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""" |
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Credits to https://github.com/CompVis/taming-transformers |
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""" |
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from typing import Tuple |
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from einops import rearrange |
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import torch |
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import torch.nn as nn |
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from .models.lpips import LPIPS |
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from .models.nets import Encoder, Decoder |
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class Tokenizer(nn.Module): |
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def __init__(self, config: dict, with_lpips: bool = True) -> None: |
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super().__init__() |
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self.vocab_size = config["vocab_size"] |
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self.embed_dim = config["embed_dim"] |
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self.encoder = Encoder(config["encoder"]) |
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self.pre_quant_conv = torch.nn.Conv2d(encoder.config.z_channels, self.embed_dim, 1) |
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self.embedding = nn.Embedding(self.vocab_size, self.embed_dim) |
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self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, decoder.config.z_channels, 1) |
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self.decoder = Decoder(config["decoder"]) |
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self.embedding.weight.data.uniform_(-1.0 / self.vocab_size, 1.0 / self.vocab_size) |
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self.lpips = LPIPS().eval() if with_lpips else None |
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def __repr__(self) -> str: |
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return "tokenizer" |
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def forward(self, x: torch.Tensor, should_preprocess: bool = False, should_postprocess: bool = False) -> Tuple[torch.Tensor]: |
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outputs = self.encode(x, should_preprocess) |
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decoder_input = outputs.z + (outputs.z_quantized - outputs.z).detach() |
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reconstructions = self.decode(decoder_input, should_postprocess) |
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return outputs.z, outputs.z_quantized, reconstructions |
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def encode(self, x: torch.Tensor, should_preprocess: bool = False) -> dict: |
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if should_preprocess: |
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x = self.preprocess_input(x) |
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shape = x.shape |
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x = x.view(-1, *shape[-3:]) |
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z = self.encoder(x) |
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z = self.pre_quant_conv(z) |
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b, e, h, w = z.shape |
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z_flattened = rearrange(z, 'b e h w -> (b h w) e') |
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dist_to_embeddings = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + torch.sum(self.embedding.weight**2, dim=1) - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) |
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tokens = dist_to_embeddings.argmin(dim=-1) |
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z_q = rearrange(self.embedding(tokens), '(b h w) e -> b e h w', b=b, e=e, h=h, w=w).contiguous() |
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z = z.reshape(*shape[:-3], *z.shape[1:]) |
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z_q = z_q.reshape(*shape[:-3], *z_q.shape[1:]) |
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tokens = tokens.reshape(*shape[:-3], -1) |
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return { |
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"z": z, |
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"z_quantized": z_q, |
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"tokens": tokens |
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} |
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def decode(self, z_q: torch.Tensor, should_postprocess: bool = False) -> torch.Tensor: |
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shape = z_q.shape |
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z_q = z_q.view(-1, *shape[-3:]) |
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z_q = self.post_quant_conv(z_q) |
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rec = self.decoder(z_q) |
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rec = rec.reshape(*shape[:-3], *rec.shape[1:]) |
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if should_postprocess: |
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rec = self.postprocess_output(rec) |
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return rec |
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@torch.no_grad() |
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def encode_decode(self, x: torch.Tensor, should_preprocess: bool = False, should_postprocess: bool = False) -> torch.Tensor: |
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z_q = self.encode(x, should_preprocess).z_quantized |
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return self.decode(z_q, should_postprocess) |
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def preprocess_input(self, x: torch.Tensor) -> torch.Tensor: |
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"""x is supposed to be channels first and in [0, 1]""" |
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return x.mul(2).sub(1) |
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def postprocess_output(self, y: torch.Tensor) -> torch.Tensor: |
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"""y is supposed to be channels first and in [-1, 1]""" |
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return y.add(1).div(2) |
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