# Copyright 2018 The Sonnet Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ # This is an alternative implementation of VQVAE that uses convolutions with kernels of size 5 and # a "standard" upsampler rather than ConvTranspose. import torch from torch import nn from torch.nn import functional as F import torch.distributed as distributed from trainer.networks import register_model from utils.util import checkpoint, opt_get # Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper. class UpsampleConv(nn.Module): def __init__(self, in_filters, out_filters, kernel_size, padding): super().__init__() self.conv = nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding) def forward(self, x): up = torch.nn.functional.interpolate(x, scale_factor=2) return self.conv(up) class Quantize(nn.Module): def __init__(self, dim, n_embed, decay=0.99, eps=1e-5): super().__init__() self.dim = dim self.n_embed = n_embed self.decay = decay self.eps = eps embed = torch.randn(dim, n_embed) self.register_buffer("embed", embed) self.register_buffer("cluster_size", torch.zeros(n_embed)) self.register_buffer("embed_avg", embed.clone()) def forward(self, input): flatten = input.reshape(-1, self.dim) dist = ( flatten.pow(2).sum(1, keepdim=True) - 2 * flatten @ self.embed + self.embed.pow(2).sum(0, keepdim=True) ) _, embed_ind = (-dist).max(1) embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype) embed_ind = embed_ind.view(*input.shape[:-1]) quantize = self.embed_code(embed_ind) if self.training: embed_onehot_sum = embed_onehot.sum(0) embed_sum = flatten.transpose(0, 1) @ embed_onehot if distributed.is_initialized() and distributed.get_world_size() > 1: distributed.all_reduce(embed_onehot_sum) distributed.all_reduce(embed_sum) self.cluster_size.data.mul_(self.decay).add_( embed_onehot_sum, alpha=1 - self.decay ) self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay) n = self.cluster_size.sum() cluster_size = ( (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n ) embed_normalized = self.embed_avg / cluster_size.unsqueeze(0) self.embed.data.copy_(embed_normalized) diff = (quantize.detach() - input).pow(2).mean() quantize = input + (quantize - input).detach() return quantize, diff, embed_ind def embed_code(self, embed_id): return F.embedding(embed_id, self.embed.transpose(0, 1)) class ResBlock(nn.Module): def __init__(self, in_channel, channel): super().__init__() self.conv = nn.Sequential( nn.ReLU(inplace=True), nn.Conv2d(in_channel, channel, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(channel, in_channel, 1), ) def forward(self, input): out = self.conv(input) out += input return out class Encoder(nn.Module): def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride): super().__init__() if stride == 4: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.ReLU(inplace=True), nn.Conv2d(channel // 2, channel, 5, stride=2, padding=2), nn.ReLU(inplace=True), nn.Conv2d(channel, channel, 3, padding=1), ] elif stride == 2: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.ReLU(inplace=True), nn.Conv2d(channel // 2, channel, 3, padding=1), ] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.ReLU(inplace=True)) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class Decoder(nn.Module): def __init__( self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride ): super().__init__() blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.ReLU(inplace=True)) if stride == 4: blocks.extend( [ UpsampleConv(channel, channel // 2, 5, padding=2), nn.ReLU(inplace=True), UpsampleConv( channel // 2, out_channel, 5, padding=2 ), ] ) elif stride == 2: blocks.append( UpsampleConv(channel, out_channel, 5, padding=2) ) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class VQVAE(nn.Module): def __init__( self, in_channel=3, channel=128, n_res_block=2, n_res_channel=32, codebook_dim=64, codebook_size=512, decay=0.99, ): super().__init__() self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4) self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2) self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1) self.quantize_t = Quantize(codebook_dim, codebook_size) self.dec_t = Decoder( codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2 ) self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1) self.quantize_b = Quantize(codebook_dim, codebook_size*2) self.upsample_t = UpsampleConv( codebook_dim, codebook_dim, 5, padding=2 ) self.dec = Decoder( codebook_dim + codebook_dim, in_channel, channel, n_res_block, n_res_channel, stride=4, ) def forward(self, input): quant_t, quant_b, diff, _, _ = self.encode(input) dec = self.decode(quant_t, quant_b) return dec, diff def encode(self, input): enc_b = checkpoint(self.enc_b, input) enc_t = checkpoint(self.enc_t, enc_b) quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1) quant_t, diff_t, id_t = self.quantize_t(quant_t) quant_t = quant_t.permute(0, 3, 1, 2) diff_t = diff_t.unsqueeze(0) dec_t = checkpoint(self.dec_t, quant_t) enc_b = torch.cat([dec_t, enc_b], 1) quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1) quant_b, diff_b, id_b = self.quantize_b(quant_b) quant_b = quant_b.permute(0, 3, 1, 2) diff_b = diff_b.unsqueeze(0) return quant_t, quant_b, diff_t + diff_b, id_t, id_b def decode(self, quant_t, quant_b): upsample_t = self.upsample_t(quant_t) quant = torch.cat([upsample_t, quant_b], 1) dec = checkpoint(self.dec, quant) return dec def decode_code(self, code_t, code_b): quant_t = self.quantize_t.embed_code(code_t) quant_t = quant_t.permute(0, 3, 1, 2) quant_b = self.quantize_b.embed_code(code_b) quant_b = quant_b.permute(0, 3, 1, 2) dec = self.decode(quant_t, quant_b) return dec @register_model def register_vqvae_normalized(opt_net, opt): kw = opt_get(opt_net, ['kwargs'], {}) return VQVAE(**kw) if __name__ == '__main__': v = VQVAE() print(v(torch.randn(1,3,128,128))[0].shape)