325 lines
12 KiB
Python
325 lines
12 KiB
Python
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# Heavily based on the lucidrains stylegan2 discriminator implementation.
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from functools import partial
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from math import log2
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from random import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import grad as torch_grad
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import trainer.losses as L
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from vector_quantize_pytorch import VectorQuantize
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from models.styled_sr.stylegan2_base import attn_and_ff, PermuteToFrom, Blur, leaky_relu, exists
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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class DiscriminatorBlock(nn.Module):
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def __init__(self, input_channels, filters, downsample=True):
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super().__init__()
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self.filters = filters
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self.conv_res = nn.Conv2d(input_channels, filters, 1, stride=(2 if downsample else 1))
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self.net = nn.Sequential(
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nn.Conv2d(input_channels, filters, 3, padding=1),
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leaky_relu(),
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nn.Conv2d(filters, filters, 3, padding=1),
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leaky_relu()
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)
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self.downsample = nn.Sequential(
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Blur(),
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nn.Conv2d(filters, filters, 3, padding=1, stride=2)
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) if downsample else None
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def forward(self, x):
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res = self.conv_res(x)
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x = self.net(x)
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if exists(self.downsample):
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x = self.downsample(x)
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x = (x + res) * (1 / math.sqrt(2))
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return x
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class StyleGan2Discriminator(nn.Module):
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def __init__(self, image_size, network_capacity=16, fq_layers=[], fq_dict_size=256, attn_layers=[],
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transparent=False, fmap_max=512, input_filters=3, quantize=False, do_checkpointing=False, mlp=False):
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super().__init__()
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num_layers = int(log2(image_size) - 1)
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blocks = []
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filters = [input_filters] + [(64) * (2 ** i) for i in range(num_layers + 1)]
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set_fmap_max = partial(min, fmap_max)
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filters = list(map(set_fmap_max, filters))
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chan_in_out = list(zip(filters[:-1], filters[1:]))
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blocks = []
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attn_blocks = []
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quantize_blocks = []
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for ind, (in_chan, out_chan) in enumerate(chan_in_out):
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num_layer = ind + 1
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is_not_last = ind != (len(chan_in_out) - 1)
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block = DiscriminatorBlock(in_chan, out_chan, downsample=is_not_last)
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blocks.append(block)
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attn_fn = attn_and_ff(out_chan) if num_layer in attn_layers else None
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attn_blocks.append(attn_fn)
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if quantize:
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quantize_fn = PermuteToFrom(VectorQuantize(out_chan, fq_dict_size)) if num_layer in fq_layers else None
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quantize_blocks.append(quantize_fn)
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else:
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quantize_blocks.append(None)
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self.blocks = nn.ModuleList(blocks)
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self.attn_blocks = nn.ModuleList(attn_blocks)
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self.quantize_blocks = nn.ModuleList(quantize_blocks)
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self.do_checkpointing = do_checkpointing
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chan_last = filters[-1]
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latent_dim = 2 * 2 * chan_last
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self.final_conv = nn.Conv2d(chan_last, chan_last, 3, padding=1)
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self.flatten = nn.Flatten()
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if mlp:
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self.to_logit = nn.Sequential(nn.Linear(latent_dim, 100),
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leaky_relu(),
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nn.Linear(100, 1))
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else:
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self.to_logit = nn.Linear(latent_dim, 1)
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self._init_weights()
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def forward(self, x):
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b, *_ = x.shape
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quantize_loss = torch.zeros(1).to(x)
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for (block, attn_block, q_block) in zip(self.blocks, self.attn_blocks, self.quantize_blocks):
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if self.do_checkpointing:
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x = checkpoint(block, x)
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else:
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x = block(x)
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if exists(attn_block):
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x = attn_block(x)
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if exists(q_block):
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x, _, loss = q_block(x)
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quantize_loss += loss
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x = self.final_conv(x)
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x = self.flatten(x)
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x = self.to_logit(x)
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if exists(q_block):
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return x.squeeze(), quantize_loss
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else:
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return x.squeeze()
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def _init_weights(self):
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for m in self.modules():
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if type(m) in {nn.Conv2d, nn.Linear}:
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
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# Configures the network as partially pre-trained. This means:
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# 1) The top (high-resolution) `num_blocks` will have their weights re-initialized.
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# 2) The haed (linear layers) will also have their weights re-initialized
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# 3) All intermediate blocks will be frozen until step `frozen_until_step`
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# These settings will be applied after the weights have been loaded (network_loaded())
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def configure_partial_training(self, bypass_blocks=0, num_blocks=2, frozen_until_step=0):
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self.bypass_blocks = bypass_blocks
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self.num_blocks = num_blocks
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self.frozen_until_step = frozen_until_step
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# Called after the network weights are loaded.
