def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--model_path', help='Path to model', type=str)
parser = parser.parse_args(args)
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
dataset_val.image_ids = dataset_val.image_ids[:50] # TEST
# Create the model
retinanet = model.resnet50(num_classes=dataset_val.num_classes(), pretrained=True)
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet.load_state_dict(torch.load(parser.model_path))
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet.load_state_dict(torch.load(parser.model_path))
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = False
retinanet.eval()
retinanet.module.freeze_bn()
coco_eval.evaluate_coco(dataset_val, retinanet)
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--model_path', help='Path to model', type=str)
parser = parser.parse_args(args)
model_path = './model_final.pt'
test_path = './test'
dataset_test = AIZOODataset(test_path, transforms=transforms.Compose([Normalizer(), Resizer()]))
# Create the model
retinanet = model.resnet50(num_classes=3, pretrained=False)
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.load(model_path)
#retinanet.load_state_dict(checkpoint.module)
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet.load_state_dict(torch.load(model_path))
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = False
retinanet.eval()
#retinanet.freeze_bn()
coco_eval.evaluate_coco(dataset_test, retinanet)
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--load_model_path',
help='Path to model(.pt file)',
type=str)
parser = parser.parse_args(args)
# dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
# transform=transforms.Compose([Normalizer(), Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
# Create the model
# retinanet = model.resnet50(num_classes=dataset_val.num_classes(), pretrained=True)
retinanet = torch.load(parser.load_model_path)
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = False
retinanet.eval()
retinanet.module.freeze_bn()
coco_eval.evaluate_coco(dataset_val, retinanet)
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--config', help='Config file path that contains scale and ratio values', type=str)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=50)
parser.add_argument('--init-lr', help='Initial learning rate for training process', type=float, default=1e-3)
parser.add_argument('--batch-size', help='Number of input images per step', type=int, default=1)
parser.add_argument('--num-workers', help='Number of worker used in dataloader', type=int, default=1)
# For resuming training from saved checkpoint
parser.add_argument('--resume', help='Whether to resume training from checkpoint', action='store_true')
parser.add_argument('--saved-ckpt', help='Resume training from this checkpoint', type=str)
parser.add_argument('--multi-gpus', help='Allow to use multi gpus for training task', action='store_true')
parser.add_argument('--snapshots', help='Location to save training snapshots', type=str, default="snapshots")
parser.add_argument('--log-dir', help='Location to save training logs', type=str, default="logs")
parser.add_argument('--expr-augs', help='Allow to use use experiment augmentation methods', action='store_true')
parser.add_argument('--aug-methods', help='(Experiment) Augmentation methods to use, separate by comma symbol', type=str, default="rotate,hflip,brightness,contrast")
parser.add_argument('--aug-prob', help='Probability of applying (experiment) augmentation in range [0.,1.]', type=float, default=0.5)
parser = parser.parse_args(args)
train_transforms = [Normalizer(), Resizer(), Augmenter()]
# Define transform methods
if parser.expr_augs:
aug_map = get_aug_map(p=parser.aug_prob)
aug_methods = parser.aug_methods.split(",")
for aug in aug_methods:
if aug in aug_map.keys():
train_transforms.append(aug_map[aug])
else:
print(f"{aug} is not available.")
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path, set_name='train2017',
transform=transforms.Compose(train_transforms))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose(train_transforms))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False)
dataloader_train = DataLoader(dataset_train, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=parser.batch_size, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler_val)
config = dict({"scales": None,
"ratios": None})
if parser.config:
config = load_config(parser.config, config)
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"])
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"])
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"])
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"])
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"])
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
optimizer = optim.Adam(retinanet.parameters(), lr=parser.init_lr)
if parser.resume:
if not parser.saved_ckpt:
print("No saved checkpoint provided for resuming training. Exiting now...")
return
if not os.path.exists(parser.saved_ckpt):
print("Invalid saved checkpoint path. Exiting now...")
return
# Restore last state
retinanet, optimizer, start_epoch = load_ckpt(parser.saved_ckpt, retinanet, optimizer)
if parser.epochs <= start_epoch:
print("Number of epochs must be higher than number of trained epochs of saved checkpoint.")
return
use_gpu = True
if use_gpu:
print("Using GPU for training process")
if torch.cuda.is_available():
if parser.multi_gpus:
print("Using multi-gpus for training process")
retinanet = torch.nn.DataParallel(retinanet.cuda(), device_ids=[0,1])
else:
retinanet = torch.nn.DataParallel(retinanet.cuda())
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# Tensorboard writer
writer = SummaryWriter(parser.log_dir)
# Save snapshots dir
if not os.path.exists(parser.snapshots):
os.makedirs(parser.snapshots)
best_mAP = 0
start_epoch = 0 if not parser.resume else start_epoch
for epoch_num in range(start_epoch, parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
epoch_csf_loss = []
epoch_reg_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
with torch.cuda.device(0):
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
epoch_csf_loss.append(float(classification_loss))
epoch_reg_loss.append(float(regression_loss))
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'\rEpoch: {}/{} | Iteration: {}/{} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
(epoch_num + 1), parser.epochs, (iter_num + 1), len(dataloader_train), float(classification_loss), float(regression_loss), np.mean(loss_hist)), end='')
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
# writer.add_scalar("Loss/train", loss, epoch_num)
_epoch_loss = np.mean(epoch_loss)
_epoch_csf_loss = np.mean(epoch_reg_loss)
_epoch_reg_loss = np.mean(epoch_reg_loss)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
scheduler.step(_epoch_loss)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('\nEvaluating dataset')
APs = csv_eval.evaluate(dataset_val, retinanet)
mAP = round(mean(APs[ap][0] for ap in APs.keys()), 5)
print("mAP: %f" %mAP)
writer.add_scalar("validate/mAP", mAP, epoch_num)
# Handle lr_scheduler wuth mAP value
scheduler.step(mAP)
lr = get_lr(optimizer)
writer.add_scalar("train/classification-loss", _epoch_csf_loss, epoch_num)
writer.add_scalar("train/regression-loss", _epoch_reg_loss, epoch_num)
writer.add_scalar("train/loss", _epoch_loss, epoch_num)
writer.add_scalar("train/learning-rate", lr, epoch_num)
# Save model file, optimizer and epoch number
checkpoint = {
'epoch': epoch_num,
'state_dict': retinanet.state_dict(),
'optimizer': optimizer.state_dict(),
}
# torch.save(retinanet.module, os.path.join(parser.snapshots, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)))
# Check whether this epoch's model achieves highest mAP value
is_best = False
if best_mAP < mAP:
best_mAP = mAP
is_best = True
save_ckpt(checkpoint, is_best, parser.snapshots, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num + 1))
print('\n')
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
writer.flush()
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=150)
parser.add_argument('--gpu_num', help='default gpu', type=int, default=5)
parser.add_argument('--saved_dir',
help='saved dir',
default='trained_models/coco/resnet50/')
parser = parser.parse_args(args)
# GPU 할당 변경하기
GPU_NUM = parser.gpu_num
device = torch.device(
f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu')
torch.cuda.set_device(device) # change allocation of current GPU
print(device)
print('Current cuda device ', torch.cuda.current_device()) # check
device_ids = [5, 4, 3, 1, 7]
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=8,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=8,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
device=device,
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
device=device,
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
device=device,
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
device=device,
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
device=device,
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.to(device)
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet,
device_ids=[5, 4, 3, 1, 7],
output_device=GPU_NUM).to(device)
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
criterion = FocalLoss(device)
criterion = criterion.to(device)
# optimizer = optim.Adam(retinanet.parameters(), lr = 1e-7)
# scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=30, T_mult=2, eta_max=0.0004, T_up=10, gamma=0.5)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
loss_per_epoch = 2
start_time = time.time()
for iter_num, data in enumerate((dataloader_train)):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
outputs = retinanet(
[data['img'].to(device).float(), data['annot']])
else:
outputs = retinanet([data['img'].float(), data['annot']])
classification, regression, anchors, annotations = (outputs)
classification_loss, regression_loss = criterion(
classification, regression, anchors, annotations)
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
if iter_num % 500 == 0:
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num,
float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
print('epoch time :', time.time() - start_time)
if loss_per_epoch > np.mean(loss_hist):
print('best model is saved')
torch.save(retinanet.state_dict(),
parser.saved_dir + 'best_model.pt')
loss_per_epoch = np.mean(loss_hist)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument('--exp_name',
help='Path to folder for saving the model and log',
type=str)
parser.add_argument('--output_folder',
help='Path to folder for saving all the experiments',
type=str)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100) # 100
parser.add_argument('--batch_size', help='Batch size', type=int, default=2)
parser.add_argument('--lr',
help='Number of epochs',
type=float,
default=1e-5)
parser.add_argument('--caption',
help='Any thing in particular about the experiment',
type=str)
parser.add_argument('--server',
help='seerver name',
type=str,
default='ultron')
parser.add_argument('--detector',
help='detection algo',
type=str,
default='RetinaNet')
parser.add_argument('--arch', help='model architecture', type=str)
parser.add_argument('--pretrain', default=False, action='store_true')
parser.add_argument('--freeze_batchnorm',
default=False,
action='store_true')
parser = parser.parse_args(args)
output_folder_path = os.path.join(parser.output_folder, parser.exp_name)
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
PARAMS = {
'dataset': parser.dataset,
'exp_name': parser.exp_name,
'depth': parser.depth,
'epochs': parser.epochs,
'batch_size': parser.batch_size,
'lr': parser.lr,
'caption': parser.caption,
'server': parser.server,
'arch': parser.arch,
'pretrain': parser.pretrain,
'freeze_batchorm': parser.freeze_batchnorm
}
exp = neptune.create_experiment(
name=parser.exp_name,
params=PARAMS,
tags=[parser.arch, parser.detector, parser.dataset, parser.server])
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18 and parser.arch == 'Resnet':
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.depth == 10 and parser.arch == 'Resnet':
retinanet = model.resnet10(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.depth == 18 and parser.arch == 'BiRealNet18':
checkpoint_path = None
if parser.pretrain:
checkpoint_path = '/media/Rozhok/Bi-Real-net/pytorch_implementation/BiReal18_34/models/imagenet_baseline/checkpoint.pth.tar'
retinanet = birealnet18(checkpoint_path,
num_classes=dataset_train.num_classes())
elif parser.depth == 34 and parser.arch == 'Resnet':
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.depth == 50 and parser.arch == 'Resnet':
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.depth == 101 and parser.arch == 'Resnet':
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrain)
elif parser.arch == 'ofa':
print("Model is ResNet50D.")
