def train_infinite_collect_stats(args, model, device, source_train_loader,
target_train_loader, optimizer,
lambda_mec_loss, target_test_loader):
source_iter = iter(source_train_loader)
target_iter = iter(target_train_loader)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[6000],
gamma=0.1)
for i in range(args.num_iters):
model.train()
exp_lr_scheduler.step()
try:
source_data, source_y = next(source_iter)
except:
source_iter = iter(source_train_loader)
source_data, source_y = next(source_iter)
try:
target_data, target_data_dup, _ = next(target_iter)
except:
target_iter = iter(target_train_loader)
target_data, target_data_dup, _ = next(target_iter)
data = torch.cat((source_data, target_data, target_data_dup),
dim=0) # concat the source and target mini-batches
data, source_y = data.to(device), source_y.to(device)
optimizer.zero_grad()
output = model(data)
source_output, target_output, target_output_dup = torch.split(
output, split_size_or_sections=output.shape[0] // 3, dim=0)
mec_criterion = consensus_loss.MinEntropyConsensusLoss(
num_classes=args.num_classes, device=device)
cls_loss = F.nll_loss(F.log_softmax(source_output), source_y)
mec_loss = lambda_mec_loss * mec_criterion(target_output,
target_output_dup)
loss = cls_loss + mec_loss
loss.backward()
optimizer.step()
if i % args.log_interval == 0:
print(
'Train Iter: [{}/{}]\tClassification Loss: {:.6f} \t MEC Loss: {:.6f}'
.format(i, args.num_iters, cls_loss.item(), mec_loss.item()))
if (i + 1) % args.check_acc_step == 0:
test(args, model, device, target_test_loader)
print("Training is complete...")
print(
"Running a bunch of forward passes to estimate the population statistics of target..."
)
eval_pass_collect_stats(args, model, device, target_test_loader)
print("Finally computing the precision on the test set...")
test(args, model, device, target_test_loader)
def train_meshnet(opt):
device = torch.device("cpu" if opt.gpu_idx < 0 else "cuda:%d" %
opt.gpu_idx)
print('Training on {} GPUs'.format(torch.cuda.device_count()))
print('Training on ' + (
'cpu' if opt.gpu_idx < 0 else torch.cuda.get_device_name(opt.gpu_idx)))
# colored console output
green = lambda x: '\033[92m' + x + '\033[0m'
blue = lambda x: '\033[94m' + x + '\033[0m'
log_dirname = os.path.join(opt.logdir, opt.name)
params_filename = os.path.join(opt.outdir, '%s_params.pth' % opt.name)
model_filename = os.path.join(opt.outdir, '%s_model.pth' % opt.name)
desc_filename = os.path.join(opt.outdir, '%s_description.txt' % opt.name)
if os.path.exists(log_dirname) or os.path.exists(model_filename):
if opt.name != 'test':
response = input(
'A training run named "%s" already exists, overwrite? (y/n) ' %
opt.name)
if response == 'y':
del_log = True
else:
return
else:
del_log = True
if del_log:
if os.path.exists(log_dirname):
try:
shutil.rmtree(log_dirname)
except OSError:
print("Can't delete " + log_dirname)
# get indices in targets and predictions corresponding to each output
target_features = []
output_target_ind = []
output_pred_ind = []
output_names = []
output_loss_weights = dict()
pred_dim = 0
for o in opt.outputs:
if o == 'imp_surf':
if o not in target_features:
target_features.append(o)
output_names.append(o)
output_target_ind.append(target_features.index(o))
output_pred_ind.append(pred_dim)
output_loss_weights[o] = 1.0
pred_dim += 1
elif o == 'imp_surf_magnitude':
if o not in target_features:
target_features.append(o)
output_names.append(o)
output_target_ind.append(target_features.index(o))
output_pred_ind.append(pred_dim)
# output_loss_weights[o] = 10.0
output_loss_weights[o] = 1.0
pred_dim += 1
elif o == 'imp_surf_sign':
if o not in target_features:
target_features.append(o)
output_names.append(o)
output_target_ind.append(target_features.index(o))
output_pred_ind.append(pred_dim)
output_loss_weights[o] = 1.0
pred_dim += 1
elif o == 'p_index':
if o not in target_features:
target_features.append(o)
output_target_ind.append(target_features.index(o))
elif o == 'patch_pts_ids':
if o not in target_features:
target_features.append(o)
output_target_ind.append(target_features.index(o))
else:
raise ValueError('Unknown output: %s' % o)
if pred_dim <= 0:
raise ValueError('Prediction is empty for the given outputs.')
# create model
use_query_point = any([
f in opt.outputs
for f in ['imp_surf', 'imp_surf_magnitude', 'imp_surf_sign']
])
meshnet = PointsToSurfModel(
net_size_max=opt.net_size,
num_points=opt.points_per_patch,
output_dim=pred_dim,
use_point_stn=opt.use_point_stn,
use_feat_stn=opt.use_feat_stn,
sym_op=opt.sym_op,
use_query_point=use_query_point,
sub_sample_size=opt.sub_sample_size,
do_augmentation=True,
single_transformer=opt.single_transformer,
shared_transformer=opt.shared_transformer,
)
start_epoch = 0
if opt.refine != '':
print(f'Refining weights from {opt.refine}')
meshnet.cuda(device=device) # same order as in training
meshnet = torch.nn.DataParallel(meshnet)
meshnet.load_state_dict(torch.load(opt.refine))
try:
# expecting a file name like 'vanilla_model_50.pth'
model_file = str(opt.refine)
last_underscore_pos = model_file.rfind('_')
last_dot_pos = model_file.rfind('.')
start_epoch = int(
model_file[last_underscore_pos + 1:last_dot_pos]) + 1
print(f'Continuing training from epoch {start_epoch}')
except:
print(
f'Warning: {opt.refine} has no epoch in the name. The Tensorboard log will continue at '
f'epoch 0 and might be messed up!')
if opt.seed < 0:
opt.seed = random.randint(1, 10000)
print("Random Seed: %d" % opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
# create train and test dataset loaders
train_dataset = data_loader.PointcloudPatchDataset(
root=opt.indir,
shape_list_filename=opt.trainset,
points_per_patch=opt.points_per_patch,
patch_features=target_features,
point_count_std=opt.patch_point_count_std,
seed=opt.seed,
identical_epochs=opt.identical_epochs,
center=opt.patch_center,
cache_capacity=opt.cache_capacity,
pre_processed_patches=True,
sub_sample_size=opt.sub_sample_size,
num_workers=int(opt.workers),
patch_radius=opt.patch_radius,
epsilon=-1, # not necessary for training
uniform_subsample=opt.uniform_subsample,
)
if opt.training_order == 'random':
train_datasampler = data_loader.RandomPointcloudPatchSampler(
train_dataset,
patches_per_shape=opt.patches_per_shape,
seed=opt.seed,
identical_epochs=opt.identical_epochs)
elif opt.training_order == 'random_shape_consecutive':
train_datasampler = data_loader.SequentialShapeRandomPointcloudPatchSampler(
train_dataset,
patches_per_shape=opt.patches_per_shape,
seed=opt.seed,
identical_epochs=opt.identical_epochs)
else:
raise ValueError('Unknown training order: %s' % opt.training_order)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
sampler=train_datasampler,
batch_size=opt.batchSize,
num_workers=int(
opt.workers))
test_dataset = data_loader.PointcloudPatchDataset(
root=opt.indir,
shape_list_filename=opt.testset,
points_per_patch=opt.points_per_patch,
patch_features=target_features,
point_count_std=opt.patch_point_count_std,
seed=opt.seed,
identical_epochs=opt.identical_epochs,
center=opt.patch_center,
cache_capacity=opt.cache_capacity,
pre_processed_patches=True,
sub_sample_size=opt.sub_sample_size,
patch_radius=opt.patch_radius,
num_workers=int(opt.workers),
epsilon=-1, # not necessary for training
uniform_subsample=opt.uniform_subsample,
)
if opt.training_order == 'random':
test_datasampler = data_loader.RandomPointcloudPatchSampler(
test_dataset,
patches_per_shape=opt.patches_per_shape,
seed=opt.seed,
identical_epochs=opt.identical_epochs)
elif opt.training_order == 'random_shape_consecutive':
test_datasampler = data_loader.SequentialShapeRandomPointcloudPatchSampler(
test_dataset,
patches_per_shape=opt.patches_per_shape,
seed=opt.seed,
identical_epochs=opt.identical_epochs)
else:
raise ValueError('Unknown training order: %s' % opt.training_order)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
sampler=test_datasampler,
batch_size=opt.batchSize,
num_workers=int(opt.workers))
# keep the exact training shape names for later reference
opt.train_shapes = train_dataset.shape_names
opt.test_shapes = test_dataset.shape_names
print(
'training set: %d patches (in %d batches) - test set: %d patches (in %d batches)'
% (len(train_datasampler), len(train_dataloader),
len(test_datasampler), len(test_dataloader)))
try:
os.makedirs(opt.outdir)
except OSError:
pass
train_fraction_done = 0.0
log_writer = SummaryWriter(log_dirname, comment=opt.name)
log_writer.add_scalar('LR', opt.lr, 0)
# milestones in number of optimizer iterations
optimizer = optim.SGD(meshnet.parameters(),
lr=opt.lr,
momentum=opt.momentum)
# SGD changes lr depending on training progress
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[], gamma=0.1) # constant lr
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=opt.scheduler_steps,
gamma=0.1)
if opt.refine == '':
meshnet.cuda(device=device)
meshnet = torch.nn.DataParallel(meshnet)
train_num_batch = len(train_dataloader)
test_num_batch = len(test_dataloader)
# save parameters
torch.save(opt, params_filename)
# save description
with open(desc_filename, 'w+') as text_file:
print(opt.desc, file=text_file)
for epoch in range(start_epoch, opt.nepoch, 1):
train_enum = enumerate(train_dataloader, 0)
test_batchind = -1
test_fraction_done = 0.0
test_enum = enumerate(test_dataloader, 0)
for train_batchind, batch_data_train in train_enum:
# batch data to GPU
for key in batch_data_train.keys():
batch_data_train[key] = batch_data_train[key].cuda(
non_blocking=True)
# set to training mode
meshnet.train()
# zero gradients
optimizer.zero_grad()
pred_train = meshnet(batch_data_train)
loss_train = compute_loss(pred=pred_train,
batch_data=batch_data_train,
outputs=opt.outputs,
output_loss_weights=output_loss_weights,
fixed_radius=opt.patch_radius > 0.0)
loss_total = sum(loss_train)
# back-propagate through entire network to compute gradients of loss w.r.t. parameters
loss_total.backward()
# parameter optimization step
optimizer.step()
# update and log lr
lr_before_update = scheduler.get_lr()
if isinstance(lr_before_update, list):
lr_before_update = lr_before_update[0]
scheduler.step(epoch)
lr_after_update = scheduler.get_lr()
if isinstance(lr_after_update, list):
lr_after_update = lr_after_update[0]
if lr_before_update != lr_after_update:
print('LR changed from {} to {} in epoch {}'.format(
lr_before_update, lr_after_update, epoch))
current_step = (
epoch + train_fraction_done) * train_num_batch * opt.batchSize
log_writer.add_scalar('LR', lr_after_update, current_step)
train_fraction_done = (train_batchind + 1) / train_num_batch
if debug:
from source import evaluation
evaluation.visualize_patch(
patch_pts_ps=batch_data_train['patch_pts_ps'][0].cpu(),
query_point_ps=batch_data_train['imp_surf_query_point_ps']
[0].cpu(),
pts_sub_sample_ms=batch_data_train['pts_sub_sample_ms']
[0].cpu(),
query_point_ms=batch_data_train['imp_surf_query_point_ms']
[0].cpu(),
file_path='debug/patch_train.off')
metrics_dict = calc_metrics(outputs=opt.outputs,
pred=pred_train,
gt_data=batch_data_train)
do_logging(writer=log_writer,
log_prefix=green('train'),
epoch=epoch,
opt=opt,
loss=loss_train,
batchind=train_batchind,
fraction_done=train_fraction_done,
num_batch=train_num_batch,
train=True,
output_names=output_names,
metrics_dict=metrics_dict)
while test_fraction_done <= train_fraction_done and test_batchind + 1 < test_num_batch:
# set to evaluation mode, no auto-diff
meshnet.eval()
test_batchind, batch_data_test = next(test_enum)
# batch data to GPU
for key in batch_data_test.keys():
batch_data_test[key] = batch_data_test[key].cuda(
non_blocking=True)
# forward pass
with torch.no_grad():
pred_test = meshnet(batch_data_test)
loss_test = compute_loss(
pred=pred_test,
batch_data=batch_data_test,
outputs=opt.outputs,
output_loss_weights=output_loss_weights,
fixed_radius=opt.patch_radius > 0.0)
metrics_dict = calc_metrics(outputs=opt.outputs,
pred=pred_test,
gt_data=batch_data_test)
test_fraction_done = (test_batchind + 1) / test_num_batch
do_logging(writer=log_writer,
log_prefix=blue('test'),
epoch=epoch,
opt=opt,
loss=loss_test,
batchind=test_batchind,
fraction_done=test_fraction_done,
num_batch=train_num_batch,
train=False,
output_names=output_names,
metrics_dict=metrics_dict)
# end of epoch save model, overwriting the old model
if epoch % opt.saveinterval == 0 or epoch == opt.nepoch - 1:
torch.save(meshnet.state_dict(), model_filename)
