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67f827d | 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 | import torchvision.transforms as T
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from tqdm import tqdm
from dataset import *
from model import *
from utils import *
spacy_eng = spacy.load('en')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# init seed
seed = torch.randint(100, (1,))
torch.manual_seed(seed)
shuffle = True
# src folders
root_folder = "/content/flickr8k/Images" # change this
csv_file = "/content/flickr8k/captions.txt" # change this
# image transforms and augmentation
transforms = T.Compose([
T.Resize(226),
T.RandomCrop(224),
T.ToTensor(),
T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# define dataset
dataset = FlickrDataset(root_folder, csv_file, transforms)
# split dataset
val_size = 512
test_size = 256
train_size = len(dataset) - val_size - test_size
train_ds, val_ds, test_ds = random_split(dataset,
[train_size, val_size, test_size])
# Define data loader parameters
num_workers = 4
pin_memory = True
batch_size_train = 256
batch_size_val_test = 128
pad_idx = dataset.vocab.stoi["<PAD>"]
# define loaders
dataloader_train = DataLoader(train_ds,
batch_size=batch_size_train,
pin_memory=pin_memory,
num_workers=num_workers,
shuffle=shuffle,
collate_fn=CapsCollate(pad_idx=pad_idx, batch_first=True))
dataloader_validation = DataLoader(val_ds,
batch_size=batch_size_val_test,
pin_memory=pin_memory,
num_workers=num_workers,
shuffle=shuffle,
collate_fn=CapsCollate(pad_idx=pad_idx, batch_first=True))
dataloader_test = DataLoader(test_ds,
batch_size=batch_size_val_test,
pin_memory=pin_memory,
num_workers=num_workers,
shuffle=shuffle,
collate_fn=CapsCollate(pad_idx=pad_idx, batch_first=True))
# model parameters
embed_wts, embed_size = load_embeding("/content/glove.42B.300d.txt", dataset.vocab) # change path
vocab_size = len(dataset.vocab)
attention_dim = 256
encoder_dim = 2048
decoder_dim = 512
fc_dims = 256
learning_rate = 5e-4
model = EncoderDecoder(embed_size,
vocab_size,
attention_dim,
encoder_dim,
decoder_dim,
fc_dims,
p=0.3,
embeddings=embed_wts).to(device)
loss_fn = nn.CrossEntropyLoss(ignore_index=dataset.vocab.stoi["<PAD>"])
optimizer = optim.Adam(params=model.parameters(), lr=learning_rate)
# training parmeters
num_epochs = 35
train_loss_arr = []
val_loss_arr = []
def training(dataset, dataloader, loss_criteria, optimize, grad_clip=5.):
total_loss = 0
for i, (img, cap) in enumerate(tqdm(dataloader, total=len(dataloader))):
img, cap = img.to(device), cap.to(device)
optimize.zero_grad()
output, attention = model(img, cap)
targets = cap[:, 1:]
loss = loss_criteria(output.view(-1, vocab_size), targets.reshape(-1))
total_loss += (loss.item())
loss.backward()
if grad_clip:
nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
optimize.step()
total_loss = total_loss / len(dataloader)
return total_loss
@torch.no_grad()
def validate(dataset, dataloader, loss_cr):
total_loss = 0
for val_img, val_cap in tqdm(dataloader, total=len(dataloader)):
val_img, val_cap = val_img.to(device), val_cap.to(device)
output, attention = model(val_img, val_cap)
targets = val_cap[:, 1:]
loss = loss_cr(output.view(-1, vocab_size), targets.reshape(-1))
total_loss += (loss.item())
total_loss /= len(dataloader)
return total_loss
# for see results while training
@torch.no_grad()
def test_on_img(data, dataloader):
dataiter = iter(dataloader)
img, cap = next(dataiter)
features = model.EncoderCNN(img[0:1].to(device))
caps, alphas = model.DecoderLSTM.gen_captions(features, vocab=data.vocab)
caption = ' '.join(caps)
show_img(img[0], caption)
def main():
best_val_loss = 6.0
for epoch in range(num_epochs):
print(f"Epoch: {epoch + 1}/{num_epochs}")
model.train()
train_loss = training(dataset, dataloader_train, loss_fn, optimizer)
train_loss_arr.append(train_loss)
model.eval()
val_loss = validate(dataset, dataloader_validation, loss_fn)
val_loss_arr.append(val_loss)
print(f"train_loss: {train_loss} validation_loss: {val_loss}")
test_on_img(dataset, dataloader_validation)
if len(val_loss_arr) == 1 or val_loss < best_val_loss:
best_val_loss = val_loss
save_model(model, epoch, optimizer, train_loss, val_loss, vocab=dataset.vocab)
print("best model saved successfully")
if __name__ == "__main__":
print(torch.cuda.is_available())
main()
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