| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from tokenizer.vqgan.layer import Encoder, Decoder |
| from tokenizer.vqgan.quantize import VectorQuantizer2 as VectorQuantizer |
|
|
|
|
| VQGAN_FROM_TAMING = { |
| 'vqgan_imagenet_f16_1024': ( |
| 'tokenizer/vqgan/configs/vqgan_imagenet_f16_1024.yaml', |
| 'pretrained_models/vqgan_imagenet_f16_1024/ckpts/last.pth'), |
| 'vqgan_imagenet_f16_16384': ( |
| 'tokenizer/vqgan/configs/vqgan_imagenet_f16_16384.yaml', |
| 'pretrained_models/vqgan_imagenet_f16_16384/ckpts/last.pth'), |
| 'vqgan_openimage_f8_256': ( |
| 'tokenizer/vqgan/configs/vqgan_openimage_f8_256.yaml', |
| 'pretrained_models/vq-f8-n256/model.pth'), |
| 'vqgan_openimage_f8_16384': ( |
| 'tokenizer/vqgan/configs/vqgan_openimage_f8_16384.yaml', |
| 'pretrained_models/vq-f8/model.pth'), |
| } |
|
|
| class VQModel(nn.Module): |
| def __init__(self, |
| ddconfig, |
| n_embed, |
| embed_dim, |
| ckpt_path=None, |
| ignore_keys=[], |
| image_key="image", |
| colorize_nlabels=None, |
| monitor=None, |
| remap=None, |
| sane_index_shape=False, |
| **kwargs, |
| ): |
| super().__init__() |
| self.image_key = image_key |
| self.encoder = Encoder(**ddconfig) |
| self.decoder = Decoder(**ddconfig) |
| self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, |
| remap=remap, sane_index_shape=sane_index_shape) |
| self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) |
| self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) |
| if ckpt_path is not None: |
| self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) |
| self.image_key = image_key |
| if colorize_nlabels is not None: |
| assert type(colorize_nlabels)==int |
| self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) |
| if monitor is not None: |
| self.monitor = monitor |
|
|
| def init_from_ckpt(self, path, ignore_keys=list(), logging=True): |
| model_weight = torch.load(path, map_location="cpu")["state_dict"] |
| keys = list(model_weight.keys()) |
| for k in keys: |
| for ik in ignore_keys: |
| if k.startswith(ik): |
| print("Deleting key {} from state_dict.".format(k)) |
| del model_weight[k] |
| missing, unexpected = self.load_state_dict(model_weight, strict=False) |
| if logging: |
| print(f"Restored from {path}") |
| print(f"Missing Keys in State Dict: {missing}") |
| print(f"Unexpected Keys in State Dict: {unexpected}") |
|
|
| def encode(self, x): |
| h = self.encoder(x) |
| h = self.quant_conv(h) |
| quant, emb_loss, info = self.quantize(h) |
| return quant, emb_loss, info |
|
|
| def decode(self, quant): |
| quant = self.post_quant_conv(quant) |
| dec = self.decoder(quant) |
| return dec |
|
|
| def decode_code(self, code_b, shape, channel_first=True): |
| quant_b = self.quantize.get_codebook_entry(code_b, shape, channel_first) |
| dec = self.decode(quant_b) |
| return dec |
|
|
| def forward(self, input): |
| quant, diff, _ = self.encode(input) |
| dec = self.decode(quant) |
| return dec, diff |
|
|
