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train.py
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train.py
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import os
import sys
import argparse
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torch.autograd import Variable
from utils import get_model, WarmUpLR
from dataset import get_imagefolder_train_loader, get_imagefoler_val_loader, get_custom_train_loader, get_custom_val_loader
import conf
def train(model, epoch, train_loader, loss_function, optimizer, warmup_scheduler, use_gpu):
model.train()
for batch_index, (images, labels) in enumerate(train_loader):
if epoch <= conf.WARM_EPOCH:
warmup_scheduler.step()
images = Variable(images)
labels = Variable(labels)
if use_gpu:
labels = labels.cuda()
images = images.cuda()
optimizer.zero_grad()
outputs = model(images)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
print('Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLR: {:0.6f}'.format(
loss.item(),
optimizer.param_groups[0]['lr'],
epoch=epoch,
trained_samples=batch_index * conf.TRAINING_BATCH_SIZE + len(images),
total_samples=len(train_loader.dataset)
))
return
def eval(model, epoch, val_loader, loss_function, use_gpu):
model.eval()
loss = 0.0
correct = 0.0
for (images, labels) in val_loader:
images = Variable(images)
labels = Variable(labels)
if use_gpu:
images = images.cuda()
labels = labels.cuda()
outputs = model(images)
loss = loss_function(outputs, labels)
loss += loss.item()
_, preds = outputs.max(1)
correct += preds.eq(labels).sum()
print('Test set: Average loss: {:.4f}, Accuracy: {:.4f}'.format(
loss / len(val_loader.dataset),
correct.float() / len(val_loader.dataset)
))
return correct.float() / len(val_loader.dataset)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-model', type = str, required = True, help = 'model type')
parser.add_argument('-gpu', action="store_true", help = 'use gpu or not')
args = parser.parse_args()
print(args.model)
print(args.gpu)
model = get_model(model_type = args.model, use_gpu = args.gpu)
train_loader = get_imagefolder_train_loader()
#train_loader = get_custom_train_loader()
print('get train loader done')
val_loader = get_imagefoler_val_loader()
#val_loader = get_custom_val_loader()
print('get val loader done')
checkpoints_path = os.path.join(conf.CHECKPOINTS_PATH, args.model, datetime.now().isoformat())
if not os.path.exists(checkpoints_path):
os.makedirs(checkpoints_path)
checkpoints_path = os.path.join(checkpoints_path, '{model}-{epoch}-{type}.pth')
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=conf.LEARNING_RATE, momentum=conf.MOMENTUM, weight_decay=conf.WEIGHT_DECAY)
train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=conf.MILESTONES, gamma=conf.GAMMA)
iter_per_epoch = len(train_loader)
warmup_scheduler = WarmUpLR(optimizer, iter_per_epoch * conf.WARM_EPOCH)
best_acc = 0.0
for epoch in range(1, conf.EPOCH):
if epoch > conf.WARM_EPOCH:
train_scheduler.step(epoch)
train(model, epoch, train_loader, loss_function, optimizer, warmup_scheduler, args.gpu)
acc = eval(model, epoch, val_loader, loss_function, args.gpu)
if best_acc < acc:
torch.save(model.state_dict(), checkpoints_path.format(model=args.model, epoch=epoch, type='best'))
best_acc = acc
continue
#if not epoch % conf.SAVE_EPOCH:
# torch.save(model.state_dict(), checkpoints_path.format(model=args.model, epoch=epoch, type='regular'))