File size: 18,743 Bytes
616e7e7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6d1a894
80d3d8e
616e7e7
 
 
 
 
 
 
 
 
b0832a1
616e7e7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
217926f
616e7e7
 
 
7b8f1e0
616e7e7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6d1a894
616e7e7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
'''
 * Tag2Text
 * Written by Xinyu Huang
'''
import warnings
warnings.filterwarnings("ignore")

from models.vit import VisionTransformer, interpolate_pos_embed
from models.swin_transformer import SwinTransformer, interpolate_relative_pos_embed
from models.med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer

import torch
from torch import nn
import torch.nn.functional as F

import os
from urllib.parse import urlparse
from timm.models.hub import download_cached_file
from data.tag_class import tra_array
import json
import math
import numpy as np

def read_json(rpath):
    with open(rpath, 'r') as f:
        return json.load(f)

delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]
        
class Tag2Text_Caption(nn.Module):
    def __init__(self,                 
                 med_config = 'configs/med_config.json',  
                 image_size = 384,
                 vit = 'base',
                 vit_grad_ckpt = False,
                 vit_ckpt_layer = 0,
                 prompt = 'a picture of ',
                 threshold = 0.7,
                 ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """            
        super().__init__()

        if vit=='swin_b':
            if image_size == 224:
                vision_config_path = 'configs/swin/config_swinB_224.json'
            elif image_size == 384:
                vision_config_path = 'configs/swin/config_swinB_384.json'
            vision_config = read_json(vision_config_path)
            assert image_size == vision_config['image_res']
            # assert config['patch_size'] == 32
            vision_width = vision_config['vision_width']

            self.visual_encoder = SwinTransformer(img_size=vision_config['image_res'],
                                            patch_size=4,
                                            in_chans=3,
                                            embed_dim=vision_config['embed_dim'],
                                            depths=vision_config['depths'],
                                            num_heads=vision_config['num_heads'],
                                            window_size=vision_config['window_size'],
                                            mlp_ratio=4.,
                                            qkv_bias=True,
                                            drop_rate=0.0,
                                            drop_path_rate=0.1,
                                            ape=False,
                                            patch_norm=True,
                                            use_checkpoint=False)
        
        else:
            self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)


        self.tokenizer = init_tokenizer()   

        # create the decoder
        decoder_config = BertConfig.from_json_file(med_config)
        decoder_config.encoder_width = 768
        self.text_decoder = BertLMHeadModel(config=decoder_config)     

        # create encoder
        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.tag_encoder = BertModel(config=encoder_config, add_pooling_layer=False)
        
        self.prompt = prompt
        self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1

        self.threshold = threshold
        num_features = 768
        self.num_class = 3429

        q2l_config = BertConfig.from_json_file('configs/q2l_config.json')
        q2l_config.encoder_width = vision_width
        self.vision_multi = BertModel.from_pretrained(config=q2l_config, add_pooling_layer=False)
        self.vision_multi.resize_token_embeddings(len(self.tokenizer)) 
        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)
        self.fc =  GroupWiseLinear(self.num_class, num_features, bias=True)
        self.del_selfattention()

        tie_encoder_decoder_weights(self.tag_encoder,self.vision_multi,'',' ')
        self.tag_array = tra_array

    def del_selfattention(self):
        del self.vision_multi.embeddings
        for layer in self.vision_multi.encoder.layer:
            del layer.attention
        
    def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0, tag_input = None, return_tag_predict = False):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)

        #==============generate tag==============#
        if tag_input == None:
            image_spatial_embeds = image_embeds[:,1:,:]
            image_cls_embeds = image_embeds[:,0,:]

            bs = image_spatial_embeds.shape[0]
            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs,1,1)
            mlr_tagembedding = self.vision_multi(encoder_embeds = label_embed,
                                encoder_hidden_states = image_embeds,
                                encoder_attention_mask = image_atts,      
                                return_dict = False,
                                mode = 'mlr',
                                )  

            logits = self.fc(mlr_tagembedding[0])
            
