radames florentgbelidji HF staff commited on
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Duplicate from florentgbelidji/blip_image_embeddings

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Co-authored-by: Florent Gbelidji <florentgbelidji@users.noreply.huggingface.co>

.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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1
+ ---
2
+ tags:
3
+ - feature-extraction
4
+ - endpoints-template
5
+ license: bsd-3-clause
6
+ library_name: generic
7
+ duplicated_from: florentgbelidji/blip_image_embeddings
8
+ ---
9
+ # Fork of [salesforce/BLIP](https://github.com/salesforce/BLIP) for a `feature-extraction` task on 🤗Inference endpoint.
10
+ This repository implements a `custom` task for `feature-extraction` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/blip-embeddings/blob/main/pipeline.py).
11
+ To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_
12
+ ### expected Request payload
13
+ ```json
14
+ {
15
+ "image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes
16
+ }
17
+ ```
18
+ below is an example on how to run a request using Python and `requests`.
19
+ ## Run Request
20
+ 1. prepare an image.
21
+ ```bash
22
+ !wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
23
+ ```
24
+ 2.run request
25
+ ```python
26
+ import json
27
+ from typing import List
28
+ import requests as r
29
+ import base64
30
+ ENDPOINT_URL = ""
31
+ HF_TOKEN = ""
32
+ def predict(path_to_image: str = None):
33
+ with open(path_to_image, "rb") as i:
34
+ b64 = base64.b64encode(i.read())
35
+ payload = {"inputs": {"image": b64.decode("utf-8")}}
36
+ response = r.post(
37
+ ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
38
+ )
39
+ return response.json()
40
+ prediction = predict(
41
+ path_to_image="palace.jpg"
42
+ )
43
+ ```
44
+ expected output
45
+ ```python
46
+ {'feature_vector': [0.016450975090265274,
47
+ -0.5551009774208069,
48
+ 0.39800673723220825,
49
+ -0.6809228658676147,
50
+ 2.053842782974243,
51
+ -0.4712907075881958,...]
52
+ }
53
+ ```
configs/med_config.json ADDED
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+ {
2
+ "architectures": [
3
+ "BertModel"
4
+ ],
5
+ "attention_probs_dropout_prob": 0.1,
6
+ "hidden_act": "gelu",
7
+ "hidden_dropout_prob": 0.1,
8
+ "hidden_size": 768,
9
+ "initializer_range": 0.02,
10
+ "intermediate_size": 3072,
11
+ "layer_norm_eps": 1e-12,
12
+ "max_position_embeddings": 512,
13
+ "model_type": "bert",
14
+ "num_attention_heads": 12,
15
+ "num_hidden_layers": 12,
16
+ "pad_token_id": 0,
17
+ "type_vocab_size": 2,
18
+ "vocab_size": 30524,
19
+ "encoder_width": 768,
20
+ "add_cross_attention": true
21
+ }
model_large_retrieval_coco.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:18e10e32c7a152f087afbb72ee6ddc817ca18e10641607b9fb72e20f9b4a5f63
3
+ size 3687945348
models/.ipynb_checkpoints/blip_decoder-checkpoint.py ADDED
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1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ '''
8
+ import warnings
9
+ warnings.filterwarnings("ignore")
10
+
11
+ from models.vit import VisionTransformer, interpolate_pos_embed
12
+ from models.med import BertConfig, BertModel, BertLMHeadModel
13
+ from transformers import BertTokenizer
14
+
15
+ import torch
16
+ from torch import nn
17
+ import torch.nn.functional as F
18
+
19
+ import os
20
+ from urllib.parse import urlparse
21
+ from timm.models.hub import download_cached_file
22
+
23
+ class BLIP_Decoder(nn.Module):
24
+ def __init__(self,
25
+ med_config = 'configs/med_config.json',
26
+ image_size = 384,
27
+ vit = 'base',
28
+ vit_grad_ckpt = False,
29
+ vit_ckpt_layer = 0,
30
+ prompt = 'a picture of ',
31
+ ):
32
+ """
33
+ Args:
34
+ med_config (str): path for the mixture of encoder-decoder model's configuration file
35
+ image_size (int): input image size
36
+ vit (str): model size of vision transformer
37
+ """
38
+ super().__init__()
39
+
40
+ self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
41
+ self.tokenizer = init_tokenizer()
42
+ med_config = BertConfig.from_json_file(med_config)
43
+ med_config.encoder_width = vision_width
44
+ self.text_decoder = BertLMHeadModel(config=med_config)
45
+
46
+ self.prompt = prompt
47
+ self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1
48
+
49
+
50
+ def forward(self, image, caption):
51
+
52
+ image_embeds = self.visual_encoder(image)
53
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
54
+
55
+ text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device)
56
+
57
+ text.input_ids[:,0] = self.tokenizer.bos_token_id
58
+
59
+ decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)
60
+ decoder_targets[:,:self.prompt_length] = -100
61
+
62
+ decoder_output = self.text_decoder(text.input_ids,
63
+ attention_mask = text.attention_mask,
64
+ encoder_hidden_states = image_embeds,
65
+ encoder_attention_mask = image_atts,
66
+ labels = decoder_targets,
67
+ return_dict = True,
68
+ )
69
+ loss_lm = decoder_output.loss
70
+
71
+ return loss_lm
72
+
73
+ def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):
74
+ image_embeds = self.visual_encoder(image)
75
+
76
+ if not sample:
77
+ image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
78
+
79
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
80
+ model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
81
+
82
+ prompt = [self.prompt] * image.size(0)
83
+ input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device)
84
+ input_ids[:,0] = self.tokenizer.bos_token_id
85
+ input_ids = input_ids[:, :-1]
86
+
87
+ if sample:
88
+ #nucleus sampling
89
+ outputs = self.text_decoder.generate(input_ids=input_ids,
90
+ max_length=max_length,
91
+ min_length=min_length,
92
+ do_sample=True,
93
+ top_p=top_p,
94
+ num_return_sequences=1,
95
+ eos_token_id=self.tokenizer.sep_token_id,
96
+ pad_token_id=self.tokenizer.pad_token_id,
97
+ repetition_penalty=1.1,
98
+ **model_kwargs)
99
+ else:
100
+ #beam search
101
+ outputs = self.text_decoder.generate(input_ids=input_ids,
102
+ max_length=max_length,
103
+ min_length=min_length,
104
+ num_beams=num_beams,
105
+ eos_token_id=self.tokenizer.sep_token_id,
106
+ pad_token_id=self.tokenizer.pad_token_id,
107
+ repetition_penalty=repetition_penalty,
108
+ **model_kwargs)
109
+
110
+ captions = []
111
+ for output in outputs:
112
+ caption = self.tokenizer.decode(output, skip_special_tokens=True)
113
+ captions.append(caption[len(self.prompt):])
114
+ return captions
115
+
116
+
117
+ def blip_decoder(pretrained='',**kwargs):
118
+ model = BLIP_Decoder(**kwargs)
119
+ if pretrained:
120
+ model,msg = load_checkpoint(model,pretrained)
121
+ assert(len(msg.missing_keys)==0)
122
+ return model
123
+
124
+ def init_tokenizer():
125
+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
126
+ tokenizer.add_special_tokens({'bos_token':'[DEC]'})
127
+ tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
128
+ tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
129
+ return tokenizer
130
+
131
+
132
+ def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
133
+
134
+ assert vit in ['base', 'large'], "vit parameter must be base or large"
135
+ if vit=='base':
136
+ vision_width = 768
137
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
138
+ num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
139
+ drop_path_rate=0 or drop_path_rate
140
+ )
141
+ elif vit=='large':
142
+ vision_width = 1024
143
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
144
+ num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
145
+ drop_path_rate=0.1 or drop_path_rate
146
+ )
147
+ return visual_encoder, vision_width
148
+
149
+ def is_url(url_or_filename):
150
+ parsed = urlparse(url_or_filename)
151
+ return parsed.scheme in ("http", "https")
152
+
153
+ def load_checkpoint(model,url_or_filename):
154
+ if is_url(url_or_filename):
155
+ cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
156
+ checkpoint = torch.load(cached_file, map_location='cpu')
157
+ elif os.path.isfile(url_or_filename):
158
+ checkpoint = torch.load(url_or_filename, map_location='cpu')
159
+ else:
160
+ raise RuntimeError('checkpoint url or path is invalid')
161
+
162
+ state_dict = checkpoint['model']
163
+
164
+ state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
165
+ if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
166
+ state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
167
+ model.visual_encoder_m)
168
+ for key in model.state_dict().keys():
169
+ if key in state_dict.keys():
