add remote code and hf-format "pytorch_model.bin"
#20
by
chuhac
- opened
- config.json +103 -0
- configuration_biomed_clip.py +60 -0
- modeling_biomed_clip.py +938 -0
- preprocessor_config.json +20 -0
- processing_biomed_clip.py +153 -0
- pytorch_model.bin +3 -0
config.json
ADDED
@@ -0,0 +1,103 @@
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1 |
+
{
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"_name_or_path": "",
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"architectures": [
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"BiomedCLIPModel"
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],
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"auto_map": {
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"AutoConfig": "configuration_biomed_clip.BiomedCLIPConfig",
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"AutoProcessor": "processing_biomed_clip.BiomedCLIPProcessor",
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"AutoModel": "modeling_biomed_clip.BiomedCLIPModel",
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"AutoModelForImageClassification": "modeling_biomed_clip.BiomedCLIPForImageClassification"
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},
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"initializer_factor": 1.0,
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+
"logit_scale_init_value": 4.4454,
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"model_type": "clip",
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"projection_dim": 512,
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"text_config": {
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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+
"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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+
"layer_norm_eps": 1e-12,
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+
"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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+
"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.6.0.dev0",
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+
"type_vocab_size": 2,
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+
"use_cache": true,
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+
"vocab_size": 30522
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},
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"text_config_dict": {
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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+
"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.6.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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},
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"text_projection_config": {
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"hidden_size": 768,
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"intermediate_size": 640,
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"projection_dim": 512,
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"hidden_act": "gelu"
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},
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"text_projection_config_dict": {
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"hidden_size": 768,
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"intermediate_size": 640,
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"projection_dim": 512,
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"hidden_act": "gelu",
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"num_hidden_layers": 2
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},
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"torch_dtype": "float32",
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"transformers_version": null,
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"vision_config": {
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"attention_probs_dropout_prob": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 12,
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"qkv_bias": true
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},
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"vision_config_dict": {
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"attention_probs_dropout_prob": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 12,
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"qkv_bias": true
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}
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}
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configuration_biomed_clip.py
ADDED
@@ -0,0 +1,60 @@
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import os
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from typing import *
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from transformers.configuration_utils import PretrainedConfig
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from transformers.models.clip.configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
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class BiomedCLIPTextProjectionConfig(PretrainedConfig):
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=640,
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projection_dim=512,
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num_hidden_layers=2,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from CLIPConfig
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if config_dict.get("model_type") == "clip":
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config_dict = config_dict["text_projection_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class BiomedCLIPConfig(CLIPConfig):
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def __init__(
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self, text_config=None, text_projection_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
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):
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# If `_config_dict` exist, we use them for the backward compatibility.
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# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
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# of confusion!).
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super().__init__(text_config, vision_config, projection_dim, logit_scale_init_value, **kwargs)
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text_projection_config_dict = kwargs.pop("text_projection_config_dict", None)
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if text_projection_config is None:
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if text_projection_config_dict is not None:
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text_projection_config = {}
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_text_projection_config_dict = BiomedCLIPTextProjectionConfig(**text_projection_config_dict)
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text_projection_config.update(_text_projection_config_dict)
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else:
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text_projection_config = BiomedCLIPTextProjectionConfig(**text_projection_config)
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self.text_projection_config = text_projection_config
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modeling_biomed_clip.py
ADDED
@@ -0,0 +1,938 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Modified by chuhac for a timm-free implementation
|
3 |
+
# Model can be directly imported with ``from_pretrained`` and ``trust_remote_code = True`` in the huggingface format
|
4 |
+
# Diff from HF CLIP Implementation:
|
5 |
+
# 1. pre-norm instead of post-norm in Vision Tower (the original implementation is right but the module registration order is misleading)