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def network_loaded(self):
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if not hasattr(self, 'frozen_until_step'):
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return
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if self.bypass_blocks > 0:
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self.blocks = self.blocks[self.bypass_blocks:]
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self.blocks[0] = DiscriminatorBlock(3, self.blocks[0].filters, downsample=True).to(next(self.parameters()).device)
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reset_blocks = [self.to_logit]
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for i in range(self.num_blocks):
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reset_blocks.append(self.blocks[i])
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for bl in reset_blocks:
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for m in bl.modules():
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if type(m) in {nn.Conv2d, nn.Linear}:
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nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
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for p in m.parameters(recurse=True):
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p._NEW_BLOCK = True
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for p in self.parameters():
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if not hasattr(p, '_NEW_BLOCK'):
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p.DO_NOT_TRAIN_UNTIL = self.frozen_until_step
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# helper classes
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def DiffAugment(x, types=[]):
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for p in types:
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for f in AUGMENT_FNS[p]:
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x = f(x)
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return x.contiguous()
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def random_hflip(tensor, prob):
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if prob > random():
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return tensor
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return torch.flip(tensor, dims=(3,))
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def rand_brightness(x):
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x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
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return x
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def rand_saturation(x):
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x_mean = x.mean(dim=1, keepdim=True)
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
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return x
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def rand_contrast(x):
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x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
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x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
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return x
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def rand_translation(x, ratio=0.125):
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shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
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translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
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translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
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grid_batch, grid_x, grid_y = torch.meshgrid(
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torch.arange(x.size(0), dtype=torch.long, device=x.device),
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torch.arange(x.size(2), dtype=torch.long, device=x.device),
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torch.arange(x.size(3), dtype=torch.long, device=x.device),
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)
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grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
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grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
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x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
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x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
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return x
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def rand_cutout(x, ratio=0.5):
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cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
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offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
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offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
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grid_batch, grid_x, grid_y = torch.meshgrid(
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torch.arange(x.size(0), dtype=torch.long, device=x.device),
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torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
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torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
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)
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grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
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grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
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mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
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mask[grid_batch, grid_x, grid_y] = 0
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x = x * mask.unsqueeze(1)
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return x
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AUGMENT_FNS = {
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'color': [rand_brightness, rand_saturation, rand_contrast],
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'translation': [rand_translation],
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'cutout': [rand_cutout],
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}
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class DiscAugmentor(nn.Module):
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def __init__(self, D, image_size, types, prob):
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super().__init__()
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self.D = D
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self.prob = prob
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self.types = types
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def forward(self, images, detach=False):
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if random() < self.prob:
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images = random_hflip(images, prob=0.5)
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images = DiffAugment(images, types=self.types)
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if detach:
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images = images.detach()
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# Save away for use elsewhere (e.g. unet loss)
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self.aug_images = images
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return self.D(images)
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def network_loaded(self):
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self.D.network_loaded()
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def loss_backwards(fp16, loss, optimizer, loss_id, **kwargs):
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if fp16:
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with amp.scale_loss(loss, optimizer, loss_id) as scaled_loss:
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scaled_loss.backward(**kwargs)
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else:
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loss.backward(**kwargs)
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def gradient_penalty(images, output, weight=10, return_structured_grads=False):
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batch_size = images.shape[0]
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gradients = torch_grad(outputs=output, inputs=images,
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grad_outputs=torch.ones(output.size(), device=images.device),
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create_graph=True, retain_graph=True, only_inputs=True)[0]
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flat_grad = gradients.reshape(batch_size, -1)
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penalty = weight * ((flat_grad.norm(2, dim=1) - 1) ** 2).mean()
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if return_structured_grads:
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return penalty, gradients
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else:
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return penalty
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class StyleSrGanDivergenceLoss(L.ConfigurableLoss):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.real = opt['real']
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self.fake = opt['fake']
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self.discriminator = opt['discriminator']
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self.for_gen = opt['gen_loss']
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self.gp_frequency = opt['gradient_penalty_frequency']
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self.noise = opt['noise'] if 'noise' in opt.keys() else 0
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def forward(self, net, state):
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real_input = state[self.real]
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fake_input = state[self.fake]
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if self.noise != 0:
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fake_input = fake_input + torch.rand_like(fake_input) * self.noise
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real_input = real_input + torch.rand_like(real_input) * self.noise
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D = self.env['discriminators'][self.discriminator]
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fake = D(fake_input)
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if self.for_gen:
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return fake.mean()
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else:
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real_input.requires_grad_() # <-- Needed to compute gradients on the input.
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real = D(real_input)
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divergence_loss = (F.relu(1 + real) + F.relu(1 - fake)).mean()
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# Apply gradient penalty. TODO: migrate this elsewhere.
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if self.env['step'] % self.gp_frequency == 0:
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gp = gradient_penalty(real_input, real)
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self.metrics.append(("gradient_penalty", gp.clone().detach()))
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divergence_loss = divergence_loss + gp
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real_input.requires_grad_(requires_grad=False)
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return divergence_loss
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@register_model
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def register_styledsr_discriminator(opt_net, opt):
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attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
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disc = StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn,
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do_checkpointing=opt_get(opt_net, ['do_checkpointing'], False),
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quantize=opt_get(opt_net, ['quantize'], False),
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mlp=opt_get(opt_net, ['mlp_head'], True))
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if 'use_partial_pretrained' in opt_net.keys():
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disc.configure_partial_training(opt_net['bypass_blocks'], opt_net['partial_training_blocks'], opt_net['intermediate_blocks_frozen_until'])
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return DiscAugmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
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