170
+ if state_dict[key].shape!=model.state_dict()[key].shape:
171
+ del state_dict[key]
172
+
173
+ msg = model.load_state_dict(state_dict,strict=False)
174
+ print('load checkpoint from %s'%url_or_filename)
175
+ return model,msg
models/.ipynb_checkpoints/blip_feature_extractor-checkpoint.py ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ '''
8
+ import warnings
9
+ warnings.filterwarnings("ignore")
10
+
11
+ from models.vit import VisionTransformer, interpolate_pos_embed
12
+ from models.med import BertConfig, BertModel, BertLMHeadModel
13
+ from transformers import BertTokenizer
14
+
15
+ import torch
16
+ from torch import nn
17
+ import torch.nn.functional as F
18
+
19
+ import os
20
+ from urllib.parse import urlparse
21
+ from timm.models.hub import download_cached_file
22
+
23
+ class BLIP_Base(nn.Module):
24
+ def __init__(self,
25
+ med_config = 'configs/med_config.json',
26
+ image_size = 224,
27
+ vit = 'base',
28
+ vit_grad_ckpt = False,
29
+ vit_ckpt_layer = 0,
30
+ ):
31
+ """
32
+ Args:
33
+ med_config (str): path for the mixture of encoder-decoder model's configuration file
34
+ image_size (int): input image size
35
+ vit (str): model size of vision transformer
36
+ """
37
+ super().__init__()
38
+
39
+ self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
40
+ self.tokenizer = init_tokenizer()
41
+ med_config = BertConfig.from_json_file(med_config)
42
+ med_config.encoder_width = vision_width
43
+ self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
44
+
45
+
46
+ def forward(self, image, caption, mode):
47
+
48
+ assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
49
+ text = self.tokenizer(caption, return_tensors="pt").to(image.device)
50
+
51
+ if mode=='image':
52
+ # return image features
53
+ image_embeds = self.visual_encoder(image)
54
+ return image_embeds
55
+
56
+ elif mode=='text':
57
+ # return text features
58
+ text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
59
+ return_dict = True, mode = 'text')
60
+ return text_output.last_hidden_state
61
+
62
+ elif mode=='multimodal':
63
+ # return multimodel features
64
+ image_embeds = self.visual_encoder(image)
65
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
66
+
67
+ text.input_ids[:,0] = self.tokenizer.enc_token_id
68
+ output = self.text_encoder(text.input_ids,
69
+ attention_mask = text.attention_mask,
70
+ encoder_hidden_states = image_embeds,
71
+ encoder_attention_mask = image_atts,
72
+ return_dict = True,
73
+ )
74
+ return output.last_hidden_state
75
+
76
+
77
+ def blip_feature_extractor(pretrained='',**kwargs):
78
+ model = BLIP_Base(**kwargs)
79
+ if pretrained:
80
+ model,msg = load_checkpoint(model,pretrained)
81
+ assert(len(msg.missing_keys)==0)
82
+ return model
83
+
84
+ def init_tokenizer():
85
+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
86
+ tokenizer.add_special_tokens({'bos_token':'[DEC]'})
87
+ tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
88
+ tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
89
+ return tokenizer
90
+
91
+
92
+ def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
93
+
94
+ assert vit in ['base', 'large'], "vit parameter must be base or large"
95
+ if vit=='base':
96
+ vision_width = 768
97
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
98
+ num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
99
+ drop_path_rate=0 or drop_path_rate
100
+ )
101
+ elif vit=='large':
102
+ vision_width = 1024
103
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
104
+ num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
105
+ drop_path_rate=0.1 or drop_path_rate
106
+ )
107
+ return visual_encoder, vision_width
108
+
109
+ def is_url(url_or_filename):
110
+ parsed = urlparse(url_or_filename)
111
+ return parsed.scheme in ("http", "https")
112
+
113
+ def load_checkpoint(model,url_or_filename):
114
+ if is_url(url_or_filename):
115
+ cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
116
+ checkpoint = torch.load(cached_file, map_location='cpu')
117
+ elif os.path.isfile(url_or_filename):
118
+ checkpoint = torch.load(url_or_filename, map_location='cpu')
119
+ else:
120
+ raise RuntimeError('checkpoint url or path is invalid')
121
+
122
+ state_dict = checkpoint['model']
123
+
124
+ state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
125
+ if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
126
+ state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
127
+ model.visual_encoder_m)
128
+ for key in model.state_dict().keys():
129
+ if key in state_dict.keys():
130
+ if state_dict[key].shape!=model.state_dict()[key].shape:
131
+ del state_dict[key]
132
+
133
+ msg = model.load_state_dict(state_dict,strict=False)
134
+ print('load checkpoint from %s'%url_or_filename)
135
+ return model,msg
models/__pycache__/blip_decoder.cpython-37.pyc ADDED
Binary file (5.54 kB). View file
 
models/__pycache__/blip_feature_extractor.cpython-37.pyc ADDED
Binary file (4.51 kB). View file
 
models/__pycache__/med.cpython-37.pyc ADDED
Binary file (28.2 kB). View file
 
models/__pycache__/vit.cpython-37.pyc ADDED
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models/blip_decoder.py ADDED
@@ -0,0 +1,175 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ '''
8
+ import warnings
9
+ warnings.filterwarnings("ignore")
10
+
11
+ from models.vit import VisionTransformer, interpolate_pos_embed
12
+ from models.med import BertConfig, BertModel, BertLMHeadModel
13
+ from transformers import BertTokenizer
14
+
15
+ import torch
16
+ from torch import nn
17
+ import torch.nn.functional as F
18
+
19
+ import os
20
+ from urllib.parse import urlparse
21
+ from timm.models.hub import download_cached_file
22
+
23
+ class BLIP_Decoder(nn.Module):
24
+ def __init__(self,
25
+ med_config = 'configs/med_config.json',
26
+ image_size = 384,
27
+ vit = 'base',
28
+ vit_grad_ckpt = False,
29
+ vit_ckpt_layer = 0,
30
+ prompt = 'a picture of ',
31
+ ):
32
+ """
33
+ Args:
34
+ med_config (str): path for the mixture of encoder-decoder model's configuration file
35
+ image_size (int): input image size
36
+ vit (str): model size of vision transformer
37
+ """
38
+ super().__init__()
39
+
40
+ self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
41
+ self.tokenizer = init_tokenizer()
42
+ med_config = BertConfig.from_json_file(med_config)
43
+ med_config.encoder_width = vision_width
44
+ self.text_decoder = BertLMHeadModel(config=med_config)
45
+
46
+ self.prompt = prompt
47
+ self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1
48
+
49
+
50
+ def forward(self, image, caption):
51
+
52
+ image_embeds = self.visual_encoder(image)
53
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
54
+
55
+ text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device)
56
+
57
+ text.input_ids[:,0] = self.tokenizer.bos_token_id
58
+
59
+ decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)
60
+ decoder_targets[:,:self.prompt_length] = -100
61
+
62
+ decoder_output = self.text_decoder(text.input_ids,
63
+ attention_mask = text.attention_mask,
64
+ encoder_hidden_states = image_embeds,
65
+ encoder_attention_mask = image_atts,
66
+ labels = decoder_targets,
67
+ return_dict = True,
68
+ )
69
+ loss_lm = decoder_output.loss
70
+
71
+ return loss_lm
72
+
73
+ def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):
74
+ image_embeds = self.visual_encoder(image)
75
+
76
+ if not sample:
77
+ image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
78
+
79
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
80
+ model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts}
81
+
82
+ prompt = [self.prompt] * image.size(0)
83
+ input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device)
84
+ input_ids[:,0] = self.tokenizer.bos_token_id
85
+ input_ids = input_ids[:, :-1]
86
+
87
+ if sample:
88
+ #nucleus sampling
89
+ outputs = self.text_decoder.generate(input_ids=input_ids,
90
+ max_length=max_length,
91
+ min_length=min_length,
92
+ do_sample=True,
93
+ top_p=top_p,
94
+ num_return_sequences=1,
95
+ eos_token_id=self.tokenizer.sep_token_id,
96
+ pad_token_id=self.tokenizer.pad_token_id,
97
+ repetition_penalty=1.1,
98
+ **model_kwargs)
99
+ else:
100
+ #beam search
101
+ outputs = self.text_decoder.generate(input_ids=input_ids,
102
+ max_length=max_length,
103
+ min_length=min_length,
104
+ num_beams=num_beams,
105
+ eos_token_id=self.tokenizer.sep_token_id,
106
+ pad_token_id=self.tokenizer.pad_token_id,
107
+ repetition_penalty=repetition_penalty,
108
+ **model_kwargs)
