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train.py
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train.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from utils import save_checkpoint, load_checkpoint, print_examples
from loader import get_loader
from model import CNNtoRNN
def train():
transform = transforms.Compose([
transforms.Resize((356, 356)),
transforms.RandomCrop((299,299)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_loader, dataset = get_loader(
root_folder = 'flickr8k/images/',
annotation_file = 'flickr8k/captions.txt',
transform = transform,
num_workers = 2
)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
load_model = False
save_model = True
embed_size = 256
hidden_size = 256
vocab_size = len(dataset.vocab)
num_layers = 1
learning_rate = 3e-4
num_epochs = 100
writer = SummaryWriter('logs/flickr')
step = 0
model = CNNtoRNN(embed_size, hidden_size, vocab_size, num_layers).to(device)
criterion = nn.CrossEntropyLoss(ignore_index=dataset.vocab.stoi['<PAD>'])
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
if load_model:
step = load_checkpoint(torch.load('my_ckpt.pth.tar'), model, optimizer)
model.train()
for epoch in range(num_epochs):
print_examples(model, device, epoch)
if save_model:
checkpoint = {
'state_dict' : model.state_dict(),
'optimizer' : optimizer.state_dict(),
'step' : step,
}
save_checkpoint(checkpoint)
for idx, (imgs, captions) in enumerate(train_loader):
imgs = imgs.to(device)
captions = captions.to(device)
outputs = model(imgs, captions)[:-1]
loss = criterion(outputs.reshape(-1, outputs.shape[2]), captions.reshape(-1))
writer.add_scalar('loss', loss.item(), global_step=step)
step += 1
optimizer.zero_grad()
loss.backward()
optimizer.step()
if __name__ == '__main__':
train()