Merge pull request #2 from 920232796/master

fix bugs

3 files changed, 0 insertions, 371 deletions

diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py
deleted file mode 100644
index e69de29b..00000000
--- a/ldm/modules/encoders/__init__.py
+++ /dev/null
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)
\ No newline at end of file
diff --git a/ldm/modules/encoders/xlmr.py b/ldm/modules/encoders/xlmr.py
deleted file mode 100644
index beab3fdf..00000000
--- a/ldm/modules/encoders/xlmr.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from transformers import BertPreTrainedModel,BertModel,BertConfig
-import torch.nn as nn
-import torch
-from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
-from transformers import XLMRobertaModel,XLMRobertaTokenizer
-from typing import Optional
-
-class BertSeriesConfig(BertConfig):
-    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
-
-        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
-        self.project_dim = project_dim
-        self.pooler_fn = pooler_fn
-        self.learn_encoder = learn_encoder
-
-class RobertaSeriesConfig(XLMRobertaConfig):
-    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
-        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-        self.project_dim = project_dim
-        self.pooler_fn = pooler_fn
-        self.learn_encoder = learn_encoder
-
-
-class BertSeriesModelWithTransformation(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-    config_class = BertSeriesConfig
-
-    def __init__(self, config=None, **kargs):
-        # modify initialization for autoloading 
-        if config is None:
-            config = XLMRobertaConfig()
-            config.attention_probs_dropout_prob= 0.1
-            config.bos_token_id=0
-            config.eos_token_id=2
-            config.hidden_act='gelu'
-            config.hidden_dropout_prob=0.1
-            config.hidden_size=1024
-            config.initializer_range=0.02
-            config.intermediate_size=4096
-            config.layer_norm_eps=1e-05
-            config.max_position_embeddings=514
-
-            config.num_attention_heads=16
-            config.num_hidden_layers=24
-            config.output_past=True
-            config.pad_token_id=1
-            config.position_embedding_type= "absolute"
-
-            config.type_vocab_size= 1
-            config.use_cache=True
-            config.vocab_size= 250002
-            config.project_dim = 768
-            config.learn_encoder = False
-        super().__init__(config)
-        self.roberta = XLMRobertaModel(config)
-        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
-        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
-        self.pooler = lambda x: x[:,0]
-        self.post_init()
-
-    def encode(self,c):
-        device = next(self.parameters()).device
-        text = self.tokenizer(c,
-                        truncation=True,
-                        max_length=77,
-                        return_length=False,
-                        return_overflowing_tokens=False,
-                        padding="max_length",
-                        return_tensors="pt")
-        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
-        text["attention_mask"] = torch.tensor(
-            text['attention_mask']).to(device)
-        features = self(**text)
-        return features['projection_state'] 
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) :
-        r"""
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-
-        outputs = self.roberta(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=True,
-            return_dict=return_dict,
-        )
-
-        # last module outputs
-        sequence_output = outputs[0]
-
-
-        # project every module
-        sequence_output_ln = self.pre_LN(sequence_output)
-
-        # pooler
-        pooler_output = self.pooler(sequence_output_ln)
-        pooler_output = self.transformation(pooler_output)
-        projection_state = self.transformation(outputs.last_hidden_state)
-
-        return {
-            'pooler_output':pooler_output,
-            'last_hidden_state':outputs.last_hidden_state,
-            'hidden_states':outputs.hidden_states,
-            'attentions':outputs.attentions,
-            'projection_state':projection_state,
-            'sequence_out': sequence_output
-        }
-
-
-class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
-    base_model_prefix = 'roberta'
-    config_class= RobertaSeriesConfig
\ No newline at end of file