# save model in a separate file in epochs 0,5,10,50,100,500,1000, ...
if epoch % (5 * 10**math.floor(math.log10(max(2, epoch - 1)))
) == 0 or epoch % 100 == 0 or epoch == opt.nepoch - 1:
torch.save(
meshnet.state_dict(),
os.path.join(opt.outdir,
'%s_model_%d.pth' % (opt.name, epoch)))
log_writer.flush()
log_writer.close()
def main():
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print("Initializing dataset {}".format(args.dataset))
dataset = data_manager.init_imgreid_dataset(
root=args.root,
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
)
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
transform_test = T.Compose([
T.Resize((args.height, args.width)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
pin_memory = True if use_gpu else False
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dataset.num_train_pids,
loss={'xent'},
use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
if args.label_smooth:
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
else:
criterion = nn.CrossEntropyLoss()
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.fixbase_epoch > 0:
if hasattr(model, 'classifier') and isinstance(model.classifier,
nn.Module):
optimizer_tmp = init_optim(args.optim,
model.classifier.parameters(),
args.fixbase_lr, args.weight_decay)
else:
print(
"Warn: model has no attribute 'classifier' and fixbase_epoch is reset to 0"
)
args.fixbase_epoch = 0
if args.load_weights:
# load pretrained weights but ignore layers that don't match in size
if check_isfile(args.load_weights):
checkpoint = torch.load(args.load_weights)
pretrain_dict = checkpoint['state_dict']
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
print("Loaded pretrained weights from '{}'".format(
args.load_weights))
if args.resume:
if check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(
args.start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
if args.fixbase_epoch > 0:
print(
"Train classifier for {} epochs while keeping base network frozen".
format(args.fixbase_epoch))
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch,
model,
criterion,
optimizer_tmp,
trainloader,
use_gpu,
freeze_bn=True)
train_time += round(time.time() - start_train_time)
del optimizer_tmp
print("Now open all layers for training")
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_step > 0 and (
epoch + 1) % args.eval_step == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_epoch = epoch + 1
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': rank1,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(
best_rank1, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
if args.pretrained:
optimizer = torch.optim.SGD(
[{'params': filter(lambda p: p.requires_grad, model.parameters()), 'lr': args.lr},
{'params': filter(lambda p: p.requires_grad, metric.parameters()), 'lr': 10 * args.lr}],
weight_decay=args.weight_decay, momentum=args.momentum)
else:
optimizer = torch.optim.SGD(
[{'params': filter(lambda p: p.requires_grad, model.parameters()), 'lr': args.lr},
{'params': filter(lambda p: p.requires_grad, metric.parameters()), 'lr': args.lr}],
weight_decay=args.weight_decay, momentum=args.momentum)
print('init_lr={}, weight_decay={}, momentum={}'.format(args.lr, args.weight_decay, args.momentum))
if args.scheduler == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.lr_step, gamma=args.lr_gamma, last_epoch=-1)
elif args.scheduler == 'multi':
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[150, 225], gamma=args.lr_gamma, last_epoch=-1)
# savepath
savepath = os.path.join(args.savepath, args.model_name)
savepath = savepath + '_' + args.metric
savepath = savepath + '_' + args.loss + '_' + str(args.inp_size) + '_' + args.scheduler
if args.seed is not None:
savepath = savepath + '_s' + str(args.seed)
if not args.pretrained:
savepath = savepath + '_' + str(args.eps)
print('savepath:', savepath)
def create_optimizer(optimizer_config, model):
"""Creates optimizer and schedule from configuration
Parameters
----------
optimizer_config : dict
Dictionary containing the configuration options for the optimizer.
model : Model
The network model.
Returns
-------
optimizer : Optimizer
The optimizer.
scheduler : LRScheduler
The learning rate scheduler.
"""
if optimizer_config["classifier_lr"] != -1:
# Separate classifier parameters from all others
net_params = []
classifier_params = []
for k, v in model.named_parameters():
if k.find("fc") != -1:
classifier_params.append(v)
else:
net_params.append(v)
params = [
{"params": net_params},
{"params": classifier_params, "lr": optimizer_config["classifier_lr"]},
]
else:
params = model.parameters()
if optimizer_config["type"] == "SGD":
optimizer = optim.SGD(params,
lr=optimizer_config["learning_rate"],
momentum=optimizer_config["momentum"],
weight_decay=optimizer_config["weight_decay"],
nesterov=optimizer_config["nesterov"])
elif optimizer_config["type"] == "Adam":
optimizer = optim.Adam(params,
lr=optimizer_config["learning_rate"],
weight_decay=optimizer_config["weight_decay"])
else:
raise KeyError("unrecognized optimizer {}".format(optimizer_config["type"]))
if optimizer_config["schedule"]["type"] == "step":
scheduler = lr_scheduler.StepLR(optimizer, **optimizer_config["schedule"]["params"])
elif optimizer_config["schedule"]["type"] == "multistep":
scheduler = lr_scheduler.MultiStepLR(optimizer, **optimizer_config["schedule"]["params"])
elif optimizer_config["schedule"]["type"] == "exponential":
scheduler = lr_scheduler.ExponentialLR(optimizer, **optimizer_config["schedule"]["params"])
elif optimizer_config["schedule"]["type"] == "constant":
scheduler = lr_scheduler.LambdaLR(optimizer, lambda epoch: 1.0)
elif optimizer_config["schedule"]["type"] == "linear":
def linear_lr(it):
return it * optimizer_config["schedule"]["params"]["alpha"] + optimizer_config["schedule"]["params"]["beta"]
scheduler = lr_scheduler.LambdaLR(optimizer, linear_lr)
return optimizer, scheduler
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu:
use_gpu = False
log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
sys.stderr = sys.stdout = Logger(osp.join(args.save_dir, log_name))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU, however, GPU is highly recommended")
print("Initializing image data manager")
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
trainloader, testloader_dict = dm.return_dataloaders()
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dm.num_train_pids,
loss={'xent'},
use_gpu=use_gpu,
args=vars(args))
print(model)
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion = get_criterion(dm.num_train_pids, use_gpu, args)
regularizer = get_regularizer(vars(args))
optimizer = init_optimizer(model.parameters(), **optimizer_kwargs(args))
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.load_weights and check_isfile(args.load_weights):
# load pretrained weights but ignore layers that don't match in size
try:
checkpoint = torch.load(args.load_weights)
except Exception as e:
print(e)
checkpoint = torch.load(args.load_weights,
map_location={'cuda:0': 'cpu'})
pretrain_dict = checkpoint['state_dict']
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
print("Loaded pretrained weights from '{}'".format(args.load_weights))
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
state = model.state_dict()
state.update(checkpoint['state_dict'])
model.load_state_dict(state)
# args.start_epoch = checkpoint['epoch'] + 1
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch,
checkpoint['rank1']))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query'], testloader_dict[
name]['query_flip']
galleryloader = testloader_dict[name]['gallery'], testloader_dict[
name]['gallery_flip']
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
args.save_dir, 'ranked_results',
name),
topk=20)
return
start_time = time.time()
ranklogger = RankLogger(args.source_names, args.target_names)
train_time = 0
print("==> Start training")
if args.fixbase_epoch > 0:
oldenv = os.environ.get('sa', '')
os.environ['sa'] = ''
print(
"Train {} for {} epochs while keeping other layers frozen".format(
args.open_layers, args.fixbase_epoch))
initial_optim_state = optimizer.state_dict()
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch,
model,
criterion,
regularizer,
optimizer,
trainloader,
use_gpu,
fixbase=True)
train_time += round(time.time() - start_train_time)
print("Done. All layers are open to train for {} epochs".format(
args.max_epoch))
optimizer.load_state_dict(initial_optim_state)
os.environ['sa'] = oldenv
max_r1 = 0
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
print(epoch)
print(criterion)
train(epoch,
model,
criterion,
regularizer,
optimizer,
trainloader,
use_gpu,
fixbase=False)
train_time += round(time.time() - start_train_time)
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': 0,
'epoch': epoch,
}, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (
epoch + 1) % args.eval_freq == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query'], testloader_dict[
name]['query_flip']
galleryloader = testloader_dict[name][
'gallery'], testloader_dict[name]['gallery_flip']
rank1 = test(model, queryloader, galleryloader, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
if max_r1 < rank1:
print('Save!', max_r1, rank1)
save_checkpoint(
{
'state_dict': state_dict,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir,
'checkpoint_best.pth.tar'))
max_r1 = rank1
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
ranklogger.show_summary()
def __init__(self, opt):
super(ICPR_model, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G1(opt).to(self.device) # G1
if self.is_train:
self.netV = networks.define_D(opt).to(self.device) # G1
self.netD = networks.define_D2(opt).to(self.device)
self.netQ = networks.define_Q(opt).to(self.device)
self.netG.train()
self.netV.train()
self.netD.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
self.weight_kl = 1e-2
self.weight_D = 1e-4
self.l_gan_w = 1e-4
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False,Rlu=True).to(self.device) #Rlu=True if feature taken before relu, else false
