def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
# args.rank = int(os.environ["RANK"])
args.rank = 1
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
checkpoint = []
if (args.resume is not None):
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
params = checkpoint['parser']
args.num_class = params.num_class
args.network = params.network
args.start_epoch = params.start_epoch + 1
del params
model = EfficientDet(num_classes=args.num_class,
network=args.network,
W_bifpn=EFFICIENTDET[args.network]['W_bifpn'],
D_bifpn=EFFICIENTDET[args.network]['D_bifpn'],
D_class=EFFICIENTDET[args.network]['D_class'],
gpu=args.gpu)
if (args.resume is not None):
model.load_state_dict(checkpoint['state_dict'])
del checkpoint
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(
(args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
print('Run with DistributedDataParallel with divice_ids....')
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
print('Run with DistributedDataParallel without device_ids....')
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
print('Run with DataParallel ....')
model = torch.nn.DataParallel(model).cuda()
# Training dataset
train_dataset = []
if (args.dataset == 'VOC'):
# train_dataset = VOCDetection(root=args.dataset_root,
# transform=get_augumentation(phase='train', width=EFFICIENTDET[args.network]['input_size'], height=EFFICIENTDET[args.network]['input_size']))
train_dataset = VOCDetection(root=args.dataset_root,
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer()
]))
elif (args.dataset == 'COCO'):
train_dataset = CocoDataset(
root_dir=args.dataset_root,
set_name='train2017',
transform=get_augumentation(
phase='train',
width=EFFICIENTDET[args.network]['input_size'],
height=EFFICIENTDET[args.network]['input_size']))
# train_loader = DataLoader(train_dataset,
# batch_size=args.batch_size,
# num_workers=args.workers,
# shuffle=True,
# collate_fn=detection_collate,
# pin_memory=True)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=True,
collate_fn=collater,
pin_memory=True)
# define loss function (criterion) , optimizer, scheduler
optimizer = optim.AdamW(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
cudnn.benchmark = True
for epoch in range(args.start_epoch, args.num_epoch):
train(train_loader, model, scheduler, optimizer, epoch, args)
state = {
'epoch': epoch,
'parser': args,
'state_dict': get_state_dict(model)
}
torch.save(
state,
'./weights/checkpoint_{}_{}_{}.pth'.format(args.dataset,
args.network, epoch))
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 or ycb.")
parser.add_argument("--path", help="Path to dataset 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("--evaluate_every", default=20, type=int)
parser.add_argument("--print_every", default=20, type=int)
parser.add_argument('--distributed',
action="store_true",
help='Run model in distributed mode with DataParallel')
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == "coco":
if parser.path is None:
raise ValueError(
"Must provide --path when training on non-CSV datasets")
dataset_train = CocoDataset(parser.path,
ann_file="instances_train2014.json",
set_name="train2014",
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer(min_side=512, max_side=512)
]))
dataset_val = CocoDataset(parser.path,
ann_file="instances_val2014.cars.json",
set_name="val2014",
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == "ycb":
dataset_train = YCBDataset(parser.path,
"image_sets/train.txt",
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer(min_side=512, max_side=512)
]),
train=True)
dataset_val = YCBDataset(parser.path,
"image_sets/val.txt",
transform=transforms.Compose(
[Normalizer(), Resizer()]),
train=False)
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=12,
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=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")
print("CUDA available: {}".format(torch.cuda.is_available()))
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
retinanet = retinanet.to(device)
if parser.distributed:
retinanet = torch.nn.DataParallel(retinanet)
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)))
best_mean_avg_prec = 0.0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data["img"].to(device).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.item()))
epoch_loss.append(float(loss.item()))
if parser.print_every % iter_num == 0:
print("Epoch: {} | Iteration: {}/{} | "
"Classification loss: {:1.5f} | "
"Regression loss: {:1.5f} | "
"Running loss: {:1.5f}".format(
epoch_num, iter_num, len(dataloader_train),
float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if ((epoch_num + 1) % parser.evaluate_every
== 0) or epoch_num + 1 == parser.epochs:
mAP = 0.0
if parser.dataset == "coco":
print("Evaluating dataset")
mAP = coco_eval.evaluate_coco(dataset_val, retinanet)
else:
print("Evaluating dataset")
AP = eval.evaluate(dataset_val, retinanet)
mAP = np.asarray([x[0] for x in AP.values()]).mean()
print("Val set mAP: ", mAP)
if mAP > best_mean_avg_prec:
best_mean_avg_prec = mAP
torch.save(
retinanet.state_dict(),
"{}_retinanet_best_mean_ap_{}.pt".format(
parser.dataset, epoch_num))
scheduler.step(np.mean(epoch_loss))
retinanet.eval()