109
+
110
+ captions = []
111
+ for output in outputs:
112
+ caption = self.tokenizer.decode(output, skip_special_tokens=True)
113
+ captions.append(caption[len(self.prompt):])
114
+ return captions
115
+
116
+
117
+ def blip_decoder(pretrained='',**kwargs):
118
+ model = BLIP_Decoder(**kwargs)
119
+ if pretrained:
120
+ model,msg = load_checkpoint(model,pretrained)
121
+ assert(len(msg.missing_keys)==0)
122
+ return model
123
+
124
+ def init_tokenizer():
125
+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
126
+ tokenizer.add_special_tokens({'bos_token':'[DEC]'})
127
+ tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
128
+ tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
129
+ return tokenizer
130
+
131
+
132
+ def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
133
+
134
+ assert vit in ['base', 'large'], "vit parameter must be base or large"
135
+ if vit=='base':
136
+ vision_width = 768
137
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
138
+ num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
139
+ drop_path_rate=0 or drop_path_rate
140
+ )
141
+ elif vit=='large':
142
+ vision_width = 1024
143
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
144
+ num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
145
+ drop_path_rate=0.1 or drop_path_rate
146
+ )
147
+ return visual_encoder, vision_width
148
+
149
+ def is_url(url_or_filename):
150
+ parsed = urlparse(url_or_filename)
151
+ return parsed.scheme in ("http", "https")
152
+
153
+ def load_checkpoint(model,url_or_filename):
154
+ if is_url(url_or_filename):
155
+ cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
156
+ checkpoint = torch.load(cached_file, map_location='cpu')
157
+ elif os.path.isfile(url_or_filename):
158
+ checkpoint = torch.load(url_or_filename, map_location='cpu')
159
+ else:
160
+ raise RuntimeError('checkpoint url or path is invalid')
161
+
162
+ state_dict = checkpoint['model']
163
+
164
+ state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
165
+ if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
166
+ state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
167
+ model.visual_encoder_m)
168
+ for key in model.state_dict().keys():
169
+ if key in state_dict.keys():
170
+ if state_dict[key].shape!=model.state_dict()[key].shape:
171
+ del state_dict[key]
172
+
173
+ msg = model.load_state_dict(state_dict,strict=False)
174
+ print('load checkpoint from %s'%url_or_filename)
175
+ return model,msg
models/blip_feature_extractor.py ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ '''
8
+ import warnings
9
+ warnings.filterwarnings("ignore")
10
+
11
+ from models.vit import VisionTransformer, interpolate_pos_embed
12
+ from models.med import BertConfig, BertModel, BertLMHeadModel
13
+ from transformers import BertTokenizer
14
+
15
+ import torch
16
+ from torch import nn
17
+ import torch.nn.functional as F
18
+
19
+ import os
20
+ from urllib.parse import urlparse
21
+ from timm.models.hub import download_cached_file
22
+
23
+ class BLIP_Base(nn.Module):
24
+ def __init__(self,
25
+ med_config = 'configs/med_config.json',
26
+ image_size = 224,
27
+ vit = 'base',
28
+ vit_grad_ckpt = False,
29
+ vit_ckpt_layer = 0,
30
+ ):
31
+ """
32
+ Args:
33
+ med_config (str): path for the mixture of encoder-decoder model's configuration file
34
+ image_size (int): input image size
35
+ vit (str): model size of vision transformer
36
+ """
37
+ super().__init__()
38
+
39
+ self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
40
+ self.tokenizer = init_tokenizer()
41
+ med_config = BertConfig.from_json_file(med_config)
42
+ med_config.encoder_width = vision_width
43
+ self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
44
+
45
+
46
+ def forward(self, image, caption, mode):
47
+
48
+ assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal"
49
+ text = self.tokenizer(caption, return_tensors="pt").to(image.device)
50
+
51
+ if mode=='image':
52
+ # return image features
53
+ image_embeds = self.visual_encoder(image)
54
+ return image_embeds
55
+
56
+ elif mode=='text':
57
+ # return text features
58
+ text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
59
+ return_dict = True, mode = 'text')
60
+ return text_output.last_hidden_state
61
+
62
+ elif mode=='multimodal':
63
+ # return multimodel features
64
+ image_embeds = self.visual_encoder(image)
65
+ image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
66
+
67
+ text.input_ids[:,0] = self.tokenizer.enc_token_id
68
+ output = self.text_encoder(text.input_ids,
69
+ attention_mask = text.attention_mask,
70
+ encoder_hidden_states = image_embeds,
71
+ encoder_attention_mask = image_atts,
72
+ return_dict = True,
73
+ )
74
+ return output.last_hidden_state
75
+
76
+
77
+ def blip_feature_extractor(pretrained='',**kwargs):
78
+ model = BLIP_Base(**kwargs)
79
+ if pretrained:
80
+ model,msg = load_checkpoint(model,pretrained)
81
+ assert(len(msg.missing_keys)==0)
82
+ return model
83
+
84
+ def init_tokenizer():
85
+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
86
+ tokenizer.add_special_tokens({'bos_token':'[DEC]'})
87
+ tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
88
+ tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
89
+ return tokenizer
90
+
91
+
92
+ def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
93
+
94
+ assert vit in ['base', 'large'], "vit parameter must be base or large"
95
+ if vit=='base':
96
+ vision_width = 768
97
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
98
+ num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
99
+ drop_path_rate=0 or drop_path_rate
100
+ )
101
+ elif vit=='large':
102
+ vision_width = 1024
103
+ visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
104
+ num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
105
+ drop_path_rate=0.1 or drop_path_rate
106
+ )
107
+ return visual_encoder, vision_width
108
+
109
+ def is_url(url_or_filename):
110
+ parsed = urlparse(url_or_filename)
111
+ return parsed.scheme in ("http", "https")
112
+
113
+ def load_checkpoint(model,url_or_filename):
114
+ if is_url(url_or_filename):
115
+ cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
116
+ checkpoint = torch.load(cached_file, map_location='cpu')
117
+ elif os.path.isfile(url_or_filename):
118
+ checkpoint = torch.load(url_or_filename, map_location='cpu')
119
+ else:
120
+ raise RuntimeError('checkpoint url or path is invalid')
121
+
122
+ state_dict = checkpoint['model']
123
+
124
+ state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
125
+ if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
126
+ state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
127
+ model.visual_encoder_m)
128
+ for key in model.state_dict().keys():
129
+ if key in state_dict.keys():
130
+ if state_dict[key].shape!=model.state_dict()[key].shape:
131
+ del state_dict[key]
132
+
133
+ msg = model.load_state_dict(state_dict,strict=False)
134
+ print('load checkpoint from %s'%url_or_filename)
135
+ return model,msg
models/med.py ADDED
@@ -0,0 +1,953 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ * Based on huggingface code base
8
+ * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
9
+ '''
10
+
11
+ import math
12
+ import os
13
+ import warnings
14
+ from dataclasses import dataclass
15
+ from typing import Optional, Tuple
16
+
17
+ import torch
18
+ from torch import Tensor, device, dtype, nn
19
+ import torch.utils.checkpoint
20
+ from torch import nn
21
+ from torch.nn import CrossEntropyLoss
22
+ import torch.nn.functional as F
23
+
24
+ from transformers.activations import ACT2FN
25
+ from transformers.file_utils import (
26
+ ModelOutput,
27
+ )
28
+ from transformers.modeling_outputs import (
29
+ BaseModelOutputWithPastAndCrossAttentions,
30
+ BaseModelOutputWithPoolingAndCrossAttentions,
31
+ CausalLMOutputWithCrossAttentions,
32
+ MaskedLMOutput,
33
+ MultipleChoiceModelOutput,
34
+ NextSentencePredictorOutput,
35
+ QuestionAnsweringModelOutput,
36
+ SequenceClassifierOutput,
37
+ TokenClassifierOutput,
38
+ )
39
+ from transformers.modeling_utils import (
40
+ PreTrainedModel,
41
+ apply_chunking_to_forward,
42
+ find_pruneable_heads_and_indices,
43
+ prune_linear_layer,
44
+ )
45
+ from transformers.utils import logging
46
+ from transformers.models.bert.configuration_bert import BertConfig
47
+
48
+
49
+ logger = logging.get_logger(__name__)
50
+
51
+
52
+ class BertEmbeddings(nn.Module):
53
+ """Construct the embeddings from word and position embeddings."""