|
6 |
+
# 2. CLS Pooling with MLP in Text Tower
|
7 |
+
# 3. Remove pre norm in Vision Tower
|
8 |
+
# 4. CNN bias in Vision Tower
|
9 |
+
# 5. Change layer_norm eps from 1e-5 to 1e-12, which introduce a little numerical variations (1e-5 level)
|
10 |
+
## ******************************** ##
|
11 |
+
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
|
12 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
13 |
+
# you may not use this file except in compliance with the License.
|
14 |
+
# You may obtain a copy of the License at
|
15 |
+
#
|
16 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
17 |
+
#
|
18 |
+
# Unless required by applicable law or agreed to in writing, software
|
19 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
20 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
21 |
+
# See the License for the specific language governing permissions and
|
22 |
+
# limitations under the License.
|
23 |
+
""" PyTorch BiomedCLIP model """
|
24 |
+
""" No need for timm or open-clip-torch """
|
25 |
+
|
26 |
+
|
27 |
+
from dataclasses import dataclass
|
28 |
+
from typing import Any, Optional, Tuple, Union, List
|
29 |
+
|
30 |
+
import math
|
31 |
+
import torch
|
32 |
+
import torch.utils.checkpoint
|
33 |
+
from torch import nn
|
34 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
35 |
+
|
36 |
+
from transformers.activations import ACT2FN
|
37 |
+
from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
|
38 |
+
from transformers.modeling_outputs import (
|
39 |
+
BaseModelOutput,
|
40 |
+
BaseModelOutputWithPooling,
|
41 |
+
ImageClassifierOutput,
|
42 |
+
BaseModelOutputWithPoolingAndCrossAttentions,
|
43 |
+
BaseModelOutputWithPastAndCrossAttentions
|
44 |
+
)
|
45 |
+
from transformers.modeling_utils import PreTrainedModel
|
46 |
+
from transformers.utils import (
|
47 |
+
ModelOutput,
|
48 |
+
add_code_sample_docstrings,
|
49 |
+
add_start_docstrings,
|
50 |
+
add_start_docstrings_to_model_forward,
|
51 |
+
logging,
|
52 |
+
replace_return_docstrings,
|
53 |
+
)
|
54 |
+
from transformers.models.clip.configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
|
55 |
+
from transformers.models.clip.modeling_clip import *
|
56 |
+
|
57 |
+
from .configuration_biomed_clip import BiomedCLIPTextProjectionConfig, BiomedCLIPConfig
|
58 |
+
|
59 |
+
|
60 |
+
logger = logging.get_logger(__name__)
|
61 |
+
|
62 |
+
|
63 |
+
|
64 |
+
# contrastive loss function, adapted from
|
65 |
+
# https://sachinruk.github.io/blog/2021-03-07-clip.html
|
66 |
+
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
|
67 |
+
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
|
68 |
+
|
69 |
+
|
70 |
+
def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
|
71 |
+
caption_loss = contrastive_loss(similarity)
|
72 |
+
image_loss = contrastive_loss(similarity.t())
|
73 |
+
return (caption_loss + image_loss) / 2.0
|
74 |
+
|
75 |
+
|
76 |
+
class BiomedCLIPVisionEmbeddings(CLIPVisionEmbeddings):
|
77 |
+
def __init__(self, config: CLIPVisionConfig):
|
78 |
+
super().__init__(config)
|
79 |
+
|
80 |
+
self.patch_embedding = nn.Conv2d(
|
81 |
+
in_channels=config.num_channels,
|
82 |
+
out_channels=self.embed_dim,
|
83 |
+
kernel_size=self.patch_size,
|
84 |
+
stride=self.patch_size,
|
85 |
+
# True in open_clip
|
86 |
+
bias=True,
|
87 |
+
)
|
88 |
+
|
89 |
+
# TODO
|
90 |
+
class BiomedCLIPTextEmbeddings(nn.Module):
|
91 |
+
def __init__(self, config: CLIPTextConfig):
|
92 |
+
super().__init__()
|
93 |
+
embed_dim = config.hidden_size
|
94 |
+
|
95 |
+
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
|
96 |
+
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
|
97 |
+
self.token_type_embedding = nn.Embedding(config.type_vocab_size, embed_dim)
|
98 |
+
|
99 |
+
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
100 |
+
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
101 |
+
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
102 |
+
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
103 |
+
|
104 |
+
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
105 |
+
self.register_buffer(
|
106 |
+
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
|
107 |
+
)
|
108 |
+
self.register_buffer(
|
109 |
+
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
|
110 |
+
)
|
111 |
+
|
112 |
+
def forward(
|
113 |
+
self,
|
114 |
+
input_ids: Optional[torch.LongTensor] = None,
|
115 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
116 |
+
position_ids: Optional[torch.LongTensor] = None,
|
117 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
118 |
+
past_key_values_length: int = 0,
|
119 |
+
) -> torch.Tensor:
|
120 |
+
|
121 |
+
if input_ids is not None:
|
122 |
+
input_shape = input_ids.size()
|
123 |
+
else:
|
124 |
+
input_shape = inputs_embeds.size()[:-1]
|
125 |
+
|
126 |
+
seq_length = input_shape[1]
|
127 |
+
|
128 |
+
if position_ids is None:
|
129 |
+
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
|
130 |
+
|
131 |
+
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
|
132 |
+
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
|
133 |
+
# issue #5664
|
134 |
+
if token_type_ids is None:
|
135 |
+
if hasattr(self, "token_type_ids"):
|
136 |
+
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
137 |
+
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
138 |
+
token_type_ids = buffered_token_type_ids_expanded
|
139 |
+
else:
|
140 |
+
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
141 |
+
|
142 |
+
if inputs_embeds is None:
|
143 |
+
inputs_embeds = self.token_embedding(input_ids)
|
144 |
+
token_type_embeddings = self.token_type_embedding(token_type_ids)
|
145 |
+
|
146 |
+
embeddings = inputs_embeds + token_type_embeddings
|
147 |
+
if self.position_embedding_type == "absolute":
|
148 |
+
position_embeddings = self.position_embedding(position_ids)
|
149 |
+
embeddings += position_embeddings
|
150 |
+
|
151 |
+
embeddings = self.layer_norm(embeddings)
|
152 |
+
embeddings = self.dropout(embeddings)
|
153 |
+
return embeddings
|
154 |
+
|
155 |
+
|
156 |
+
class BiomedCLIPAttention(nn.Module):
|
157 |
+
def __init__(self, config, position_embedding_type=None):
|
158 |
+
super().__init__()
|
159 |
+
super().__init__()
|
160 |
+
self.config = config
|
161 |
+
self.embed_dim = config.hidden_size
|
162 |
+
self.num_heads = config.num_attention_heads
|
163 |
+
self.head_dim = self.embed_dim // self.num_heads
|
164 |
+
if self.head_dim * self.num_heads != self.embed_dim:
|
165 |
+
raise ValueError(
|
166 |
+
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
167 |
+
f" {self.num_heads})."
|
168 |
+
)
|
169 |
+
self.scale = self.head_dim**-0.5
|
170 |
+
self.dropout = nn.Dropout(config.attention_dropout)
|
171 |
+
|
172 |
+
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
173 |
+
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
174 |
+
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
175 |
+
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
|
176 |
+
|
177 |
+
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
|
178 |
+
new_x_shape = x.size()[:-1] + (self.num_heads, self.head_dim)
|
179 |
+
x = x.view(new_x_shape)
|
180 |
+
return x.permute(0, 2, 1, 3)
|
181 |
+
|
182 |
+
def forward(
|
183 |
+
self,
|
184 |
+
hidden_states: torch.Tensor,
|
185 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
186 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
187 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
188 |
+
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
189 |
+
output_attentions: Optional[bool] = False,
|
190 |
+
) -> Tuple[torch.Tensor]:
|
191 |
+
|
192 |
+
mixed_query_layer = self.q_proj(hidden_states)
|
193 |
+
|
194 |
+
# If this is instantiated as a cross-attention module, the keys
|
195 |
+
# and values come from an encoder; the attention mask needs to be
|
196 |
+
# such that the encoder's padding tokens are not attended to.
|
197 |
+
is_cross_attention = encoder_hidden_states is not None
|
198 |
+
|
199 |
+
key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
|
200 |
+
value_layer = self.transpose_for_scores(self.v_proj(hidden_states))
|
201 |
+
|
202 |
+
query_layer = self.transpose_for_scores(mixed_query_layer)