            targets = torch.where(torch.sigmoid(logits) > self.threshold , torch.tensor(1.0).to(image.device), torch.zeros(self.num_class).to(image.device))

            tag = targets.cpu().numpy()
            tag[:,delete_tag_index] = 0
            bs = image.size(0)
            tag_input = []
            for b in range(bs):
                index = np.argwhere(tag[b] == 1)
                token = self.tag_array[index].squeeze(axis = 1)
                tag_input.append(' | '.join(token))            
        #========================================#
        
        if not sample:
            image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)
            tag_input_temp = []
            for tag in tag_input:
                for i in range(num_beams):
                    tag_input_temp.append(tag)
            tag_input = tag_input_temp


        tag_input_tokenzier = self.tokenizer(tag_input, padding='max_length', truncation=True, max_length=40, 
                              return_tensors="pt").to(image.device)  
        encoder_input_ids = tag_input_tokenzier.input_ids
        encoder_input_ids[:,0] = self.tokenizer.enc_token_id

        output_tagembedding = self.tag_encoder(encoder_input_ids,
                                       attention_mask = tag_input_tokenzier.attention_mask,
                                       encoder_hidden_states = image_embeds,
                                       encoder_attention_mask = image_atts,      
                                       return_dict = True,
                                      )  
        
        prompt = [self.prompt] * image.size(0)
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) 
        input_ids[:,0] = self.tokenizer.bos_token_id
        input_ids = input_ids[:, :-1] 

        if sample:
            #nucleus sampling
            model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None}
            outputs = self.text_decoder.generate(input_ids=input_ids,
                                                max_length=max_length,
                                                min_length=min_length,
                                                do_sample=True,
                                                top_p=top_p,
                                                num_return_sequences=1,
                                                eos_token_id=self.tokenizer.sep_token_id,
                                                pad_token_id=self.tokenizer.pad_token_id, 
                                                repetition_penalty=1.1,                                            
                                                **model_kwargs)
        else:
            #beam search
            model_kwargs = {"encoder_hidden_states": output_tagembedding.last_hidden_state, "encoder_attention_mask":None}
            outputs = self.text_decoder.generate(input_ids=input_ids,
                                                max_length=max_length,
                                                min_length=min_length,
                                                num_beams=num_beams,
                                                eos_token_id=self.tokenizer.sep_token_id,
                                                pad_token_id=self.tokenizer.pad_token_id,     
                                                repetition_penalty=repetition_penalty,
                                                **model_kwargs)            
            
        captions = []    
        for output in outputs:
            caption = self.tokenizer.decode(output, skip_special_tokens=True)    
            captions.append(caption[len(self.prompt):])
        if return_tag_predict == True:
            if sample:
                return captions, tag_input
            else:
                return captions, tag_input[0:int(len(tag_input)/num_beams)]            
        return captions


def tag2text_caption(pretrained='',**kwargs):
    model = Tag2Text_Caption(**kwargs)
    if pretrained:
        if kwargs['vit'] == 'swin_b':
            model,msg = load_checkpoint_swinbase(model,pretrained,kwargs)
        else:
            model,msg = load_checkpoint(model,pretrained)
        print('vit:',kwargs['vit'])
        print('msg_v2',msg)
    return model    


from typing import List
def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
    uninitialized_encoder_weights: List[str] = []
    if decoder.__class__ != encoder.__class__:
        logger.info(
            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
        )

    def tie_encoder_to_decoder_recursively(
        decoder_pointer: nn.Module,
        encoder_pointer: nn.Module,
        module_name: str,
        uninitialized_encoder_weights: List[str],
        skip_key: str,
        depth=0,
    ):
        assert isinstance(decoder_pointer, nn.Module) and isinstance(
            encoder_pointer, nn.Module
        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
            assert hasattr(encoder_pointer, "weight")
            encoder_pointer.weight = decoder_pointer.weight
            if hasattr(decoder_pointer, "bias"):
                assert hasattr(encoder_pointer, "bias")
                encoder_pointer.bias = decoder_pointer.bias                
            print(module_name+' is tied')    
            return