54
+
55
+ def __init__(self, config):
56
+ super().__init__()
57
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
58
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
59
+
60
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
61
+ # any TensorFlow checkpoint file
62
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
63
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
64
+
65
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
66
+ self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
67
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
68
+
69
+ self.config = config
70
+
71
+ def forward(
72
+ self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
73
+ ):
74
+ if input_ids is not None:
75
+ input_shape = input_ids.size()
76
+ else:
77
+ input_shape = inputs_embeds.size()[:-1]
78
+
79
+ seq_length = input_shape[1]
80
+
81
+ if position_ids is None:
82
+ position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
83
+
84
+ if inputs_embeds is None:
85
+ inputs_embeds = self.word_embeddings(input_ids)
86
+
87
+ embeddings = inputs_embeds
88
+
89
+ if self.position_embedding_type == "absolute":
90
+ position_embeddings = self.position_embeddings(position_ids)
91
+ embeddings += position_embeddings
92
+ embeddings = self.LayerNorm(embeddings)
93
+ embeddings = self.dropout(embeddings)
94
+ return embeddings
95
+
96
+
97
+ class BertSelfAttention(nn.Module):
98
+ def __init__(self, config, is_cross_attention):
99
+ super().__init__()
100
+ self.config = config
101
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
102
+ raise ValueError(
103
+ "The hidden size (%d) is not a multiple of the number of attention "
104
+ "heads (%d)" % (config.hidden_size, config.num_attention_heads)
105
+ )
106
+
107
+ self.num_attention_heads = config.num_attention_heads
108
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
109
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
110
+
111
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
112
+ if is_cross_attention:
113
+ self.key = nn.Linear(config.encoder_width, self.all_head_size)
114
+ self.value = nn.Linear(config.encoder_width, self.all_head_size)
115
+ else:
116
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
117
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
118
+
119
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
120
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
121
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
122
+ self.max_position_embeddings = config.max_position_embeddings
123
+ self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
124
+ self.save_attention = False
125
+
126
+ def save_attn_gradients(self, attn_gradients):
127
+ self.attn_gradients = attn_gradients
128
+
129
+ def get_attn_gradients(self):
130
+ return self.attn_gradients
131
+
132
+ def save_attention_map(self, attention_map):
133
+ self.attention_map = attention_map
134
+
135
+ def get_attention_map(self):
136
+ return self.attention_map
137
+
138
+ def transpose_for_scores(self, x):
139
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
140
+ x = x.view(*new_x_shape)
141
+ return x.permute(0, 2, 1, 3)
142
+
143
+ def forward(
144
+ self,
145
+ hidden_states,
146
+ attention_mask=None,
147
+ head_mask=None,
148
+ encoder_hidden_states=None,
149
+ encoder_attention_mask=None,
150
+ past_key_value=None,
151
+ output_attentions=False,
152
+ ):
153
+ mixed_query_layer = self.query(hidden_states)
154
+
155
+ # If this is instantiated as a cross-attention module, the keys
156
+ # and values come from an encoder; the attention mask needs to be
157
+ # such that the encoder's padding tokens are not attended to.
158
+ is_cross_attention = encoder_hidden_states is not None
159
+
160
+ if is_cross_attention:
161
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
162
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
163
+ attention_mask = encoder_attention_mask
164
+ elif past_key_value is not None:
165
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
166
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
167
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
168
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
169
+ else:
170
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
171
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
172
+
173
+ query_layer = self.transpose_for_scores(mixed_query_layer)
174
+
175
+ past_key_value = (key_layer, value_layer)
176
+
177
+ # Take the dot product between "query" and "key" to get the raw attention scores.
178
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
179
+
180
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
181
+ seq_length = hidden_states.size()[1]
182
+ position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
183
+ position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
184
+ distance = position_ids_l - position_ids_r
185
+ positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
186
+ positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
187
+
188
+ if self.position_embedding_type == "relative_key":
189
+ relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
190
+ attention_scores = attention_scores + relative_position_scores
191
+ elif self.position_embedding_type == "relative_key_query":
192
+ relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
193
+ relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
194
+ attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
195
+
196
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
197
+ if attention_mask is not None:
198
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
199
+ attention_scores = attention_scores + attention_mask
200
+
201
+ # Normalize the attention scores to probabilities.
202
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
203
+
204
+ if is_cross_attention and self.save_attention:
205
+ self.save_attention_map(attention_probs)
206
+ attention_probs.register_hook(self.save_attn_gradients)
207
+
208
+ # This is actually dropping out entire tokens to attend to, which might
209
+ # seem a bit unusual, but is taken from the original Transformer paper.
210
+ attention_probs_dropped = self.dropout(attention_probs)
211
+
212
+ # Mask heads if we want to
213
+ if head_mask is not None:
214
+ attention_probs_dropped = attention_probs_dropped * head_mask
215
+
216
+ context_layer = torch.matmul(attention_probs_dropped, value_layer)
217
+
218
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
219
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
220
+ context_layer = context_layer.view(*new_context_layer_shape)
221
+
222
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
223
+
224
+ outputs = outputs + (past_key_value,)
225
+ return outputs
226
+
227
+
228
+ class BertSelfOutput(nn.Module):
229
+ def __init__(self, config):
230
+ super().__init__()
231
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
232
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
233
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
234
+
235
+ def forward(self, hidden_states, input_tensor):
236
+ hidden_states = self.dense(hidden_states)
237
+ hidden_states = self.dropout(hidden_states)
238
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
239
+ return hidden_states
240
+
241
+
242
+ class BertAttention(nn.Module):
243
+ def __init__(self, config, is_cross_attention=False):
244
+ super().__init__()
245
+ self.self = BertSelfAttention(config, is_cross_attention)
246
+ self.output = BertSelfOutput(config)
247
+ self.pruned_heads = set()
248
+
249
+ def prune_heads(self, heads):
250
+ if len(heads) == 0:
251
+ return
252
+ heads, index = find_pruneable_heads_and_indices(
253
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
254
+ )
255
+
256
+ # Prune linear layers
257
+ self.self.query = prune_linear_layer(self.self.query, index)
258
+ self.self.key = prune_linear_layer(self.self.key, index)
259
+ self.self.value = prune_linear_layer(self.self.value, index)
260
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
261
+
262
+ # Update hyper params and store pruned heads
263
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
264
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
265
+ self.pruned_heads = self.pruned_heads.union(heads)
266
+
267
+ def forward(
268
+ self,
269
+ hidden_states,
270
+ attention_mask=None,
271
+ head_mask=None,
272
+ encoder_hidden_states=None,
273
+ encoder_attention_mask=None,
274
+ past_key_value=None,
275
+ output_attentions=False,
276
+ ):
277
+ self_outputs = self.self(
278
+ hidden_states,
279
+ attention_mask,
280
+ head_mask,
281