|
203 |
+
|
204 |
+
|
205 |
+
# Take the dot product between "query" and "key" to get the raw attention scores.
|
206 |
+
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
207 |
+
|
208 |
+
|
209 |
+
attention_scores = attention_scores / math.sqrt(self.head_dim)
|
210 |
+
if attention_mask is not None:
|
211 |
+
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
|
212 |
+
attention_scores = attention_scores + attention_mask
|
213 |
+
|
214 |
+
# Normalize the attention scores to probabilities.
|
215 |
+
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
216 |
+
|
217 |
+
# This is actually dropping out entire tokens to attend to, which might
|
218 |
+
# seem a bit unusual, but is taken from the original Transformer paper.
|
219 |
+
attention_probs = self.dropout(attention_probs)
|
220 |
+
|
221 |
+
|
222 |
+
context_layer = torch.matmul(attention_probs, value_layer)
|
223 |
+
|
224 |
+
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
225 |
+
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
|
226 |
+
context_layer = context_layer.view(new_context_layer_shape).contiguous()
|
227 |
+
|
228 |
+
outputs = self.out_proj(context_layer)
|
229 |
+
return outputs, attention_probs
|
230 |
+
|
231 |
+
|
232 |
+
|
233 |
+
|
234 |
+
class BiomedCLIPEncoderLayer(nn.Module):
|
235 |
+
def __init__(self, config: BiomedCLIPConfig, norm='pre'):
|
236 |
+
super().__init__()
|
237 |
+
self.embed_dim = config.hidden_size
|
238 |
+
# pre-norm
|
239 |
+
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
240 |
+
self.self_attn = BiomedCLIPAttention(config)
|
241 |
+
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
242 |
+
self.mlp = CLIPMLP(config)
|
243 |
+
self.norm = norm
|
244 |
+
|
245 |
+
if self.norm == 'pre':
|
246 |
+
self.forward = self.pre_norm_forward
|
247 |
+
elif self.norm == 'post':
|
248 |
+
self.forward = self.post_norm_forward
|
249 |
+
|
250 |
+
|
251 |
+
def pre_norm_forward(
|
252 |
+
self,
|
253 |
+
hidden_states: torch.Tensor,
|
254 |
+
attention_mask: torch.Tensor,
|
255 |
+
output_attentions: Optional[bool] = False,
|
256 |
+
) -> Tuple[torch.FloatTensor]:
|
257 |
+
"""
|
258 |
+
Args:
|
259 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
260 |
+
attention_mask (`torch.FloatTensor`): attention mask of size
|
261 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
262 |
+
`(config.encoder_attention_heads,)`.
|
263 |
+
output_attentions (`bool`, *optional*):
|
264 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
265 |
+
returned tensors for more detail.
|
266 |
+
"""
|
267 |
+
residual = hidden_states
|
268 |
+
|
269 |
+
hidden_states = self.layer_norm1(hidden_states)
|
270 |
+
hidden_states, attn_weights = self.self_attn(
|
271 |
+
hidden_states=hidden_states,
|
272 |
+
attention_mask=attention_mask,
|
273 |
+
output_attentions=output_attentions,
|
274 |
+
)
|
275 |
+
hidden_states = residual + hidden_states
|
276 |
+
|
277 |
+
residual = hidden_states
|
278 |
+
hidden_states = self.layer_norm2(hidden_states)
|
279 |
+
hidden_states = self.mlp(hidden_states)
|
280 |
+
hidden_states = residual + hidden_states
|
281 |
+
|
282 |
+
outputs = (hidden_states,)
|
283 |
+
|
284 |
+
if output_attentions:
|
285 |
+
outputs += (attn_weights,)
|
286 |
+
|
287 |
+
return outputs
|
288 |
+
|
289 |
+
def post_norm_forward(
|
290 |
+
self,
|
291 |
+
hidden_states: torch.Tensor,
|
292 |
+
attention_mask: torch.Tensor,
|
293 |
+
output_attentions: Optional[bool] = False,
|
294 |
+
) -> Tuple[torch.FloatTensor]:
|
295 |
+
"""
|
296 |
+
Args:
|
297 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
298 |
+
attention_mask (`torch.FloatTensor`): attention mask of size
|
299 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
300 |
+
`(config.encoder_attention_heads,)`.
|
301 |
+
output_attentions (`bool`, *optional*):
|
302 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
303 |
+
returned tensors for more detail.
|
304 |
+
"""
|
305 |
+
residual = hidden_states
|
306 |
+
|
307 |
+
hidden_states, attn_weights = self.self_attn(
|
308 |
+
hidden_states=hidden_states,
|
309 |
+
attention_mask=attention_mask,
|
310 |
+
output_attentions=output_attentions,
|
311 |
+
)
|
312 |
+
hidden_states = residual + hidden_states
|
313 |
+
|
314 |
+
hidden_states = self.layer_norm1(hidden_states)
|
315 |
+
|
316 |
+
residual = hidden_states
|
317 |
+
hidden_states = self.mlp(hidden_states)
|
318 |
+
hidden_states = residual + hidden_states
|
319 |
+
hidden_states = self.layer_norm2(hidden_states)
|
320 |
+
outputs = (hidden_states,)
|
321 |
+
|
322 |
+
if output_attentions:
|
323 |
+
outputs += (attn_weights,)
|
324 |
+
|
325 |
+
return outputs
|
326 |
+
|
327 |
+
|
328 |
+
class BiomedCLIPTextProjection(nn.Module):
|
329 |
+
def __init__(self, config):
|
330 |
+
super().__init__()
|
331 |
+
self.config = config
|
332 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
333 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
334 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.projection_dim, bias=False)
|
335 |
+
|
336 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
337 |
+
hidden_states = self.fc1(hidden_states)
|
338 |
+
hidden_states = self.activation_fn(hidden_states)
|
339 |
+
hidden_states = self.fc2(hidden_states)
|
340 |
+
return hidden_states
|
341 |
+
|
342 |
+
|
343 |
+
class BiomedCLIPEncoder(nn.Module):
|
344 |
+
"""
|
345 |
+
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
346 |
+
[`BiomedCLIPEncoderLayer`].
