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Diffstat (limited to 'ldm/modules/encoders/modules.py')
| -rw-r--r-- | ldm/modules/encoders/modules.py | 234 |
1 files changed, 0 insertions, 234 deletions
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py deleted file mode 100644 index ededbe43..00000000 --- a/ldm/modules/encoders/modules.py +++ /dev/null @@ -1,234 +0,0 @@ -import torch -import torch.nn as nn -from functools import partial -import clip -from einops import rearrange, repeat -from transformers import CLIPTokenizer, CLIPTextModel -import kornia - -from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - - -class ClassEmbedder(nn.Module): - def __init__(self, embed_dim, n_classes=1000, key='class'): - super().__init__() - self.key = key - self.embedding = nn.Embedding(n_classes, embed_dim) - - def forward(self, batch, key=None): - if key is None: - key = self.key - # this is for use in crossattn - c = batch[key][:, None] - c = self.embedding(c) - return c - - -class TransformerEmbedder(AbstractEncoder): - """Some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): - super().__init__() - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer)) - - def forward(self, tokens): - tokens = tokens.to(self.device) # meh - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, x): - return self(x) - - -class BERTTokenizer(AbstractEncoder): - """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" - def __init__(self, device="cuda", vq_interface=True, max_length=77): - super().__init__() - from transformers import BertTokenizerFast # TODO: add to reuquirements - self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") - self.device = device - self.vq_interface = vq_interface - self.max_length = max_length - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - return tokens - - @torch.no_grad() - def encode(self, text): - tokens = self(text) - if not self.vq_interface: - return tokens - return None, None, [None, None, tokens] - - def decode(self, text): - return text - - -class BERTEmbedder(AbstractEncoder): - """Uses the BERT tokenizr model and add some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, - device="cuda",use_tokenizer=True, embedding_dropout=0.0): - super().__init__() - self.use_tknz_fn = use_tokenizer - if self.use_tknz_fn: - self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer), - emb_dropout=embedding_dropout) - - def forward(self, text): - if self.use_tknz_fn: - tokens = self.tknz_fn(text)#.to(self.device) - else: - tokens = text - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, text): - # output of length 77 - return self(text) - - -class SpatialRescaler(nn.Module): - def __init__(self, - n_stages=1, - method='bilinear', - multiplier=0.5, - in_channels=3, - out_channels=None, - bias=False): - super().__init__() - self.n_stages = n_stages - assert self.n_stages >= 0 - assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] - self.multiplier = multiplier - self.interpolator = partial(torch.nn.functional.interpolate, mode=method) - self.remap_output = out_channels is not None - if self.remap_output: - print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') - self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) - - def forward(self,x): - for stage in range(self.n_stages): - x = self.interpolator(x, scale_factor=self.multiplier) - - - if self.remap_output: - x = self.channel_mapper(x) - return x - - def encode(self, x): - return self(x) - -class FrozenCLIPEmbedder(AbstractEncoder): - """Uses the CLIP transformer encoder for text (from Hugging Face)""" - def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): - super().__init__() - self.tokenizer = CLIPTokenizer.from_pretrained(version) - self.transformer = CLIPTextModel.from_pretrained(version) - self.device = device - self.max_length = max_length - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) - - -class FrozenCLIPTextEmbedder(nn.Module): - """ - Uses the CLIP transformer encoder for text. - """ - def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True): - super().__init__() - self.model, _ = clip.load(version, jit=False, device="cpu") - self.device = device - self.max_length = max_length - self.n_repeat = n_repeat - self.normalize = normalize - - def freeze(self): - self.model = self.model.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - tokens = clip.tokenize(text).to(self.device) - z = self.model.encode_text(tokens) - if self.normalize: - z = z / torch.linalg.norm(z, dim=1, keepdim=True) - return z - - def encode(self, text): - z = self(text) - if z.ndim==2: - z = z[:, None, :] - z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat) - return z - - -class FrozenClipImageEmbedder(nn.Module): - """ - Uses the CLIP image encoder. - """ - def __init__( - self, - model, - jit=False, - device='cuda' if torch.cuda.is_available() else 'cpu', - antialias=False, - ): - super().__init__() - self.model, _ = clip.load(name=model, device=device, jit=jit) - - self.antialias = antialias - - self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) - self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) - - def preprocess(self, x): - # normalize to [0,1] - x = kornia.geometry.resize(x, (224, 224), - interpolation='bicubic',align_corners=True, - antialias=self.antialias) - x = (x + 1.) / 2. - # renormalize according to clip - x = kornia.enhance.normalize(x, self.mean, self.std) - return x - - def forward(self, x): - # x is assumed to be in range [-1,1] - return self.model.encode_image(self.preprocess(x)) - - -if __name__ == "__main__": - from ldm.util import count_params - model = FrozenCLIPEmbedder() - count_params(model, verbose=True)
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