num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
min_mean_error = 1000.0
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
# print(epoch)
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = utils.lr_poly(p['lr'], epoch, nEpochs, 0.9)
print('(poly lr policy) learning rate: ', lr_)
optimizer = optim.Adam(net.parameters(),
lr=p['lr'],
weight_decay=p['wd'])
net.train()
for ii, sample_batched in enumerate(trainloader):
# inputs, labels = sample_batched['image'], sample_batched['label']
inputs, labels = sample_batched[0], sample_batched[1]
# Forward-Backward of the mini-batch
inputs, labels = Variable(inputs,
requires_grad=True), Variable(labels)
global_step += inputs.data.shape[0]
utils.generate_param_report(os.path.join(save_dir, exp_name + '.txt'), p)
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
print("Training Network")
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = utils.lr_poly(p['lr'], epoch, nEpochs, 0.9)
print('(poly lr policy) learning rate: ', lr_)
optimizer = optim.SGD(net.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd'])
net.train()
for ii, sample_batched in enumerate(trainloader):
inputs, gts = sample_batched['image'], sample_batched['gt']
# Forward-Backward of the mini-batch
inputs, gts = Variable(inputs, requires_grad=True), Variable(gts)
if gpu_id >= 0:
inputs, gts = inputs.cuda(), gts.cuda()
output = net.forward(inputs)