|
347 |
+
|
348 |
+
Args:
|
349 |
+
config: BiomedCLIPConfig
|
350 |
+
"""
|
351 |
+
def __init__(self, config, norm='pre'):
|
352 |
+
super().__init__()
|
353 |
+
self.config = config
|
354 |
+
self.norm = norm
|
355 |
+
self.layers = nn.ModuleList([BiomedCLIPEncoderLayer(config, norm) for _ in range(config.num_hidden_layers)])
|
356 |
+
self.gradient_checkpointing = False
|
357 |
+
|
358 |
+
def forward(
|
359 |
+
self,
|
360 |
+
hidden_states: torch.Tensor,
|
361 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
362 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
363 |
+
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
364 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
365 |
+
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
366 |
+
use_cache: Optional[bool] = None,
|
367 |
+
output_attentions: Optional[bool] = False,
|
368 |
+
output_hidden_states: Optional[bool] = False,
|
369 |
+
return_dict: Optional[bool] = True,
|
370 |
+
) :
|
371 |
+
all_hidden_states = () if output_hidden_states else None
|
372 |
+
all_self_attentions = () if output_attentions else None
|
373 |
+
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
374 |
+
|
375 |
+
if self.gradient_checkpointing and self.training:
|
376 |
+
if use_cache:
|
377 |
+
logger.warning_once(
|
378 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
379 |
+
)
|
380 |
+
use_cache = False
|
381 |
+
|
382 |
+
next_decoder_cache = () if use_cache else None
|
383 |
+
for i, layer_module in enumerate(self.layers):
|
384 |
+
if output_hidden_states:
|
385 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
386 |
+
|
387 |
+
layer_head_mask = head_mask[i] if head_mask is not None else None
|
388 |
+
past_key_value = past_key_values[i] if past_key_values is not None else None
|
389 |
+
|
390 |
+
if self.gradient_checkpointing and self.training:
|
391 |
+
layer_outputs = self._gradient_checkpointing_func(
|
392 |
+
layer_module.__call__,
|
393 |
+
hidden_states,
|
394 |
+
attention_mask,
|
395 |
+
output_attentions,
|
396 |
+
)
|
397 |
+
else:
|
398 |
+
layer_outputs = layer_module(
|
399 |
+
hidden_states,
|
400 |
+
attention_mask,
|
401 |
+
output_attentions,
|
402 |
+
)
|
403 |
+
|
404 |
+
hidden_states = layer_outputs[0]
|
405 |
+
if use_cache:
|
406 |
+
next_decoder_cache += (layer_outputs[-1],)
|
407 |
+
if output_attentions:
|
408 |
+
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
409 |
+
if self.config.add_cross_attention:
|
410 |
+
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
411 |
+
|
412 |
+
if output_hidden_states:
|
413 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
414 |
+
|
415 |
+
if not return_dict:
|
416 |
+
return tuple(
|
417 |
+
v
|
418 |
+
for v in [
|
419 |
+
hidden_states,
|
420 |
+
next_decoder_cache,
|
421 |
+
all_hidden_states,
|
422 |
+
all_self_attentions,
|
423 |
+
all_cross_attentions,
|
424 |
+
]
|
425 |
+
if v is not None
|
426 |
+
)
|
427 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
428 |
+
last_hidden_state=hidden_states,
|
429 |
+
past_key_values=next_decoder_cache,
|
430 |
+
hidden_states=all_hidden_states,
|
431 |
+
attentions=all_self_attentions,
|
432 |
+
cross_attentions=all_cross_attentions,
|
433 |
+
)
|
434 |
+
|
435 |
+
|
436 |
+
|
437 |
+
class BiomedCLIPTextTransformer(CLIPPreTrainedModel):
|
438 |
+
def __init__(self, config: CLIPTextConfig):
|
439 |
+
super().__init__(config)
|
440 |
+
self.config = config
|
441 |
+
embed_dim = config.hidden_size
|
442 |
+
self.embeddings = BiomedCLIPTextEmbeddings(config)
|
443 |
+
self.encoder = BiomedCLIPEncoder(config, norm='post')
|
444 |
+
# no final_ln
|
445 |
+
# self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
446 |
+
|
447 |
+
# For `pooled_output` computation
|
448 |
+
|
449 |
+
def forward(
|
450 |
+
self,
|
451 |
+
input_ids: Optional[torch.Tensor] = None,
|
452 |
+
attention_mask: Optional[torch.Tensor] = None,
|
453 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
454 |
+
position_ids: Optional[torch.Tensor] = None,
|
455 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
456 |
+
encoder_hidden_states: Optional[torch.Tensor] = None,
|
457 |
+
encoder_attention_mask: Optional[torch.Tensor] = None,
|
458 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
459 |
+
use_cache: Optional[bool] = None,
|
460 |
+
output_attentions: Optional[bool] = None,
|
461 |
+
output_hidden_states: Optional[bool] = None,
|
462 |
+
return_dict: Optional[bool] = None,
|
463 |
+
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
464 |
+
r"""
|
465 |
+
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
466 |
+
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
467 |
+
the model is configured as a decoder.
|
468 |
+
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
469 |
+
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
470 |
+
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
471 |
+
|
472 |
+
- 1 for tokens that are **not masked**,
|
473 |
+
- 0 for tokens that are **masked**.
|
474 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
475 |
+
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
476 |
+
|
477 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
478 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
479 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
480 |
+
use_cache (`bool`, *optional*):
|
481 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
482 |
+
`past_key_values`).