bn_momentum = 0.1
bn_eps = 1e-5
retinanet = ResNet50D(
n_classes=dataset_train.num_classes(),
bn_param=(bn_momentum, bn_eps),
dropout_rate=0,
width_mult=1.0,
depth_param=3,
expand_ratio=0.35,
)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
print(retinanet)
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=parser.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
if parser.freeze_batchnorm:
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
exp.log_metric('Current lr', float(optimizer.param_groups[0]['lr']))
exp.log_metric('Current epoch', int(epoch_num))
retinanet.train()
if parser.freeze_batchnorm:
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
exp.log_metric('Training: Classification loss',
float(classification_loss))
exp.log_metric('Training: Regression loss',
float(regression_loss))
exp.log_metric('Training: Totalloss', float(loss))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val,
retinanet,
output_folder_path,
exp=exp)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
os.path.join(
output_folder_path,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)))
retinanet.eval()
torch.save(retinanet, os.path.join(output_folder_path, 'model_final.pt'))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.',
default='csv')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)',
default='data/train_retinanet.csv')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)',
default='data/class_retinanet.csv')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)',
default='data/val_retinanet.csv')
parser.add_argument('--model_path',
default='coco_resnet_50_map_0_335_state_dict.pt',
help='Path to file containing pretrained retinanet')
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs_detection',
help='Number of epochs for detection',
type=int,
default=50)
parser.add_argument('--epochs_classification',
help='Number of epochs for classification',
type=int,
default=50)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=1,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if parser.model_path is not None:
print('loading ', parser.model_path)
if 'coco' in parser.model_path:
retinanet.load_state_dict(torch.load(parser.model_path),
strict=False)
else:
retinanet = torch.load(parser.model_path)
print('Pretrained model loaded!')
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
#Here training the detection
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=4,
verbose=True)
loss_hist = collections.deque(maxlen=500)
loss_style_classif = nn.CrossEntropyLoss()
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
mAP_list = []
mAPbest = 0
for epoch_num in range(parser.epochs_detection):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
[classification_loss, regression_loss], style = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
[classification_loss, regression_loss
], style = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
if torch.cuda.is_available():
style_loss = loss_style_classif(
style,
torch.tensor(data['style']).cuda())
else:
style_loss = loss_style_classif(
style, torch.tensor(data['style']))
loss = classification_loss + regression_loss + style_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.4f} | Regression loss: {:1.4f} | Style loss: {:1.4f} | Running loss: {:1.4f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), float(style_loss),
np.mean(loss_hist)))
del classification_loss
del regression_loss
del style_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet)
mAP_list.append(mAP)
print('mAP_list', mAP_list)
if mAP > mAPbest:
print('Saving best checkpoint')
torch.save(retinanet, 'model_best.pt')
mAPbest = mAP
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
# Here we aggregate all the data to don't have to appy the Retinanet during training.
retinanet.load_state_dict(torch.load('model_best.pt').state_dict())
List_feature = []
List_target = []
retinanet.training = False
retinanet.eval()
retinanet.module.style_inference = True
retinanet.module.freeze_bn()
epoch_loss = []
with torch.no_grad():
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
_, _, feature_vec = retinanet(data['img'].cuda().float())
else:
_, _, feature_vec = retinanet(data['img'].float())
List_feature.append(torch.squeeze(feature_vec).cpu())
List_target.append(data['style'][0])
except Exception as e:
print(e)
continue
print('END of preparation of the data for classification of style')
# Here begins Style training. Need to set to style_train. They are using the same loader, as it was expected to train both at the same time.
batch_size_classification = 64
dataloader_train_style = torch.utils.data.DataLoader(
StyleDataset(List_feature, List_target),
batch_size=batch_size_classification)
retinanet.load_state_dict(torch.load('model_best.pt').state_dict())
# Here training the detection
retinanet.module.style_inference = False
retinanet.module.style_train(True)
retinanet.training = True
retinanet.train()
optimizer = optim.Adam(
retinanet.module.styleClassificationModel.parameters(),
lr=5e-3,
weight_decay=1e-3)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='max',
patience=4,
verbose=True)
loss_hist = collections.deque(maxlen=500)
loss_style_classif = nn.CrossEntropyLoss()
retinanet.train()
retinanet.module.freeze_bn()
criterion = nn.CrossEntropyLoss()
accu_list = []
accubest = 0
for epoch_num in range(parser.epochs_classification):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
total = 0
correct = 0
for iter_num, data in enumerate(dataloader_train_style):
try:
optimizer.zero_grad()
inputs, targets = data
if torch.cuda.is_available():
inputs, targets = inputs.cuda(), targets.cuda()
outputs = retinanet.module.styleClassificationModel(
inputs, 0, 0, 0, True)
loss = criterion(outputs, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
total += targets.size(0)
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum()
print(
'| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f Acc@1: %.3f%%'
%
(epoch_num, parser.epochs_classification, iter_num + 1,
(len(dataloader_train_style) // batch_size_classification)
+ 1, loss.item(), 100. * correct / total))
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet)
accu_list.append(accu)
print('mAP_list', mAP_list, 'accu_list', accu_list)
if accu > accubest:
print('Saving best checkpoint')
torch.save(retinanet.module, 'model_best_classif.pt')
accubest = accu
scheduler.step(accu)
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet.module, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
type=str,
default='csv',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/train.csv',
help='Path to file containing training annotations (see readme)')
parser.add_argument(
'--csv_classes',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/class.csv',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/val.csv',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--model_save_path',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/model/resnet101+PFA+CFPN/',
help='Path to save model')
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=101)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=150)
parser.add_argument('--iter_num',
help='Iter number of saving checkpoint',
type=int,
default=5)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
# 将自定义的Dataset根据batch size大小、是否shuffle等封装成一个Batch Size大小的Tensor，用于后面的训练
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# add gap save model count variable
n = 0
for epoch_num in range(parser.epochs):
n += 1
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
# try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
# except Exception as e:
# print(e)
# continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if n % parser.iter_num == 0:
torch.save(
retinanet.module, parser.model_save_path + '/' +
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, parser.model_save_path + '/' + 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description="Simple training script for training a RetinaNet network.")
parser.add_argument("--dataset",
help="Dataset type, must be one of csv or coco.")
parser.add_argument("--coco_path", help="Path to COCO directory")
parser.add_argument(
"--csv_train",
help="Path to file containing training annotations (see readme)")
parser.add_argument("--csv_classes",
help="Path to file containing class list (see readme)")
parser.add_argument(
"--csv_val",
help=
"Path to file containing validation annotations (optional, see readme)",
)
parser.add_argument(
"--depth",
help="Resnet depth, must be one of 18, 34, 50, 101, 152",
type=int,
default=50,
)
parser.add_argument("--batch_size", help="Batch size", type=int, default=2)
parser.add_argument("--epochs",
help="Number of epochs",
type=int,
default=100)
parser.add_argument("--workers",
help="Number of workers of dataleader",
type=int,
default=4)
parser = parser.parse_args(args)
writer = SummaryWriter("logs")
# Create the data loaders
if parser.dataset == "coco":
if parser.coco_path is None:
raise ValueError("Must provide --coco_path when training on COCO,")
dataset_train = CocoDataset(
parser.coco_path,
set_name="train2017",
transform=transforms.Compose(
[Normalizer(), Augmenter(),
Resizer()]),
)
dataset_val = CocoDataset(
parser.coco_path,
set_name="val2017",
transform=transforms.Compose([Normalizer(),
Resizer()]),
)
elif parser.dataset == "csv":
if parser.csv_train is None:
raise ValueError("Must provide --csv_train when training on COCO,")
if parser.csv_classes is None:
raise ValueError(
"Must provide --csv_classes when training on COCO,")
dataset_train = CSVDataset(
train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(), Augmenter(),
Resizer()]),
)
if parser.csv_val is None:
dataset_val = None
print("No validation annotations provided.")
else:
dataset_val = CSVDataset(
train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(),
Resizer()]),
)
else:
raise ValueError(
"Dataset type not understood (must be csv or coco), exiting.")
