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train.py
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train.py
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import os
import torch.utils.data
from torch.nn import DataParallel
from datetime import datetime
from torch.optim.lr_scheduler import MultiStepLR
from config import BATCH_SIZE, PROPOSAL_NUM, SAVE_FREQ, LR, WD, resume, save_dir
from core import model, dataset
from core.utils import init_log, progress_bar
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
start_epoch = 1
save_dir = os.path.join(save_dir, datetime.now().strftime('%Y%m%d_%H%M%S'))
if os.path.exists(save_dir):
raise NameError('model dir exists!')
os.makedirs(save_dir)
logging = init_log(save_dir)
_print = logging.info
# read dataset
trainset = dataset.CUB(root='./CUB_200_2011', is_train=True, data_len=None)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=8, drop_last=False)
testset = dataset.CUB(root='./CUB_200_2011', is_train=False, data_len=None)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=8, drop_last=False)
# define model
net = model.attention_net(topN=PROPOSAL_NUM)
if resume:
ckpt = torch.load(resume)
net.load_state_dict(ckpt['net_state_dict'])
start_epoch = ckpt['epoch'] + 1
creterion = torch.nn.CrossEntropyLoss()
# define optimizers
raw_parameters = list(net.pretrained_model.parameters())
part_parameters = list(net.proposal_net.parameters())
concat_parameters = list(net.concat_net.parameters())
partcls_parameters = list(net.partcls_net.parameters())
raw_optimizer = torch.optim.SGD(raw_parameters, lr=LR, momentum=0.9, weight_decay=WD)
concat_optimizer = torch.optim.SGD(concat_parameters, lr=LR, momentum=0.9, weight_decay=WD)
part_optimizer = torch.optim.SGD(part_parameters, lr=LR, momentum=0.9, weight_decay=WD)
partcls_optimizer = torch.optim.SGD(partcls_parameters, lr=LR, momentum=0.9, weight_decay=WD)
schedulers = [MultiStepLR(raw_optimizer, milestones=[60, 100], gamma=0.1),
MultiStepLR(concat_optimizer, milestones=[60, 100], gamma=0.1),
MultiStepLR(part_optimizer, milestones=[60, 100], gamma=0.1),
MultiStepLR(partcls_optimizer, milestones=[60, 100], gamma=0.1)]
net = net.cuda()
net = DataParallel(net)
for epoch in range(start_epoch, 500):
for scheduler in schedulers:
scheduler.step()
# begin training
_print('--' * 50)
net.train()
for i, data in enumerate(trainloader):
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
raw_optimizer.zero_grad()
part_optimizer.zero_grad()
concat_optimizer.zero_grad()
partcls_optimizer.zero_grad()
raw_logits, concat_logits, part_logits, _, top_n_prob = net(img)
part_loss = model.list_loss(part_logits.view(batch_size * PROPOSAL_NUM, -1),
label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1)).view(batch_size, PROPOSAL_NUM)
raw_loss = creterion(raw_logits, label)
concat_loss = creterion(concat_logits, label)
rank_loss = model.ranking_loss(top_n_prob, part_loss)
partcls_loss = creterion(part_logits.view(batch_size * PROPOSAL_NUM, -1),
label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1))
total_loss = raw_loss + rank_loss + concat_loss + partcls_loss
total_loss.backward()
raw_optimizer.step()
part_optimizer.step()
concat_optimizer.step()
partcls_optimizer.step()
progress_bar(i, len(trainloader), 'train')
if epoch % SAVE_FREQ == 0:
train_loss = 0
train_correct = 0
total = 0
net.eval()
for i, data in enumerate(trainloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
_, concat_logits, _, _, _ = net(img)
# calculate loss
concat_loss = creterion(concat_logits, label)
# calculate accuracy
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
train_correct += torch.sum(concat_predict.data == label.data)
train_loss += concat_loss.item() * batch_size
progress_bar(i, len(trainloader), 'eval train set')
train_acc = float(train_correct) / total
train_loss = train_loss / total
_print(
'epoch:{} - train loss: {:.3f} and train acc: {:.3f} total sample: {}'.format(
epoch,
train_loss,
train_acc,
total))
# evaluate on test set
test_loss = 0
test_correct = 0
total = 0
for i, data in enumerate(testloader):
with torch.no_grad():
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
_, concat_logits, _, _, _ = net(img)
# calculate loss
concat_loss = creterion(concat_logits, label)
# calculate accuracy
_, concat_predict = torch.max(concat_logits, 1)
total += batch_size
test_correct += torch.sum(concat_predict.data == label.data)
test_loss += concat_loss.item() * batch_size
progress_bar(i, len(testloader), 'eval test set')
test_acc = float(test_correct) / total
test_loss = test_loss / total
_print(
'epoch:{} - test loss: {:.3f} and test acc: {:.3f} total sample: {}'.format(
epoch,
test_loss,
test_acc,
total))
# save model
net_state_dict = net.module.state_dict()
if not os.path.exists(save_dir):
os.mkdir(save_dir)
torch.save({
'epoch': epoch,
'train_loss': train_loss,
'train_acc': train_acc,
'test_loss': test_loss,
'test_acc': test_acc,
'net_state_dict': net_state_dict},
os.path.join(save_dir, '%03d.ckpt' % epoch))
print('finishing training')