|
483 |
+
"""
|
484 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
485 |
+
output_hidden_states = (
|
486 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
487 |
+
)
|
488 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
489 |
+
|
490 |
+
if self.config.is_decoder:
|
491 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
492 |
+
else:
|
493 |
+
use_cache = False
|
494 |
+
|
495 |
+
if input_ids is not None and inputs_embeds is not None:
|
496 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
497 |
+
elif input_ids is not None:
|
498 |
+
input_shape = input_ids.size()
|
499 |
+
elif inputs_embeds is not None:
|
500 |
+
input_shape = inputs_embeds.size()[:-1]
|
501 |
+
else:
|
502 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
503 |
+
|
504 |
+
batch_size, seq_length = input_shape
|
505 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
506 |
+
|
507 |
+
# past_key_values_length
|
508 |
+
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
509 |
+
|
510 |
+
if token_type_ids is None:
|
511 |
+
if hasattr(self.embeddings, "token_type_ids"):
|
512 |
+
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
513 |
+
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
514 |
+
token_type_ids = buffered_token_type_ids_expanded
|
515 |
+
else:
|
516 |
+
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
517 |
+
|
518 |
+
embedding_output = self.embeddings(
|
519 |
+
input_ids=input_ids,
|
520 |
+
position_ids=position_ids,
|
521 |
+
token_type_ids=token_type_ids,
|
522 |
+
inputs_embeds=inputs_embeds,
|
523 |
+
past_key_values_length=past_key_values_length,
|
524 |
+
)
|
525 |
+
|
526 |
+
if attention_mask is None:
|
527 |
+
attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device)
|
528 |
+
|
529 |
+
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
530 |
+
# ourselves in which case we just need to make it broadcastable to all heads.
|
531 |
+
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
|
532 |
+
|
533 |
+
# If a 2D or 3D attention mask is provided for the cross-attention
|
534 |
+
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
535 |
+
if self.config.is_decoder and encoder_hidden_states is not None:
|
536 |
+
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
537 |
+
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
538 |
+
if encoder_attention_mask is None:
|
539 |
+
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
540 |
+
|
541 |
+
if use_sdpa_attention_masks:
|
542 |
+
# Expand the attention mask for SDPA.
|
543 |
+
# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
|
544 |
+
encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
|
545 |
+
encoder_attention_mask, embedding_output.dtype, tgt_len=seq_length
|
546 |
+
)
|
547 |
+
else:
|
548 |
+
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
549 |
+
else:
|
550 |
+
encoder_extended_attention_mask = None
|
551 |
+
|
552 |
+
|
553 |
+
encoder_outputs = self.encoder(
|
554 |
+
embedding_output,
|
555 |
+
attention_mask=extended_attention_mask,
|
556 |
+
output_attentions=output_attentions,
|
557 |
+
output_hidden_states=output_hidden_states,
|
558 |
+
return_dict=return_dict,
|
559 |
+
)
|
560 |
+
sequence_output = encoder_outputs[0]
|
561 |
+
|
562 |
+
return (sequence_output, sequence_output[:, 0, :])
|
563 |
+
|
564 |
+
|
565 |
+
|
566 |
+
class BiomedCLIPVisionTransformer(nn.Module):
|
567 |
+
def __init__(self, config: CLIPVisionConfig):
|
568 |
+
super().__init__()
|
569 |
+
self.config = config
|
570 |
+
embed_dim = config.hidden_size
|
571 |
+
|
572 |
+
self.embeddings = BiomedCLIPVisionEmbeddings(config)
|
573 |
+
# No pre_norm in open_clip Vision Tower
|
574 |
+
# self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
575 |
+
self.encoder = BiomedCLIPEncoder(config)
|
576 |
+
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
577 |
+
|
578 |
+
def forward(
|
579 |
+
self,
|
580 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
581 |
+
output_attentions: Optional[bool] = None,
|
582 |
+
output_hidden_states: Optional[bool] = None,
|
583 |
+
return_dict: Optional[bool] = None,
|
584 |
+
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
585 |
+
r"""
|
586 |
+
Returns:
|
587 |
+
|
588 |
+
"""
|
589 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
590 |
+
output_hidden_states = (
|
591 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
592 |
+
)
|
593 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
594 |
+
|
595 |
+
if pixel_values is None:
|
596 |
+
raise ValueError("You have to specify pixel_values")
|
597 |
+
|
598 |
+
hidden_states = self.embeddings(pixel_values)
|
599 |
+
# hidden_states = self.pre_layrnorm(hidden_states)
|
600 |
+
|
601 |
+
encoder_outputs = self.encoder(
|
602 |
+
hidden_states=hidden_states,
|
603 |
+
output_attentions=output_attentions,
|
604 |
+
output_hidden_states=output_hidden_states,
|
605 |
+
return_dict=return_dict,
|
606 |
+
)
|
607 |
+
|
608 |
+
last_hidden_state = encoder_outputs[0]
|
609 |
+
pooled_output = last_hidden_state[:, 0, :]
|
610 |
+
pooled_output = self.post_layernorm(pooled_output)
|
611 |
+
|
612 |
+
if not return_dict:
|
613 |
+
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
614 |
+
|
615 |
+
return BaseModelOutputWithPooling(
|
616 |
+
last_hidden_state=last_hidden_state,
|
617 |
+
pooler_output=pooled_output,
|
618 |
+
hidden_states=encoder_outputs.hidden_states,
|
619 |
+
attentions=encoder_outputs.attentions,
|
620 |
+
)
|
621 |
+
|
622 |
+
|
623 |
+
class BiomedCLIPModel(CLIPPreTrainedModel):
|
624 |
+
config_class = BiomedCLIPConfig
|
625 |
+
_no_split_modules = ["BiomedCLIPTextEmbeddings", "BiomedCLIPEncoderLayer"]
|
626 |
+
|
627 |
+
def __init__(self, config: BiomedCLIPConfig):
|
628 |
+
super().__init__(config)
|
629 |
+
|
630 |
+
if not isinstance(config.text_config, CLIPTextConfig):
|
631 |
+
raise ValueError(
|
632 |
+
"config.text_config is expected to be of type CLIPTextConfig but is of type"
|
633 |
+
f" {type(config.text_config)}."
|
634 |
+
)
|
635 |
+
|
636 |
+
if not isinstance(config.vision_config, CLIPVisionConfig):
|
637 |
+
raise ValueError(
|
638 |
+
"config.vision_config is expected to be of type CLIPVisionConfig but is of type"
|
639 |
+
f" {type(config.vision_config)}."
