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