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
#D
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D)
self.optimizer_V = torch.optim.Adam(self.netV.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_V)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
if __name__ == '__main__':
args.workspace = os.path.join(args.workspace, args.exp_name)
os.makedirs(args.workspace, exist_ok=True)
logger = setup_logger(os.path.join(args.workspace, 'train_icdar15_log'))
criterion = Loss()
device = torch.device("cuda")
model = EAST()
data_parallel = False
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
data_parallel = True
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[args.epoch_iter // 2],
gamma=0.1)
# 先产生第一次的pseudo-label
logger.info("loading pretrained model from ", args.resume)
# model.load_state_dict(torch.load(args.resume))
#
# target domain
trainset = ICDAR15(args.train_data, args.train_gt)
train_loader_target = data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
drop_last=True)
def main(opt):
lr1 = opt.lr
lr_steps = opt.lr_steps
gpu_idx = opt.gpu_idx
batch_size = opt.batch_size
arch = opt.arch
logdir = os.path.join(opt.logdir, arch)
os.makedirs(logdir, exist_ok=True)
n_epochs = opt.n_epochs
train_writer = SummaryWriter(os.path.join(logdir, 'train'))
test_writer = SummaryWriter(os.path.join(logdir, 'test'))
if not gpu_idx == 999:
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_idx) # non-functional
torch.cuda.set_device(0)
device = torch.device("cuda:0")
best_prec1 = 0
res = 256
center_crop = 224
train_transform = transforms.Compose([
transforms.Resize(res),
transforms.CenterCrop(center_crop),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), # because inpus dtype is PIL Image
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
val_transform = transforms.Compose([
transforms.Resize(res),
transforms.CenterCrop(center_crop),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
# Data loaders
dataset_path = opt.dataset_path
training_set = torchvision.datasets.ImageFolder(os.path.join(
dataset_path, 'train'),
transform=train_transform)
if opt.data_sampler == 'weighted':
train_sampler_weights = make_weights_for_balanced_classes(
training_set.imgs, len(training_set.classes))
train_sampler_weights = torch.DoubleTensor(train_sampler_weights)
train_sampler = torch.utils.data.sampler.WeightedRandomSampler(
train_sampler_weights, len(train_sampler_weights))
train_loader = data.DataLoader(training_set,
sampler=train_sampler,
batch_size=batch_size,
num_workers=8,
pin_memory=True)
else:
train_loader = data.DataLoader(training_set,
batch_size=batch_size,
num_workers=5,
pin_memory=True)
val_set = torchvision.datasets.ImageFolder(os.path.join(
dataset_path, 'test'),
transform=val_transform)
val_loader = data.DataLoader(val_set,
batch_size=batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
num_classes = 33
if opt.arch == 'vgg':
# use vgg architecture
# model_ft = vgg19(pretrained=True) ##### Model Structure Here
model_ft = models.__dict__['vgg19'](pretrained=True)
model_ft.classifier[6] = nn.Linear(
4096,
num_classes) # change last layer to fit the number of classes
elif 'resnet' in opt.arch:
# use resnet architecture
model_ft = models.__dict__[opt.arch](pretrained=True)
if opt.arch == 'resnet18' or opt.arch == 'resnet34':
model_ft.fc = nn.Linear(512, num_classes)
elif opt.arch == 'resnet50' or opt.arch == 'resnet101' or opt.arch == 'resnet152':
model_ft = models.__dict__[opt.arch](pretrained=True)
model_ft.fc = nn.Linear(2048, num_classes)
else:
raise ValueError("unsupported architecture")
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model_ft = nn.DataParallel(model_ft)
if opt.refine:
if opt.refine_epoch == 0:
raise ValueError(
"You set the refine epoch to 0. No need to refine, just retrain."
)
refine_model_filename = os.path.join(
logdir, 'classifier{}.pth'.format(opt.refine_epoch))
model_ft.load_state_dict(torch.load(refine_model_filename))
model_ft.to(device)
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(model_ft.parameters(),
lr=lr1,
momentum=0.9,
weight_decay=5e-4)
scheduler = lr_scheduler.MultiStepLR(
optimizer, milestones=opt.lr_steps,
gamma=0.1) # milestones in number of optimizer iterations
refine_flag = True
for epoch in range(1, n_epochs):
if epoch <= opt.refine_epoch and opt.refine and refine_flag:
scheduler.step()
continue
else:
refine_flag = False
# adjust_learning_rate(optimizer, epoch, lr1, lr_steps)
train_fraction_done = 0.0
test_batchind = -1
test_fraction_done = 0.0
test_enum = enumerate(val_loader, 0)
# train for one epoch
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top3 = utils.AverageMeter()
model_ft.train()
optimizer.zero_grad()
train_num_batch = len(train_loader)
test_num_batch = len(val_loader)
for train_batchind, (im_data, im_class) in enumerate(train_loader):
model_ft.train
im_data = im_data.to(device)
im_class = im_class.to(device)
# batch_size = im_data.shape[0]
optimizer.zero_grad()
output = model_ft(im_data)
# measure accuracy and record loss
prec1, prec3 = utils.accuracy(output.data.detach(),
im_class,
topk=(1, 3))
loss = criterion(output, im_class)
loss.backward()
# compute gradient and do SGD step
optimizer.step()
losses.update(loss.item(), im_data.size(0))
top1.update(prec1.item(), im_data.size(0))
top3.update(prec3.item(), im_data.size(0))
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@3 {top3.val:.3f} ({top3.avg:.3f})'.format(
epoch,
train_batchind + 1,
len(train_loader) + 1,
loss=losses,
top1=top1,
top3=top3))
train_fraction_done = (train_batchind + 1) / train_num_batch
train_writer.add_scalar('loss', losses.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
train_writer.add_scalar('top1', top1.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
train_writer.add_scalar('top3', top3.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
train_fraction_done = (train_batchind + 1) / train_num_batch
# evaluate on a fraction of the validation set
if test_fraction_done <= train_fraction_done and test_batchind + 1 < test_num_batch:
test_losses = utils.AverageMeter()
test_top1 = utils.AverageMeter()
test_top3 = utils.AverageMeter()
# switch to evaluate mode
model_ft.eval()
test_batchind, (im_data, im_class) = next(test_enum)
with torch.no_grad():
im_data = im_data.to(device)
im_class = im_class.to(device)
# compute output
output = model_ft(im_data)
test_loss = criterion(output, im_class)
# measure accuracy and record loss
prec1, prec3 = utils.accuracy(output.data,
im_class,
topk=(1, 3))
test_losses.update(test_loss.item(), im_data.size(0))
test_top1.update(prec1.item(), im_data.size(0))
test_top3.update(prec3.item(), im_data.size(0))
print('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@3 {top3.val:.3f} ({top3.avg:.3f})\t'.format(
test_batchind,
len(val_loader),
loss=test_losses,
top1=test_top1,
top3=test_top3))
test_writer.add_scalar('loss', test_losses.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
test_writer.add_scalar('top1', test_top1.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
test_writer.add_scalar('top3', test_top3.val,
(epoch + train_fraction_done) *
train_num_batch * batch_size)
test_writer.add_scalar('lr',
optimizer.param_groups[0]['lr'],
(epoch + train_fraction_done) *
train_num_batch * batch_size)
test_fraction_done = (test_batchind + 1) / test_num_batch
scheduler.step()
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
if (epoch + 1) % 2 == 0:
# save model
model_tmp = copy.deepcopy(model_ft.state_dict())
model_ft.load_state_dict(model_tmp)
torch.save(
model_ft.state_dict(),
os.path.join(logdir, 'classifier' + str(epoch) + '.pth'))
if (is_best):
model_tmp = copy.deepcopy(model_ft.state_dict())
model_ft.load_state_dict(model_tmp)
torch.save(model_ft.state_dict(),
os.path.join(logdir, 'best_classifier.pth'))
batch_size=BATCH_SIZE ,
shuffle=True, num_workers=1)
## Initializing r, theta
P,Pall = gridRing(N)
Drr = abs(P)
Drr = torch.from_numpy(Drr).float()
Dtheta = np.angle(P)
Dtheta = torch.from_numpy(Dtheta).float()
# What and where is gamma
## Create the model
model = OFModel(Drr, Dtheta, T, PRE, gpu_id)
model.cuda(gpu_id)
optimizer = torch.optim.Adam(model.parameters(), lr=LR)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[100,150], gamma=0.1) # if Kitti: milestones=[100,150], UCF [50,100]
loss_mse = nn.MSELoss()
start_epoch = 1
## If want to continue training from a checkpoint
if(load_ckpt):
loadedcheckpoint = torch.load(ckpt_file)
start_epoch = loadedcheckpoint['epoch']
model.load_state_dict(loadedcheckpoint['state_dict'])
optimizer.load_state_dict(loadedcheckpoint['optimizer'])
print("Training from epoch: ", start_epoch)
print('-' * 25)
start = time.time()
count = 0
print(nets)
gpus = [int(gpu) for gpu in args.gpus.split(',')]
if len(gpus) > 1:
print("Using GPUs {}.".format(gpus))
for net in nets:
net = nn.DataParallel(net, device_ids=gpus)
params = [{'params': net.parameters()} for net in nets]
solver = optim.Adam(
params,
lr=args.lr)
milestones = [int(s) for s in args.schedule.split(',')]
scheduler = LS.MultiStepLR(solver, milestones=milestones, gamma=args.gamma)
if not os.path.exists(args.model_dir):
print("Creating directory %s." % args.model_dir)
os.makedirs(args.model_dir)
############### Checkpoints ###############
def resume(model_name, index):
names = ['encoder', 'binarizer', 'decoder', 'unet', 'd2']
for net_idx, net in enumerate(nets):
if net is not None:
name = names[net_idx]
checkpoint_path = '{}/{}_{}_{:08d}.pth'.format(
args.model_dir, model_name,
name, index)
def main(args):
# ***** parameters *****
batch_size = 8
num_workers = 1
num_epoch = 200
resume_epoch = 0
resume = False
epoch_samples = 4421
NUM_POINTS = 2048
lr = 0.001
weigh_decay = 1e-4
milestones = [60] # [30, 60]
which_dir = args.dir
OBJ_CLASS = [args.cat]
# load data
train_dataset = PartDataset(num_ptrs=NUM_POINTS,
plane_num=32,
class_choice=OBJ_CLASS,
random_selection=True,
random_jitter=True,
random_scale=True,
random_translation=False,
which_dir=which_dir,
split='trainval')
