First init

app.py

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+import gradio as gr
+from source.predict_sample import main_caption
+
+
+iface = gr.Interface(fn=main_caption,
+                     inputs=gr.Image(image_mode='RGB'),
+                     outputs="text",
+                     title="Image Caption Generator",
+                     description="Upload an image, and the system will generate a caption.")
+iface.launch()

requirements.txt

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+gradio==4.23.0
+nltk==3.8.1
+Pillow==9.1.0
+torch==2.1.2
+torchvision==0.16.2

source/config.py

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+import os
+import torch
+
+
+class Config(object):
+
+    def __init__(self) -> None:
+
+        self.DEVICE = torch.device("cpu")
+
+        self.VOCAB_FILE = 'source/vocabulary.txt'
+        self.VOCAB_SIZE = 5000
+
+        self.NUM_LAYER = 1
+        self.IMAGE_EMB_DIM = 256
+        self.WORD_EMB_DIM = 256
+        self.HIDDEN_DIM = 512
+
+        self.EMBEDDING_WEIGHT_FILE = 'source/weights/embeddings-32B-512H-1L-e5.pt'
+        self.ENCODER_WEIGHT_FILE = 'source/weights/encoder-32B-512H-1L-e5.pt'
+        self.DECODER_WEIGHT_FILE = 'source/weights/decoder-32B-512H-1L-e5.pt'
+
+        self.ROOT = os.path.join(os.path.expanduser('~'), 'Huggingface', 'ImageCaption')

source/model.py

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+import torch
+import torch._utils
+import torch.nn as nn
+import torchvision.models as models
+from typing import Tuple
+from config import Config
+
+
+class Encoder(nn.Module):
+    def __init__(self, image_emb_dim: int, device: torch.device):
+        """ Image encoder to obtain features from images. Contains pretrained Resnet50 with last layer removed
+            and a linear layer with the output dimension of (BATCH, image_emb_dim)
+        """
+
+        super(Encoder, self).__init__()
+        self.image_emb_dim = image_emb_dim
+        self.device = device
+
+        # pretrained Resnet50 model with freezed parameters
+        resnet = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+        for param in resnet.parameters():
+            param.requires_grad_(False)
+
+        # remove last layer
+        modules = list(resnet.children())[:-1]
+        self.resnet = nn.Sequential(*modules)
+
+        # define a final classifier
+        self.fc = nn.Linear(in_features=resnet.fc.in_features, out_features=self.image_emb_dim)
+
+    def forward(self, images: torch.Tensor) -> torch.Tensor:
+        """ Forward operation of encoder, passing images through resnet and then linear layer.
+
+        Args:
+            > images (torch.Tensor): (BATCH, 3, 224, 224)
+
+        Returns:
+            > features (torch.Tensor): (BATCH, IMAGE_EMB_DIM)
+        """
+
+        features = self.resnet(images)
+        # features: (BATCH, 2048, 1, 1)
+
+        features = features.reshape(features.size(0), -1).to(self.device)
+        # features: (BATCH, 2048)
+
+        features = self.fc(features).to(self.device)
+        # features: (BATCH, IMAGE_EMB_DIM)
+
+        return features
+
+
+class Decoder(nn.Module):
+    def __init__(self,
+                 image_emb_dim: int,
+                 word_emb_dim: int,
+                 hidden_dim: int,
+                 num_layers: int,
+                 vocab_size: int,
+                 device: torch.device):
+        """
+        Decoder taking as input for the LSTM layer the concatenation of features obtained from the encoder
+        and embedded captions obtained from the embedding layer. Hidden and cell states are randomly initialized.
+        Final classifier is a linear layer with output dimension of the size of a vocabulary.
+        """
+
+        super(Decoder, self).__init__()
+
+        self.config = Config()
+
+        self.image_emd_dim = image_emb_dim
+        self.word_emb_dim = word_emb_dim
+        self.hidden_dim = hidden_dim
+        self.num_layer = num_layers
+        self.vocab_size = vocab_size
+        self.device = device
+
+        self.hidden_state_0 = nn.Parameter(torch.zeros((self.num_layer, 1, self.hidden_dim)))
+        self.cell_state_0 = nn.Parameter(torch.zeros((self.num_layer, 1, self.hidden_dim)))
+
+        self.lstm = nn.LSTM(input_size=self.image_emd_dim + self.word_emb_dim,
+                            hidden_size=self.hidden_dim,
+                            num_layers=self.num_layer,
+                            bidirectional=False)
+
+        self.fc = nn.Sequential(
+            nn.Linear(in_features=self.hidden_dim, out_featuers=self.vocab_size),
+            nn.LogSoftmax(dim=2)
+        )
+
+    def forward(self,
+                embedded_captions: torch.Tensor,
+                features: torch.Tensor,
+                hidden: torch.Tensor,
+                cell: torch.Tensor) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Forward operation of (word-by-word) decoder.
+        The LSTM input (concatenation of embedded_captions and features) is passed through LSTM and then linear layer.
+
+        Args:
+
+            > embedded_captions(torch.Tensor): (SEQ_LENGTH = 1, BATCH, WORD_EMB_DIM)
+            > features (torch.Tensor): (1, BATCH, IMAGE_EMB_DIM)
+            > hidden (torch.Tensor): (NUM_LAYER, BATCH, HIDDEN_DIM)
+            > cell (torch.Tensor): (NUM_LAYER, BATCH, HIDDEN_DIM)
+
+        Returns:
+
+            > output (torch.Tensor): (1, BATCH, VOCAB_SIZE)
+            > (hidden, cell) (torch.Tensor, torch.Tensor): (NUM_LAYER, BATCH, HIDDEN_DIM)
+        """
+
+        lstm_input = torch.cat((embedded_captions, features), dim=2)
+
+        output, (hidden, cell) = self.lstm(lstm_input, (hidden, cell))
+        # output : (SEQ_LENGTH, BATCH, HIDDEN_DIM)
+        # hidden : (NUM_LAYER, BATCH, HIDDEN_DIM)
+
+        output = output.to(self.device)
+
+        output = self.fc(output)
+        # output : (SEQ_LENGTH, BATCH, VOCAB_SIZE)
+
+        return output, (hidden, cell)