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(
dataset_train,
num_workers=parser.workers,
collate_fn=collater,
batch_sampler=sampler,
)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=parser.workers,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
"Unsupported model depth, must be one of 18, 34, 50, 101, 152")
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=10,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print("Num training images: {}".format(len(dataset_train)))
global_step = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
global_step = iter_num + epoch_num * len(dataloader_train)
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data["img"].cuda().float(), data["annot"]])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
if iter_num % 10 == 0:
print(
"Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}"
.format(
epoch_num,
iter_num,
float(classification_loss),
float(regression_loss),
np.mean(loss_hist),
))
writer.add_scalars(
"training",
{
"loss": loss,
"loss_cls": classification_loss,
"loss_reg": regression_loss,
},
global_step,
)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == "coco":
print("Evaluating dataset")
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == "csv" and parser.csv_val is not None:
print("Evaluating dataset")
mAP = csv_eval.evaluate(dataset_val, retinanet)
valid_mAP = [x[0] for x in mAP.values() if x[1] > 0]
mmAP = sum(valid_mAP) / len(mAP)
writer.add_scalars("validation", {"mmAP": mmAP}, global_step)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
"checkpoints/{}_retinanet_{}.pt".format(parser.dataset, epoch_num),
)
retinanet.eval()
torch.save(retinanet, "checkpoints/odel_final.pt")
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', default='csv', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', default='dataset/pascal_train.csv', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', default='dataset/classes.csv', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', default='dataset/pascal_val.csv', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=100)
parser.add_argument('--weights_folder', help='path to save weight', type=str, required=True)
parser = parser.parse_args(args)
if not os.path.exists(parser.weights_folder):
os.makedirs(parser.weights_folder)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path, set_name='train2017',
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size=5, drop_last=False)
dataloader_train = DataLoader(dataset_train, num_workers=4, collate_fn=collater, batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=8, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=4, collate_fn=collater, batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# import ipdb; ipdb.set_trace()
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
total_loss = 0
total_regression_loss = 0
total_classification_loss = 0
with tqdm(dataloader_train, unit="batch") as tepoch:
for data in tepoch:
# for iter_num, data in tepoch:#enumerate(dataloader_train):
tepoch.set_description(f"Epoch {epoch_num}")
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
total_loss = total_loss + loss
total_regression_loss = total_regression_loss + regression_loss
total_classification_loss = total_classification_loss + classification_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
# print(
# 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
# epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)))
tepoch.set_postfix(cls_loss="{:1.5f}".format(classification_loss), reg_loss="{:1.5f}".format(regression_loss))
time.sleep(0.1)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
tb.add_scalar('Training loss', total_loss, epoch_num)
tb.add_scalar('Training regression loss', total_regression_loss, epoch_num)
tb.add_scalar('Training accuracy loss', total_classification_loss, epoch_num)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module, '{}/{}_retinanet_{}.pt'.format(parser.weights_folder,parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, '{}/model_final.pt'.format(parser.weights_folder))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument(
'--dataset', help='Dataset type, must be one of csv or coco.') #数据集类型
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50) #选择与训练模型
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
#决定图片数据集的顺序和batch_size,返回的是图片的分组
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
#多GPU运行
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
#collections:模块实现了特定目标的容器，以提供Python标准内建容器 dict、list、set、tuple 的替代选择
#collections.deque:返回双向队列对象，最长长度为500
loss_hist = collections.deque(maxlen=500)
# model.train() ：启用 BatchNormalization 和 Dropout
# model.eval() ：不启用 BatchNormalization 和 Dropout
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
#反向传播
loss.backward()
#梯度裁剪，梯度小于/大于阈值时，更新的梯度为阈值（此处为小于0.1）
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
#更新所有的参数，一旦梯度被如backward()之类的函数计算好后，我们就可以调用这个函数
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
#optimizer.step()通常用在每个mini-batch之中，而scheduler.step()通常用在epoch里面
#有用了optimizer.step()，模型才会更新，而scheduler.step()是对lr进行调整。
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--iou',default='05')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=100)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path, set_name='train2017',
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
val_dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size=8, drop_last=False)
dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=8, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=5e-5)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
multistep_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5,8,11,20], gamma=0.2)
loss_hist = collections.deque(maxlen=500)
val_loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
val_epoch_loss=[]
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Train: Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f} | Epoch loss: {:1.5f} '.format(
epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist),epoch_loss[-1]))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
for iter_num, data in enumerate(dataloader_val):
try:
#optimizer.zero_grad()
#retinanet.eval()
with torch.no_grad():
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet((data['img'].cuda().float(), data['annot']))
else:
classification_loss, regression_loss = retinanet((data['img'].float(), data['annot']))
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
#loss.backward()
#torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
#optimizer.step()
val_loss_hist.append(float(loss))
val_epoch_loss.append(float(loss))
print(
'Val: Epoch: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f} | Epoch loss: {:1.5f} '.format(
epoch_num, float(classification_loss), float(regression_loss), np.mean(val_loss_hist),val_epoch_loss[-1]))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
#mAP_train = csv_eval.evaluate(val_dataset_train,retinanet,iou_threshold=float(parser.iou)/10)
mAP_val = csv_eval.evaluate(dataset_val, retinanet,iou_threshold=float(parser.iou)/10)
#writer.add_scalar('train_mAP_Questions',mAP_train[0][0],epoch_num)
writer.add_scalar('val_mAP_Questions', mAP_val[0][0], epoch_num)
writer.add_scalar('val_loss',np.mean(val_epoch_loss),epoch_num)
writer.add_scalar('train_loss',np.mean(epoch_loss),epoch_num)
lr_scheduler.step(np.mean(epoch_loss))
#one_scheduler.step()
multistep_scheduler.step()
torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.iou, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=100)
parser.add_argument('--dcn_layers', type =str, help = 'comma seperated str where laters to be used, 0..3',default = None)
parser.add_argument('--use_depth', action='store_true', help='if specified, use depth for deformconv')
parser = parser.parse_args(args)
use_dcn = [False, False, False, False]
if parser.dcn_layers is not None:
_t = parser.dcn_layers.split(',')
for __t in _t:
use_dcn[int(__t)] = True
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path, set_name='train2017',
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size=128, drop_last=False)
dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True, use_dcn = use_dcn, use_depth = parser.use_depth)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
writer = SummaryWriter()
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
#mAP = csv_eval.evaluate(dataset_val, retinanet)
global_step = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
global_step += 1
if torch.cuda.is_available():
if parser.use_depth and 'depth' in data:
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']],depth = data['depth'].cuda())
else:
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
else:
if parser.use_depth and 'depth' in data:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']],depth=data['depth'])
else:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
writer.add_scalar('CLS Loss',classification_loss,global_step)
writer.add_scalar('REG Loss',regression_loss,global_step)
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
writer.close()
def main(args=None):
parser = argparse.ArgumentParser(description = 'Simple training script for training a RetinaNet network.')