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
test_dataset = PartDataset(num_ptrs=NUM_POINTS,
plane_num=32,
class_choice=OBJ_CLASS,
split='test',
which_dir=which_dir)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=8,
shuffle=False,
num_workers=num_workers)
print('Training set size:', len(train_dataset))
print('Test set size:', len(test_dataset))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seg_classes = train_dataset.seg_classes
seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
for label in seg_classes[cat]:
seg_label_to_cat[label] = cat
NUM_CLASS = len(seg_classes[OBJ_CLASS[0]])
# ***** specify model and log output directory *****
time_stamp = time.strftime("%Y%m%d-%H%M%S")
curr_dir = os.path.dirname(os.path.abspath(__file__))
model_out_dir = '/media/pwu/Data/saved_models/point_cloud/shapepart/RCNet/'
log_out_dir = os.path.join(curr_dir, 'results')
try:
os.makedirs(log_out_dir)
except OSError:
pass
save_model_dir_root = check_dir(
os.path.join(
'/media/pwu/Data/saved_models/point_cloud/shapepart/RCNet/',
'save_' + str(which_dir)))
save_model_dir_class = check_dir(
os.path.join(save_model_dir_root, OBJ_CLASS[0]))
save_model_dir = check_dir(os.path.join(save_model_dir_class, time_stamp))
# ***** specify logger *****
# log_dir = os.path.join(log_out_dir, 'log-' + time_stamp + '.txt')
# logging.basicConfig(level=logging.INFO,
# format='%(asctime)s %(message)s',
# filename=log_dir,
# filemode='w')
save_weights_name = time_stamp
# ***** build model *****
classifier = EnsembleRCNet(device, which_dir, NUM_CLASS, NUM_POINTS)
print(classifier)
temp = sum(p.numel() for p in classifier.parameters() if p.requires_grad)
print("num_parameter", temp)
# ***** load existing model *****
model_path = os.path.join(model_out_dir,
'cls_model_' + str(resume_epoch) + '.pth')
if model_path != '' and resume is True:
classifier.load_state_dict(torch.load(model_path))
# ***** define optimizer *****
optimizer = optim.Adam(classifier.parameters(),
lr=lr,
weight_decay=weigh_decay,
amsgrad=False)
classifier.to(device)
# ***** scheduler *****
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=milestones,
gamma=0.1)
# exp_lr_scheduler = CosineLRWithRestarts(optimizer, batch_size, epoch_samples, restart_period=5, t_mult=2)
num_batch = len(train_dataset) / batch_size
if resume:
start_epoch = resume_epoch + 1
else:
start_epoch = 0
curr_shape_ious = None
for epoch in range(start_epoch, num_epoch):
exp_lr_scheduler.step()
classifier.train()
# statistic data
single_shape_ious = []
for b, data in enumerate(train_dataloader):
target, points, quantiles, ori_points_num, gather_idx, ori_point_idx = data
target = target - seg_classes[OBJ_CLASS[0]][0]
target = target.to(device)
points = points.to(device)
# ***************************************************************
# first, prepare the input to rnn
seq_data, seq_len, inverse_index = prepare_input_first_level(
points, quantiles)
seq_data = torch.from_numpy(seq_data.astype(np.float32))
seq_data = seq_data.to(device)
# next, prepare for the data index for convolution
batch_num = quantiles.shape[0]
plane_num = quantiles.shape[1]
items_indices = np.array([], dtype=np.int32)
cnt = 0
for i in range(batch_num):
plane_slice = []
for j in range(plane_num):
item = []
for k in range(plane_num):
num = quantiles[i, j, k]
if num != 0:
items_indices = np.append(items_indices, cnt)
cnt = cnt + 1
# ***************************************************************
optimizer.zero_grad()
pred = classifier(points, quantiles, seq_data, seq_len,
inverse_index, items_indices, gather_idx,
ori_point_idx)
loss = F.cross_entropy(
pred.view(-1, NUM_CLASS), target.view(-1)
) # should use nll_loss, but seems like there is no difference?
loss.backward()
optimizer.step()
# compute ious
cur_pred_val_logits = pred.data.cpu().numpy()
cur_pred_val = np.zeros(
(pred.size(0), NUM_POINTS)).astype(np.int32)
ori_points_num = ori_points_num.numpy().squeeze().tolist()
target = target.data.cpu().numpy()
for i in range(pred.size(0)):
logits = cur_pred_val_logits[i, :, :]
cur_pred_val[i, 0:ori_points_num[i]] = np.argmax(
logits, 1)[0:ori_points_num[i]]
for i in range(pred.size(0)):
segp = cur_pred_val[i, 0:ori_points_num[i]]
segl = target[i, 0:ori_points_num[i]]
cat = OBJ_CLASS[0]
part_ious = [0.0 for _ in range(NUM_CLASS)]
for l in range(NUM_CLASS):
if (np.sum(segl == l) == 0) and (
np.sum(segp == l) == 0
): # part is not present, no prediction as well
part_ious[l] = 1.0
else:
part_ious[l] = np.sum(
(segl == l) & (segp == l)) / float(
np.sum((segl == l) | (segp == l)))
single_shape_ious.append(np.mean(part_ious))
curr_shape_ious = np.mean(single_shape_ious)
msg = '[{0:d}: {1:d}/{2:d}] mean IoUs: {3:f}'.format(
epoch, b, trunc(num_batch), curr_shape_ious)
print(msg)
curr_shape_ious = np.mean(single_shape_ious)
msg = '*** train epoch {}, mean IoUs: {}'.format(
epoch, curr_shape_ious)
# logging.info(msg)
print(msg)
# evaluate
single_shape_ious = []
classifier.eval()
ttime = []
for b, data in enumerate(test_dataloader):
target, points, quantiles, ori_points_num, gather_idx, ori_point_idx = data
target = target - seg_classes[OBJ_CLASS[0]][0]
target = target.to(device)
points = points.to(device)
# ***************************************************************
# first, prepare the input to rnn
seq_data, seq_len, inverse_index = prepare_input_first_level(
points, quantiles)
seq_data = torch.from_numpy(seq_data.astype(np.float32))
seq_data = seq_data.to(device)
# next, prepare for the data index for convolution
batch_num = quantiles.shape[0]
plane_num = quantiles.shape[1]
items_indices = np.array([], dtype=np.int32)
cnt = 0
for i in range(batch_num):
plane_slice = []
for j in range(plane_num):
item = []
for k in range(plane_num):
num = quantiles[i, j, k]
if num != 0:
items_indices = np.append(items_indices, cnt)
cnt = cnt + 1
# ***************************************************************
start = timeit.default_timer()
pred = classifier(points, quantiles, seq_data, seq_len,
inverse_index, items_indices, gather_idx,
ori_point_idx)
stop = timeit.default_timer()
print("time >>", stop - start)
ttime.append(stop - start)
# compute ious
cur_pred_val_logits = pred.data.cpu().numpy()
cur_pred_val = np.zeros(
(pred.size(0), NUM_POINTS)).astype(np.int32)
ori_points_num = ori_points_num.numpy().squeeze().tolist()
target = target.data.cpu().numpy()
for i in range(pred.size(0)):
logits = cur_pred_val_logits[i, :, :]
cur_pred_val[i, 0:ori_points_num[i]] = np.argmax(
logits, 1)[0:ori_points_num[i]]
for i in range(pred.size(0)):
segp = cur_pred_val[i, 0:ori_points_num[i]]
segl = target[i, 0:ori_points_num[i]]
cat = OBJ_CLASS[0]
part_ious = [0.0 for _ in range(NUM_CLASS)]
for l in range(NUM_CLASS):
if (np.sum(segl == l) == 0) and (
np.sum(segp == l) == 0
): # part is not present, no prediction as well
part_ious[l] = 1.0
else:
part_ious[l] = np.sum(
(segl == l) & (segp == l)) / float(
np.sum((segl == l) | (segp == l)))
single_shape_ious.append(np.mean(part_ious))
curr_shape_ious = np.mean(single_shape_ious)
msg = '*** Test mean IoUs: {0:f}'.format(curr_shape_ious)
# logging.info(msg)
print(msg)
#if epoch % 10 == 0:
# torch.save(classifier.state_dict(), '{}/{}.pth'.format(save_model_dir, curr_shape_ious))
# logging.info(msg)
# torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (model_out_dir, epoch))
return curr_shape_ious
# {'params': model.classifierD0.parameters(), 'lr': opt.lr},
# {'params': model.classifierB3.parameters(), 'lr': opt.lr},
# {'params': model.classifierB4.parameters(), 'lr': opt.lr},
# {'params': model.classifierB5.parameters(), 'lr': opt.lr},
# {'params': model.classifierC2.parameters(), 'lr': opt.lr},
# {'params': model.classifierC3.parameters(), 'lr': opt.lr},
# {'params': model.classifier.parameters(), 'lr': opt.lr},
# ], 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.MultiStepLR(optimizer,
milestones=[40, 70],
gamma=0.1)
######################################################################
# Train and evaluate
# --------
#
dir_name = os.path.join('./logs', opt.name)
if not os.path.isdir(dir_name):
os.mkdir(dir_name)
# record every run
copyfile('./train_irid.py', dir_name + '/train_irid.py')
copyfile('models/base_model.py', dir_name + '/base_model.py')
if opt.LSTM:
copyfile('models/lstm_model.py', dir_name + '/lstm_model.py')
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 6)
if use_gpu:
model_ft = model_ft.cuda()
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
# exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer_ft, [55, 75],
gamma=0.5,
last_epoch=-1)
'''
Result recording
'''
result_dir = 'result/1234'
# folder
if not os.path.exists(result_dir):
os.makedirs(result_dir)
# image sample display
model_ft = train_model(model_ft,
optimizer_ft,
exp_lr_scheduler,
num_epochs=100)
def __init__(self, opt):
super(SRFeatModel, self).__init__(opt)
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device) # G
if self.is_train:
self.netD1 = networks.define_D(opt).to(self.device) # D
self.netD2 = networks.define_DF(opt).to(self.device) # D
self.netG.train()
self.netD1.train()
self.netD2.train()
self.load() # load G and D if needed
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters are for WGAN
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
if train_opt['gan_type'] == 'wgan-gp':
self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
# gradient penalty loss
self.cri_gp = GradientPenaltyLoss(device=self.device).to(self.device)
self.l_gp_w = train_opt['gp_weigth']
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'], \
weight_decay=wd_G, betas=(train_opt['beta1_G'], 0.999))
self.optimizers.append(self.optimizer_G)
# D1 and D2
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D1 = torch.optim.Adam(self.netD1.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D1)
self.optimizer_D2 = torch.optim.Adam(self.netD2.parameters(), lr=train_opt['lr_D'], \
weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999))
self.optimizers.append(self.optimizer_D2)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepLR(optimizer, \