        encoder_modules = encoder_pointer._modules
        decoder_modules = decoder_pointer._modules
        if len(decoder_modules) > 0:
            assert (
                len(encoder_modules) > 0
            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"

            all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
            encoder_layer_pos = 0
            for name, module in decoder_modules.items():
                if name.isdigit():
                    encoder_name = str(int(name) + encoder_layer_pos)
                    decoder_name = name
                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
                        encoder_modules
                    ) != len(decoder_modules):
                        # this can happen if the name corresponds to the position in a list module list of layers
                        # in this case the decoder has added a cross-attention that the encoder does not have
                        # thus skip this step and subtract one layer pos from encoder
                        encoder_layer_pos -= 1
                        continue
                elif name not in encoder_modules:
                    continue
                elif depth > 500:
                    raise ValueError(
                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
                    )
                else:
                    decoder_name = encoder_name = name
                tie_encoder_to_decoder_recursively(
                    decoder_modules[decoder_name],
                    encoder_modules[encoder_name],
                    module_name + "/" + name,
                    uninitialized_encoder_weights,
                    skip_key,
                    depth=depth + 1,
                )
                all_encoder_weights.remove(module_name + "/" + encoder_name)

            uninitialized_encoder_weights += list(all_encoder_weights)

    # tie weights recursively
    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  


class GroupWiseLinear(nn.Module):
    # could be changed to: 
    # output = torch.einsum('ijk,zjk->ij', x, self.W)
    # or output = torch.einsum('ijk,jk->ij', x, self.W[0])
    def __init__(self, num_class, hidden_dim, bias=True):
        super().__init__()
        self.num_class = num_class
        self.hidden_dim = hidden_dim
        self.bias = bias

        self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim))
        if bias:
            self.b = nn.Parameter(torch.Tensor(1, num_class))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.W.size(2))
        for i in range(self.num_class):
            self.W[0][i].data.uniform_(-stdv, stdv)
        if self.bias:
            for i in range(self.num_class):
                self.b[0][i].data.uniform_(-stdv, stdv)

    def forward(self, x):
        # x: B,K,d
        x = (self.W * x).sum(-1)
        if self.bias:
            x = x + self.b
        return x


def init_tokenizer():
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
    return tokenizer


def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
        
    assert vit in ['base', 'large'], "vit parameter must be base or large"
    if vit=='base':
        vision_width = 768
        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, 
                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
                                           drop_path_rate=0 or drop_path_rate
                                          )   
    elif vit=='large':
        vision_width = 1024
        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, 
                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
                                           drop_path_rate=0.1 or drop_path_rate
                                          )   
    return visual_encoder, vision_width

def is_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")

def load_checkpoint(model,url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
        
    state_dict = checkpoint['model']
    
    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
                                                                         model.visual_encoder_m)    
    for key in model.state_dict().keys():
        if key in state_dict.keys():
            if state_dict[key].shape!=model.state_dict()[key].shape:
                del state_dict[key]
    
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model,msg
    

def load_checkpoint_swinbase(model,url_or_filename,kwargs):
    if kwargs['image_size'] == 224:
        vision_config_path = 'configs/swin/config_swinB_224.json'
    elif kwargs['image_size'] == 384:
        vision_config_path = 'configs/swin/config_swinB_384.json'
    elif kwargs['image_size'] == 480:
        vision_config_path = 'configs/swin/config_swinB_480.json'
    elif kwargs['image_size'] == 576:
        vision_config_path = 'configs/swin/config_swinB_576.json'
    elif kwargs['image_size'] == 608:
        vision_config_path = 'configs/swin/config_swinB_608.json'
    window_size = read_json(vision_config_path)['window_size']
    print('--------------')
    print(url_or_filename)
    print('--------------')
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
        
    state_dict = checkpoint['model']

    for k in list(state_dict.keys()):
        if 'relative_position_bias_table' in k:
            dst_num_pos = (2 * window_size - 1) ** 2
            state_dict[k] = interpolate_relative_pos_embed(state_dict[k], dst_num_pos, param_name=k)
        elif ('relative_position_index' in k) or ('attn_mask' in k):
            del state_dict[k]
    
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model,msg