parser.add_argument('--s', help = 'training session', type = int)
parser.add_argument('--bs', help = 'batch size', type = int, default = 4)
parser.add_argument('--lr', help = 'learning rate', type = float, default = 0.001)
parser.add_argument('--save_int', help = 'interval for saving model', type = int)
parser.add_argument('--dataset', help = 'Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help = 'Path to COCO directory')
parser.add_argument('--csv_train', help = 'Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', help = 'Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help = 'Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--depth', help = 'Resnet depth, must be one of 18, 34, 50, 101, 152', type = int, default = 50)
parser.add_argument('--epochs', help = 'Number of epochs', type = int, default = 100)
parser.add_argument('--use_tb', help = 'whether to use tensorboard', action = 'store_true')
parser.add_argument('--use_aug', help = 'whether to use data augmentation', action = 'store_true')
parser = parser.parse_args(args)
session = parser.s
session_dir = 'session_{:02d}'.format(session)
assert os.path.isdir('models'), '[ERROR] models folder not exist'
assert os.path.isdir('logs'), '[ERROR] logs folder not exist'
model_dir = os.path.join('models', session_dir)
logs_dir = os.path.join('logs', session_dir)
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
if not os.path.isdir(logs_dir):
os.mkdir(logs_dir)
# set up tensorboard logger
tb_writer = None
if parser.use_tb:
tb_writer = SummaryWriter('logs')
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
if parser.use_aug:
#transform = transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform = transforms.Compose([Normalizer(), Augmenter(), ToTensor()]))
else:
dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform = transforms.Compose([Normalizer(), ToTensor()]))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform = transforms.Compose([Normalizer(), ToTensor()]))
#transform = transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), ToTensor()]))
#transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), ToTensor()]))
#transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size = parser.bs, drop_last = False)
dataloader_train = DataLoader(dataset_train, num_workers = 0, collate_fn = collater, batch_sampler = sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size = parser.bs, drop_last = False)
dataloader_val = DataLoader(dataset_val, num_workers = 0, collate_fn = collater, batch_sampler = sampler_val)
print('# classes: {}'.format(dataset_train.num_classes))
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes = dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes = dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes = dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes = dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes = dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
# disable multi-GPU train
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr = parser.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience = 3, verbose = True)
loss_hist = collections.deque(maxlen = 500)
retinanet.train()
#retinanet.module.freeze_bn() if DataParallel activated
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
# retinanet.module.freeze_bn() if DataParallel activated
retinanet.module.freeze_bn()
epoch_loss = []
iter_per_epoch = len(dataloader_train)
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
assert data['img'][0].shape[0] == 3, '[ERROR] data first dim should be 3! ({})'.format(data['img'][0].shape)
# data['img']: (B, C, H, W)
# data['annot']: [x1, y1, x2, y2, class_id]
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
# epoch starts from 0
if (iter_num + 1) % 1 == 0:
print(
'Epoch: {} | Iteration: {} | Total loss: {:1.5f} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
epoch_num, iter_num, float(loss), float(classification_loss), float(regression_loss), np.mean(loss_hist)
)
)
# update tensorboard
if tb_writer is not None:
crt_iter = (epoch_num) * iter_per_epoch + (iter_num + 1)
tb_dict = {
'total_loss': float(loss),
'classification_loss': float(classification_loss),
'regression_loss': float(regression_loss)
}
tb_writer.add_scalars('session_{:02d}/loss'.format(session), tb_dict, crt_iter)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if (epoch_num + 1) % parser.save_int == 0:
# retinanet (before DataParallel): <class 'retinanet.model.ResNet'>, no self.module
# retinanet (after DataParallel): <class 'torch.nn.parallel.data_parallel.DataParallel>, self.module available
# retinanet.module (after DataParallel): <class 'retinanet.model.ResNet'>
torch.save(retinanet.module.state_dict(), os.path.join(model_dir, 'retinanet_s{:02d}_e{:03d}.pth'.format(session, epoch_num)))
if parser.use_tb:
tb_writer.close()
retinanet.eval()
torch.save(retinanet.module.state_dict(), os.path.join(model_dir, 'retinanet_s{:02d}_e{:03d}.pth'.format(session, epoch_num)))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=25)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
# create samplers for both training and validation
# using muti CPU cores to accelerate data loading
sampler_train1 = torch.utils.data.SequentialSampler(dataset_train)
sampler_train2 = torch.utils.data.BatchSampler(sampler_train1,
batch_size=1,
drop_last=True)
dataloader_train = DataLoader(dataset_train,
num_workers=10,
collate_fn=collater,
batch_sampler=sampler_train2)
sampler_val1 = torch.utils.data.SequentialSampler(dataset_val)
sampler_val2 = torch.utils.data.BatchSampler(sampler_val1,
batch_size=1,
drop_last=True)
dataloader_val = DataLoader(dataset_val,
num_workers=10,
collate_fn=collater,
batch_sampler=sampler_val2)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
# ADAM optimizer
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# using tensorboardX to show training process
writer = SummaryWriter('log')
iter_sum = 0
time_sum = 0
frame_num = 8
for epoch_num in range(parser.epochs):
# only work for frame_num > 8
frame_list = collections.deque(maxlen=frame_num)
anno_list = collections.deque(maxlen=frame_num)
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for index, data in enumerate(dataloader_train):
try:
frame_list.append(data['img'])
anno_list.append(data['annot'])
# if frame_num != 32:
if index < 31:
continue
if index >= 697 and index <= 697 + 32:
continue
# real_frame is the frame we used for fish detection
# It's the last frame in the batch group
real_frame = frame_list[-1]
# the annotation for real_frame
annot = anno_list[-1]
# drop useless frames
data['img'] = torch.cat(list(frame_list), dim=0)
optimizer.zero_grad()
classification_loss, regression_loss = retinanet([
data['img'].cuda().float(),
real_frame.cuda().float(),
annot.cuda().float()
])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
writer.add_scalar('loss_hist', np.mean(loss_hist), iter_sum)
writer.add_scalar('classification_loss',
float(classification_loss), iter_sum)
writer.add_scalar('regression_loss', float(regression_loss),
iter_sum)
writer.add_scalar('loss', float(loss), iter_sum)
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, index, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
iter_sum = iter_sum + 1
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
# evaluate coco
coco_eval.evaluate_coco(dataset_val, dataloader_val, retinanet,
frame_num)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
'checkpoint/{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'save/model_final.pt')
writer.close()
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
epoch_loss_mem = -999999
in_a_row = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
map_val = float(mAP.get(0)[0])
if map_val < epoch_loss_mem:
in_a_row += 1
if in_a_row >= PATIENCE:
print('Early Stop, Epoch', epoch_num)
break
else:
print('Validation Performance Decreased for', in_a_row,
'Run(s)')
else:
epoch_loss_mem = map_val
in_a_row = 0
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.',
default='csv')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--batch_size', help='Batch size', type=int, default=2)
parser.add_argument('--num_workers',
help='Number of workers',
type=int,
default=4)
parser.add_argument('--models_out',
help='The directory to save models',
type=str)
parser = parser.parse_args(args)
if not os.path.exists(parser.models_out):
os.makedirs(parser.models_out)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=parser.num_workers,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=parser.num_workers,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
writer = SummaryWriter(log_dir="tensor_log/" + parser.models_out)
global_steps = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
running_loss = np.mean(loss_hist)
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), running_loss))
global_steps += 1
writer.add_scalar("Loss/Classification",
float(classification_loss), global_steps)
writer.add_scalar("Loss/Regression", float(regression_loss),
global_steps)
writer.add_scalar("Loss/Running", running_loss, global_steps)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
#for k, v in mAP.items():
# writer.add_scalar("Accuracy/map_{}".format(k), v, epoch_num)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
os.path.join(
parser.models_out,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)))
retinanet.eval()
torch.save(retinanet, os.path.join(parser.models_out, 'model_final.pt'))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.',
default='coco')
parser.add_argument(
'--coco_path',
help='Path to COCO directory',
default=
'/media/zhuzhu/ec114170-f406-444f-bee7-a3dc0a86cfa2/dataset/coco')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--use-gpu',
help='training on cpu or gpu',
action='store_false',
default=True)
parser.add_argument('--device-ids', help='GPU device ids', default=[0])
args = parser.parse_args()
# ------------------------------ Create the data loaders -----------------------------
if args.dataset == 'coco':
if args.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(args.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(args.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
sampler_train = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_train)
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if args.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=False)
elif args.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
if args.use_gpu:
retinanet = nn.DataParallel(retinanet,
device_ids=args.device_ids).cuda()
# retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(args.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
nn.utils.clip_grad_norm_(retinanet.parameters(),
0.1) # 梯度的最大范数为0.1
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if args.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(args.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--model', help='Path to model (.pt) file.')