train_opt['lr_steps'], train_opt['lr_gamma']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
# print network
self.print_network()
#checkpoint = torch.load('./model/model_mulstep.pth')
net = googlenet()
net = nn.DataParallel(net)
#net.load_state_dict(checkpoint)
#net.load_state_dict(torch.load('./model/model_mulstep.pth'))
#net = torch.load('./model/model_mulstep.pth')
#net = nn.DataParallel(net)
net.cuda()
# 定义损失函数和优化方式
criterion = nn.CrossEntropyLoss() #损失函数为交叉熵，多用于多分类问题
optimizer = optim.SGD(
net.parameters(), lr=LR, momentum=0.9,
weight_decay=5e-4) #优化方式为mini-batch momentum-SGD，并采用L2正则化（权重衰减）
lr_schedule = lr_scheduler.MultiStepLR(optimizer, [60, 80], 0.1)
#lr_schedule = lr_scheduler.ExponentialLR(optimizer,gamma=1)
# 训练
if __name__ == "__main__":
#if not os.path.exists(args.outf):
#os.makedirs(args.outf)
best_acc = 0 #2 初始化best test accuracy
print("Start Training, googlenet!") # 定义遍历数据集的次数
with open("ci-acc16.txt", "w") as f:
with open("ci-log16.txt", "w") as f2:
for epoch in range(0, EPOCH):
print('\nEpoch: %d' % (epoch + 1))
net.train()
sum_loss = 0.0
correct = 0.0
def experiment(args):
# load data
data = pd.read_pickle(os.path.join(args.dataset_root, 'dataset.pkl'))
data = data[data.sensor.str.contains('|'.join(args.sensors))] # filter sensors
# load splits
splits = pd.read_csv(os.path.join(args.dataset_root, 'train_test_split.csv'))
train_patients = splits[splits.split.str.contains('train')].patient_hash.tolist()
test_patients = splits[splits.split.str.contains('test')].patient_hash.tolist()
# get data accorting to patient split
train_data = data[data.patient_hash.str.contains('|'.join(train_patients))]
test_data = data[data.patient_hash.str.contains('|'.join(test_patients))]
# subset the dataset
train_dataset = COVID19Dataset(args, train_data, get_transforms(args, 'train'))
test_dataset = COVID19Dataset(args, test_data, get_transforms(args, 'test'))
# For unbalanced dataset we create a weighted sampler
train_labels = [sum(l) for l in train_data.label.tolist()]
weights = get_weights_for_balanced_classes(train_labels, len(list(set(train_labels))))
weights = torch.DoubleTensor(weights)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights=weights, num_samples=len(weights))
nclasses = len(list(set(train_labels)))
# dataloaders from subsets
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=sampler,
num_workers=args.num_workers,
drop_last=True)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=None,
num_workers=args.num_workers,
drop_last=False)
# create directories
args.weights_dir = os.path.join('logs', args.run_name, 'weights')
os.makedirs(args.weights_dir, exist_ok=True)
args.train_viz_dir = os.path.join('logs', args.run_name, 'viz_train')
os.makedirs(args.train_viz_dir, exist_ok=True)
args.test_viz_dir = os.path.join('logs', args.run_name, 'viz_test')
os.makedirs(args.test_viz_dir, exist_ok=True)
model = CNNConStn(args.img_size, nclasses, args.fixed_scale)
print(model)
print('Number of params in the model: {}'.format(
*[sum([p.data.nelement() for p in net.parameters()]) for net in [model]]))
model = model.cuda()
# fixed samples for stn visualization
fixed_samples_iter = iter(train_loader)
fixed_samples_train, fixed_y_train = fixed_samples_iter.next()
fixed_samples_iter = iter(test_loader)
fixed_samples_test, _ = fixed_samples_iter.next()
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[70], gamma=0.1) # 10, 50
state_dict = {'best_f1': 0., 'precision': 0., 'recall': 0., 'accuracy': 0.}
for epoch in range(args.epochs):
model = train(args, model, train_loader, len(list(set(train_labels))), optimizer, epoch,
fixed_samples_train, fixed_y_train)
test(args, model, test_loader, len(list(set(train_labels))), epoch, state_dict, args.weights_dir,
fixed_samples_test)
exp_lr_scheduler.step()
def train(opt):
""" dataset preparation """
if opt.select_data == 'baidu':
train_set = BAIDUset(opt, opt.train_csv)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=BaiduCollate(opt.imgH, opt.imgW, keep_ratio=False))
val_set = BAIDUset(opt, opt.val_csv)
valid_loader = torch.utils.data.DataLoader(
val_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=BaiduCollate(opt.imgH, opt.imgW, keep_ratio=False),
pin_memory=True)
else:
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
train_dataset = Batch_Balanced_Dataset(opt)
AlignCollate_valid = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
valid_dataset = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=opt.batch_size,
shuffle=
True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid,
pin_memory=True)
print('-' * 80)
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
elif 'Bert' in opt.Prediction:
converter = TransformerConverter(opt.character, opt.max_seq)
elif 'SRN' in opt.Prediction:
converter = SRNConverter(opt.character, opt.SRN_PAD)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial,
opt.input_channel, opt.output_channel, opt.hidden_size,
opt.num_class, opt.batch_max_length, opt.Transformation,
opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).cuda()
model.train()
if opt.continue_model != '':
print(f'loading pretrained model from {opt.continue_model}')
model.load_state_dict(torch.load(opt.continue_model))
print("Model:")
print(model)
""" setup loss """
if 'CTC' in opt.Prediction:
criterion = torch.nn.CTCLoss(zero_infinity=True).cuda()
elif 'Bert' in opt.Prediction:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).cuda()
elif 'SRN' in opt.Prediction:
criterion = cal_performance
else:
criterion = torch.nn.CrossEntropyLoss(
ignore_index=0).cuda() # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.adam:
optimizer = optim.Adam(filtered_parameters,
lr=opt.lr,
betas=(opt.beta1, 0.999))
elif opt.ranger:
optimizer = Ranger(filtered_parameters, lr=opt.lr)
else:
optimizer = optim.Adadelta(filtered_parameters,
lr=opt.lr,
rho=opt.rho,
eps=opt.eps)
print("Optimizer:")
print(optimizer)
lrScheduler = lr_scheduler.MultiStepLR(optimizer, [5, 20, 30],
gamma=0.5) # 减小学习速率
""" final options """
# print(opt)
with open(f'./saved_models/{opt.experiment_name}/opt.txt',
'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.continue_model != '':
start_iter = int(opt.continue_model.split('_')[-1].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
start_time = time.time()
best_accuracy = -1
best_norm_ED = 1e+6
i = start_iter
if opt.select_data == 'baidu':
train_iter = iter(train_loader)
step_per_epoch = len(train_set) / opt.batch_size
print('一代有多少step:', step_per_epoch)
else:
step_per_epoch = train_dataset.nums_samples / opt.batch_size
print('一代有多少step:', step_per_epoch)
while (True):
# try:
# train part
for p in model.parameters():
p.requires_grad = True
if opt.select_data == 'baidu':
try:
image_tensors, labels = train_iter.next()
except:
train_iter = iter(train_loader)
image_tensors, labels = train_iter.next()
else:
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.cuda()
text, length = converter.encode(labels)
batch_size = image.size(0)
if 'CTC' in opt.Prediction:
preds = model(image, text).log_softmax(2)
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
preds = preds.permute(1, 0, 2) # to use CTCLoss format
cost = criterion(preds, text, preds_size, length)
elif 'Bert' in opt.Prediction:
pad_mask = None
# print(image.shape)
preds = model(image, pad_mask)
cost = criterion(preds[0].view(-1, preds[0].shape[-1]), text.contiguous().view(-1)) + \
criterion(preds[1].view(-1, preds[1].shape[-1]), text.contiguous().view(-1))
elif 'SRN' in opt.Prediction:
preds = model(image, None)
cost, n_correct = criterion(preds, text)
else:
preds = model(image, text[:, :-1]) # align with Attention.forward
target = text[:, 1:] # without [GO] Symbol
cost = criterion(preds.view(-1, preds.shape[-1]),
target.contiguous().view(-1))
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(),
opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
if i % opt.disInterval == 0:
elapsed_time = time.time() - start_time
print(
f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}'
)
start_time = time.time()
# validation part
if i % opt.valInterval == 0 and i > start_iter:
elapsed_time = time.time() - start_time
print(
f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}'
)
# for log
with open(f'./saved_models/{opt.experiment_name}/log_train.txt',
'a') as log:
log.write(
f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n'
)
loss_avg.reset()
model.eval()
valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation(
model, criterion, valid_loader, converter, opt)
model.train()
for pred, gt in zip(preds[:5], labels[:5]):
if 'Attn' in opt.Prediction:
pred = pred[:pred.find('[s]')]
gt = gt[:gt.find('[s]')]
print(
f'pred: {pred:20s}, gt: {gt:20s}, {str(pred == gt)}')
log.write(
f'pred: {pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n'
)
valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}'
valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}'
print(valid_log)
log.write(valid_log + '\n')
# keep best accuracy model
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(
model.state_dict(),
f'./saved_models/{opt.experiment_name}/best_accuracy.pth'
)
if current_norm_ED < best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(
model.state_dict(),
f'./saved_models/{opt.experiment_name}/best_norm_ED.pth'
)
best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}'
print(best_model_log)
log.write(best_model_log + '\n')
# save model per 1e+5 iter.
if (i + 1) % opt.saveInterval == 0:
torch.save(model.state_dict(),
f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth')
if i == opt.num_iter:
print('end the training')
sys.exit()
if i > 0 and i % step_per_epoch == 0: # 调整学习速率
lrScheduler.step()
i += 1
model_weights = os.listdir(save_root)
model_weights.remove("optimizer.pt")
model_weights.sort()
last_model_name = model_weights[-1]
model.load_state_dict(torch.load(PJ(save_root, last_model_name)))
optimizer.load_state_dict(torch.load(PJ(save_root, "optimizer.pt")))
print(f"Loading model {last_model_name} successed!")