+ encoder_hidden_states,
282
+ encoder_attention_mask,
283
+ past_key_value,
284
+ output_attentions,
285
+ )
286
+ attention_output = self.output(self_outputs[0], hidden_states)
287
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
288
+ return outputs
289
+
290
+
291
+ class BertIntermediate(nn.Module):
292
+ def __init__(self, config):
293
+ super().__init__()
294
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
295
+ if isinstance(config.hidden_act, str):
296
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
297
+ else:
298
+ self.intermediate_act_fn = config.hidden_act
299
+
300
+ def forward(self, hidden_states):
301
+ hidden_states = self.dense(hidden_states)
302
+ hidden_states = self.intermediate_act_fn(hidden_states)
303
+ return hidden_states
304
+
305
+
306
+ class BertOutput(nn.Module):
307
+ def __init__(self, config):
308
+ super().__init__()
309
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
310
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
311
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
312
+
313
+ def forward(self, hidden_states, input_tensor):
314
+ hidden_states = self.dense(hidden_states)
315
+ hidden_states = self.dropout(hidden_states)
316
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
317
+ return hidden_states
318
+
319
+
320
+ class BertLayer(nn.Module):
321
+ def __init__(self, config, layer_num):
322
+ super().__init__()
323
+ self.config = config
324
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
325
+ self.seq_len_dim = 1
326
+ self.attention = BertAttention(config)
327
+ self.layer_num = layer_num
328
+ if self.config.add_cross_attention:
329
+ self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
330
+ self.intermediate = BertIntermediate(config)
331
+ self.output = BertOutput(config)
332
+
333
+ def forward(
334
+ self,
335
+ hidden_states,
336
+ attention_mask=None,
337
+ head_mask=None,
338
+ encoder_hidden_states=None,
339
+ encoder_attention_mask=None,
340
+ past_key_value=None,
341
+ output_attentions=False,
342
+ mode=None,
343
+ ):
344
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
345
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
346
+ self_attention_outputs = self.attention(
347
+ hidden_states,
348
+ attention_mask,
349
+ head_mask,
350
+ output_attentions=output_attentions,
351
+ past_key_value=self_attn_past_key_value,
352
+ )
353
+ attention_output = self_attention_outputs[0]
354
+
355
+ outputs = self_attention_outputs[1:-1]
356
+ present_key_value = self_attention_outputs[-1]
357
+
358
+ if mode=='multimodal':
359
+ assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
360
+
361
+ cross_attention_outputs = self.crossattention(
362
+ attention_output,
363
+ attention_mask,
364
+ head_mask,
365
+ encoder_hidden_states,
366
+ encoder_attention_mask,
367
+ output_attentions=output_attentions,
368
+ )
369
+ attention_output = cross_attention_outputs[0]
370
+ outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
371
+ layer_output = apply_chunking_to_forward(
372
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
373
+ )
374
+ outputs = (layer_output,) + outputs
375
+
376
+ outputs = outputs + (present_key_value,)
377
+
378
+ return outputs
379
+
380
+ def feed_forward_chunk(self, attention_output):
381
+ intermediate_output = self.intermediate(attention_output)
382
+ layer_output = self.output(intermediate_output, attention_output)
383
+ return layer_output
384
+
385
+
386
+ class BertEncoder(nn.Module):
387
+ def __init__(self, config):
388
+ super().__init__()
389
+ self.config = config
390
+ self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
391
+ self.gradient_checkpointing = False
392
+
393
+ def forward(
394
+ self,
395
+ hidden_states,
396
+ attention_mask=None,
397
+ head_mask=None,
398
+ encoder_hidden_states=None,
399
+ encoder_attention_mask=None,
400
+ past_key_values=None,
401
+ use_cache=None,
402
+ output_attentions=False,
403
+ output_hidden_states=False,
404
+ return_dict=True,
405
+ mode='multimodal',
406
+ ):
407
+ all_hidden_states = () if output_hidden_states else None
408
+ all_self_attentions = () if output_attentions else None
409
+ all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
410
+
411
+ next_decoder_cache = () if use_cache else None
412
+
413
+ for i in range(self.config.num_hidden_layers):
414
+ layer_module = self.layer[i]
415
+ if output_hidden_states:
416
+ all_hidden_states = all_hidden_states + (hidden_states,)
417
+
418
+ layer_head_mask = head_mask[i] if head_mask is not None else None
419
+ past_key_value = past_key_values[i] if past_key_values is not None else None
420
+
421
+ if self.gradient_checkpointing and self.training:
422
+
423
+ if use_cache:
424
+ logger.warn(
425
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
426
+ )
427
+ use_cache = False
428
+
429
+ def create_custom_forward(module):
430
+ def custom_forward(*inputs):
431
+ return module(*inputs, past_key_value, output_attentions)
432
+
433
+ return custom_forward
434
+
435
+ layer_outputs = torch.utils.checkpoint.checkpoint(
436
+ create_custom_forward(layer_module),
437
+ hidden_states,
438
+ attention_mask,
439
+ layer_head_mask,
440
+ encoder_hidden_states,
441
+ encoder_attention_mask,
442
+ mode=mode,
443
+ )
444
+ else:
445
+ layer_outputs = layer_module(
446
+ hidden_states,
447
+ attention_mask,
448
+ layer_head_mask,
449
+ encoder_hidden_states,
450
+ encoder_attention_mask,
451
+ past_key_value,
452
+ output_attentions,
453
+ mode=mode,
454
+ )
455
+
456
+ hidden_states = layer_outputs[0]
457
+ if use_cache:
458
+ next_decoder_cache += (layer_outputs[-1],)
459
+ if output_attentions:
460
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
461
+
462
+ if output_hidden_states:
463
+ all_hidden_states = all_hidden_states + (hidden_states,)
464
+
465
+ if not return_dict:
466
+ return tuple(
467
+ v
468
+ for v in [
469
+ hidden_states,
470
+ next_decoder_cache,
471
+ all_hidden_states,
472
+ all_self_attentions,
473
+ all_cross_attentions,
474
+ ]
475
+ if v is not None
476
+ )
477
+ return BaseModelOutputWithPastAndCrossAttentions(
478
+ last_hidden_state=hidden_states,
479
+ past_key_values=next_decoder_cache,
480
+ hidden_states=all_hidden_states,
481
+ attentions=all_self_attentions,
482
+ cross_attentions=all_cross_attentions,
483
+ )
484
+
485
+
486
+ class BertPooler(nn.Module):
487
+ def __init__(self, config):
488
+ super().__init__()
489
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
490
+ self.activation = nn.Tanh()
491
+
492
+ def forward(self, hidden_states):
493
+ # We "pool" the model by simply taking the hidden state corresponding
494
+ # to the first token.
495
+ first_token_tensor = hidden_states[:, 0]
496
+ pooled_output = self.dense(first_token_tensor)
497
+ pooled_output = self.activation(pooled_output)
498
+ return pooled_output
499
+
500
+
501
+ class BertPredictionHeadTransform(nn.Module):
502
+ def __init__(self, config):
503
+ super().__init__()
504
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
505
+ if isinstance(config.hidden_act, str):
506
+ self.transform_act_fn = ACT2FN[config.hidden_act]
507
+ else:
508
+ self.transform_act_fn = config.hidden_act
509
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
510
+
511
+ def forward(self, hidden_states):
512
+ hidden_states = self.dense(hidden_states)
513
+ hidden_states = self.transform_act_fn(hidden_states)
514
+ hidden_states = self.LayerNorm(hidden_states)
515
+ return hidden_states
516
+
517
+
518
+ class BertLMPredictionHead(nn.Module):
519
+ def __init__(self, config):
520
+ super().__init__()
521
+ self.transform = BertPredictionHeadTransform(config)
522
+
523
+ # The output weights are the same as the input embeddings, but there is
524
+ # an output-only bias for each token.
525
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
526
+
527
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
528
+
529
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
530
+ self.decoder.bias = self.bias
531
+
532
+ def forward(self, hidden_states):
533
+ hidden_states = self.transform(hidden_states)
534
+ hidden_states = self.decoder(hidden_states)
535
+ return hidden_states
536
+
537
+
538
+ class BertOnlyMLMHead(nn.Module):
539
+ def __init__(self, config):
540
+ super().__init__()
541
+ self.predictions = BertLMPredictionHead(config)
542
+
543
+ def forward(self, sequence_output):
544
+ prediction_scores = self.predictions(sequence_output)
545
+ return prediction_scores
546
+
547
+
548
+ class BertPreTrainedModel(PreTrainedModel):
549
+ """
550
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
551
+ models.
