def main_train():
f = open(args.save_dir + 'train_log.txt', 'w')
copyfile('./train.py', args.save_dir + '/train.py')
copyfile('./model.py', args.save_dir + '/model.py')
model = PCB(len(datas["class"]))
if gpu:
model = model.cuda()
if is_parallel_train:
model = nn.DataParallel(model, device_ids=gpu_ids)
criterion = nn.CrossEntropyLoss()
model = pcb_train(model, criterion, f, "PCB", 60)
if args.RPP:
model = get_net(is_parallel_train, model).convert_to_rpp()
if use_gpu:
model = model.cuda()
if is_parallel_train:
model = nn.DataParallel(model, device_ids=gpu_ids)
model = rpp_train(model, criterion, f, "RPP", 5)
model = full_train(model, criterion, f, "full", 10)
f.close()
#
if opt.use_dense:
model = ft_net_dense(len(class_names))
else:
model = ft_net(len(class_names))
if opt.PCB:
model = PCB(len(class_names))
model_verif = verif_net()
# print(model)
# print(model_verif)
if use_gpu:
model = model.cuda()
model_verif = model_verif.cuda()
criterion = nn.CrossEntropyLoss()
if not opt.PCB:
ignored_params = list(map(id, model.model.fc.parameters())) + list(map(id, model.classifier.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params, model.parameters())
optimizer_ft = optim.SGD([
{'params': base_params, 'lr': 0.1 * opt.lr},
{'params': model.model.fc.parameters(), 'lr': opt.lr},
{'params': model.classifier.parameters(), 'lr': opt.lr},
{'params': model_verif.classifier.parameters(), 'lr': opt.lr}
], weight_decay=5e-4, momentum=0.9, nesterov=True)
else:
ignored_params = list(map(id, model.model.fc.parameters()))
######################################################################
# Train and evaluate
# ^^^^^^^^^^^^^^^^^^
#
# It should take around 1-2 hours on GPU.
#
dir_name = os.path.join('./model', name)
if not os.path.exists('model'):
os.mkdir('model')
print('class_num = %d' % (class_num))
model = PCB(class_num, train=True)
if use_gpu:
model.cuda()
# print('model structure')
# print(model)
criterion = nn.CrossEntropyLoss()
criterion_soft = SoftLabelLoss()
classifier_id = (list(map(id, model.classifier0.parameters())) +
list(map(id, model.classifier1.parameters())) +
list(map(id, model.classifier2.parameters())) +
list(map(id, model.classifier3.parameters())) +
list(map(id, model.classifier4.parameters())) +
list(map(id, model.classifier5.parameters())))
classifier_params = filter(lambda p: id(p) in classifier_id,
model.parameters())
base_params = filter(lambda p: id(p) not in classifier_id, model.parameters())
def train(opt):
version = torch.__version__
fp16 = opt.fp16
data_dir = opt.data_dir
name = opt.name
str_ids = opt.gpu_ids.split(',')
gpu_ids = []
for str_id in str_ids:
gid = int(str_id)
if gid >= 0:
gpu_ids.append(gid)
# set gpu ids
if len(gpu_ids) > 0:
torch.cuda.set_device(gpu_ids[0])
cudnn.benchmark = True
######################################################################
# Load Data
# ---------
#
transform_train_list = [
# transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
transforms.Resize((256, 128), interpolation=3),
transforms.Pad(10),
transforms.RandomCrop((256, 128)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_val_list = [
transforms.Resize(size=(256, 128), interpolation=3), # Image.BICUBIC
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if opt.PCB:
transform_train_list = [
transforms.Resize((384, 192), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_val_list = [
transforms.Resize(size=(384, 192),
interpolation=3), # Image.BICUBIC
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + [
RandomErasing(probability=opt.erasing_p, mean=[0.0, 0.0, 0.0])
]
if opt.color_jitter:
transform_train_list = [
transforms.ColorJitter(
brightness=0.1, contrast=0.1, saturation=0.1, hue=0)
] + transform_train_list
# print(transform_train_list)
data_transforms = {
'train': transforms.Compose(transform_train_list),
'val': transforms.Compose(transform_val_list),
}
train_all = ''
if opt.train_all:
train_all = '_all'
image_datasets = {}
image_datasets['train'] = datasets.ImageFolder(
os.path.join(data_dir, 'train' + train_all), data_transforms['train'])
image_datasets['val'] = datasets.ImageFolder(os.path.join(data_dir, 'val'),
data_transforms['val'])
dataloaders = {
x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=opt.batchsize,
shuffle=True,
num_workers=8,
pin_memory=True)
# 8 workers may work faster
for x in ['train', 'val']
}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
use_gpu = torch.cuda.is_available()
#since = time.time()
#inputs, classes = next(iter(dataloaders['train']))
#print('time used for loading data: %ds' %(time.time() - since))