source/predict_sample.py

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+
+from torchvision import transforms
+import torch
+import torch.utils.data
+from PIL import Image
+
+from vocab import Vocab
+from model import Decoder, Encoder
+from config import Config
+
+
+def generate_caption(image: torch.Tensor,
+                     image_encoder: Encoder,
+                     emb_layer: torch.nn.Embedding,
+                     image_decoder: Decoder,
+                     vocab: Vocab,
+                     device: torch.device) -> list[str]:
+    """ Generate caption of a single image of size (1, 3, 224, 224)
+
+    Returns:
+        list[str]: caption for given image
+    """
+
+    image = image.to(device)
+    # image: (3, 224, 224)
+    image = image.unsqueeze(0)
+    # image: (1, 3, 224, 224)
+
+    features = image_encoder.forward(image)
+    # features: (1, IMAGE_EMB_DIM)
+    features = features.to(device)
+    features = features.unsqueeze(0)
+    # features: (1, 1, IMAGE_EMB_DIM)
+
+    hidden = image_decoder.hidden_state_0
+    cell = image_decoder.cell_state_0
+    # hidden, cell : (NUM_LAYER, 1, HIDDEN_DIM)
+
+    sentence = []
+
+    # start with '<sos>' as first word
+    previous_word = vocab.index2word[vocab.SOS]
+
+    MAX_LENGTH = 20
+
+    for i in range(MAX_LENGTH):
+
+        input_word_id = vocab.word_to_index(previous_word)
+        input_word_tensor = torch.tensor([input_word_id]).unsqueeze(0)
+        # input_word_tensor : (1, 1)
+
+        input_word_tensor = input_word_tensor.to(device)
+        lstm_input = emb_layer.forward(input_word_tensor)
+        # lstm_input : (1, 1, WORD_EMB_DIM)
+
+        next_word_pred, (hidden, cell) = image_decoder.forward(lstm_input, features, hidden, cell)
+        # next_word_pred : (1, 1, VOCAB_SIZE)
+
+        next_word_pred = next_word_pred[0, 0, :]
+        # next_word_pred : (VOCAB_SIZE)
+
+        next_word_pred = torch.argmax(next_word_pred)
+        next_word_pred = vocab.index_to_word(int(next_word_pred.item()))
+
+        # stop if we predict '<eos>'
+        if next_word_pred == vocab.index2word[vocab.EOS]:
+            break
+
+        sentence.append(next_word_pred)
+        previous_word = next_word_pred
+
+    return sentence
+
+
+def main_caption(image):
+
+    config = Config()
+
+    vocab = Vocab()
+    vocab.load_vocab(config.VOCAB_FILE)
+
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+
+    transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+    image = transform(image)
+
+    image_encoder = Encoder(image_emb_dim=config.IMAGE_EMB_DIM,
+                            device=config.DEVICE)
+    emb_layer = torch.nn.Embedding(num_embeddings=config.VOCAB_SIZE,
+                                   embedding_dim=config.WORD_EMB_DIM,
+                                   padding_idx=vocab.PADDING_INDEX)
+    image_decoder = Decoder(image_emb_dim=config.IMAGE_EMB_DIM,
+                            word_emb_dim=config.WORD_EMB_DIM,
+                            hidden_dim=config.HIDDEN_DIM,
+                            num_layers=config.NUM_LAYER,
+                            vocab_size=config.VOCAB_SIZE,
+                            device=config.DEVICE)
+
+    emb_layer.eval()
+    image_encoder.eval()
+    image_decoder.eval()
+
+    emb_layer.load_state_dict(torch.load(f=config.EMBEDDING_WEIGHT_FILE, map_location=config.DEVICE))
+    image_encoder.load_state_dict(torch.load(f=config.ENCODER_WEIGHT_FILE, map_location=config.DEVICE))
+    image_decoder.load_state_dict(torch.load(f=config.DECODER_WEIGHT_FILE, map_location=config.DEVICE))
+
+    emb_layer = emb_layer.to(config.DEVICE)
+    image_encoder = image_encoder.to(config.DEVICE)
+    image_decoder = image_decoder.to(config.DEVICE)
+    image = image.to(config.DEVICE)
+
+    sentence = generate_caption(image, image_encoder, emb_layer, image_decoder, vocab, device=config.DEVICE)
+    description = ' '.join(word for word in sentence)
+
+    return description