print(f"Loading optimizer successed!")
iteration, last_epoch = int(last_model_name[-11:-3]), int(
last_model_name[:4])
iteration, last_epoch = (iteration, last_epoch) if resume else (0, 1)
# Learning rate decay scheduler
scheduler = lr_scheduler.MultiStepLR(optimizer,
config["step_size"],
config["gamma"],
last_epoch=last_epoch - 2)
########################################
# Start training
########################################
print("\n> Training")
for epoch in range(last_epoch, config["max_epoch"] + 1):
# scheduler step in each epoch
scheduler.step()
for it, (labels, images) in enumerate(trainloader):
optimizer.zero_grad()
# Drop images and labels into GPU
images = images.cuda().detach()
labels = labels.cuda().detach()
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(
DEVICE) # keep images in the [0, 1] range
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
optimizer = SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.adv == 0:
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.lr_step,
gamma=args.lr_decay)
else:
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[60, 120, 160],
gamma=0.2)
attacker = DDN(steps=args.steps, device=DEVICE)
max_loss = torch.log(torch.tensor(10.)).item() # for callback
best_acc = 0
best_epoch = 0
for epoch in range(args.epochs):
scheduler.step()
cudnn.benchmark = True
model.train()
requires_grad_(m, True)
accs = AverageMeter()
losses = AverageMeter()
def train(cfg):
# prepare dataset
train_loader, val_loader, num_query, num_classes, num_classes2, image_map_label2 = make_data_loader(
cfg)
#print('\n\n*** image_map_label2:')
# prepare model
model = build_model(cfg, num_classes, num_classes2)
#print(list(model.children()))
#print(model.state_dict().keys())
#exit(0)
#print('model.named_children(): \n\n', model.named_children())
'''
kk = 1
for name, child in model.base.named_children():
print(kk, name)
kk += 1
print(len(list(model.base.children())))
exit(0)
for i in range(len(list(model.base.children()))):
print(' +++', i+1)
print(list(model.base.children())[i])
exit(0)
'''
if len(cfg.MODEL.PRETRAIN_PATH2) > 5:
print('--- resume from ', cfg.MODEL.PRETRAIN_PATH2)
#model.load_param(cfg.MODEL.PRETRAIN_PATH)
#model.loiad_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH2, map_location=lambda storage, loc: storage))
if cfg.MODEL.ONCE_LOAD == 'yes':
print('\n---ONCE_LOAD...\n')
model.load_state_dict(
torch.load(cfg.MODEL.PRETRAIN_PATH2,
map_location=lambda storage, loc: storage))
#if cfg.MODEL.FREEZE_BASE == 'yes':
# functions.freeze_layer(model, 'base', False)
#functions.freeze_global_model(model, False)
else:
functions.load_state_dict_distill(model, cfg.MODEL.PRETRAIN_PATH2,
cfg.MODEL.ONLY_BASE,
cfg.MODEL.WITHOUT_FC)
print('**** Successfully load ', cfg.MODEL.PRETRAIN_PATH2)
if cfg.MODEL.FREEZE_BASE:
#functions.freeze_layer(model, 'base', False)
functions.freeze_global_model(model, False)
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is',
cfg.MODEL.METRIC_LOSS_TYPE)
if cfg.SOLVER.MY_OPTIMIZER == "yes":
print('---* my optimizer:', cfg.SOLVER.MY_OPTIMIZER_NAME)
other_params = [
p for n, p in model.named_parameters()
if not n.startswith('base')
]
optimizer = optim.SGD([{
'params': model.base.parameters(),
'lr': cfg.SOLVER.LR / 10
}, {
'params': other_params,
'lr': cfg.SOLVER.LR
}],
momentum=0.9,
weight_decay=5e-4,
nesterov=True)
else:
print('---* not my optimizer')
optimizer = make_optimizer(cfg, model)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
#_C.SOLVER.MY_SCHEDULER = "no"
#_C.SOLVER.MY_WARMUP = "no"
loss_func = make_loss(cfg, num_classes) # modified by gu
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(
cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')
[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace(
'model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD,
start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
if cfg.SOLVER.MY_SCHEDULER == "yes":
print('cfg.SOLVER.MY_SCHEDULER_STEP:',
cfg.SOLVER.MY_SCHEDULER_STEP)
print('---* my scheduler: ', cfg.SOLVER.MY_SCHEDULER_NAME)
if cfg.SOLVER.MY_SCHEDULER_NAME == 'SL':
scheduler = lr_scheduler.StepLR(
optimizer,
step_size=cfg.SOLVER.MY_SCHEDULER_STEP[0],
gamma=0.1)
elif cfg.SOLVER.MY_SCHEDULER_NAME == 'MSL':
scheduler = lr_scheduler.MultiStepLR(
optimizer, cfg.SOLVER.MY_SCHEDULER_STEP, gamma=0.1)
else:
print(cfg.SOLVER.MY_SCHEDULER_NAME, ' not found!')
eixt(0)
else:
print('---* not my scheduler')
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS,
cfg.SOLVER.WARMUP_METHOD)
else:
print(
'Only support pretrain_choice for imagenet and self, but got {}'
.format(cfg.MODEL.PRETRAIN_CHOICE))
arguments = {}
print('************ do_train')
do_train(
cfg,
model,
train_loader,
val_loader,
optimizer,
scheduler, # modify for using self trained model
loss_func,
num_query,
start_epoch, # add for using self trained model
image_map_label2,
num_classes2)
# elif cfg.MODEL.IF_WITH_CENTER == 'yes':
# print('Train with center loss, the loss type is', cfg.MODEL.METRIC_LOSS_TYPE)
# loss_func, center_criterion = make_loss_with_center(cfg, num_classes) # modified by gu
# optimizer, optimizer_center = make_optimizer_with_center(cfg, model, center_criterion)
# # scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# # cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
#
# arguments = {}
#
# # Add for using self trained model
# if cfg.MODEL.PRETRAIN_CHOICE == 'self':
# start_epoch = eval(cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')[-1])
# print('Start epoch:', start_epoch)
# path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer')
# print('Path to the checkpoint of optimizer:', path_to_optimizer)
# path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer_center')
# print('Path to the checkpoint of optimizer_center:', path_to_optimizer_center)
# model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
# optimizer.load_state_dict(torch.load(path_to_optimizer))
# optimizer_center.load_state_dict(torch.load(path_to_optimizer_center))
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD, start_epoch)
# elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
# start_epoch = 0
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
# else:
# print('Only support pretrain_choice for imagenet and self, but got {}'.format(cfg.MODEL.PRETRAIN_CHOICE))
#
# do_train_with_center(
# cfg,
# model,
# center_criterion,
# train_loader,
# val_loader,
# optimizer,
# optimizer_center,
# scheduler, # modify for using self trained model
# loss_func,
# num_query,
# start_epoch # add for using self trained model
# )
else:
print(
"Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n"
.format(cfg.MODEL.IF_WITH_CENTER))
def train(self, train_queue, val_queue=None):
''' Given data queues, train the network '''
# Parameter directory
save_dir = os.path.join(cfg.DIR.OUT_PATH)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Timer for the training op and parallel data loading op.
train_timer = Timer()
data_timer = Timer()
training_losses = []
# Setup learning rates
lr_steps = [int(k) for k in cfg.TRAIN.LEARNING_RATES.keys()]
#Setup the lr_scheduler
self.lr_scheduler = lr_scheduler.MultiStepLR(self.optimizer,
lr_steps,
gamma=0.1)
start_iter = 0
# Resume training
if cfg.TRAIN.RESUME_TRAIN:
self.load(cfg.CONST.WEIGHTS)
start_iter = cfg.TRAIN.INITIAL_ITERATION
# Main training loop
for train_ind in range(start_iter, cfg.TRAIN.NUM_ITERATION + 1):
self.lr_scheduler.step()
data_timer.tic()
batch_img, batch_voxel = train_queue.get()
data_timer.toc()
if self.net.is_x_tensor4:
batch_img = batch_img[0]
# Apply one gradient step
train_timer.tic()
loss = self.train_loss(batch_img, batch_voxel)
train_timer.toc()
training_losses.append(loss.data[0])
# Decrease learning rate at certain points
if train_ind in lr_steps:
#for pytorch optimizer, learning rate can only be set when the optimizer is created
#or using torch.optim.lr_scheduler
print('Learing rate decreased to %f: ' %
cfg.TRAIN.LEARNING_RATES[str(train_ind)])
# Debugging modules
#
# Print status, run validation, check divergence, and save model.
if train_ind % cfg.TRAIN.PRINT_FREQ == 0:
# Print the current loss
print('%s Iter: %d Loss: %f' %
(datetime.now(), train_ind, loss))
if train_ind % cfg.TRAIN.VALIDATION_FREQ == 0 and val_queue is not None:
# Print test loss and params to check convergence every N iterations
val_losses = 0
for i in range(cfg.TRAIN.NUM_VALIDATION_ITERATIONS):
batch_img, batch_voxel = val_queue.get()
val_loss = self.train_loss(batch_img, batch_voxel)
val_losses += val_loss
var_losses_mean = val_losses / cfg.TRAIN.NUM_VALIDATION_ITERATIONS
print('%s Test loss: %f' % (datetime.now(), var_losses_mean))
if train_ind % cfg.TRAIN.NAN_CHECK_FREQ == 0:
# Check that the network parameters are all valid
nan_or_max_param = max_or_nan(self.net.parameters())
if has_nan(nan_or_max_param):
print('NAN detected')
break
if train_ind % cfg.TRAIN.SAVE_FREQ == 0 and not train_ind == 0:
self.save(training_losses, save_dir, train_ind)
#loss is a Variable containing torch.FloatTensor of size 1
if loss.data[0] > cfg.TRAIN.LOSS_LIMIT:
print("Cost exceeds the threshold. Stop training")
break
)
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.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},
], weight_decay=5e-4, momentum=0.9, nesterov=True)
if opt.adam:
optimizer_ft = optim.Adam(model.parameters(), opt.lr, weight_decay=5e-4)
# 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)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer_ft, milestones=[60-start_epoch, 75-start_epoch], gamma=0.1)
######################################################################
# Train and evaluate
# ^^^^^^^^^^^^^^^^^^
#
# It should take around 1-2 hours on GPU.
#
dir_name = os.path.join('./data/outputs',name)
if not opt.resume:
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')
num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print('The number of parameters of model is', num_params)
if args.resume is not None:
checkpoint = torch.load('./save_model/' + args.resume)
net.load_state_dict(checkpoint['net'])
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
decay_epoch = [150, 225]
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=decay_epoch,
gamma=0.1)
writer = SummaryWriter(args.logdir)
def train(epoch, global_steps):
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(train_loader):
global_steps += 1
inputs = inputs.to(device)
def train():
cfg = opt.cfg
data = opt.data
img_size = opt.img_size
epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
if 'pw' not in opt.arc: # remove BCELoss positive weights
hyp['cls_pw'] = 1.
hyp['obj_pw'] = 1.
