437 lines
15 KiB
Python
437 lines
15 KiB
Python
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
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'''
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VQGAN code, adapted from the original created by the Unleashing Transformers authors:
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https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
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'''
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import numpy as np
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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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import copy
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from basicsr.utils import get_root_logger
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from basicsr.utils.registry import ARCH_REGISTRY
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def normalize(in_channels):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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@torch.jit.script
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def swish(x):
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return x*torch.sigmoid(x)
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# Define VQVAE classes
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class VectorQuantizer(nn.Module):
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def __init__(self, codebook_size, emb_dim, beta):
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super(VectorQuantizer, self).__init__()
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self.codebook_size = codebook_size # number of embeddings
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self.emb_dim = emb_dim # dimension of embedding
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self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
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self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
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def forward(self, z):
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.emb_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
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2 * torch.matmul(z_flattened, self.embedding.weight.t())
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mean_distance = torch.mean(d)
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# find closest encodings
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# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
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min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
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# [0-1], higher score, higher confidence
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min_encoding_scores = torch.exp(-min_encoding_scores/10)
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min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
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min_encodings.scatter_(1, min_encoding_indices, 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
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# compute loss for embedding
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loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# perplexity
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e_mean = torch.mean(min_encodings, dim=0)
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perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q, loss, {
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"perplexity": perplexity,
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"min_encodings": min_encodings,
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"min_encoding_indices": min_encoding_indices,
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"min_encoding_scores": min_encoding_scores,
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"mean_distance": mean_distance
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}
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def get_codebook_feat(self, indices, shape):
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# input indices: batch*token_num -> (batch*token_num)*1
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# shape: batch, height, width, channel
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indices = indices.view(-1,1)
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min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
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min_encodings.scatter_(1, indices, 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
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if shape is not None: # reshape back to match original input shape
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z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
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return z_q
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class GumbelQuantizer(nn.Module):
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def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
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super().__init__()
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self.codebook_size = codebook_size # number of embeddings
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self.emb_dim = emb_dim # dimension of embedding
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self.straight_through = straight_through
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self.temperature = temp_init
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self.kl_weight = kl_weight
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self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits
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self.embed = nn.Embedding(codebook_size, emb_dim)
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def forward(self, z):
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hard = self.straight_through if self.training else True
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logits = self.proj(z)
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soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
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z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
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# + kl divergence to the prior loss
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qy = F.softmax(logits, dim=1)
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diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
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min_encoding_indices = soft_one_hot.argmax(dim=1)
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return z_q, diff, {
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"min_encoding_indices": min_encoding_indices
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}
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class Downsample(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x):
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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return x
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class Upsample(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x):
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x = F.interpolate(x, scale_factor=2.0, mode="nearest")
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x = self.conv(x)
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return x
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class ResBlock(nn.Module):
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def __init__(self, in_channels, out_channels=None):
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super(ResBlock, self).__init__()
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self.in_channels = in_channels
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self.out_channels = in_channels if out_channels is None else out_channels
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self.norm1 = normalize(in_channels)
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.norm2 = normalize(out_channels)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if self.in_channels != self.out_channels:
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self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x_in):
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x = x_in
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x = self.norm1(x)
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x = swish(x)
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x = self.conv1(x)
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x = self.norm2(x)
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x = swish(x)
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x = self.conv2(x)
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if self.in_channels != self.out_channels:
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x_in = self.conv_out(x_in)
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return x + x_in
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class AttnBlock(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, c, h*w)
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q = q.permute(0, 2, 1)
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k = k.reshape(b, c, h*w)
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w_ = torch.bmm(q, k)
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w_ = w_ * (int(c)**(-0.5))
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w_ = F.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h*w)
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w_ = w_.permute(0, 2, 1)
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h_ = torch.bmm(v, w_)
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x+h_
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class Encoder(nn.Module):
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def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
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super().__init__()
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self.nf = nf
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.attn_resolutions = attn_resolutions
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curr_res = self.resolution
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in_ch_mult = (1,)+tuple(ch_mult)
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blocks = []
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# initial convultion
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blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
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# residual and downsampling blocks, with attention on smaller res (16x16)
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for i in range(self.num_resolutions):
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block_in_ch = nf * in_ch_mult[i]
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block_out_ch = nf * ch_mult[i]
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for _ in range(self.num_res_blocks):
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blocks.append(ResBlock(block_in_ch, block_out_ch))
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block_in_ch = block_out_ch
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if curr_res in attn_resolutions:
