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from torchvision import transforms
import torch
import torch.utils.data
from PIL import Image
from source.vocab import Vocab
from source.model import Decoder, Encoder
from source.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 (3, 224, 224).
Generating of caption starts with <sos>, and each next predicted word ID
is appended for the next LSTM input until the sentence reaches MAX_LENGTH or <eos>.
Returns:
list[str]: caption for given image
"""
image = image.to(device)
# image: (3, 224, 224)
image = image.unsqueeze(0)
# image: (1, 3, 224, 224)
hidden = image_decoder.hidden_state_0
cell = image_decoder.cell_state_0
# hidden, cell : (NUM_LAYER, 1, HIDDEN_DIM)
sentence = []
# initialize LSTM input to SOS token = 1
input_words = [vocab.SOS]
MAX_LENGTH = 20
for i in range(MAX_LENGTH):
features = image_encoder.forward(image)
# features: (1, IMAGE_EMB_DIM)
features = features.to(device)
features = features.unsqueeze(0)
# features: (1, 1, IMAGE_EMB_DIM)
input_words_tensor = torch.tensor([input_words])
# input_word_tensor : (B=1, SEQ_LENGTH)
input_words_tensor = input_words_tensor.to(device)
lstm_input = emb_layer.forward(input_words_tensor)
# lstm_input : (B=1, SEQ_LENGTH, WORD_EMB_DIM)
lstm_input = lstm_input.permute(1, 0, 2)
# lstm_input : (SEQ_LENGTH, B=1, WORD_EMB_DIM)
SEQ_LENGTH = lstm_input.shape[0]
features = features.repeat(SEQ_LENGTH, 1, 1)
# features : (SEQ_LENGTH, B=1, IMAGE_EMB_DIM)
next_id_pred, (hidden, cell) = image_decoder.forward(lstm_input, hidden, cell)
# next_id_pred : (SEQ_LENGTH, 1, VOCAB_SIZE)
next_id_pred = next_id_pred[-1, 0, :]
# next_id_pred : (VOCAB_SIZE)
next_id_pred = torch.argmax(next_id_pred)
# append it to input_words which will be again as input for LSTM
input_words.append(next_id_pred.item())
# id --> word
next_word_pred = vocab.index_to_word(int(next_id_pred.item()))
if next_word_pred == vocab.index2word[vocab.EOS]:
break
sentence.append(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(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
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