source/vocab.py

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+import os
+from collections import Counter
+from nltk.tokenize import RegexpTokenizer
+from config import Config
+
+
+class Vocab:
+    """
+    Offers word2index and index2word functionality after counting words in input sentences.
+    Allows choosing the size of the vocabulary by taking the most common words. Explicitly reserves four indices:
+    <pad>, <sos>, <eos> and <unk>.
+    """
+    def __init__(self, sentence_splitter=None):
+        """
+        Args:
+        sentence_splitter: tokenizing function
+        """
+        self.config = Config()
+
+        self.counter = Counter()
+        self.word2index = dict()
+        self.index2word = dict()
+        self.size = 0
+
+        # predefined tokens
+        self.PADDING_INDEX = 0
+        self.SOS = 1
+        self.EOS = 2
+        self.UNKNOWN_WORD_INDEX = 3
+
+        if sentence_splitter is None:
+            # matches sequences of characters including ones between < >
+            word_regex = r'(?:\w+|<\w+>)'
+            # tokenize the string into words
+            sentence_splitter = RegexpTokenizer(word_regex).tokenize
+        self.splitter = sentence_splitter
+
+    def add_sentence(self, sentence: str):
+        """
+        Update word counts from sentence after tokenizing it into words
+        """
+        self.counter.update(self.splitter(sentence))
+
+    def build_vocab(self, vocab_size: int, file_name: str):
+        """ Build vocabulary dictionaries word2index and index2word from a text file at config.ROOT path
+
+        Args:
+
+            vocab_size (int): size of vocabulary (including 4 predefined tokens: <pad>, <sos>, <eos>, <unk>)
+
+            file_name (str): name of the text file from which the vocabulary will be built.
+                Note: the lines in file are assumed to be in form: 'word SPACE index' and
+                it asssumes a header line (for example: 'captions.txt')
+        """
+
+        filepath = os.path.join(self.config.ROOT, file_name)
+
+        try:
+            with open(filepath, 'r', encoding='utf-8') as file:
+                for i, line in enumerate(file):
+                    # ignore header line
+                    if i == 0:
+                        continue
+                    caption = line.strip().lower().split(",", 1)[1]  # id=0, caption=1
+                    self.add_sentence(caption)
+        except Exception as e:
+            print(f"Error processing file {filepath}: {e}")
+            return
+
+        # adding predefined tokens in the vocabulary
+        self._add_predefined_tokens()
+
+        words = self.counter.most_common(vocab_size - 4)
+        # (index + 4) because first 4 tokens are the predefined ones
+        for index, (word, _) in enumerate(words, start=4):
+            self.word2index[word] = index
+            self.index2word[index] = word
+
+        self.size = len(self.word2index)
+
+    def _add_predefined_tokens(self):
+        predefined_tokens = ['<pad>', '<sos>', '<eos>', '<unk>']
+        for index, token in enumerate(predefined_tokens):
+            self.word2index[token] = index
+            self.index2word[index] = token
+
+    def word_to_index(self, word: str) -> int:
+        """ Map word to index from word2index dictionary in vocabulary
+
+        Args:
+            word (str): word to be mapped
+
+        Returns:
+            int: index matched to the word
+        """
+        try:
+            return self.word2index[word]
+        except KeyError:
+            return self.UNKNOWN_WORD_INDEX
+
+    def index_to_word(self, index: int) -> str:
+        """ Map word to index from index2word dictionary in vocabulary
+
+        Args:
+            word (str): index to be mapped
+
+        Returns:
+            str: word matched to the index
+        """
+        try:
+            return self.index2word[index]
+        except KeyError:
+            return self.index2word[self.UNKNOWN_WORD_INDEX]
+
+    def load_vocab(self, file_name: str):
+        """ Load the word2index and index2word dictionaries from a text file at config.ROOT path
+
+        Args:
+            file_name (str): name of the text file where the vocabulary is saved (i.e 'word2index.txt')
+                Note: the lines in file are assumed to be in form: 'word SPACE index' and it asssumes a header line
+        """
+
+        filepath = os.path.join(self.config.ROOT, file_name)
+
+        self.word2index = dict()
+        self.index2word = dict()
+
+        try:
+            with open(filepath) as file:
+                for line in file:
+                    line = line.strip().split(' ')
+                    word, index = line[0], line[1]
+                    self.word2index[word] = int(index)
+                    self.index2word[int(index)] = word
+        except Exception as e:
+            print(f"Error loading vocabulary from file {filepath}: {e}")