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blocks.append(AttnBlock(block_in_ch))
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if i != self.num_resolutions - 1:
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blocks.append(Downsample(block_in_ch))
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curr_res = curr_res // 2
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# non-local attention block
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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blocks.append(AttnBlock(block_in_ch))
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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# normalise and convert to latent size
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blocks.append(normalize(block_in_ch))
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blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
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self.blocks = nn.ModuleList(blocks)
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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return x
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class Generator(nn.Module):
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def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
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super().__init__()
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self.nf = nf
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self.ch_mult = ch_mult
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self.num_resolutions = len(self.ch_mult)
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self.num_res_blocks = res_blocks
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self.resolution = img_size
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self.attn_resolutions = attn_resolutions
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self.in_channels = emb_dim
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self.out_channels = 3
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block_in_ch = self.nf * self.ch_mult[-1]
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curr_res = self.resolution // 2 ** (self.num_resolutions-1)
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blocks = []
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# initial conv
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blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
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# non-local attention block
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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blocks.append(AttnBlock(block_in_ch))
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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for i in reversed(range(self.num_resolutions)):
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block_out_ch = self.nf * self.ch_mult[i]
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for _ in range(self.num_res_blocks):
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blocks.append(ResBlock(block_in_ch, block_out_ch))
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block_in_ch = block_out_ch
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if curr_res in self.attn_resolutions:
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blocks.append(AttnBlock(block_in_ch))
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if i != 0:
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blocks.append(Upsample(block_in_ch))
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curr_res = curr_res * 2
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blocks.append(normalize(block_in_ch))
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blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
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self.blocks = nn.ModuleList(blocks)
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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return x
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@ARCH_REGISTRY.register()
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class VQAutoEncoder(nn.Module):
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def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
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beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
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super().__init__()
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logger = get_root_logger()
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self.in_channels = 3
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self.nf = nf
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self.n_blocks = res_blocks
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self.codebook_size = codebook_size
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self.embed_dim = emb_dim
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self.ch_mult = ch_mult
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self.resolution = img_size
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self.attn_resolutions = attn_resolutions
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self.quantizer_type = quantizer
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self.encoder = Encoder(
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self.in_channels,
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self.nf,
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self.embed_dim,
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self.ch_mult,
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self.n_blocks,
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self.resolution,
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self.attn_resolutions
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)
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if self.quantizer_type == "nearest":
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self.beta = beta #0.25
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self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
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elif self.quantizer_type == "gumbel":
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self.gumbel_num_hiddens = emb_dim
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self.straight_through = gumbel_straight_through
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self.kl_weight = gumbel_kl_weight
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self.quantize = GumbelQuantizer(
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self.codebook_size,
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self.embed_dim,
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self.gumbel_num_hiddens,
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self.straight_through,
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self.kl_weight
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)
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self.generator = Generator(
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self.nf,
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self.embed_dim,
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self.ch_mult,
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self.n_blocks,
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self.resolution,
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self.attn_resolutions
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)
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if model_path is not None:
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chkpt = torch.load(model_path, map_location='cpu')
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if 'params_ema' in chkpt:
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
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logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
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elif 'params' in chkpt:
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
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logger.info(f'vqgan is loaded from: {model_path} [params]')
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else:
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raise ValueError('Wrong params!')
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def forward(self, x):
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x = self.encoder(x)
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quant, codebook_loss, quant_stats = self.quantize(x)
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x = self.generator(quant)
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return x, codebook_loss, quant_stats
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# patch based discriminator
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@ARCH_REGISTRY.register()
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class VQGANDiscriminator(nn.Module):
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def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
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super().__init__()
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layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
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ndf_mult = 1
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ndf_mult_prev = 1
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for n in range(1, n_layers): # gradually increase the number of filters
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ndf_mult_prev = ndf_mult
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ndf_mult = min(2 ** n, 8)
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layers += [
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nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(ndf * ndf_mult),
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nn.LeakyReLU(0.2, True)
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]
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ndf_mult_prev = ndf_mult
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ndf_mult = min(2 ** n_layers, 8)
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layers += [
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nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(ndf * ndf_mult),
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nn.LeakyReLU(0.2, True)
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]
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layers += [
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nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map
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self.main = nn.Sequential(*layers)
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if model_path is not None:
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chkpt = torch.load(model_path, map_location='cpu')
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if 'params_d' in chkpt:
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
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elif 'params' in chkpt:
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
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else:
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raise ValueError('Wrong params!')
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def forward(self, x):
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return self.main(x) |