parser.add_argument('--finetune',
help='if load trained retina model',
type=bool,
default=False)
parser.add_argument('--gpu', help='', type=bool, default=False)
parser.add_argument('--batch_size', help='', type=int, default=2)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
#sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False)
sampler = AspectRatioBasedSampler(dataset_train,
parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
'''
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
'''
use_gpu = parser.gpu
#import pdb
#pdb.set_trace()
#读coco预训练模型
retinanet = model.resnet50(num_classes=80, pretrained=True)
retinanet.load_state_dict(torch.load(parser.model))
for param in retinanet.parameters():
param.requires_grad = False
retinanet.regressionModel = model.RegressionModel(256)
retinanet.classificationModel = model.ClassificationModel(
256, num_classes=dataset_train.num_classes())
prior = 0.01
retinanet.classificationModel.output.weight.data.fill_(0)
retinanet.classificationModel.output.bias.data.fill_(-math.log(
(1.0 - prior) / prior))
retinanet.regressionModel.output.weight.data.fill_(0)
retinanet.regressionModel.output.bias.data.fill_(0)
# for m in retinanet.classificationModel.modules():
# if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
# elif isinstance(m, nn.BatchNorm2d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()
# for m in retinanet.regressionModel.modules():
# if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
# elif isinstance(m, nn.BatchNorm2d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if use_gpu and torch.cuda.is_available():
#retinanet.load_state_dict(torch.load(parser.model))
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
#retinanet.load_state_dict(torch.load(parser.model))
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(
[{
'params': retinanet.module.regressionModel.parameters()
}, {
'params': retinanet.module.classificationModel.parameters()
}], 1e-6)
#optimizer = optim.Adam(retinanet.parameters(), lr=1e-6)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
#import pdb
#pdb.set_trace()
optimizer.zero_grad()
if use_gpu and torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if epoch_num % 5 == 0:
torch.save(
retinanet.module,
'{}_freezinetune_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument('--model_save_path',
help='Path to save model',
type=str)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=8,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# add draw tensorboard code
writer = SummaryWriter(log_dir='./logs/416*416/', flush_secs=60)
# if Cuda:
# graph_inputs = torch.from_numpy(np.random.rand(1, 3, input_shape[0], input_shape[1])).type(
# torch.FloatTensor).cuda()
# else:
# graph_inputs = torch.from_numpy(np.random.rand(1, 3, input_shape[0], input_shape[1])).type(torch.FloatTensor)
# writer.add_graph(model, (graph_inputs,))
# add gap save model count variable
n = 0
for epoch_num in range(parser.epochs):
n += 1
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
### begin calculate train loss
for iter_num, data in enumerate(dataloader_train):
# try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
# except Exception as e:
# print(e)
# continue
### begin calculate valid loss
for iter_num, data in enumerate(dataloader_val):
# try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss_hist.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Valid-Classification loss: {:1.5f} | Valid-Regression loss: {:1.5f} | Running Valid loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
print('Epoch: {} | mAP: {:.3f}'.format(epoch_num, float(mAP)))
scheduler.step(np.mean(epoch_loss))
if n % 10 == 0:
torch.save(
retinanet.module, parser.model_save_path +
'/' + '{}_retinanet_{}_{:.3f}.pt'.format(
parser.dataset, epoch_num, mAP))
retinanet.eval()
torch.save(retinanet, parser.model_save_path + '/' + 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--local_rank', help='Local rank', type=int, default=0)
parser.add_argument('--distributed', action='store_true')
parser.add_argument('--pretrained', action='store_true')
parser = parser.parse_args(args)
torch.cuda.set_device(parser.local_rank)
DISTRIBUTED = parser.distributed and config.DISTRIBUTED
if DISTRIBUTED:
distributed.init_process_group(backend="nccl")
device = torch.device(f'cuda:{parser.local_rank}')
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
if DISTRIBUTED:
sampler = DistributedSampler(dataset_train)
dataloader_train = DataLoader(dataset_train,
num_workers=4,
batch_size=batch_size,
collate_fn=collater,
sampler=sampler,
pin_memory=True,
drop_last=True)
if dataset_val is not None:
sampler_val = DistributedSampler(dataset_val)
dataloader_val = DataLoader(dataset_val,
batch_size=1,
num_workers=4,
collate_fn=collater,
sampler=sampler_val,
pin_memory=True,
drop_last=True)
else:
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=4,
collate_fn=collater,
batch_sampler=sampler,
pin_memory=True)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=4,
collate_fn=collater,
batch_sampler=sampler_val,
pin_memory=True)
# Create the model
if parser.depth == 18:
retinanet = model.retinanet18(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrained)
elif parser.depth == 34:
retinanet = model.retinanet34(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrained)
elif parser.depth == 50:
retinanet = model.retinanet50(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrained)
elif parser.depth == 101:
retinanet = model.retinanet101(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrained)
elif parser.depth == 152:
retinanet = model.retinanet152(num_classes=dataset_train.num_classes(),
pretrained=parser.pretrained)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
if use_cuda:
retinanet = retinanet.cuda()
if RESTORE:
retinanet.load_state_dict(torch.load(RESTORE))
if DISTRIBUTED:
retinanet = torch.nn.parallel.DistributedDataParallel(
retinanet, device_ids=[parser.local_rank])
print("Let's use", parser.local_rank, "GPU!")
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
if DISTRIBUTED:
retinanet.module.freeze_bn()
else:
retinanet.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
save_to_disk = parser.local_rank == 0
retinanet.train()
if DISTRIBUTED:
retinanet.module.freeze_bn()
else:
retinanet.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if use_cuda:
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
if save_to_disk:
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num,
float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if save_to_disk:
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if DISTRIBUTED:
torch.save(
retinanet.module.state_dict(),
'{}/{}_retinanet_{}.pt'.format(checkpoints_dir,
parser.dataset, epoch_num))
else:
torch.save(
retinanet.state_dict(),
'{}/{}_retinanet_{}.pt'.format(checkpoints_dir,
parser.dataset, epoch_num))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--finetune',
help='if load trained retina model',
type=bool,
default=False)
parser.add_argument('--gpu', help='', type=bool, default=False)
parser.add_argument('--batch_size', help='', type=int, default=2)
parser.add_argument('--c',
help='continue with formal model',
type=bool,
default=False)
parser.add_argument('--model', help='model path')
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
#sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False)
sampler = AspectRatioBasedSampler(dataset_train,
parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=16,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=8,
collate_fn=collater,
batch_sampler=sampler_val)
epochpassed = 0
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
if parser.c:
retinanet = torch.load(parser.model)
#import pdb
#pdb.set_trace()
epochpassed = int(parser.model.split('.')[1].split('_')[-1])
use_gpu = parser.gpu
#torch.cuda.set_device(5)
#import pdb
#pdb.set_trace()
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if use_gpu and torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) #original:1e-5
#optimizer =optim.SGD(retinanet.parameters(), lr=0.01,weight_decay=0.0001, momentum=0.9)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
writer = SummaryWriter()
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
epoch_classification_loss = []
epoch_regression_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
#import pdb
#pdb.set_trace()
optimizer.zero_grad()
if use_gpu and torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
epoch_classification_loss.append(float(classification_loss))
epoch_regression_loss.append(float(regression_loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}\r'
.format(epoch_num + epochpassed, iter_num,
float(classification_loss), float(regression_loss),
np.mean(loss_hist)),
end='')
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
print(
'Epoch: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}'
.format(epoch_num + epochpassed,
np.mean(epoch_classification_loss),
np.mean(epoch_regression_loss), np.mean(epoch_loss)))
writer.add_scalar('lossrecord/regressionloss',
np.mean(epoch_regression_loss),
epoch_num + epochpassed)
writer.add_scalar('lossrecord/classificationloss',
np.mean(epoch_regression_loss),
epoch_num + epochpassed)
writer.add_scalar('lossrecord/epochloss', np.mean(epoch_loss),
epoch_num + epochpassed)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if epoch_num % 10 == 0:
torch.save(
retinanet.module,
'./models/{}_retinanet{}_highResolution4fold_{}.pt'.format(
parser.dataset, parser.depth, epoch_num + epochpassed))
#retinanet.eval()
torch.save(
retinanet.module,
'./models/{}_retinanet{}_highResolution4fold_{}.pt'.format(
parser.dataset, parser.depth, parser.epochs + epochpassed))
writer.close()
def main():
global args, results, val_image_ids, logger
args = parse().parse_args()
try:
os.makedirs(args.logdir, exist_ok=True)
except Exception as exc:
raise exc
log_file = os.path.join(args.logdir, "train.log")
logger = get_logger(__name__, log_file)
try:
init_distributed_mode(args)
distributed = True
except KeyError:
args.rank = 0
distributed = False
if args.dist_mode == "DP":
distributed = True
args.rank = 0
if args.rank == 0:
logger.info(f"distributed mode: {args.dist_mode if distributed else 'OFF'}")
if args.val_image_dir is None:
if args.rank == 0:
logger.info(
"No validation image directory specified, will assume the same image directory for train and val"
)
args.val_image_dir = args.image_dir
writer = SummaryWriter(logdir=args.logdir)
img_dim = parse_resize(args.resize)
if args.rank == 0:
logger.info(f"training image dimensions: {img_dim[0]},{img_dim[1]}")
## print out basic info
if args.rank == 0:
logger.info("CUDA available: {}".format(torch.cuda.is_available()))
logger.info(f"torch.__version__ = {torch.__version__}")
# Create the data loaders
if args.dataset == "coco":
# if args.coco_path is None:
# raise ValueError("Must provide --coco_path when training on COCO,")
train_transforms = [Normalizer()]
if args.augs is None:
train_transforms.append(Resizer(img_dim))
else:
p = 0.5
if args.augs_prob is not None:
p = args.augs_prob
aug_map = get_aug_map(p=p)
for aug in args.augs:
if aug in aug_map.keys():
train_transforms.append(aug_map[aug])
else:
logger.info(f"{aug} is not available.")