source/vocabulary.txt

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+<pad> 0
+<sos> 1
+<eos> 2
+<unk> 3
+a 4
+in 5
+the 6
+on 7
+and 8
+man 9
+is 10
+of 11
+with 12
+woman 13
+two 14
+are 15
+to 16
+people 17
+at 18
+an 19
+wearing 20
+shirt 21
+white 22
+young 23
+black 24
+while 25
+his 26
+blue 27
+red 28
+girl 29
+sitting 30
+men 31
+boy 32
+standing 33
+dog 34
+playing 35
+street 36
+group 37
+down 38
+front 39
+her 40
+walking 41
+holding 42
+one 43
+water 44
+three 45
+by 46
+women 47
+green 48
+up 49
+looking 50
+child 51
+as 52
+for 53
+little 54
+large 55
+outside 56
+brown 57
+yellow 58
+person 59
+children 60
+through 61
+hat 62
+from 63
+their 64
+other 65
+ball 66
+small 67
+next 68
+into 69
+over 70
+some 71
+dressed 72
+out 73
+another 74
+running 75
+building 76
+jacket 77
+riding 78
+around 79
+orange 80
+near 81
+field 82
+crowd 83
+stands 84
+beach 85
+background 86
+pink 87
+behind 88
+sidewalk 89
+hair 90
+jumping 91
+table 92
+girls 93
+snow 94
+sits 95
+grass 96
+bike 97
+that 98
+camera 99
+looks 100
+top 101
+air 102
+asian 103
+dogs 104
+stand 105
+wall 106
+city 107
+s 108
+player 109
+older 110
+off 111
+four 112
+has 113
+gray 114
+working 115
+soccer 116
+blond 117
+dress 118
+something 119
+it 120
+several 121
+talking 122
+shorts 123
+park 124
+lady 125
+him 126
+guitar 127
+picture 128
+walks 129
+pants 130
+boys 131
+hand 132
+smiling 133
+jeans 134
+plays 135
+play 136
+along 137
+dark 138
+road 139
+together 140
+game 141
+carrying 142
+holds 143
+car 144
+food 145
+bench 146
+them 147
+side 148
+walk 149
+long 150
+face 151
+baby 152
+there 153
+old 154
+glasses 155
+head 156
+stage 157
+sit 158
+back 159
+bicycle 160
+hands 161
+tree 162
+taking 163
+doing 164
+pool 165
+watching 166
+rock 167
+t 168
+guy 169
+male 170
+each 171
+couple 172
+baseball 173
+area 174
+ground 175
+who 176
+dirt 177
+he 178
+female 179
+middle 180
+being 181
+race 182
+striped 183
+boat 184
+day 185
+performing 186
+jumps 187
+mouth 188
+look 189
+construction 190
+suit 191
+coat 192
+purple 193
+room 194
+across 195
+many 196
+sunglasses 197
+eating 198
+football 199
+under 200
+sign 201
+horse 202
+runs 203
+band 204
+kids 205
+haired 206
+sand 207
+using 208
+uniform 209
+microphone 210
+helmet 211
+reading 212
+its 213
+tennis 214
+or 215
+watch 216
+players 217
+workers 218
+be 219
+store 220
+toy 221
+watches 222
+floor 223
+all 224
+shirts 225
+wooden 226
+rides 227
+against 228
+mountain 229
+this 230
+train 231
+ocean 232
+window 233
+during 234
+basketball 235
+cap 236
+about 237
+covered 238
+work 239
+chair 240
+dancing 241
+elderly 242
+busy 243
+clothing 244
+bag 245
+team 246
+climbing 247
+waiting 248
+getting 249
+having 250
+trees 251
+swimming 252
+outdoor 253
+past 254
+they 255
+restaurant 256
+colorful 257
+making 258
+posing 259
+inside 260
+singing 261
+trying 262
+fence 263
+skateboard 264
+five 265
+tan 266
+light 267
+high 268
+line 269
+market 270
+brick 271
+ice 272
+she 273
+hats 274
+very 275
+big 276
+ride 277