552
+ """
553
+
554
+ config_class = BertConfig
555
+ base_model_prefix = "bert"
556
+ _keys_to_ignore_on_load_missing = [r"position_ids"]
557
+
558
+ def _init_weights(self, module):
559
+ """ Initialize the weights """
560
+ if isinstance(module, (nn.Linear, nn.Embedding)):
561
+ # Slightly different from the TF version which uses truncated_normal for initialization
562
+ # cf https://github.com/pytorch/pytorch/pull/5617
563
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
564
+ elif isinstance(module, nn.LayerNorm):
565
+ module.bias.data.zero_()
566
+ module.weight.data.fill_(1.0)
567
+ if isinstance(module, nn.Linear) and module.bias is not None:
568
+ module.bias.data.zero_()
569
+
570
+
571
+ class BertModel(BertPreTrainedModel):
572
+ """
573
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
574
+ cross-attention is added between the self-attention layers, following the architecture described in `Attention is
575
+ all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
576
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
577
+ argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
578
+ input to the forward pass.
579
+ """
580
+
581
+ def __init__(self, config, add_pooling_layer=True):
582
+ super().__init__(config)
583
+ self.config = config
584
+
585
+ self.embeddings = BertEmbeddings(config)
586
+
587
+ self.encoder = BertEncoder(config)
588
+
589
+ self.pooler = BertPooler(config) if add_pooling_layer else None
590
+
591
+ self.init_weights()
592
+
593
+
594
+ def get_input_embeddings(self):
595
+ return self.embeddings.word_embeddings
596
+
597
+ def set_input_embeddings(self, value):
598
+ self.embeddings.word_embeddings = value
599
+
600
+ def _prune_heads(self, heads_to_prune):
601
+ """
602
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
603
+ class PreTrainedModel
604
+ """
605
+ for layer, heads in heads_to_prune.items():
606
+ self.encoder.layer[layer].attention.prune_heads(heads)
607
+
608
+
609
+ def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
610
+ """
611
+ Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
612
+ Arguments:
613
+ attention_mask (:obj:`torch.Tensor`):
614
+ Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
615
+ input_shape (:obj:`Tuple[int]`):
616
+ The shape of the input to the model.
617
+ device: (:obj:`torch.device`):
618
+ The device of the input to the model.
619
+ Returns:
620
+ :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
621
+ """
622
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
623
+ # ourselves in which case we just need to make it broadcastable to all heads.
624
+ if attention_mask.dim() == 3:
625
+ extended_attention_mask = attention_mask[:, None, :, :]
626
+ elif attention_mask.dim() == 2:
627
+ # Provided a padding mask of dimensions [batch_size, seq_length]
628
+ # - if the model is a decoder, apply a causal mask in addition to the padding mask
629
+ # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
630
+ if is_decoder:
631
+ batch_size, seq_length = input_shape
632
+
633
+ seq_ids = torch.arange(seq_length, device=device)
634
+ causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
635
+ # in case past_key_values are used we need to add a prefix ones mask to the causal mask
636
+ # causal and attention masks must have same type with pytorch version < 1.3
637
+ causal_mask = causal_mask.to(attention_mask.dtype)
638
+
639
+ if causal_mask.shape[1] < attention_mask.shape[1]:
640
+ prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
641
+ causal_mask = torch.cat(
642
+ [
643
+ torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
644
+ causal_mask,
645
+ ],
646
+ axis=-1,
647
+ )
648
+
649
+ extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
650
+ else:
651
+ extended_attention_mask = attention_mask[:, None, None, :]
652
+ else:
653
+ raise ValueError(
654
+ "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
655
+ input_shape, attention_mask.shape
656
+ )
657
+ )
658
+
659
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
660
+ # masked positions, this operation will create a tensor which is 0.0 for
661
+ # positions we want to attend and -10000.0 for masked positions.
662
+ # Since we are adding it to the raw scores before the softmax, this is
663
+ # effectively the same as removing these entirely.
664
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
665
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
666
+ return extended_attention_mask
667
+
668
+ def forward(
669
+ self,
670
+ input_ids=None,
671
+ attention_mask=None,
672
+ position_ids=None,
673
+ head_mask=None,
674
+ inputs_embeds=None,
675
+ encoder_embeds=None,
676
+ encoder_hidden_states=None,
677
+ encoder_attention_mask=None,
678
+ past_key_values=None,
679
+ use_cache=None,
680
+ output_attentions=None,
681
+ output_hidden_states=None,
682
+ return_dict=None,
683
+ is_decoder=False,
684
+ mode='multimodal',
685
+ ):
686
+ r"""
687
+ encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
688
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
689
+ the model is configured as a decoder.
690
+ encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
691
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
692
+ the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
693
+ - 1 for tokens that are **not masked**,
694
+ - 0 for tokens that are **masked**.
695
+ past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
696
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
697
+ If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
698
+ (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
699
+ instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
700
+ use_cache (:obj:`bool`, `optional`):
701
+ If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
702
+ decoding (see :obj:`past_key_values`).
703
+ """
704
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
705
+ output_hidden_states = (
706
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
707
+ )
708
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
709
+
710
+ if is_decoder:
711
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
712
+ else:
713
+ use_cache = False
714
+
715
+ if input_ids is not None and inputs_embeds is not None:
716
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
717
+ elif input_ids is not None:
718
+ input_shape = input_ids.size()
719
+ batch_size, seq_length = input_shape
720
+ device = input_ids.device
721
+ elif inputs_embeds is not None:
722
+ input_shape = inputs_embeds.size()[:-1]
723
+ batch_size, seq_length = input_shape
724
+ device = inputs_embeds.device
725
+ elif encoder_embeds is not None:
726
+ input_shape = encoder_embeds.size()[:-1]
727
+ batch_size, seq_length = input_shape
728
+ device = encoder_embeds.device
729
+ else:
730
+ raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
731
+
732
+ # past_key_values_length
733
+ past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
734
+
735
+ if attention_mask is None:
736
+ attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
737
+
738
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
739
+ # ourselves in which case we just need to make it broadcastable to all heads.
740
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape,
741
+ device, is_decoder)
742
+
743
+ # If a 2D or 3D attention mask is provided for the cross-attention
744
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
745
+ if encoder_hidden_states is not None:
746
+ if type(encoder_hidden_states) == list:
747
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
748
+ else:
749
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
750
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
751
+
752
+ if type(encoder_attention_mask) == list:
753
+ encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
754
+ elif encoder_attention_mask is None:
755
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
756
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
757
+ else:
758
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
759
+ else:
760
+ encoder_extended_attention_mask = None
761
+
762
+ # Prepare head mask if needed
763
+ # 1.0 in head_mask indicate we keep the head
764
+ # attention_probs has shape bsz x n_heads x N x N
765
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
766
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
767
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
768
+
769
+ if encoder_embeds is None:
770
+ embedding_output = self.embeddings(
771
+ input_ids=input_ids,
772
+ position_ids=position_ids,
773
+ inputs_embeds=inputs_embeds,
774
+ past_key_values_length=past_key_values_length,
775
+ )
776
+ else:
777
+ embedding_output = encoder_embeds
778
+
779
+ encoder_outputs = self.encoder(
780
+ embedding_output,
781
+ attention_mask=extended_attention_mask,
782
+ head_mask=head_mask,
783
+ encoder_hidden_states=encoder_hidden_states,
784
+ encoder_attention_mask=encoder_extended_attention_mask,
785
+ past_key_values=past_key_values,
786
+ use_cache=use_cache,
787
+ output_attentions=output_attentions,
788
+ output_hidden_states=output_hidden_states,
789
+ return_dict=return_dict,
790
+ mode=mode,
791
+ )
792
+ sequence_output = encoder_outputs[0]
793
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
794
+
795
+ if not return_dict:
796
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
797
+
798
+ return BaseModelOutputWithPoolingAndCrossAttentions(
799
+ last_hidden_state=sequence_output,
800
+ pooler_output=pooled_output,
801
+ past_key_values=encoder_outputs.past_key_values,
802
+ hidden_states=encoder_outputs.hidden_states,
803
+ attentions=encoder_outputs.attentions,
804
+ cross_attentions=encoder_outputs.cross_attentions,
805
+ )
806
+
807
+
808
+
809
+ class BertLMHeadModel(BertPreTrainedModel):
810
+
811
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
812
+ _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
813
+
814
+ def __init__(self, config):
815
+ super().__init__(config)
816
+
817
+ self.bert = BertModel(config, add_pooling_layer=False)
818
+ self.cls = BertOnlyMLMHead(config)
819
+
820
+ self.init_weights()
821
+
822
+ def get_output_embeddings(self):
823
+ return self.cls.predictions.decoder
824
+
825
+ def set_output_embeddings(self, new_embeddings):
826
+ self.cls.predictions.decoder = new_embeddings
827
+
828
+ def forward(
829
+ self,
830
+ input_ids=None,
831
+ attention_mask=None,
832
+ position_ids=None,
833
+ head_mask=None,
834
+ inputs_embeds=None,
835
+ encoder_hidden_states=None,
836
+ encoder_attention_mask=None,
837
+ labels=None,
838
+ past_key_values=None,
839
+ use_cache=None,
840
+ output_attentions=None,
841
+ output_hidden_states=None,
842
+ return_dict=None,
843
+ return_logits=False,
844
+ is_decoder=True,
845
+ reduction='mean',
846
+ mode='multimodal',
847
+ ):
848
+ r"""
849
+ encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
850
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
851
+ the model is configured as a decoder.
