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train.py
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train.py
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import os
import pdb
import sys
from timeit import default_timer as timer
import torch
import numpy as np
from loss import CtdetLoss
from torch.utils.data import DataLoader
from dataset import ctDataset
from DLAnet import DlaNet
import torch.nn as nn
import torch.optim as optim
import argparse
from utils import snapshot, setup_logs
run_name = "zjn-cennet"
print(run_name)
class ScheduledOptim(object):
"""A simple wrapper class for learning rate scheduling"""
def __init__(self, optimizer, n_warmup_steps):
self.optimizer = optimizer
self.d_model = 128
self.n_warmup_steps = n_warmup_steps
self.n_current_steps = 0
self.delta = 1
def state_dict(self):
self.optimizer.state_dict()
def step(self):
"""Step by the inner optimizer"""
self.optimizer.step()
def zero_grad(self):
"""Zero out the gradients by the inner optimizer"""
self.optimizer.zero_grad()
def increase_delta(self):
self.delta *= 2
def update_learning_rate(self):
"""Learning rate scheduling per step"""
self.n_current_steps += self.delta
new_lr = np.power(self.d_model, -0.5) * np.min([
np.power(self.n_current_steps, -0.5),
np.power(self.n_warmup_steps, -1.5) * self.n_current_steps])
for param_group in self.optimizer.param_groups:
param_group['lr'] = new_lr
return new_lr
def main():
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--epochs', type=int, default=200, metavar='N',
help='number of epochs to train')
parser.add_argument('--n-warmup-steps', type=int, default=50)
parser.add_argument('--logging-dir', required=True,
help='model save directory')
args = parser.parse_args()
use_gpu = torch.cuda.is_available()
print("Use CUDA? ", use_gpu)
logger = setup_logs(args.logging_dir, run_name) # setup logs
model = DlaNet(34)
if (use_gpu):
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# model = nn.DataParallel(model)
# print('Using ', torch.cuda.device_count(), "CUDAs")
print('cuda', torch.cuda.current_device(), torch.cuda.device_count())
device = torch.device("cuda")
model.cuda()
else:
device = torch.device("cpu")
loss_weight = {'hm_weight':1, 'wh_weight':0.1, 'reg_weight':0.1}
criterion = CtdetLoss(loss_weight)
model.train()
#learning_rate = 5e-4
#num_epochs = args.epochs
# different learning rate
# params=[]
# params_dict = dict(model.named_parameters())
# for key, value in params_dict.items():
# params += [{'params':[value], 'lr':learning_rate}]
#optimizer = torch.optim.Adam(params, lr=learning_rate, weight_decay=1e-4)
optimizer = ScheduledOptim(
optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
betas=(0.9, 0.98), eps=1e-09, weight_decay=1e-4, amsgrad=True),args.n_warmup_steps)
# split into training and testing set
full_dataset = ctDataset()
full_dataset_len = full_dataset.__len__()
print("Full dataset has ", full_dataset_len, " images.")
train_size = int(0.8 * full_dataset_len)
test_size = full_dataset_len - train_size
torch.manual_seed(42)
train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])
print("Training set and testing set has: ", train_dataset.__len__(), \
" and ", test_dataset.__len__(), " images respectively.")
logger.info('===> loading train, validation and eval dataset')
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=0)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False, num_workers=0)
model_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info('### Model summary below###\n {}\n'.format(str(model)))
logger.info('===> Model total parameter: {}\n'.format(model_params))
best_test_loss = np.inf
loss_log = np.empty((0, 3))
best_acc = 0
best_loss = np.inf
best_epoch = -1
for epoch in range(1, args.epochs + 1):
epoch_timer = timer()
model.train()
# if epoch == 45:
# learning_rate = learning_rate * 0.1
# if epoch == 60:
# learning_rate = learning_rate * (0.1 ** 2)
# for param_group in optimizer.param_groups:
# param_group['lr'] = learning_rate
total_loss = 0.0
#pdb.set_trace()
for i, sample in enumerate(train_loader):
for k in sample:
if k != 'ori_index':
sample[k] = sample[k].to(device=device, non_blocking=True)
pred = model(sample['input'])
loss = criterion(pred, sample)
total_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr = optimizer.update_learning_rate()
if (i+1) % 5 == 0:
print ('Epoch [%d/%d], Iter [%d/%d] lr: %.4f, Loss: %.4f, average_loss: %.4f'
%(epoch, args.epochs, i+1, len(train_loader), lr, loss.data, total_loss / (i+1)))
# validation
validation_loss = 0.0
model.eval()
for i, sample in enumerate(test_loader):
if use_gpu:
for k in sample:
if k != 'ori_index':
sample[k] = sample[k].to(device=device, non_blocking=True)
pred = model(sample['input'])
loss = criterion(pred, sample)
validation_loss += loss.item()
validation_loss /= len(test_loader)
print('Epoch [%d/%d] Validation loss %.5f' % (epoch, args.epochs, validation_loss))
loss_log = np.append(loss_log, [[epoch, total_loss / len(train_loader), validation_loss]], axis=0)
np.savetxt('../loss_log.csv', loss_log, delimiter=',')
# Save
# if validation_loss > best_acc:
# best_acc = max(validation_loss, best_acc)
if validation_loss < best_loss:
best_loss = min(validation_loss, best_loss)
snapshot(args.logging_dir, run_name, {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'validation_loss': validation_loss,
'optimizer': optimizer.state_dict(),
})
best_epoch = epoch + 1
elif epoch - best_epoch > 2:
optimizer.increase_delta()
best_epoch = epoch + 1
end_epoch_timer = timer()
logger.info("#### End epoch {}/{}, elapsed time: {}".format(epoch, args.epochs, end_epoch_timer - epoch_timer))
if __name__ == "__main__":
main()