train_transforms.append(Resizer(img_dim))
if args.rank == 0:
if len(train_transforms) == 2:
logger.info(
"Not applying any special augmentations, using only {}".format(train_transforms)
)
else:
logger.info(
"Applying augmentations {} with probability {}".format(train_transforms, p)
)
dataset_train = CocoDataset(
args.image_dir, args.train_json_path, transform=transforms.Compose(train_transforms),
)
elif args.dataset == "csv":
if args.csv_train is None:
raise ValueError("Must provide --csv_train when training on COCO,")
if args.csv_classes is None:
raise ValueError("Must provide --csv_classes when training on COCO,")
dataset_train = CSVDataset(
train_file=args.csv_train,
class_list=args.csv_classes,
# transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]),
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer(img_dim)]),
)
if args.csv_val is None:
dataset_val = None
print("No validation annotations provided.")
else:
dataset_val = CSVDataset(
train_file=args.csv_val,
class_list=args.csv_classes,
# transform=transforms.Compose([Normalizer(), Resizer()]),
transform=transforms.Compose([Normalizer(), Resizer(img_dim)]),
)
else:
raise ValueError("Dataset type not understood (must be csv or coco), exiting.")
if dist.is_available() and distributed and args.dist_mode == "DDP":
sampler = DistributedSampler(dataset_train)
dataloader_train = DataLoader(
dataset_train,
sampler=sampler,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=collater,
)
elif args.nsr is not None:
logger.info(f"using WeightedRandomSampler with negative (image) sample rate = {args.nsr}")
weighted_sampler = WeightedRandomSampler(
dataset_train.weights, len(dataset_train), replacement=True
)
dataloader_train = DataLoader(
dataset_train,
num_workers=args.num_workers,
collate_fn=collater,
sampler=weighted_sampler,
batch_size=args.batch_size,
pin_memory=True,
)
else:
sampler = AspectRatioBasedSampler(
dataset_train, batch_size=args.batch_size, drop_last=False
)
dataloader_train = DataLoader(
dataset_train,
num_workers=args.num_workers,
collate_fn=collater,
batch_sampler=sampler,
pin_memory=True,
)
if args.val_json_path is not None:
dataset_val = CocoDataset(
args.val_image_dir,
args.val_json_path,
transform=transforms.Compose([Normalizer(), Resizer(img_dim)]),
return_ids=True,
)
# Create the model
if args.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif args.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif args.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True)
elif args.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif args.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError("Unsupported model depth, must be one of 18, 34, 50, 101, 152")
# Load checkpoint if provided.
retinanet = load_checkpoint(retinanet, args.weights, args.depth)
use_gpu = True
if torch.cuda.is_available():
if dist.is_available() and distributed:
if args.dist_mode == "DDP":
retinanet = nn.SyncBatchNorm.convert_sync_batchnorm(retinanet)
retinanet = retinanet.cuda()
elif args.dist_mode == "DP":
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
raise NotImplementedError
else:
torch.cuda.set_device(torch.device("cuda:0"))
retinanet = retinanet.cuda()
# swav = torch.load("/home/bishwarup/Desktop/swav_ckp-50.pth", map_location=torch.device("cpu"))[
# "state_dict"
# ]
# swav_dict = collections.OrderedDict()
# for k, v in swav.items():
# k = k[7:] # discard the module. part
# if k in retinanet.state_dict():
# swav_dict[k] = v
# logger.info(f"SwAV => {len(swav_dict)} keys matched")
# model_dict = copy.deepcopy(retinanet.state_dict())
# model_dict.update(swav_dict)
# retinanet.load_state_dict(model_dict)
# if use_gpu:
# if torch.cuda.is_available():
# if torch.cuda.is_available():
# retinanet = torch.nn.DataParallel(retinanet).cuda()
# else:
# retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=0.001)
# optimizer = torch.optim.SGD(
# retinanet.parameters(), lr=4.2, momentum=0.9, weight_decay=1e-4,
# )
if dist.is_available() and distributed and args.dist_mode == "DDP":
optimizer = LARC(optimizer=optimizer, trust_coefficient=0.001, clip=True)
# optimizer = optim.SGD(retinanet.parameters(), lr=0.0001, momentum=0.95)
# scheduler = optim.lr_scheduler.CosineAnnealingLR(
# optimizer, T_max=args.epochs, eta_min=1e-6
# )
warmup_lr_schedule = np.linspace(
args.start_warmup, args.base_lr, len(dataloader_train) * args.warmup_epochs
)
iters = np.arange(len(dataloader_train) * (args.epochs - args.warmup_epochs))
cosine_lr_schedule = np.array(
[
args.final_lr
+ 0.5
* (args.base_lr - args.final_lr)
* (
1
+ math.cos(
math.pi * t / (len(dataloader_train) * (args.epochs - args.warmup_epochs))
)
)
for t in iters
]
)
lr_schedule = np.concatenate((warmup_lr_schedule, cosine_lr_schedule))
if distributed and dist.is_available() and args.dist_mode == "DDP":
retinanet = nn.parallel.DistributedDataParallel(
retinanet, device_ids=[args.gpu_to_work_on], find_unused_parameters=True
)
# scheduler_warmup = GradualWarmupScheduler(
# optimizer, multiplier=100, total_epoch=5, after_scheduler=scheduler
# )
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
# scheduler = optim.lr_scheduler.OneCycleLR(
# optimizer,
# max_lr=1e-4,
# total_steps=args.epochs * len(dataloader_train),
# pct_start=0.2,
# max_momentum=0.95,
# )
loss_hist = collections.deque(maxlen=500)
if dist.is_available() and distributed:
retinanet.module.train()
retinanet.module.freeze_bn()
else:
retinanet.train()
retinanet.freeze_bn()
# retinanet.module.freeze_bn()
if args.rank == 0:
logger.info("Number of training images: {}".format(len(dataset_train)))
if dataset_val is not None:
logger.info("Number of validation images: {}".format(len(dataset_val)))
# scaler = amp.GradScaler()
global best_map
best_map = 0
n_iter = 0
scaler = amp.GradScaler(enabled=True)
global keep_pbar
keep_pbar = not (distributed and args.dist_mode == "DDP")
for epoch_num in range(args.epochs):
# scheduler_warmup.step(epoch_num)
if dist.is_available() and distributed:
if args.dist_mode == "DDP":
dataloader_train.sampler.set_epoch(epoch_num)
retinanet.module.train()
retinanet.module.freeze_bn()
else:
retinanet.train()
retinanet.freeze_bn()
# retinanet.module.freeze_bn()
epoch_loss = []
results = []
val_image_ids = []
pbar = tqdm(enumerate(dataloader_train), total=len(dataloader_train), leave=keep_pbar)
for iter_num, data in pbar:
n_iter = epoch_num * len(dataloader_train) + iter_num
for param_group in optimizer.param_groups:
lr = lr_schedule[n_iter]
param_group["lr"] = lr
optimizer.zero_grad()
if torch.cuda.is_available():
with amp.autocast(enabled=False):
classification_loss, regression_loss = retinanet(
[data["img"].cuda().float(), data["annot"].cuda()]
)
else:
classification_loss, regression_loss = retinanet(
[data["img"].float(), data["annot"]]
)
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
# for param_group in optimizer.param_groups:
# lr = param_group["lr"]
if args.rank == 0:
writer.add_scalar("Learning rate", lr, n_iter)
pbar_desc = f"Epoch: {epoch_num} | lr = {lr:0.6f} | batch: {iter_num} | cls: {classification_loss:.4f} | reg: {regression_loss:.4f}"
pbar.set_description(pbar_desc)
pbar.update(1)
if bool(loss == 0):
continue
# loss.backward()
scaler.scale(loss).backward()
# unscale the gradients for grad clipping
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
# optimizer.step()
# scheduler.step() # one cycle lr operates at batch level
scaler.step(optimizer)
scaler.update()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
del classification_loss
del regression_loss
if args.dataset == "coco":
# print("Evaluating dataset")
# if args.plot:
# stats = coco_eval.evaluate_coco(
# dataset_val,
# retinanet,
# args.logdir,
# args.batch_size,
# args.num_workers,
# writer,
# n_iter,
# )
# else:
# stats = coco_eval.evaluate_coco(
# dataset_val,
# retinanet,
# args.logdir,
# args.batch_size,
# args.num_workers,
# )
if len(dataset_val) > 0:
if dist.is_available() and distributed and args.dist_mode == "DDP":
sampler_val = DistributedSampler(dataset_val)
dataloader_val = DataLoader(
dataset_val,
sampler=sampler_val,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=eval_collate,
pin_memory=True,
)
else:
dataloader_val = DataLoader(
dataset_val,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=eval_collate,
pin_memory=True,
drop_last=False,
)
validate(retinanet, dataset_val, dataloader_val)
if args.rank == 0:
if len(results):
with open(os.path.join(args.logdir, "val_bbox_results.json"), "w") as f:
json.dump(results, f, indent=4)
stats = coco_eval.evaluate_coco(dataset_val, val_image_ids, args.logdir)
map_avg, map_50, map_75, map_small = stats[:4]
else:
map_avg, map_50, map_75, map_small = [-1] * 4
if map_50 > best_map:
torch.save(
retinanet.state_dict(),
os.path.join(args.logdir, f"retinanet_resnet{args.depth}_best.pt"),
)
best_map = map_50
writer.add_scalar("eval/[email protected]:0.95", map_avg, epoch_num * len(dataloader_train))
writer.add_scalar("eval/[email protected]", map_50, epoch_num * len(dataloader_train))
writer.add_scalar("eval/[email protected]", map_75, epoch_num * len(dataloader_train))
writer.add_scalar("eval/map_small", map_small, epoch_num * len(dataloader_train))
logger.info(
f"Epoch: {epoch_num} | lr = {lr:.6f} |[email protected]:0.95 = {map_avg:.4f} | [email protected] = {map_50:.4f} | [email protected] = {map_75:.4f} | map-small = {map_small:.4f}"
)
elif args.dataset == "csv" and args.csv_val is not None:
