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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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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 |
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self.emb_dim = emb_dim |
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self.beta = beta |
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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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z = z.permute(0, 2, 3, 1).contiguous() |
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z_flattened = z.view(-1, self.emb_dim) |
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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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min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) |
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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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z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) |
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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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z_q = z + (z_q - z).detach() |
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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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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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"mean_distance": mean_distance |
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} |
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def get_codebook_feat(self, indices, shape): |
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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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z_q = torch.matmul(min_encodings.float(), self.embedding.weight) |
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if shape is not None: |
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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 |
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self.emb_dim = emb_dim |
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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) |
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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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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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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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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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blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) |
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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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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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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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blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) |
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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 |
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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(f'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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@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): |
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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)] |
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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(f'Wrong params!') |
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def forward(self, x): |
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return self.main(x) |