######################################################################
# Training the model
# ------------------
#
# Now, let's write a general function to train a model. Here, we will
# illustrate:
#
# - Scheduling the learning rate
# - Saving the best model
#
# In the following, parameter ``scheduler`` is an LR scheduler object from
# ``torch.optim.lr_scheduler``.
y_loss = {} # loss history
y_loss['train'] = []
y_loss['val'] = []
y_err = {}
y_err['train'] = []
y_err['val'] = []
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
results = []
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train(True) # Set model to training mode
else:
model.train(False) # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0.0
# Iterate over data.
pbar = tqdm(dataloaders[phase])
for inputs, labels in pbar:
# get the inputs
now_batch_size, c, h, w = inputs.shape
if now_batch_size < opt.batchsize: # skip the last batch
continue
# print(inputs.shape)
# wrap them in Variable
if use_gpu:
inputs = Variable(inputs.cuda().detach())
labels = Variable(labels.cuda().detach())
else:
inputs, labels = Variable(inputs), Variable(labels)
# if we use low precision, input also need to be fp16
# if fp16:
# inputs = inputs.half()
# zero the parameter gradients
optimizer.zero_grad()
# forward
if phase == 'val':
with torch.no_grad():
outputs = model(inputs)
else:
outputs = model(inputs)
if not opt.PCB:
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
else:
part = {}
sm = nn.Softmax(dim=1)
num_part = 6
for i in range(num_part):
part[i] = outputs[i]
score = sm(part[0]) + sm(part[1]) + sm(part[2]) + sm(
part[3]) + sm(part[4]) + sm(part[5])
_, preds = torch.max(score.data, 1)
loss = criterion(part[0], labels)
for i in range(num_part - 1):
loss += criterion(part[i + 1], labels)
# backward + optimize only if in training phase
if phase == 'train':
if fp16: # we use optimier to backward loss
with amp.scale_loss(loss,
optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
# statistics
if int(version[0]) > 0 or int(
version[2]
) > 3: # for the new version like 0.4.0, 0.5.0 and 1.0.0
running_loss += loss.item() * now_batch_size
else: # for the old version like 0.3.0 and 0.3.1
running_loss += loss.data[0] * now_batch_size
running_corrects += float(torch.sum(preds == labels.data))
pbar.set_description(
desc='loss: {:.4f}'.format(loss.item()))
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
print('\r\n{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
logging.info('epoch: {}, {} Loss: {:.4f} Acc: {:.4f}'.format(
epoch, phase, epoch_loss, epoch_acc))
y_loss[phase].append(epoch_loss)
y_err[phase].append(1.0 - epoch_acc)
# deep copy the model
if phase == 'val':
results.append({
'epoch': epoch,
'trainLoss': y_loss['train'][-1],
'trainError': y_err['train'][-1],
'valLoss': y_loss['val'][-1],
'valError': y_err['val'][-1]
})
last_model_wts = model.state_dict()
if epoch % 10 == 9:
save_network(model, epoch)
draw_curve(epoch)
write_to_csv(results)
time_elapsed = time.time() - since
print('\r\nTraining complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print()
time_elapsed = time.time() - since
print('\r\nTraining complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
# print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(last_model_wts)
save_network(model, 'last')
return model
######################################################################
# Draw Curve
# ---------------------------
x_epoch = []
fig = plt.figure()
ax0 = fig.add_subplot(121, title="loss")
ax1 = fig.add_subplot(122, title="top1err")
def draw_curve(current_epoch):
x_epoch.append(current_epoch)
ax0.plot(x_epoch, y_loss['train'], 'bo-', label='train')
ax0.plot(x_epoch, y_loss['val'], 'ro-', label='val')
ax1.plot(x_epoch, y_err['train'], 'bo-', label='train')
ax1.plot(x_epoch, y_err['val'], 'ro-', label='val')
if current_epoch == 0:
ax0.legend()
ax1.legend()
fig.savefig(os.path.join('./model', name, 'train.jpg'))
def write_to_csv(results):
path = os.path.join('./model', name, 'result.csv')
with open(path, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=list(results[0].keys()))
writer.writeheader()
writer.writerows(results)
######################################################################
# Save model
# ---------------------------
def save_network(network, epoch_label):
save_filename = 'net_%s.pth' % epoch_label
rpth = os.path.join('./model', name, 'Model Files')
if not os.path.exists(rpth):
os.makedirs(rpth)
save_path = os.path.join(rpth, save_filename)
torch.save(network.cpu().state_dict(), save_path)
if torch.cuda.is_available():
network.cuda(gpu_ids[0])