|
640 |
+
)
|
641 |
+
|
642 |
+
text_config = config.text_config
|
643 |
+
text_projection_config = config.text_projection_config
|
644 |
+
vision_config = config.vision_config
|
645 |
+
|
646 |
+
|
647 |
+
self.projection_dim = config.projection_dim
|
648 |
+
self.text_embed_dim = text_config.hidden_size
|
649 |
+
self.vision_embed_dim = vision_config.hidden_size
|
650 |
+
|
651 |
+
self.text_model = BiomedCLIPTextTransformer(text_config)
|
652 |
+
self.vision_model = BiomedCLIPVisionTransformer(vision_config)
|
653 |
+
|
654 |
+
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
|
655 |
+
|
656 |
+
self.text_projection = BiomedCLIPTextProjection(text_projection_config)
|
657 |
+
|
658 |
+
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
|
659 |
+
|
660 |
+
# Initialize weights and apply final processing
|
661 |
+
self.post_init()
|
662 |
+
|
663 |
+
def get_text_features(
|
664 |
+
self,
|
665 |
+
input_ids: Optional[torch.Tensor] = None,
|
666 |
+
attention_mask: Optional[torch.Tensor] = None,
|
667 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
668 |
+
position_ids: Optional[torch.Tensor] = None,
|
669 |
+
output_attentions: Optional[bool] = None,
|
670 |
+
output_hidden_states: Optional[bool] = None,
|
671 |
+
return_dict: Optional[bool] = None,
|
672 |
+
) -> torch.FloatTensor:
|
673 |
+
r"""
|
674 |
+
Returns:
|
675 |
+
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
|
676 |
+
applying the projection layer to the pooled output of [`CLIPTextModel`].
|
677 |
+
|
678 |
+
Examples:
|
679 |
+
|
680 |
+
```python
|
681 |
+
>>> from transformers import AutoTokenizer, CLIPModel
|
682 |
+
|
683 |
+
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
|
684 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
|
685 |
+
|
686 |
+
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
|
687 |
+
>>> text_features = model.get_text_features(**inputs)
|
688 |
+
```"""
|
689 |
+
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
|
690 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
691 |
+
output_hidden_states = (
|
692 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
693 |
+
)
|
694 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
695 |
+
|
696 |
+
text_outputs = self.text_model(
|
697 |
+
input_ids=input_ids,
|
698 |
+
attention_mask=attention_mask,
|
699 |
+
token_type_ids=token_type_ids,
|
700 |
+
position_ids=position_ids,
|
701 |
+
output_attentions=output_attentions,
|
702 |
+
output_hidden_states=output_hidden_states,
|
703 |
+
return_dict=return_dict,
|
704 |
+
)
|
705 |
+
|
706 |
+
pooled_output = text_outputs[1]
|
707 |
+
text_features = self.text_projection(pooled_output)
|
708 |
+
|
709 |
+
return text_features
|
710 |
+
|
711 |
+
def get_image_features(
|
712 |
+
self,
|
713 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
714 |
+
output_attentions: Optional[bool] = None,
|
715 |
+
output_hidden_states: Optional[bool] = None,
|
716 |
+
return_dict: Optional[bool] = None,
|
717 |
+
) -> torch.FloatTensor:
|
718 |
+
r"""
|
719 |
+
Returns:
|
720 |
+
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
|
721 |
+
applying the projection layer to the pooled output of [`CLIPVisionModel`].
|
722 |
+
|
723 |
+
Examples:
|
724 |
+
|
725 |
+
```python
|
726 |
+
>>> from PIL import Image
|
727 |
+
>>> import requests
|
728 |
+
>>> from transformers import AutoProcessor, CLIPModel
|
729 |
+
|
730 |
+
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
|
731 |
+
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
|
732 |
+
|
733 |
+
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
734 |
+
>>> image = Image.open(requests.get(url, stream=True).raw)
|
735 |
+
|
736 |
+
>>> inputs = processor(images=image, return_tensors="pt")
|
737 |
+
|
738 |
+
>>> image_features = model.get_image_features(**inputs)
|
739 |
+
```"""
|
740 |
+
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
|
741 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
742 |
+
output_hidden_states = (
|
743 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
744 |
+
)
|
745 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
746 |
+
|
747 |
+
vision_outputs = self.vision_model(
|
748 |
+
pixel_values=pixel_values,
|
749 |
+
output_attentions=output_attentions,
|
750 |
+
output_hidden_states=output_hidden_states,
|
751 |
+
return_dict=return_dict,
|
752 |
+
)
|
753 |
+
|
754 |
+
pooled_output = vision_outputs[1] # pooled_output
|
755 |
+
image_features = self.visual_projection(pooled_output)
|
756 |
+
|
757 |
+
return image_features
|
758 |
+
|
759 |
+
def forward(
|
760 |
+
self,
|
761 |
+
input_ids: Optional[torch.LongTensor] = None,
|
762 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
763 |
+
attention_mask: Optional[torch.Tensor] = None,
|
764 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
765 |
+
position_ids: Optional[torch.LongTensor] = None,
|
766 |
+
return_loss: Optional[bool] = None,
|
767 |
+
output_attentions: Optional[bool] = None,
|
768 |
+
output_hidden_states: Optional[bool] = None,
|
769 |
+
return_dict: Optional[bool] = None,
|
770 |
+
) -> Union[Tuple, CLIPOutput]:
|
771 |
+
r"""
|
772 |
+
Returns:
|
773 |
+
|
774 |
+
Examples:
|
775 |
+
|
776 |
+
```python
|
777 |
+
>>> from PIL import Image
|
778 |
+
>>> import requests
|
779 |
+
>>> from transformers import AutoProcessor, CLIPModel
|
780 |
+
|
781 |
+
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
|
782 |
+
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
|
783 |
+
|
784 |
+
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
785 |
+
>>> image = Image.open(requests.get(url, stream=True).raw)
|
786 |
+
|
787 |
+
>>> inputs = processor(
|
788 |
+
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
|
789 |
+
... )
|
790 |
+
|
791 |
+
>>> outputs = model(**inputs)
|
792 |
+
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
|
793 |
+
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
|
794 |
+
```"""
|
795 |
+
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
|
796 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
797 |
+
output_hidden_states = (
|
798 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
799 |
+
)
|
800 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
801 |
+
|
802 |
+
vision_outputs = self.vision_model(