# logger.info("Running eval...")
mAP = csv_eval.evaluate(dataset_val, retinanet)
# scheduler.step(np.mean(epoch_loss))
# scheduler.step()
# torch.save(retinanet.module, os.path.join(args.logdir, f"retinanet_{epoch_num}.pt"))
retinanet.eval()
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='show')
parser.add_argument('--coco_path', help='Path to COCO directory', default='/mnt/marathon')
parser.add_argument('--image_size', help='image size', type=int, nargs=2, default=IMAGE_SIZE)
parser.add_argument('--limit', help='limit', type=int, nargs=2, default=(0, 0))
parser.add_argument('--batch_size', help='batch size', type=int, default=BATCH_SIZE)
parser.add_argument('--num_works', help='num works', type=int, default=NUM_WORKERS)
parser.add_argument('--num_classes', help='num classes', type=int, default=3)
parser.add_argument('--merge_val', help='merge_val', type=int, default=MERGE_VAL)
parser.add_argument('--do_aug', help='do_aug', type=int, default=DO_AUG)
parser.add_argument('--lr_choice', default=LR_CHOICE, choices=['lr_scheduler', 'lr_map', 'lr_fn'], type=str)
parser.add_argument('--lr', help='lr', type=float, default=LR)
parser.add_argument("--lr_map", dest="lr_map", action=StoreDictKeyPair, default=LR_MAP)
parser.add_argument("--lr_fn", dest="lr_fn", action=StoreDictKeyPair, default=LR_FN)
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=DEPTH)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=EPOCHS)
parser = parser.parse_args(args)
print('dataset:', parser.dataset)
print('depth:', parser.depth)
print('epochs:', parser.epochs)
print('image_size:', parser.image_size)
print('batch_size:', parser.batch_size)
print('num_works:', parser.num_works)
print('merge_val:', parser.merge_val)
print('do_aug:', parser.do_aug)
print('lr_choice:', parser.lr_choice)
print('lr:', parser.lr)
print('lr_map:', parser.lr_map)
print('lr_fn:', parser.lr_fn)
print('num_classes:', parser.num_classes)
print('limit:', parser.limit)
# Create the data loaders
# dataset_train, _ = torch.utils.data.random_split(dataset_train, [NUM_COCO_DATASET_TRAIN, len(dataset_train) - NUM_COCO_DATASET_TRAIN])
# dataset_val, _ = torch.utils.data.random_split(dataset_val, [NUM_COCO_DATASET_VAL, len(dataset_val) - NUM_COCO_DATASET_VAL])
transform_train = None
transform_vail = None
collate_fn = None
if parser.do_aug:
transform_train = get_augumentation('train', parser.image_size[0], parser.image_size[1])
transform_vail = get_augumentation('test', parser.image_size[0], parser.image_size[1])
collate_fn = detection_collate
else:
transform_train = transforms.Compose([
# Normalizer(),
# Augmenter(),
Resizer(*parser.image_size)])
transform_vail = transforms.Compose([
# Normalizer(),
Resizer(*parser.image_size)])
collate_fn = collater
if parser.dataset == 'h5':
dataset_train = H5CoCoDataset('{}/train_small.hdf5'.format(parser.coco_path), 'train_small')
dataset_val = H5CoCoDataset('{}/test.hdf5'.format(parser.coco_path), 'test')
else:
dataset_train = CocoDataset(parser.coco_path, set_name='train_small', do_aug=parser.do_aug,
transform=transform_train, limit_len=parser.limit[0])
dataset_val = CocoDataset(parser.coco_path, set_name='test', do_aug=parser.do_aug,
transform=transform_vail, limit_len=parser.limit[1])
# 混合val
if parser.merge_val:
dataset_train += dataset_val
print('training images: {}'.format(len(dataset_train)))
print('val images: {}'.format(len(dataset_val)))
steps_pre_epoch = len(dataset_train) // parser.batch_size
print('steps_pre_epoch:', steps_pre_epoch)
sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False)
dataloader_train = DataLoader(dataset_train, batch_size=1, num_workers=parser.num_works, shuffle=False,
collate_fn=collate_fn, batch_sampler=sampler)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 101250:
retinanet = model.resnet101with50weight(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=parser.num_classes, pretrained=PRETRAINED)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
if parser.lr_choice == 'lr_map':
lr_now = lr_change_map(1, 0, parser.lr_map)
elif parser.lr_choice == 'lr_fn':
lr_now = float(parser.lr_fn['LR_START'])
elif parser.lr_choice == 'lr_scheduler':
lr_now = parser.lr
# optimizer = optim.Adam(retinanet.parameters(), lr=lr_now)
optimizer = optim.AdamW(retinanet.parameters(), lr=lr_now)
# optimizer = optim.SGD(retinanet.parameters(), lr=lr_now, momentum=0.9, weight_decay=5e-4)
# optimizer = optim.SGD(retinanet.parameters(), lr=lr_now)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=PATIENCE, factor=FACTOR, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
iteration_loss_path = 'iteration_loss.csv'
if os.path.isfile(iteration_loss_path):
os.remove(iteration_loss_path)
epoch_loss_path = 'epoch_loss.csv'
if os.path.isfile(epoch_loss_path):
os.remove(epoch_loss_path)
eval_train_path = 'eval_train_result.csv'
if os.path.isfile(eval_train_path):
os.remove(eval_train_path)
eval_val_path = 'eval_val_result.csv'
if os.path.isfile(eval_val_path):
os.remove(eval_val_path)
USE_KAGGLE = True if os.environ.get('KAGGLE_KERNEL_RUN_TYPE', False) else False
if USE_KAGGLE:
iteration_loss_path = '/kaggle/working/' + iteration_loss_path
epoch_loss_path = '/kaggle/working/' + epoch_loss_path
eval_val_path = '/kaggle/working/' + eval_val_path
eval_train_path = '/kaggle/working/' + eval_train_path
with open(epoch_loss_path, 'a+') as epoch_loss_file, \
open(iteration_loss_path, 'a+') as iteration_loss_file, \
open(eval_train_path, 'a+') as eval_train_file, \
open(eval_val_path, 'a+') as eval_val_file:
epoch_loss_file.write('epoch_num,mean_epoch_loss\n')
iteration_loss_file.write('epoch_num,iteration,classification_loss,regression_loss,iteration_loss\n')
eval_train_file.write('epoch_num,map50\n')
eval_val_file.write('epoch_num,map50\n')
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
optimizer.zero_grad()
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
iteration_loss = np.mean(loss_hist)
print('\rEpoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
epoch_num+1, iter_num+1, float(classification_loss), float(regression_loss), iteration_loss), end=' ' * 50)
iteration_loss_file.write('{},{},{:1.5f},{:1.5f},{:1.5f}\n'.format(epoch_num+1,
epoch_num * steps_pre_epoch + (iter_num+1), float(classification_loss), float(regression_loss),
iteration_loss))
iteration_loss_file.flush()
del classification_loss
del regression_loss
mean_epoch_loss = np.mean(epoch_loss)
epoch_loss_file.write('{},{:1.5f}\n'.format(epoch_num+1, mean_epoch_loss))
epoch_loss_file.flush()
if parser.lr_choice == 'lr_map':
lr_now = lr_change_map(epoch_num+1, lr_now, parser.lr_map)
adjust_learning_rate(optimizer, lr_now)
elif parser.lr_choice == 'lr_fn':
lr_now = lrfn(epoch_num+1, parser.lr_fn)
adjust_learning_rate(optimizer, lr_now)
elif parser.lr_choice == 'lr_scheduler':
scheduler.step(mean_epoch_loss)
# if parser.dataset != 'show':
# print('Evaluating dataset_train')
# coco_eval.evaluate_coco(dataset_train, retinanet, parser.dataset, parser.do_aug, eval_train_file, epoch_num)
print('Evaluating dataset_val')
coco_eval.evaluate_coco(dataset_val, retinanet, parser.dataset, parser.do_aug, eval_val_file, epoch_num)
return parser
def Train(self, num_epochs=2, output_model_name="final_model.pt"):
self.system_dict["output"]["saved_model"] = output_model_name
self.system_dict["params"]["num_epochs"] = num_epochs
for epoch_num in range(num_epochs):
self.system_dict["local"]["model"].train()
self.system_dict["local"]["model"].module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(
self.system_dict["local"]["dataloader_train"]):
try:
self.system_dict["local"]["optimizer"].zero_grad()
classification_loss, regression_loss = self.system_dict[
"local"]["model"]([
data['img'].to(
self.system_dict["local"]["device"]).float(),
data['annot'].to(
self.system_dict["local"]["device"])
])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.system_dict["local"]["model"].parameters(), 0.1)
self.system_dict["local"]["optimizer"].step()
self.system_dict["local"]["loss_hist"].append(float(loss))
epoch_loss.append(float(loss))
if (iter_num %
self.system_dict["params"]["print_interval"] == 0):
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(
epoch_num, iter_num,
float(classification_loss),
float(regression_loss),
np.mean(
self.system_dict["local"]["loss_hist"])))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if (self.system_dict["dataset"]["val"]["status"]):
print('Evaluating dataset')
coco_eval.evaluate_coco(
self.system_dict["local"]["dataset_val"],
self.system_dict["local"]["model"])
self.system_dict["local"]["scheduler"].step(np.mean(epoch_loss))
torch.save(self.system_dict["local"]["model"], 'resume.pt')
self.system_dict["local"]["model"].eval()
torch.save(self.system_dict["local"]["model"], output_model_name)
def Train(self, num_epochs=2, output_model_name="final_model.pt"):
self.system_dict["output"]["saved_model"] = output_model_name
self.system_dict["params"]["num_epochs"] = num_epochs
#modified.