+towards 278
+colored 279
+sweater 280
+someone 281
+beside 282
+lake 283
+jump 284
+motorcycle 285
+cart 286
+truck 287
+hill 288
+open 289
+surrounded 290
+bright 291
+cellphone 292
+grassy 293
+kitchen 294
+laying 295
+path 296
+arms 297
+outdoors 298
+stick 299
+book 300
+body 301
+track 302
+clothes 303
+vest 304
+others 305
+piece 306
+umbrella 307
+river 308
+bus 309
+swing 310
+like 311
+tank 312
+outfit 313
+shopping 314
+night 315
+worker 316
+painting 317
+above 318
+trick 319
+american 320
+enjoying 321
+uniforms 322
+event 323
+stone 324
+snowy 325
+hanging 326
+run 327
+guys 328
+hard 329
+metal 330
+drinking 331
+ready 332
+full 333
+paper 334
+toddler 335
+music 336
+sleeping 337
+african 338
+adults 339
+house 340
+arm 341
+away 342
+takes 343
+right 344
+onto 345
+shoes 346
+going 347
+backpack 348
+phone 349
+gathered 350
+pole 351
+shop 352
+bridge 353
+rocks 354
+set 355
+wave 356
+dance 357
+kid 358
+both 359
+smiles 360
+photo 361
+after 362
+family 363
+rope 364
+hockey 365
+board 366
+leaning 367
+steps 368
+flowers 369
+crowded 370
+catch 371
+what 372
+pose 373
+subway 374
+left 375
+lot 376
+short 377
+gear 378
+preparing 379
+cowboy 380
+skirt 381
+bar 382
+have 383
+shirtless 384
+flag 385
+sunny 386
+which 387
+racing 388
+costume 389
+fishing 390
+fire 391
+between 392
+fountain 393
+chairs 394
+laughing 395
+machine 396
+number 397
+corner 398
+public 399
+setting 400
+wood 401
+glass 402
+instruments 403
+drink 404
+plaid 405
+volleyball 406
+frisbee 407
+stairs 408
+graffiti 409
+appears 410
+bags 411
+adult 412
+tall 413
+plastic 414
+couch 415
+concrete 416
+selling 417
+police 418
+seated 419
+beautiful 420
+sweatshirt 421
+midair 422
+forest 423
+ramp 424
+equipment 425
+cooking 426
+scarf 427
+computer 428
+does 429
+woods 430
+slide 431
+putting 432
+beard 433
+object 434
+view 435
+edge 436
+toward 437
+skateboarder 438
+pushing 439
+2 440
+sky 441
+mountains 442
+take 443
+bed 444
+ladies 445
+distance 446
+boots 447
+nearby 448
+throwing 449
+pictures 450
+rider 451
+bikes 452
+get 453
+where 454
+poses 455
+aged 456
+just 457
+sun 458
+buildings 459
+jersey 460
+door 461
+audience 462
+cigarette 463
+performs 464
+painted 465
+playground 466
+crossing 467
+filled 468
+winter 469
+fish 470
+pulling 471
+eyes 472
+vehicle 473
+lawn 474
+smoking 475
+statue 476
+parade 477
+instrument 478
+trail 479
+leaves 480
+party 481
+rocky 482
+driving 483
+school 484
+horses 485
+station 486
+cleaning 487
+works 488
+six 489
+friends 490
+lying 491
+yard 492
+cars 493
+giving 494
+few 495
+safety 496
+gentleman 497
+cutting 498
+beer 499
+cross 500
+concert 501
+art 502
+cream 503
+apron 504
+no 505
+mother 506
+gloves 507
+net 508
+smile 509
+shore 510
+carries 511
+structure 512
+martial 513
+roof 514
+showing 515
+bottle 516
+shoulder 517
+hit 518
+court 519
+snowboarder 520
+jackets 521
+wet 522
+hold 523
+reads 524
+fruit 525
+perform 526
+rodeo 527
+cup 528
+drums 529
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