|
803 |
+
pixel_values=pixel_values,
|
804 |
+
output_attentions=output_attentions,
|
805 |
+
output_hidden_states=output_hidden_states,
|
806 |
+
return_dict=return_dict,
|
807 |
+
)
|
808 |
+
|
809 |
+
text_outputs = self.text_model(
|
810 |
+
input_ids=input_ids,
|
811 |
+
token_type_ids=token_type_ids,
|
812 |
+
attention_mask=attention_mask,
|
813 |
+
position_ids=position_ids,
|
814 |
+
output_attentions=output_attentions,
|
815 |
+
output_hidden_states=output_hidden_states,
|
816 |
+
return_dict=return_dict,
|
817 |
+
)
|
818 |
+
|
819 |
+
image_embeds = vision_outputs[1]
|
820 |
+
image_embeds = self.visual_projection(image_embeds)
|
821 |
+
|
822 |
+
text_embeds = text_outputs[1]
|
823 |
+
text_embeds = self.text_projection(text_embeds)
|
824 |
+
|
825 |
+
# normalized features
|
826 |
+
image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
|
827 |
+
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
|
828 |
+
|
829 |
+
# cosine similarity as logits
|
830 |
+
logit_scale = self.logit_scale.exp()
|
831 |
+
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
|
832 |
+
logits_per_image = logits_per_text.t()
|
833 |
+
|
834 |
+
loss = None
|
835 |
+
if return_loss:
|
836 |
+
loss = clip_loss(logits_per_text)
|
837 |
+
|
838 |
+
if not return_dict:
|
839 |
+
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
|
840 |
+
return ((loss,) + output) if loss is not None else output
|
841 |
+
|
842 |
+
return CLIPOutput(
|
843 |
+
loss=loss,
|
844 |
+
logits_per_image=logits_per_image,
|
845 |
+
logits_per_text=logits_per_text,
|
846 |
+
text_embeds=text_embeds,
|
847 |
+
image_embeds=image_embeds,
|
848 |
+
text_model_output=text_outputs,
|
849 |
+
vision_model_output=vision_outputs,
|
850 |
+
)
|
851 |
+
|
852 |
+
|
853 |
+
class BiomedCLIPForImageClassification(CLIPPreTrainedModel):
|
854 |
+
main_input_name = "pixel_values"
|
855 |
+
|
856 |
+
def __init__(self, config: BiomedCLIPConfig) -> None:
|
857 |
+
super().__init__(config)
|
858 |
+
|
859 |
+
self.num_labels = config.num_labels
|
860 |
+
self.vision_model = BiomedCLIPVisionTransformer(config.vision_config)
|
861 |
+
|
862 |
+
# Classifier head
|
863 |
+
self.classifier = (
|
864 |
+
nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
865 |
+
)
|
866 |
+
|
867 |
+
# Initialize weights and apply final processing
|
868 |
+
self.post_init()
|
869 |
+
|
870 |
+
def forward(
|
871 |
+
self,
|
872 |
+
pixel_values: Optional[torch.Tensor] = None,
|
873 |
+
labels: Optional[torch.Tensor] = None,
|
874 |
+
output_attentions: Optional[bool] = None,
|
875 |
+
output_hidden_states: Optional[bool] = None,
|
876 |
+
return_dict: Optional[bool] = None,
|
877 |
+
) -> Union[tuple, ImageClassifierOutput]:
|
878 |
+
r"""
|
879 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
880 |
+
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
|
881 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
882 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
883 |
+
"""
|
884 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
885 |
+
output_hidden_states = (
|
886 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
887 |
+
)
|
888 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
889 |
+
|
890 |
+
outputs = self.vision_model(
|
891 |
+
pixel_values,
|
892 |
+
output_attentions=output_attentions,
|
893 |
+
output_hidden_states=output_hidden_states,
|
894 |
+
return_dict=return_dict,
|
895 |
+
)
|
896 |
+
|
897 |
+
sequence_output = outputs[0]
|
898 |
+
|
899 |
+
# average pool the patch tokens
|
900 |
+
sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1)
|
901 |
+
# apply classifier
|
902 |
+
logits = self.classifier(sequence_output)
|
903 |
+
|
904 |
+
loss = None
|
905 |
+
if labels is not None:
|
906 |
+
# move labels to correct device to enable model parallelism
|
907 |
+
labels = labels.to(logits.device)
|
908 |
+
if self.config.problem_type is None:
|
909 |
+
if self.num_labels == 1:
|
910 |
+
self.config.problem_type = "regression"
|
911 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
912 |
+
self.config.problem_type = "single_label_classification"
|
913 |
+
else:
|
914 |
+
self.config.problem_type = "multi_label_classification"
|
915 |
+
|
916 |
+
if self.config.problem_type == "regression":
|
917 |
+
loss_fct = MSELoss()
|
918 |
+
if self.num_labels == 1:
|
919 |
+
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
920 |
+
else:
|
921 |
+
loss = loss_fct(logits, labels)
|
922 |
+
elif self.config.problem_type == "single_label_classification":
|
923 |
+
loss_fct = CrossEntropyLoss()
|
924 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
925 |
+
elif self.config.problem_type == "multi_label_classification":
|
926 |
+
loss_fct = BCEWithLogitsLoss()
|
927 |
+
loss = loss_fct(logits, labels)
|
928 |
+
|
929 |
+
if not return_dict:
|
930 |
+
output = (logits,) + outputs[2:]
|
931 |
+
return ((loss,) + output) if loss is not None else output
|
932 |
+
|
933 |
+
return ImageClassifierOutput(
|
934 |
+
loss=loss,
|
935 |
+
logits=logits,
|
936 |
+
hidden_states=outputs.hidden_states,
|
937 |
+
attentions=outputs.attentions,
|
938 |
+
)
|
preprocessor_config.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"crop_size": 224,
|
3 |
+
"do_center_crop": true,
|
4 |
+
"do_normalize": true,
|
5 |
+
"do_resize": true,
|
6 |
+
"image_processor_type": "CLIPImageProcessor",
|
7 |
+
"tokenizer_type": "BertTokenizer",
|
8 |
+
"image_mean": [
|
9 |
+
0.48145466,
|
10 |
+
0.4578275,
|
11 |
+
0.40821073
|
12 |
+
],
|
13 |
+
"image_std": [
|
14 |
+
0.26862954,
|
15 |
+
0.26130258,
|
16 |
+
0.27577711
|
17 |
+
],
|
18 |
+
"resample": 3,
|
19 |
+
"size": 224
|
20 |
+
}
|
processing_biomed_clip.py
ADDED
@@ -0,0 +1,153 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2021 The HuggingFace Inc. team.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""
|
16 |
+
Image/Text processor class for CLIP
|
17 |
+
"""
|
18 |
+
|
19 |
+
import warnings
|
20 |
+
|
21 |
+
from transformers.processing_utils import ProcessorMixin
|
22 |
+
from transformers.tokenization_utils_base import BatchEncoding
|
23 |
+
|
24 |
+
|
25 |
+
class BiomedCLIPProcessor(ProcessorMixin):
|
26 |
+
r"""
|
27 |
+
Constructs a CLIP processor which wraps a CLIP image processor and a CLIP tokenizer into a single processor.