852
+ encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
853
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
854
+ the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
855
+ - 1 for tokens that are **not masked**,
856
+ - 0 for tokens that are **masked**.
857
+ labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
858
+ Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
859
+ ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
860
+ ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
861
+ past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
862
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
863
+ If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
864
+ (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
865
+ instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
866
+ use_cache (:obj:`bool`, `optional`):
867
+ If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
868
+ decoding (see :obj:`past_key_values`).
869
+ Returns:
870
+ Example::
871
+ >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
872
+ >>> import torch
873
+ >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
874
+ >>> config = BertConfig.from_pretrained("bert-base-cased")
875
+ >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
876
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
877
+ >>> outputs = model(**inputs)
878
+ >>> prediction_logits = outputs.logits
879
+ """
880
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
881
+ if labels is not None:
882
+ use_cache = False
883
+
884
+ outputs = self.bert(
885
+ input_ids,
886
+ attention_mask=attention_mask,
887
+ position_ids=position_ids,
888
+ head_mask=head_mask,
889
+ inputs_embeds=inputs_embeds,
890
+ encoder_hidden_states=encoder_hidden_states,
891
+ encoder_attention_mask=encoder_attention_mask,
892
+ past_key_values=past_key_values,
893
+ use_cache=use_cache,
894
+ output_attentions=output_attentions,
895
+ output_hidden_states=output_hidden_states,
896
+ return_dict=return_dict,
897
+ is_decoder=is_decoder,
898
+ mode=mode,
899
+ )
900
+
901
+ sequence_output = outputs[0]
902
+ prediction_scores = self.cls(sequence_output)
903
+
904
+ if return_logits:
905
+ return prediction_scores[:, :-1, :].contiguous()
906
+
907
+ lm_loss = None
908
+ if labels is not None:
909
+ # we are doing next-token prediction; shift prediction scores and input ids by one
910
+ shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
911
+ labels = labels[:, 1:].contiguous()
912
+ loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
913
+ lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
914
+ if reduction=='none':
915
+ lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)
916
+
917
+ if not return_dict:
918
+ output = (prediction_scores,) + outputs[2:]
919
+ return ((lm_loss,) + output) if lm_loss is not None else output
920
+
921
+ return CausalLMOutputWithCrossAttentions(
922
+ loss=lm_loss,
923
+ logits=prediction_scores,
924
+ past_key_values=outputs.past_key_values,
925
+ hidden_states=outputs.hidden_states,
926
+ attentions=outputs.attentions,
927
+ cross_attentions=outputs.cross_attentions,
928
+ )
929
+
930
+ def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
931
+ input_shape = input_ids.shape
932
+ # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
933
+ if attention_mask is None:
934
+ attention_mask = input_ids.new_ones(input_shape)
935
+
936
+ # cut decoder_input_ids if past is used
937
+ if past is not None:
938
+ input_ids = input_ids[:, -1:]
939
+
940
+ return {
941
+ "input_ids": input_ids,
942
+ "attention_mask": attention_mask,
943
+ "past_key_values": past,
944
+ "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
945
+ "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
946
+ "is_decoder": True,
947
+ }
948
+
949
+ def _reorder_cache(self, past, beam_idx):
950
+ reordered_past = ()
951
+ for layer_past in past:
952
+ reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
953
+ return reordered_past
models/vit.py ADDED
@@ -0,0 +1,305 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ * Copyright (c) 2022, salesforce.com, inc.
3
+ * All rights reserved.
4
+ * SPDX-License-Identifier: BSD-3-Clause
5
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
6
+ * By Junnan Li
7
+ * Based on timm code base
8
+ * https://github.com/rwightman/pytorch-image-models/tree/master/timm
9
+ '''
10
+
11
+ import torch
12
+ import torch.nn as nn
13
+ import torch.nn.functional as F
14
+ from functools import partial
15
+
16
+ from timm.models.vision_transformer import _cfg, PatchEmbed
17
+ from timm.models.registry import register_model
18
+ from timm.models.layers import trunc_normal_, DropPath
19
+ from timm.models.helpers import named_apply, adapt_input_conv
20
+
21
+ from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
22
+
23
+ class Mlp(nn.Module):
24
+ """ MLP as used in Vision Transformer, MLP-Mixer and related networks
25
+ """
26
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
27
+ super().__init__()
28
+ out_features = out_features or in_features
29
+ hidden_features = hidden_features or in_features
30
+ self.fc1 = nn.Linear(in_features, hidden_features)
31
+ self.act = act_layer()
32
+ self.fc2 = nn.Linear(hidden_features, out_features)
33
+ self.drop = nn.Dropout(drop)
34
+
35
+ def forward(self, x):
36
+ x = self.fc1(x)
37
+ x = self.act(x)
38
+ x = self.drop(x)
39
+ x = self.fc2(x)
40
+ x = self.drop(x)
41
+ return x
42
+
43
+
44
+ class Attention(nn.Module):
45
+ def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
46
+ super().__init__()
47
+ self.num_heads = num_heads
48
+ head_dim = dim // num_heads
49
+ # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
50
+ self.scale = qk_scale or head_dim ** -0.5
51
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
52
+ self.attn_drop = nn.Dropout(attn_drop)
53
+ self.proj = nn.Linear(dim, dim)
54
+ self.proj_drop = nn.Dropout(proj_drop)
55
+ self.attn_gradients = None
56
+ self.attention_map = None
57
+
58
+ def save_attn_gradients(self, attn_gradients):
59
+ self.attn_gradients = attn_gradients
60
+
61
+ def get_attn_gradients(self):
62
+ return self.attn_gradients
63
+
64
+ def save_attention_map(self, attention_map):
65
+ self.attention_map = attention_map
66
+
67
+ def get_attention_map(self):
68
+ return self.attention_map
69
+
70
+ def forward(self, x, register_hook=False):
71
+ B, N, C = x.shape
72
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
73
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
74
+
75
+ attn = (q @ k.transpose(-2, -1)) * self.scale
76
+ attn = attn.softmax(dim=-1)
77
+ attn = self.attn_drop(attn)
78
+
79
+ if register_hook:
80
+ self.save_attention_map(attn)
81
+ attn.register_hook(self.save_attn_gradients)
82
+
83
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
84
+ x = self.proj(x)
85
+ x = self.proj_drop(x)
86
+ return x
87
+
88
+
89
+ class Block(nn.Module):
90
+
91
+ def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
92
+ drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
93
+ super().__init__()
94
+ self.norm1 = norm_layer(dim)
95
+ self.attn = Attention(
96
+ dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
97
+ # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
98
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
99
+ self.norm2 = norm_layer(dim)
100
+ mlp_hidden_dim = int(dim * mlp_ratio)
101
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
102
+
103
+ if use_grad_checkpointing:
104
+ self.attn = checkpoint_wrapper(self.attn)
105
+ self.mlp = checkpoint_wrapper(self.mlp)
106
+
107
+ def forward(self, x, register_hook=False):
108
+ x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
109
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
110
+ return x
111
+
112
+
113
+ class VisionTransformer(nn.Module):
114
+ """ Vision Transformer
115
+ A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
116
+ https://arxiv.org/abs/2010.11929
117
+ """
118
+ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
119
+ num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
120
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
121
+ use_grad_checkpointing=False, ckpt_layer=0):
122
+ """
123
+ Args:
124
+ img_size (int, tuple): input image size
125
+ patch_size (int, tuple): patch size
126
+ in_chans (int): number of input channels
127
+ num_classes (int): number of classes for classification head
128
+ embed_dim (int): embedding dimension
129
+ depth (int): depth of transformer
130
+ num_heads (int): number of attention heads
131
+ mlp_ratio (int): ratio of mlp hidden dim to embedding dim
132
+ qkv_bias (bool): enable bias for qkv if True
133
+ qk_scale (float): override default qk scale of head_dim ** -0.5 if set
134
+ representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
135
+ drop_rate (float): dropout rate
136
+ attn_drop_rate (float): attention dropout rate
137
+ drop_path_rate (float): stochastic depth rate
138
+ norm_layer: (nn.Module): normalization layer
