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train.py
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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from data import BBDataset
from torch.utils.data import DataLoader
from models.model import SAAN
import torch.optim as optim
from common import *
import argparse
train_dataset = BBDataset(file_dir='dataset', type='train', test=False)
val_dataset = BBDataset(file_dir='dataset', type='validation', test=True)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--lr', type=float, default=1e-5)
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--checkpoint_dir', type=str,
default='checkpoint/SAAN')
parser.add_argument('--val_freq', type=int,
default=2)
parser.add_argument('--save_freq', type=int,
default=2)
return parser.parse_args()
def validate(args, model, val_loader, epoch):
model.eval()
device = args.device
loss = nn.MSELoss()
val_loss = 0.0
with torch.no_grad():
for step, val_data in enumerate(val_loader):
image = val_data[0].to(device)
label = val_data[1].to(device).float()
predicted_label = model(image).squeeze()
val_loss += loss(predicted_label, label).item()
val_loss /= len(val_loader)
print("Epoch: %3d Validation loss: %.8f" % (epoch, val_loss))
def train(args):
device = args.device
model = SAAN(num_classes=1)
for name, param in model.named_parameters():
if 'GenAes' in name:
param.requires_grad = False
model = model.to(device)
loss = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.5, 0.999), weight_decay=5e-4)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=8)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, pin_memory=True)
for epoch in range(args.epoch):
model.train()
epoch_loss = 0.0
for step, train_data in enumerate(train_loader):
optimizer.zero_grad()
image = train_data[0].to(device)
label = train_data[1].to(device).float()
predicted_label = model(image).squeeze()
train_loss = loss(predicted_label, label)
train_loss.backward()
optimizer.step()
epoch_loss += train_loss.item()
print("Epoch: %3d Step: %5d / %5d Train loss: %.8f" % (epoch, step, len(train_loader), train_loss.item()))
adjust_learning_rate(args, optimizer, epoch)
if (epoch + 1) % args.val_freq == 0:
validate(args, model, val_loader, epoch)
if (epoch + 1) % args.save_freq == 0:
save_checkpoint(args, model, epoch)
if __name__ == '__main__':
args = parse_args()
train(args)