file_path = "/content/info_loss.txt"
with open(file_path, "w") as info_file:
print("Epoch",
"Iteration",
"Classification_Loss",
"Regression_Loss",
"Running_Loss",
file=info_file)
for epoch_num in range(num_epochs):
self.system_dict["local"]["model"].train()
self.system_dict["local"]["model"].module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(
self.system_dict["local"]["dataloader_train"]):
try:
self.system_dict["local"]["optimizer"].zero_grad()
classification_loss, regression_loss = self.system_dict[
"local"]["model"]([
data['img'].to(
self.system_dict["local"]["device"]).float(),
data['annot'].to(
self.system_dict["local"]["device"])
])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.system_dict["local"]["model"].parameters(), 0.1)
self.system_dict["local"]["optimizer"].step()
self.system_dict["local"]["loss_hist"].append(float(loss))
epoch_loss.append(float(loss))
if (iter_num %
self.system_dict["params"]["print_interval"] == 0):
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(
epoch_num, iter_num,
float(classification_loss),
float(regression_loss),
np.mean(
self.system_dict["local"]["loss_hist"])))
#appending to the log file.
# with open("/content/info_loss.txt","a") as text:
# print(epoch_num,iter_num,float(classification_loss),float(regression_loss),np.mean(self.system_dict["local"]["loss_hist"]),file=text)
#modified.
with open(os.path.abspath(file_path), "a") as another:
print(epoch_num,
iter_num,
float(classification_loss),
float(regression_loss),
np.mean(
self.system_dict["local"]["loss_hist"]),
file=another)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if (self.system_dict["dataset"]["val"]["status"]):
print('Evaluating dataset')
coco_eval.evaluate_coco(
self.system_dict["local"]["dataset_val"],
self.system_dict["local"]["model"])
self.system_dict["local"]["scheduler"].step(np.mean(epoch_loss))
torch.save(self.system_dict["local"]["model"], 'resume.pt')
self.system_dict["local"]["model"].eval()
torch.save(self.system_dict["local"]["model"], output_model_name)
def main(args=None):
parser = argparse.ArgumentParser(
description="Simple training script for training a RetinaNet network."
)
parser.add_argument("--dataset", help="Dataset type, must be one of csv or coco.")
parser.add_argument("--model", default=None, help="Path to trained model")
parser.add_argument("--coco_path", help="Path to COCO directory")
parser.add_argument(
"--csv_train", help="Path to file containing training annotations (see readme)"
)
parser.add_argument(
"--csv_classes", help="Path to file containing class list (see readme)"
)
parser.add_argument(
"--csv_val",
help="Path to file containing validation annotations (optional, see readme)",
)
parser.add_argument(
"--depth",
help="Resnet depth, must be one of 18, 34, 50, 101, 152",
type=int,
default=50,
)
parser.add_argument("--epochs", help="Number of epochs", type=int, default=100)
parser.add_argument(
"--result_dir",
default="results",
help="Path to store training results",
type=str,
)
parser.add_argument(
"--batch_num", default=8, help="Number of samples in a batch", type=int
)
parser = parser.parse_args(args)
print(parser)
# parameters
BATCH_SIZE = parser.batch_num
IMAGE_MIN_SIDE = 1440
IMAGE_MAX_SIDE = 2560
# Create the data loaders
if parser.dataset == "coco":
if parser.coco_path is None:
raise ValueError("Must provide --coco_path when training on COCO,")
# TODO: parameterize arguments for Resizer, and other transform functions
# resizer: min_side=608, max_side=1024
dataset_train = CocoDataset(
parser.coco_path,
# set_name="train2017",
set_name="train_images_full",
transform=transforms.Compose(
[Normalizer(), Augmenter(), Resizer(passthrough=True),]
),
)
dataset_val = CocoDataset(
parser.coco_path,
# set_name="val2017",
set_name="val_images_full",
transform=transforms.Compose([Normalizer(), Resizer(passthrough=True),]),
)
elif parser.dataset == "csv":
if parser.csv_train is None:
raise ValueError("Must provide --csv_train when training on COCO,")
if parser.csv_classes is None:
raise ValueError("Must provide --csv_classes when training on COCO,")
dataset_train = CSVDataset(
train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]),
)
if parser.csv_val is None:
dataset_val = None
print("No validation annotations provided.")
else:
dataset_val = CSVDataset(
train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]),
)
else:
raise ValueError("Dataset type not understood (must be csv or coco), exiting.")
sampler = AspectRatioBasedSampler(
dataset_train, batch_size=BATCH_SIZE, drop_last=False
)
dataloader_train = DataLoader(
dataset_train, num_workers=16, collate_fn=collater, batch_sampler=sampler
)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(
dataset_val, batch_size=BATCH_SIZE, drop_last=False
)
dataloader_val = DataLoader(
dataset_val, num_workers=16, collate_fn=collater, batch_sampler=sampler_val
)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(
num_classes=dataset_train.num_classes(), pretrained=True
)
elif parser.depth == 34:
retinanet = model.resnet34(
num_classes=dataset_train.num_classes(), pretrained=True
)
elif parser.depth == 50:
retinanet = model.resnet50(
num_classes=dataset_train.num_classes(), pretrained=True
)
elif parser.depth == 101:
retinanet = model.resnet101(
num_classes=dataset_train.num_classes(), pretrained=True
)
elif parser.depth == 152:
retinanet = model.resnet152(
num_classes=dataset_train.num_classes(), pretrained=True
)
else:
raise ValueError("Unsupported model depth, must be one of 18, 34, 50, 101, 152")
if parser.model:
retinanet = torch.load(parser.model)
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer, patience=3, verbose=True
)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print("Num training images: {}".format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
p_bar = tqdm(dataloader_train)
for iter_num, data in enumerate(p_bar):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data["img"].cuda().float(), data["annot"]]
)
else:
classification_loss, regression_loss = retinanet(
[data["img"].float(), data["annot"]]
)
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
mean_loss = np.mean(loss_hist)
p_bar.set_description(
f"Epoch: {epoch_num} | Iteration: {iter_num} | "
f"Class loss: {float(classification_loss.item()):.5f} | "
f"Regr loss: {float(regression_loss.item()):.5f} | "
f"Running loss: {mean_loss:.5f}"
)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == "coco":
print("Evaluating dataset")
coco_eval.evaluate_coco(
dataset_val, retinanet, result_dir=parser.result_dir
)
elif parser.dataset == "csv" and parser.csv_val is not None:
print("Evaluating dataset")
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
# TODO: Fix string formating mix (adopt homogeneous format)
torch.save(
retinanet.module,
f"{parser.result_dir}/"
+ "{}_retinanet_{}.pt".format(parser.dataset, epoch_num),
)
retinanet.eval()
torch.save(retinanet, "model_final.pt")