# Source: https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGANdeep.py import numpy as np import math import functools import torch import torch.nn as nn from torch.nn import init import torch.optim as optim import torch.nn.functional as F from torch.nn import Parameter as P import models.archs.biggan_layers as layers from models.archs.biggan_sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d # BigGAN-deep: uses a different resblock and pattern # Architectures for G # Attention is passed in in the format '32_64' to mean applying an attention # block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64. # Channel ratio is the ratio of class GBlock(nn.Module): def __init__(self, in_channels, out_channels, which_conv=nn.Conv2d, which_bn=layers.bn, activation=None, upsample=None, channel_ratio=4): super(GBlock, self).__init__() self.in_channels, self.out_channels = in_channels, out_channels self.hidden_channels = self.in_channels // channel_ratio self.which_conv, self.which_bn = which_conv, which_bn self.activation = activation # Conv layers self.conv1 = self.which_conv(self.in_channels, self.hidden_channels, kernel_size=1, padding=0) self.conv2 = self.which_conv(self.hidden_channels, self.hidden_channels) self.conv3 = self.which_conv(self.hidden_channels, self.hidden_channels) self.conv4 = self.which_conv(self.hidden_channels, self.out_channels, kernel_size=1, padding=0) # Batchnorm layers self.bn1 = self.which_bn(self.in_channels) self.bn2 = self.which_bn(self.hidden_channels) self.bn3 = self.which_bn(self.hidden_channels) self.bn4 = self.which_bn(self.hidden_channels) # upsample layers self.upsample = upsample def forward(self, x, y): # Project down to channel ratio h = self.conv1(self.activation(self.bn1(x, y))) # Apply next BN-ReLU h = self.activation(self.bn2(h, y)) # Drop channels in x if necessary if self.in_channels != self.out_channels: x = x[:, :self.out_channels] # Upsample both h and x at this point if self.upsample: h = self.upsample(h) x = self.upsample(x) # 3x3 convs h = self.conv2(h) h = self.conv3(self.activation(self.bn3(h, y))) # Final 1x1 conv h = self.conv4(self.activation(self.bn4(h, y))) return h + x def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'): arch = {} arch[256] = {'in_channels': [ch * item for item in [16, 16, 8, 8, 4, 2]], 'out_channels': [ch * item for item in [16, 8, 8, 4, 2, 1]], 'upsample': [True] * 6, 'resolution': [8, 16, 32, 64, 128, 256], 'attention': {2 ** i: (2 ** i in [int(item) for item in attention.split('_')]) for i in range(3, 9)}} arch[128] = {'in_channels': [ch * item for item in [16, 16, 8, 4, 2]], 'out_channels': [ch * item for item in [16, 8, 4, 2, 1]], 'upsample': [True] * 5, 'resolution': [8, 16, 32, 64, 128], 'attention': {2 ** i: (2 ** i in [int(item) for item in attention.split('_')]) for i in range(3, 8)}} arch[64] = {'in_channels': [ch * item for item in [16, 16, 8, 4]], 'out_channels': [ch * item for item in [16, 8, 4, 2]], 'upsample': [True] * 4, 'resolution': [8, 16, 32, 64], 'attention': {2 ** i: (2 ** i in [int(item) for item in attention.split('_')]) for i in range(3, 7)}} arch[32] = {'in_channels': [ch * item for item in [4, 4, 4]], 'out_channels': [ch * item for item in [4, 4, 4]], 'upsample': [True] * 3, 'resolution': [8, 16, 32], 'attention': {2 ** i: (2 ** i in [int(item) for item in attention.split('_')]) for i in range(3, 6)}} return arch class Generator(nn.Module): def __init__(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_classes=1000, num_G_SVs=1, num_G_SV_itrs=1, G_shared=True, shared_dim=0, hier=False, cross_replica=False, mybn=False, G_activation=nn.ReLU(inplace=False), G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8, BN_eps=1e-5, SN_eps=1e-12, G_mixed_precision=False, G_fp16=False, G_init='ortho', skip_init=False, no_optim=False, G_param='SN', norm_style='bn', **kwargs): super(Generator, self).__init__() # Channel width mulitplier self.ch = G_ch # Number of resblocks per stage self.G_depth = G_depth # Dimensionality of the latent space self.dim_z = dim_z # The initial spatial dimensions self.bottom_width = bottom_width # Resolution of the output self.resolution = resolution # Kernel size? self.kernel_size = G_kernel_size # Attention? self.attention = G_attn # number of classes, for use in categorical conditional generation self.n_classes = n_classes # Use shared embeddings? self.G_shared = G_shared # Dimensionality of the shared embedding? Unused if not using G_shared self.shared_dim = shared_dim if shared_dim > 0 else dim_z # Hierarchical latent space? self.hier = hier # Cross replica batchnorm? self.cross_replica = cross_replica # Use my batchnorm? self.mybn = mybn # nonlinearity for residual blocks self.activation = G_activation # Initialization style self.init = G_init # Parameterization style self.G_param = G_param # Normalization style self.norm_style = norm_style # Epsilon for BatchNorm? self.BN_eps = BN_eps # Epsilon for Spectral Norm? self.SN_eps = SN_eps # fp16? self.fp16 = G_fp16 # Architecture dict self.arch = G_arch(self.ch, self.attention)[resolution] # Which convs, batchnorms, and linear layers to use if self.G_param == 'SN': self.which_conv = functools.partial(layers.SNConv2d, kernel_size=3, padding=1, num_svs=num_G_SVs, num_itrs=num_G_SV_itrs, eps=self.SN_eps) self.which_linear = functools.partial(layers.SNLinear, num_svs=num_G_SVs, num_itrs=num_G_SV_itrs, eps=self.SN_eps) else: self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1) self.which_linear = nn.Linear # We use a non-spectral-normed embedding here regardless; # For some reason applying SN to G's embedding seems to randomly cripple G self.which_embedding = nn.Embedding bn_linear = (functools.partial(self.which_linear, bias=False) if self.G_shared else self.which_embedding) self.which_bn = functools.partial(layers.ccbn, which_linear=bn_linear, cross_replica=self.cross_replica, mybn=self.mybn, input_size=(self.shared_dim + self.dim_z if self.G_shared else self.n_classes), norm_style=self.norm_style, eps=self.BN_eps) # Prepare model # If not using shared embeddings, self.shared is just a passthrough self.shared = (self.which_embedding(n_classes, self.shared_dim) if G_shared else layers.identity()) # First linear layer self.linear = self.which_linear(self.dim_z + self.shared_dim, self.arch['in_channels'][0] * (self.bottom_width ** 2)) # self.blocks is a doubly-nested list of modules, the outer loop intended # to be over blocks at a given resolution (resblocks and/or self-attention) # while the inner loop is over a given block self.blocks = [] for index in range(len(self.arch['out_channels'])): self.blocks += [[GBlock(in_channels=self.arch['in_channels'][index], out_channels=self.arch['in_channels'][index] if g_index == 0 else self.arch['out_channels'][index], which_conv=self.which_conv, which_bn=self.which_bn, activation=self.activation, upsample=(functools.partial(F.interpolate, scale_factor=2) if self.arch['upsample'][index] and g_index == ( self.G_depth - 1) else None))] for g_index in range(self.G_depth)] # If attention on this block, attach it to the end if self.arch['attention'][self.arch['resolution'][index]]: print('Adding attention layer in G at resolution %d' % self.arch['resolution'][index]) self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.which_conv)] # Turn self.blocks into a ModuleList so that it's all properly registered. self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) # output layer: batchnorm-relu-conv. # Consider using a non-spectral conv here self.output_layer = nn.Sequential(layers.bn(self.arch['out_channels'][-1], cross_replica=self.cross_replica, mybn=self.mybn), self.activation, self.which_conv(self.arch['out_channels'][-1], 3)) # Initialize weights. Optionally skip init for testing. if not skip_init: self.init_weights() # Set up optimizer # If this is an EMA copy, no need for an optim, so just return now if no_optim: return self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps if G_mixed_precision: print('Using fp16 adam in G...') import utils self.optim = utils.Adam16(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) else: self.optim = optim.Adam(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) # LR scheduling, left here for forward compatibility # self.lr_sched = {'itr' : 0}# if self.progressive else {} # self.j = 0 # Initialize def init_weights(self): self.param_count = 0 for module in self.modules(): if (isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear) or isinstance(module, nn.Embedding)): if self.init == 'ortho': init.orthogonal_(module.weight) elif self.init == 'N02': init.normal_(module.weight, 0, 0.02) elif self.init in ['glorot', 'xavier']: init.xavier_uniform_(module.weight) else: print('Init style not recognized...') self.param_count += sum([p.data.nelement() for p in module.parameters()]) print('Param count for G''s initialized parameters: %d' % self.param_count) # Note on this forward function: we pass in a y vector which has # already been passed through G.shared to enable easy class-wise # interpolation later. If we passed in the one-hot and then ran it through # G.shared in this forward function, it would be harder to handle. # NOTE: The z vs y dichotomy here is for compatibility with not-y def forward(self, z, y): # If hierarchical, concatenate zs and ys if self.hier: z = torch.cat([y, z], 1) y = z # First linear layer h = self.linear(z) # Reshape h = h.view(h.size(0), -1, self.bottom_width, self.bottom_width) # Loop over blocks for index, blocklist in enumerate(self.blocks): # Second inner loop in case block has multiple layers for block in blocklist: h = block(h, y) # Apply batchnorm-relu-conv-tanh at output return torch.tanh(self.output_layer(h))