|
28 |
+
|
29 |
+
[`CLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`CLIPTokenizerFast`]. See the
|
30 |
+
[`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information.
|
31 |
+
|
32 |
+
Args:
|
33 |
+
image_processor ([`CLIPImageProcessor`], *optional*):
|
34 |
+
The image processor is a required input.
|
35 |
+
tokenizer ([`CLIPTokenizerFast`], *optional*):
|
36 |
+
The tokenizer is a required input.
|
37 |
+
"""
|
38 |
+
|
39 |
+
attributes = ["image_processor", "tokenizer"]
|
40 |
+
image_processor_class = "CLIPImageProcessor"
|
41 |
+
tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
|
42 |
+
|
43 |
+
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
|
44 |
+
feature_extractor = None
|
45 |
+
if "feature_extractor" in kwargs:
|
46 |
+
warnings.warn(
|
47 |
+
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
|
48 |
+
" instead.",
|
49 |
+
FutureWarning,
|
50 |
+
)
|
51 |
+
feature_extractor = kwargs.pop("feature_extractor")
|
52 |
+
|
53 |
+
image_processor = image_processor if image_processor is not None else feature_extractor
|
54 |
+
if image_processor is None:
|
55 |
+
raise ValueError("You need to specify an `image_processor`.")
|
56 |
+
if tokenizer is None:
|
57 |
+
raise ValueError("You need to specify a `tokenizer`.")
|
58 |
+
|
59 |
+
super().__init__(image_processor, tokenizer)
|
60 |
+
|
61 |
+
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
|
62 |
+
"""
|
63 |
+
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
64 |
+
and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
|
65 |
+
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
66 |
+
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
|
67 |
+
of the above two methods for more information.
|
68 |
+
|
69 |
+
Args:
|
70 |
+
text (`str`, `List[str]`, `List[List[str]]`):
|
71 |
+
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
72 |
+
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
73 |
+
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
74 |
+
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
75 |
+
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
76 |
+
tensor. Both channels-first and channels-last formats are supported.
|
77 |
+
|
78 |
+
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
79 |
+
If set, will return tensors of a particular framework. Acceptable values are:
|
80 |
+
|
81 |
+
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
82 |
+
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
83 |
+
- `'np'`: Return NumPy `np.ndarray` objects.
|
84 |
+
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
85 |
+
|
86 |
+
Returns:
|
87 |
+
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
|
88 |
+
|
89 |
+
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
90 |
+
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
91 |
+
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
92 |
+
`None`).
|
93 |
+
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
94 |
+
"""
|
95 |
+
tokenizer_kwargs, image_processor_kwargs = {}, {}
|
96 |
+
if kwargs:
|
97 |
+
tokenizer_kwargs = {k: v for k, v in kwargs.items() if k not in self.image_processor._valid_processor_keys}
|
98 |
+
image_processor_kwargs = {
|
99 |
+
k: v for k, v in kwargs.items() if k in self.image_processor._valid_processor_keys
|
100 |
+
}
|
101 |
+
|
102 |
+
if text is None and images is None:
|
103 |
+
raise ValueError("You have to specify either text or images. Both cannot be none.")
|
104 |
+
|
105 |
+
if text is not None:
|
106 |
+
encoding = self.tokenizer(text, return_tensors=return_tensors, **tokenizer_kwargs)
|
107 |
+
|
108 |
+
if images is not None:
|
109 |
+
image_features = self.image_processor(images, return_tensors=return_tensors, **image_processor_kwargs)
|
110 |
+
|
111 |
+
if text is not None and images is not None:
|
112 |
+
encoding["pixel_values"] = image_features.pixel_values
|
113 |
+
return encoding
|
114 |
+
elif text is not None:
|
115 |
+
return encoding
|
116 |
+
else:
|
117 |
+
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
|
118 |
+
|
119 |
+
def batch_decode(self, *args, **kwargs):
|
120 |
+
"""
|
121 |
+
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
122 |
+
refer to the docstring of this method for more information.
|
123 |
+
"""
|
124 |
+
return self.tokenizer.batch_decode(*args, **kwargs)
|
125 |
+
|
126 |
+
def decode(self, *args, **kwargs):
|
127 |
+
"""
|
128 |
+
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
129 |
+
the docstring of this method for more information.
|
130 |
+
"""
|
131 |
+
return self.tokenizer.decode(*args, **kwargs)
|
132 |
+
|
133 |
+
@property
|
134 |
+
def model_input_names(self):
|
135 |
+
tokenizer_input_names = self.tokenizer.model_input_names
|
136 |
+
image_processor_input_names = self.image_processor.model_input_names
|
137 |
+
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
138 |
+
|
139 |
+
@property
|
140 |
+
def feature_extractor_class(self):
|
141 |
+
warnings.warn(
|
142 |
+
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
|
143 |
+
FutureWarning,
|
144 |
+
)
|
145 |
+
return self.image_processor_class
|
146 |
+
|
147 |
+
@property
|
148 |
+
def feature_extractor(self):
|
149 |
+
warnings.warn(
|
150 |
+
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
|
151 |
+
FutureWarning,
|
152 |
+
)
|
153 |
+
return self.image_processor
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5bdc400de59a85620ddc7584d06913dc901c47f22647899c6addec71b9a5c9a2
|
3 |
+
size 783733062
|