# Initialize
init_seeds()
if opt.multi_scale:
img_sz_min = round(img_size / 32 / 1.5)
img_sz_max = round(img_size / 32 * 1.5)
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = int(data_dict['classes']) # number of classes
# Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Initialize model
model = Darknet(cfg, arc=opt.arc).to(device)
# Optimizer
pg0, pg1 = [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if 'Conv2d.weight' in k:
pg1 += [v] # parameter group 1 (apply weight_decay)
else:
pg0 += [v] # parameter group 0
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
del pg0, pg1
# https://github.com/alphadl/lookahead.pytorch
# optimizer = torch_utils.Lookahead(optimizer, k=5, alpha=0.5)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_fitness = float('inf')
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {
k: v
for k, v in chkpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
cutoff = load_darknet_weights(model, weights)
if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
nf = int(model.module_defs[model.yolo_layers[0] -
1]['filters']) # yolo layer size (i.e. 255)
if opt.prebias:
for p in optimizer.param_groups:
# lower param count allows more aggressive training settings: i.e. SGD ~0.1 lr0, ~0.9 momentum
p['lr'] *= 100 # lr gain
if p.get('momentum') is not None: # for SGD but not Adam
p['momentum'] *= 0.9
for p in model.parameters():
if opt.prebias and p.numel() == nf: # train (yolo biases)
p.requires_grad = True
elif opt.transfer and p.shape[
0] == nf: # train (yolo biases+weights)
p.requires_grad = True
else: # freeze layer
p.requires_grad = False
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=range(59, 70, 1), gamma=0.8) # gradual fall to 0.1*lr0
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[round(opt.epochs * x) for x in [0.8, 0.9]],
gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count() > 1:
dist.init_process_group(
backend='nccl', # 'distributed backend'
init_method=
'tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(
train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
image_weights=opt.img_weights,
cache_labels=epochs > 10,
cache_images=opt.cache_images and not opt.prebias)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.
rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Test Dataloader
if not opt.prebias:
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(
test_path,
img_size,
batch_size,
hyp=hyp,
rect=True,
cache_labels=True,
cache_images=opt.cache_images),
batch_size=batch_size,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
nb = len(dataloader)
model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
print('Using %g dataloader workers' % nw)
print('Starting %s for %g epochs...' %
('prebias' if opt.prebias else 'training', epochs))
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
freeze_backbone = False
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 -
maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min,
img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [
math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]
] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch%g.jpg' % i
plot_images(imgs=imgs,
targets=targets,
paths=paths,
fname=fname)
if tb_writer:
tb_writer.add_image(fname,
cv2.imread(fname)[:, :, ::-1],
dataformats='HWC')
# Hyperparameter burn-in
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
# if ni <= n_burn:
# for m in model.named_modules():
# if m[0].endswith('BatchNorm2d'):
# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
# g = (i / n_burn) ** 4 # gain rises from 0 - 1
# for x in optimizer.param_groups:
# x['lr'] = hyp['lr0'] * g
# x['weight_decay'] = hyp['weight_decay'] * g
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available(
) else 0 # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1),
'%.3gG' % mem, *mloss,
len(targets), img_size)
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
final_epoch = epoch + 1 == epochs
if opt.prebias:
print_model_biases(model)
else:
# Calculate mAP
if not opt.notest or final_epoch:
with torch.no_grad():
results, maps = test.test(
cfg,
data,
batch_size=batch_size,
img_size=opt.img_size,
model=model,
conf_thres=0.001
if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
save_json=final_epoch and epoch > 0
and 'coco.data' in data,
dataloader=testloader)
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket and not opt.prebias:
os.system('gsutil cp results.txt gs://%s/results%s.txt' %
(opt.bucket, opt.name))
# Write Tensorboard results
if tb_writer:
x = list(mloss) + list(results)
titles = [
'GIoU', 'Objectness', 'Classification', 'Train loss',
'Precision', 'Recall', 'mAP', 'F1', 'val GIoU',
'val Objectness', 'val Classification'
]
for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch)
# Update best mAP
fitness = sum(results[4:]) # total loss
if fitness < best_fitness:
best_fitness = fitness
# Save training results
save = (not opt.nosave) or (final_epoch
and not opt.evolve) or opt.prebias
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
model.module.state_dict()
if type(model) is nn.parallel.DistributedDataParallel else
model.state_dict(),
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if best_fitness == fitness:
torch.save(chkpt, best)
# # Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if len(opt.name) and not opt.prebias:
fresults, flast, fbest = 'results%s.txt' % opt.name, 'last%s.pt' % opt.name, 'best%s.pt' % opt.name
os.rename('results.txt', fresults)
os.rename(wdir + 'last.pt', wdir +
flast) if os.path.exists(wdir + 'last.pt') else None
os.rename(wdir + 'best.pt', wdir +
fbest) if os.path.exists(wdir + 'best.pt') else None
# save to cloud
if opt.bucket:
os.system('gsutil cp %s %s gs://%s' %
(fresults, wdir + flast, opt.bucket))
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1,
(time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def main(arg_seed, arg_timestamp):
random_seed = arg_seed
np.random.seed(random_seed)
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
torch.backends.cudnn.deterministic = True # need to set to True as well
print('Random Seed {}\n'.format(arg_seed))
# -- training parameters
num_epoch = args.epoch
milestone = [50, 75]
batch_size = args.batch
num_workers = 2
weight_decay = 1e-3
gamma = 0.2
current_delta = args.delta
lr = args.lr
start_epoch = 0
# -- specify dataset
# data augmentation
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
trainset = Animal10(split='train', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers,
worker_init_fn=_init_fn, drop_last=True)
testset = Animal10(split='test', transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size * 4, shuffle=False, num_workers=num_workers)
num_class = 10
print('train data size:', len(trainset))
print('test data size:', len(testset))
# -- create log file
if arg_timestamp:
time_stamp = time.strftime("%Y%m%d-%H%M%S")
file_name = 'Ours(' + time_stamp + ').txt'
else:
file_name = 'Ours.txt'
log_dir = check_folder('logs')
file_name = os.path.join(log_dir, file_name)
saver = open(file_name, "w")
saver.write(args.__repr__() + "\n\n")
saver.flush()
# -- set network, optimizer, scheduler, etc
net = vgg19_bn(num_classes=num_class, pretrained=False)
net = nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), lr=lr, weight_decay=weight_decay)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=milestone, gamma=gamma)
criterion = torch.nn.CrossEntropyLoss()
# -- misc
iterations = 0
f_record = torch.zeros([args.rollWindow, len(trainset), num_class])
for epoch in range(start_epoch, num_epoch):
train_correct = 0
train_loss = 0
train_total = 0
net.train()
for i, (images, labels, indices) in enumerate(trainloader):
if images.size(0) == 1: # when batch size equals 1, skip, due to batch normalization
continue
images, labels = images.to(device), labels.to(device)
outputs = net(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_total += images.size(0)
_, predicted = outputs.max(1)
train_correct += predicted.eq(labels).sum().item()
f_record[epoch % args.rollWindow, indices] = F.softmax(outputs.detach().cpu(), dim=1)
iterations += 1
if iterations % 100 == 0:
cur_train_acc = train_correct / train_total * 100.
cur_train_loss = train_loss / train_total
cprint('epoch: {}\titerations: {}\tcurrent train accuracy: {:.4f}\ttrain loss:{:.4f}'.format(
epoch, iterations, cur_train_acc, cur_train_loss), 'yellow')
if iterations % 5000 == 0:
saver.write('epoch: {}\titerations: {}\ttrain accuracy: {}\ttrain loss: {}\n'.format(
epoch, iterations, cur_train_acc, cur_train_loss))
saver.flush()
train_acc = train_correct / train_total * 100.
cprint('epoch: {}'.format(epoch), 'yellow')
cprint('train accuracy: {:.4f}\ntrain loss: {:.4f}'.format(train_acc, train_loss), 'yellow')
saver.write('epoch: {}\ntrain accuracy: {}\ntrain loss: {}\n'.format(epoch, train_acc, train_loss))
saver.flush()
exp_lr_scheduler.step()
if epoch >= args.warm_up:
f_x = f_record.mean(0)
y_tilde = trainset.targets
y_corrected, current_delta = lrt_correction(y_tilde, f_x, current_delta=current_delta, delta_increment=0.1)
logging.info('Current delta:\t{}\n'.format(current_delta))
trainset.update_corrupted_label(y_corrected)
# testing
net.eval()
test_total = 0
test_correct = 0
with torch.no_grad():
for i, (images, labels, _) in enumerate(testloader):
images, labels = images.to(device), labels.to(device)
outputs = net(images)
test_total += images.size(0)
_, predicted = outputs.max(1)
test_correct += predicted.eq(labels).sum().item()
test_acc = test_correct / test_total * 100.
