_downsampling for i in range(n_blocks): model += [ ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer) ] # upsample 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), norm_layer(int(ngf * mult / 2)), activation ] model += [ nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0) ] if last_op is not None: model += [last_op] self.model = nn.Sequential(*model) def forward(self, input): return self.model(input) # Define a resnet block class ResnetBlock(pl.LightningModule): def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False): super(ResnetBlock, self).__init__() self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout) def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout): conv_block = [] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [ nn.Conv2d(dim, dim, kernel_size=3, padding=p), norm_layer(dim), activation ] if use_dropout: conv_block += [nn.Dropout(0.5)] p = 0 if padding_type == 'reflect': conv_block += [nn.ReflectionPad2d(1)] elif padding_type == 'replicate': conv_block += [nn.ReplicationPad2d(1)] elif padding_type == 'zero': p = 1 else: raise NotImplementedError('padding [%s] is not implemented' % padding_type) conv_block += [ nn.Conv2d(dim, dim, kernel_size=3, padding=p), norm_layer(dim) ] return nn.Sequential(*conv_block) def forward(self, x): out = x + self.conv_block(x) return out class Encoder(pl.LightningModule): def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d): super(Encoder, self).__init__() self.output_nc = output_nc model = [ nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), nn.ReLU(True) ] # downsample for i in range(n_downsampling): mult = 2**i model += [ nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), norm_layer(ngf * mult * 2), nn.ReLU(True) ] # upsample 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), norm_layer(int(ngf * mult / 2)), nn.ReLU(True) ] model += [ nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh() ] self.model = nn.Sequential(*model) def forward(self, input, inst): outputs = self.model(input) # instance-wise average pooling outputs_mean = outputs.clone() inst_list = np.unique(inst.cpu().numpy().astype(int)) for i in inst_list: for b in range(input.size()[0]): indices = (inst[b:b + 1] == int(i)).nonzero() # n x 4 for j in range(self.output_nc): output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3]] mean_feat = torch.mean(output_ins).expand_as(output_ins) outputs_mean[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3]] = mean_feat return outputs_mean