139
+ """
140
+ super().__init__()
141
+ self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
142
+ norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
143
+
144
+ self.patch_embed = PatchEmbed(
145
+ img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
146
+
147
+ num_patches = self.patch_embed.num_patches
148
+
149
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
150
+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
151
+ self.pos_drop = nn.Dropout(p=drop_rate)
152
+
153
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
154
+ self.blocks = nn.ModuleList([
155
+ Block(
156
+ dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
157
+ drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
158
+ use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
159
+ )
160
+ for i in range(depth)])
161
+ self.norm = norm_layer(embed_dim)
162
+
163
+ trunc_normal_(self.pos_embed, std=.02)
164
+ trunc_normal_(self.cls_token, std=.02)
165
+ self.apply(self._init_weights)
166
+
167
+ def _init_weights(self, m):
168
+ if isinstance(m, nn.Linear):
169
+ trunc_normal_(m.weight, std=.02)
170
+ if isinstance(m, nn.Linear) and m.bias is not None:
171
+ nn.init.constant_(m.bias, 0)
172
+ elif isinstance(m, nn.LayerNorm):
173
+ nn.init.constant_(m.bias, 0)
174
+ nn.init.constant_(m.weight, 1.0)
175
+
176
+ @torch.jit.ignore
177
+ def no_weight_decay(self):
178
+ return {'pos_embed', 'cls_token'}
179
+
180
+ def forward(self, x, register_blk=-1):
181
+ B = x.shape[0]
182
+ x = self.patch_embed(x)
183
+
184
+ cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
185
+ x = torch.cat((cls_tokens, x), dim=1)
186
+
187
+ x = x + self.pos_embed[:,:x.size(1),:]
188
+ x = self.pos_drop(x)
189
+
190
+ for i,blk in enumerate(self.blocks):
191
+ x = blk(x, register_blk==i)
192
+ x = self.norm(x)
193
+
194
+ return x
195
+
196
+ @torch.jit.ignore()
197
+ def load_pretrained(self, checkpoint_path, prefix=''):
198
+ _load_weights(self, checkpoint_path, prefix)
199
+
200
+
201
+ @torch.no_grad()
202
+ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
203
+ """ Load weights from .npz checkpoints for official Google Brain Flax implementation
204
+ """
205
+ import numpy as np
206
+
207
+ def _n2p(w, t=True):
208
+ if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
209
+ w = w.flatten()
210
+ if t:
211
+ if w.ndim == 4:
212
+ w = w.transpose([3, 2, 0, 1])
213
+ elif w.ndim == 3:
214
+ w = w.transpose([2, 0, 1])
215
+ elif w.ndim == 2:
216
+ w = w.transpose([1, 0])
217
+ return torch.from_numpy(w)
218
+
219
+ w = np.load(checkpoint_path)
220
+ if not prefix and 'opt/target/embedding/kernel' in w:
221
+ prefix = 'opt/target/'
222
+
223
+ if hasattr(model.patch_embed, 'backbone'):
224
+ # hybrid
225
+ backbone = model.patch_embed.backbone
226
+ stem_only = not hasattr(backbone, 'stem')
227
+ stem = backbone if stem_only else backbone.stem
228
+ stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
229
+ stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
230
+ stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
231
+ if not stem_only:
232
+ for i, stage in enumerate(backbone.stages):
233
+ for j, block in enumerate(stage.blocks):
234
+ bp = f'{prefix}block{i + 1}/unit{j + 1}/'
235
+ for r in range(3):
236
+ getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
237
+ getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
238
+ getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
239
+ if block.downsample is not None:
240
+ block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
241
+ block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
242
+ block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
243
+ embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
244
+ else:
245
+ embed_conv_w = adapt_input_conv(
246
+ model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
247
+ model.patch_embed.proj.weight.copy_(embed_conv_w)
248
+ model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
249
+ model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
250
+ pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
251
+ if pos_embed_w.shape != model.pos_embed.shape:
252
+ pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
253
+ pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
254
+ model.pos_embed.copy_(pos_embed_w)
255
+ model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
256
+ model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
257
+ # if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
258
+ # model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
259
+ # model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
260
+ # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
261
+ # model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
262
+ # model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
263
+ for i, block in enumerate(model.blocks.children()):
264
+ block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
265
+ mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
266
+ block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
267
+ block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
268
+ block.attn.qkv.weight.copy_(torch.cat([
269
+ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
270
+ block.attn.qkv.bias.copy_(torch.cat([
271
+ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
272
+ block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
273
+ block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
274
+ for r in range(2):
275
+ getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
276
+ getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
277
+ block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
278
+ block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
279
+
280
+
281
+ def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
282
+ # interpolate position embedding
283
+ embedding_size = pos_embed_checkpoint.shape[-1]
284
+ num_patches = visual_encoder.patch_embed.num_patches
285
+ num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
286
+ # height (== width) for the checkpoint position embedding
287
+ orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
288
+ # height (== width) for the new position embedding
289
+ new_size = int(num_patches ** 0.5)
290
+
291
+ if orig_size!=new_size:
292
+ # class_token and dist_token are kept unchanged
293
+ extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
294
+ # only the position tokens are interpolated
295
+ pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
296
+ pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
297
+ pos_tokens = torch.nn.functional.interpolate(
298
+ pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
299
+ pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
300
+ new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
301
+ print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
302
+
303
+ return new_pos_embed
304
+ else:
305
+ return pos_embed_checkpoint
pipeline.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Dict, List, Any
2
+ from PIL import Image
3
+ import requests
4
+ import torch
5
+ import base64
6
+ import os
7
+ from io import BytesIO
8
+ from models.blip_feature_extractor import blip_feature_extractor
9
+ from torchvision import transforms
10
+ from torchvision.transforms.functional import InterpolationMode
11
+
12
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
13
+
14
+ class PreTrainedPipeline():
15
+ def __init__(self, path=""):
16
+ # load the optimized model
17
+ self.model_path = os.path.join(path,'model_large_retrieval_coco.pth')
18
+ self.model = blip_feature_extractor(
19
+ pretrained=self.model_path,
20
+ image_size=384,
21
+ vit='large',
22
+ med_config=os.path.join(path, 'configs/med_config.json')
23
+ )
24
+ self.model.eval()
25
+ self.model = self.model.to(device)
26
+
27
+ image_size = 384
28
+ self.transform = transforms.Compose([
29
+ transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
30
+ transforms.ToTensor(),
31
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
32
+ ])
33
+
34
+
35
+
36
+ def __call__(self, data: Any) -> Dict[str, List[float]]:
37
+ """
38
+ Args:
39
+ data (:obj:):
40
+ includes the input data and the parameters for the inference.
41
+ Return:
42
+ A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing :
43
+ - "feature_vector": A list of floats corresponding to the image embedding.
44
+ """
45
+ inputs = data.pop("inputs", data)
46
+ parameters = data.pop("parameters", {"mode": "image"})
47
+
48
+ # decode base64 image to PIL
49
+ image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
50
+ image = self.transform(image).unsqueeze(0).to(device)
51
+ text=""
52
+ with torch.no_grad():
53
+ feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist()
54
+ # postprocess the prediction
55
+ return {"feature_vector": feature_vector}
requirements.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ timm
2
+ fairscale
3
+ requests
4
+ Pillow