cprint('>> current test accuracy: {:.4f}'.format(test_acc), 'cyan')
saver.write('>> current test accuracy: {}\n'.format(test_acc))
saver.flush()
saver.close()
def main():
torch.manual_seed(args.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'), mode='a')
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print("Initializing dataset {}".format(args.dataset))
dataset = data_manager.init_imgreid_dataset(
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
)
transform_train = ST.Compose([
ST.Scale((args.height, args.width), interpolation=3),
ST.RandomHorizontalFlip(),
ST.ToTensor(),
ST.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ST.RandomErasing(0.5),
])
transform_test = T.Compose([
T.Resize((args.height, args.width), interpolation=3),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
pin_memory = True if use_gpu else False
trainloader = DataLoader(
ImageDataset_seg(dataset.train, transform=transform_train),
sampler=RandomIdentitySampler(dataset.train,
num_instances=args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dataset.num_train_pids)
print(model)
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion_xent = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
criterion_htri = TripletLoss(margin=args.margin, distance=args.distance)
criterion_mask = MaskLoss(mode=args.mode)
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.resume:
if check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(
args.start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.start_epoch, args.max_epoch):
scheduler.step()
start_train_time = time.time()
train(epoch, model, criterion_xent, criterion_htri, criterion_mask,
optimizer, trainloader)
train_time += round(time.time() - start_train_time)
if (epoch + 1) % args.eval_step == 0 or epoch == 0:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_epoch = epoch + 1
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': rank1,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(
best_rank1, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
print("==========\nArgs:{}\n==========".format(args))
def main():
args = vars(parse_args_func())
#config_file = "../configs/config_SN7.json"
config_file = args['config'] # "../configs/config_v1.json"
config_dict = json.loads(open(config_file, 'rt').read())
#config_dict = json.loads(open(sys.argv[1], 'rt').read())
file_dict = config_dict['file_path']
config = config_dict['opt_config']
input_folder = file_dict['input_path'] # '../inputs'
checkpoint_folder = file_dict['checkpoint_path'] # '../checkpoint'
model_folder = file_dict['model_path'] # '../models'
if 'False' in config['deep_supervision']:
config['deep_supervision'] = False
else:
config['deep_supervision'] = True
if 'False' in config['nesterov']:
config['nesterov'] = False
else:
config['nesterov'] = True
if 'None' in config['name']:
config['name'] = None
if config['name'] is None:
config['name'] = '%s_%s_segmodel' % (config['dataset'], config['arch'])
os.makedirs(os.path.join(model_folder, '%s' % config['name']),
exist_ok=True)
if not os.path.isdir(checkpoint_folder):
os.mkdir(checkpoint_folder)
log_name = config['name']
log_dir = os.path.join(checkpoint_folder, log_name)
writer = SummaryWriter(logdir=log_dir)
print('-' * 20)
for key in config:
print('%s: %s' % (key, config[key]))
print('-' * 20)
with open(os.path.join(model_folder, '%s/config.yml' % config['name']),
'w') as f:
yaml.dump(config, f)
# define loss function (criterion)
if config['loss'] == 'BCEWithLogitsLoss':
criterion = nn.BCEWithLogitsLoss().cuda()
else:
criterion = losses.__dict__[config['loss']]().cuda()
cudnn.benchmark = True
# create model
print("=> creating model %s" % config['arch'])
model = archs.__dict__[config['arch']](config['num_classes'],
config['input_channels'],
config['deep_supervision'])
if 'False' in config['resume']:
config['resume'] = False
else:
config['resume'] = True
resume_flag = False
if resume_flag == True:
save_path = os.path.join(model_folder, config['name'], 'model.pth')
weights = torch.load(save_path)
model.load_state_dict(weights)
name_yaml = config['name']
with open(os.path.join(model_folder, '%s/config.yml' % name_yaml),
'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
#start_epoch = config['epochs']
start_epoch = 0
else:
start_epoch = 0
model = model.cuda()
if 'effnet' in config['arch']:
eff_flag = True
else:
eff_flag = False
if eff_flag == True:
cnn_subs = list(model.encoder.eff_conv.children())[1:]
#cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs]
#cnn_params = [item for sublist in cnn_params for item in sublist]
summary(model,
(config['input_channels'], config['input_w'], config['input_h']))
params = filter(lambda p: p.requires_grad, model.parameters())
if eff_flag == True:
params = list(params) + list(model.encoder.conv_a.parameters())
model = torch.nn.DataParallel(model)
if config['optimizer'] == 'Adam':
optimizer = optim.Adam(params,
lr=config['lr'],
weight_decay=config['weight_decay'])
elif config['optimizer'] == 'SGD':
optimizer = optim.SGD(params,
lr=config['lr'],
momentum=config['momentum'],
nesterov=config['nesterov'],
weight_decay=config['weight_decay'])
else:
raise NotImplementedError
if eff_flag == True:
cnn_params = [list(sub_module.parameters()) for sub_module in cnn_subs]
cnn_params = [item for sublist in cnn_params for item in sublist]
cnn_optimizer = torch.optim.Adam(cnn_params,
lr=0.001,
weight_decay=config['weight_decay'])
#cnn_optimizer = None
else:
cnn_optimizer = None
if config['optimizer'] == 'SGD':
if config['scheduler'] == 'CosineAnnealingLR':
scheduler = lr_scheduler.CosineAnnealingLR(
optimizer, T_max=config['epochs'], eta_min=config['min_lr'])
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=config['factor'],
patience=config['patience'],
verbose=1,
min_lr=config['min_lr'])
elif config['scheduler'] == 'MultiStepLR':
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in config['milestones'].split(',')],
gamma=config['gamma'])
elif config['scheduler'] == 'ConstantLR':
scheduler = None
else:
raise NotImplementedError
else:
scheduler = None
# Data loading code
img_ids = glob(
os.path.join(input_folder, config['dataset'], 'images', 'training',
'*' + config['img_ext']))
train_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
#img_dir = os.path.join(input_folder, config['dataset'], 'images', 'training')
#mask_dir = os.path.join(input_folder, config['dataset'], 'annotations', 'training')
#train_image_mask = image_to_afile(img_dir, mask_dir, None, train_img_ids, config)
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images',
'validation', '*' + config['img_ext']))
val_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
img_ids = glob(
os.path.join(input_folder, config['val_dataset'], 'images', 'test',
'*' + config['img_ext']))
test_img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_transform = Compose([
#transforms.RandomScale ([config['scale_min'], config['scale_max']]),
#transforms.RandomRotate90(),
transforms.Rotate([config['rotate_min'], config['rotate_max']],
value=mean,
mask_value=0),
transforms.Flip(),
#transforms.HorizontalFlip (),
transforms.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=10,
val_shift_limit=10),
transforms.RandomBrightnessContrast(brightness_limit=0.10,
contrast_limit=0.10,
brightness_by_max=True),
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
val_transform = Compose([
transforms.Resize(config['input_h'], config['input_w']),
transforms.Normalize(mean=mean, std=std),
])
train_dataset = Dataset(img_ids=train_img_ids,
img_dir=os.path.join(input_folder,
config['dataset'], 'images',
'training'),
mask_dir=os.path.join(input_folder,
config['dataset'],
'annotations', 'training'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=train_transform,
from_file=None)
val_dataset = Dataset(img_ids=val_img_ids,
img_dir=os.path.join(input_folder,
config['val_dataset'], 'images',
'validation'),
mask_dir=os.path.join(input_folder,
config['val_dataset'],
'annotations', 'validation'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform,
from_file=None)
test_dataset = Dataset(img_ids=test_img_ids,
img_dir=os.path.join(input_folder,
config['val_dataset'],
'images', 'test'),
mask_dir=os.path.join(input_folder,
config['val_dataset'],
'annotations', 'test'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
input_channels=config['input_channels'],
transform=val_transform,
from_file=None)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
drop_last=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1, #config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1, #config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
log = OrderedDict([
('epoch', []),
('lr', []),
('loss', []),
('iou', []),
('dice', []),
('val_loss', []),
('val_iou', []),
('val_dice', []),
])
best_iou = 0
trigger = 0
Best_dice = 0
iou_AtBestDice = 0
for epoch in range(start_epoch, config['epochs']):
print('{:s} Epoch [{:d}/{:d}]'.format(config['arch'], epoch,
config['epochs']))
# train for one epoch
train_log = train(epoch, config, train_loader, model, criterion,
optimizer, cnn_optimizer)
if config['optimizer'] == 'SGD':
if config['scheduler'] == 'CosineAnnealingLR':
scheduler.step()
elif config['scheduler'] == 'ReduceLROnPlateau':
scheduler.step(val_log['loss'])
elif config['scheduler'] == 'MultiStepLR':
scheduler.step()
# evaluate on validation set
val_log = validate(config, val_loader, model, criterion)
test_log = validate(config, test_loader, model, criterion)
if Best_dice < test_log['dice']:
Best_dice = test_log['dice']
iou_AtBestDice = test_log['iou']
print(
'loss %.4f - iou %.4f - dice %.4f - val_loss %.4f - val_iou %.4f - val_dice %.4f - test_iou %.4f - test_dice %.4f - Best_dice %.4f - iou_AtBestDice %.4f'
% (train_log['loss'], train_log['iou'], train_log['dice'],
val_log['loss'], val_log['iou'], val_log['dice'],
test_log['iou'], test_log['dice'], Best_dice, iou_AtBestDice))
save_tensorboard(writer, train_log, val_log, test_log, epoch)
log['epoch'].append(epoch)
log['lr'].append(config['lr'])
log['loss'].append(train_log['loss'])
log['iou'].append(train_log['iou'])
log['dice'].append(train_log['dice'])
log['val_loss'].append(val_log['loss'])
log['val_iou'].append(val_log['iou'])
log['val_dice'].append(val_log['dice'])
pd.DataFrame(log).to_csv(os.path.join(model_folder,
'%s/log.csv' % config['name']),
index=False)
trigger += 1
if val_log['iou'] > best_iou:
torch.save(
model.state_dict(),
os.path.join(model_folder, '%s/model.pth' % config['name']))
best_iou = val_log['iou']
print("=> saved best model")
trigger = 0
# early stopping
if config['early_stopping'] >= 0 and trigger >= config[
'early_stopping']:
print("=> early stopping")
break
torch.cuda.empty_cache()
genusnet.to(device)
discriminator_family.to(device)
familynet.to(device)
loss_fn = torch.nn.CrossEntropyLoss()
loss_fn2 = ContrastiveLoss() ##quitar
loss_fn3 = SpecLoss() ##quitar
# -----------------------------------------------------------------------------
## etapa 1: entrenar g y h
print("||||| Stage 1 |||||")
optimizer = torch.optim.Adam(
list(encoder.parameters()) + list(classifier.parameters()) +
list(ssnet.parameters()) + list(genusnet.parameters()) +
list(familynet.parameters()),
lr=0.0001)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[30], gamma=0.1)
#herb std-mean
#tensor([0.0808, 0.0895, 0.1141])
#tensor([0.7410, 0.7141, 0.6500])
#photo std-mean
#tensor([0.1399, 0.1464, 0.1392])
#tensor([0.2974, 0.3233, 0.2370])
data_transforms = {
'train':
transforms.Compose([
#transforms.Resize((img_size, img_size)),
transforms.RandomRotation(15),
#transforms.RandomCrop((img_size, img_size)),
#transforms.RandomResizedCrop((img_size, img_size)),
lr=config['learning_rate'],
weight_decay=0,
betas=(0.9, 0.999))
init_weights(netG, init_type='kaiming', scale=0.1)
global_step = 0
# G pixel loss
cri_pix = nn.L1Loss().to(device)
# G feature loss
cri_fea = nn.L1Loss().to(device)
# load VGG perceptual loss
netF = VGGFeatureExtractor(feature_layer=34, use_bn=False).to(device)
print('# perceptual parameters:',
sum(param.numel() for param in netF.parameters()))
scheduler = lr_scheduler.MultiStepLR(optimizer_G,
[50000, 100000, 200000, 300000], 0.5)
log_dict = OrderedDict()
netG.train()
for epoch in trange(config['number_epochs']):
train_bar = tqdm(train_loader)
train_bar.set_description_str(desc=f"N epochs - {epoch}")
scheduler.step()
for step, (lr, hr) in enumerate(train_bar):
global_step += 1
lr = torch.autograd.Variable(lr, requires_grad=True).to(device)
hr = torch.autograd.Variable(hr, requires_grad=True).to(device)
sr = netG(lr)