######################################################################
# Finetuning the convnet
# ----------------------
#
# Load a pretrainied model and reset final fully connected layer.
#
if opt.use_dense:
model = ft_net_dense(len(class_names), opt.droprate)
else:
model = ft_net(len(class_names), opt.droprate, opt.stride)
if opt.PCB:
model = PCB(len(class_names))
opt.nclasses = len(class_names)
print(model)
print('model loaded')
if not opt.PCB:
ignored_params = list(map(id, model.model.fc.parameters())) + list(
map(id, model.classifier.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params,
model.parameters())
optimizer_ft = optim.SGD([{
'params': base_params,
'lr': 0.1 * opt.lr
}, {
'params': model.model.fc.parameters(),
'lr': opt.lr
}, {
'params': model.classifier.parameters(),
'lr': opt.lr
}],
weight_decay=5e-4,
momentum=0.9,
nesterov=True)
else:
ignored_params = list(map(id, model.model.fc.parameters()))
ignored_params += (
list(map(id, model.classifier0.parameters())) +
list(map(id, model.classifier1.parameters())) +
list(map(id, model.classifier2.parameters())) +
list(map(id, model.classifier3.parameters())) +
list(map(id, model.classifier4.parameters())) +
list(map(id, model.classifier5.parameters()))
# +list(map(id, model.classifier6.parameters() ))
# +list(map(id, model.classifier7.parameters() ))
)
base_params = filter(lambda p: id(p) not in ignored_params,
model.parameters())
optimizer_ft = optim.SGD(
[
{
'params': base_params,
'lr': 0.1 * opt.lr
},
{
'params': model.model.fc.parameters(),
'lr': opt.lr
},
{
'params': model.classifier0.parameters(),
'lr': opt.lr
},
{
'params': model.classifier1.parameters(),
'lr': opt.lr
},
{
'params': model.classifier2.parameters(),
'lr': opt.lr
},
{
'params': model.classifier3.parameters(),
'lr': opt.lr
},
{
'params': model.classifier4.parameters(),
'lr': opt.lr
},
{
'params': model.classifier5.parameters(),
'lr': opt.lr
},
# {'params': model.classifier6.parameters(), 'lr': 0.01},
# {'params': model.classifier7.parameters(), 'lr': 0.01}
],
weight_decay=5e-4,
momentum=0.9,
nesterov=True)
# Decay LR by a factor of 0.1 every 40 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=40,
gamma=0.1)
######################################################################
# Train and evaluate
# ^^^^^^^^^^^^^^^^^^
#
# It should take around 1-2 hours on GPU.
#
dir_name = os.path.join('./model', name)
if not os.path.isdir(dir_name):
os.mkdir(dir_name)
# record every run
copyfile('./train.py', dir_name + '/train.py')
copyfile('./model.py', dir_name + '/model.py')
# save opts
with open('%s/opts.yaml' % dir_name, 'w') as fp:
yaml.dump(vars(opt), fp, default_flow_style=False)
# model to gpu
model = model.cuda()
if fp16:
# model = network_to_half(model)
# optimizer_ft = FP16_Optimizer(optimizer_ft, static_loss_scale = 128.0)
model, optimizer_ft = amp.initialize(model,
optimizer_ft,
opt_level="O1")
criterion = losses.DualLoss()
model = train_model(model,
criterion,
optimizer_ft,
exp_lr_scheduler,
num_epochs=60)
#
# if __name__ == "__main__":
# train(opt)
if opt.use_dense:
model = ft_net_dense(len(class_names))
else:
model = ft_net(len(class_names))
if opt.PCB:
model = PCB(len(class_names))
print(model)
if use_gpu:
if len(opt.gpu_ids) >= 1:
print(opt.gpu_ids)
model_wraped = nn.DataParallel(model).cuda()
else:
model = model.cuda()
model_wraped = model.cuda()
if len(opt.gpu_ids) >= 1:
model = model_wraped.module
criterion = nn.CrossEntropyLoss().cuda()
if not opt.PCB:
ignored_params = list(map(id, model.model.fc.parameters())) + list(
map(id, model.classifier.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params,
model.parameters())
optimizer_ft = optim.SGD([{
'params': base_params,
'lr': 0.01
}, {
'params': model.model.fc.parameters(),
#
# Load a pretrainied model and reset final fully connected layer.
#
if opt.use_dense:
model = ft_net_dense(len(class_names))
else:
model = ft_net(len(class_names))
if opt.PCB:
model = PCB(len(class_names))
print(model)
if use_gpu:
model = model.cuda()
criterion = nn.CrossEntropyLoss()
if not opt.PCB:
ignored_params = list(map(id, model.model.fc.parameters() )) + list(map(id, model.classifier.parameters() ))
base_params = filter(lambda p: id(p) not in ignored_params, model.parameters())
optimizer_ft = optim.SGD([
{'params': base_params, 'lr': 0.01},
{'params': model.model.fc.parameters(), 'lr': 0.1},
{'params': model.classifier.parameters(), 'lr': 0.1}
], weight_decay=5e-4, momentum=0.9, nesterov=True)
else:
ignored_params = list(map(id, model.model.fc.parameters() ))
ignored_params += (list(map(id, model.classifier0.parameters() ))
+list(map(id, model.classifier1.parameters() ))