torch.save(retinanet.state_dict(), "retinanet_model_final.pt")
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
# args.rank = int(os.environ["RANK"])
args.rank = 1
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(
backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# args.num_class = train_dataset.num_classes()
print('dataset:', args.dataset)
print('network:', args.network)
print('num_epoch:', args.num_epoch)
print('batch_size:', args.batch_size)
print('lr_choice:', args.lr_choice)
print('lr:', args.lr)
print('lr_fn:', args.lr_fn)
print('image_size:', args.image_size)
print('workers:', args.workers)
print('num_class:', args.num_class)
print('save_folder:', args.save_folder)
print('limit:', args.limit)
if args.dataset == 'h5':
train_dataset = H5CoCoDataset('{}/train_small.hdf5'.format(args.dataset_root), 'train_small')
valid_dataset = H5CoCoDataset('{}/test.hdf5'.format(args.dataset_root), 'test')
else:
train_dataset = CocoDataset(args.dataset_root, set_name='train_small',
# transform=transforms.Compose([Normalizer(), Augmenter(), Resizer(args.image_size)]),
transform=get_augumentation('train'),
limit_len=args.limit[0])
valid_dataset = CocoDataset(args.dataset_root, set_name='test',
# transform=transforms.Compose([Normalizer(), Resizer(args.image_size)]),
transform=get_augumentation('test'),
limit_len=args.limit[1])
print('train_dataset:', len(train_dataset))
print('valid_dataset:', len(valid_dataset))
steps_pre_epoch = len(train_dataset) // args.batch_size
print('steps_pre_epoch:', steps_pre_epoch)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
valid_loader = DataLoader(valid_dataset,
batch_size=1,
num_workers=args.workers,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True)
checkpoint = []
if(args.resume is not None):
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
params = checkpoint['parser']
args.num_class = params.num_class
args.network = params.network
args.start_epoch = checkpoint['epoch'] + 1
del params
model = EfficientDet(num_classes=args.num_class,
network=args.network,
W_bifpn=EFFICIENTDET[args.network]['W_bifpn'],
D_bifpn=EFFICIENTDET[args.network]['D_bifpn'],
D_class=EFFICIENTDET[args.network]['D_class']
)
if(args.resume is not None):
model.load_state_dict(checkpoint['state_dict'])
del checkpoint
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
print('Run with DistributedDataParallel with divice_ids....')
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
print('Run with DistributedDataParallel without device_ids....')
elif args.gpu is not None:
# print('using gpu:', args.gpu)
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
model = model.cpu()
# print('Run with DataParallel ....')
model = torch.nn.DataParallel(model).cuda()
if args.lr_choice == 'lr_fn':
lr_now = float(args.lr_fn['LR_START'])
elif args.lr_choice == 'lr_scheduler':
lr_now = args.lr
optimizer = optim.Adam(model.parameters(), lr=lr_now)
# optimizer = optim.AdamW(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, factor=0.1, verbose=True)
cudnn.benchmark = True
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 in range(args.start_epoch, args.num_epoch):
train(train_loader, model, scheduler, optimizer, epoch, args, epoch_loss_file, iteration_loss_file, steps_pre_epoch)
# test
_model = model.module
_model.eval()
_model.is_training = False
with torch.no_grad():
if args.dataset != 'show':
evaluate_coco(train_dataset, _model, args.dataset, epoch, eval_train_file)
evaluate_coco(valid_dataset, _model, args.dataset, epoch, eval_val_file)
help='Checkpoint state_dict file to resume training from')
args = parser.parse_args()
if(args.weight is not None):
resume_path = str(args.weight)
print("Loading checkpoint: {} ...".format(resume_path))
checkpoint = torch.load(
args.weight, map_location=lambda storage, loc: storage)
params = checkpoint['parser']
args.num_class = params.num_class
args.network = params.network
model = EfficientDet(
num_classes=args.num_class,
network=args.network,
W_bifpn=EFFICIENTDET[args.network]['W_bifpn'],
D_bifpn=EFFICIENTDET[args.network]['D_bifpn'],
D_class=EFFICIENTDET[args.network]['D_class'],
is_training=False,
threshold=args.threshold,
iou_threshold=args.iou_threshold)
model.load_state_dict(checkpoint['state_dict'])
model = model.cuda()
if(args.dataset == 'VOC'):
valid_dataset = VOCDetection(root=args.dataset_root, image_sets=[('2007', 'test')],
transform=transforms.Compose([Normalizer(), Resizer()]))
evaluate(valid_dataset, model)
else:
valid_dataset = CocoDataset(root_dir=args.dataset_root, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
evaluate_coco(valid_dataset, model)
checkpoint = []
if(args.resume is not None):
resume_path = str(args.resume)
print("Loading checkpoint: {} ...".format(resume_path))
checkpoint = torch.load(
args.resume, map_location=lambda storage, loc: storage)
args.num_class = checkpoint['num_class']
args.network = checkpoint['network']
train_dataset = []
if(args.dataset == 'VOC'):
train_dataset = VOCDetection(root=args.dataset_root,
transform=get_augumentation(phase='train', width=EFFICIENTDET[args.network]['input_size'], height=EFFICIENTDET[args.network]['input_size']))
elif(args.dataset == 'COCO'):
train_dataset = CocoDataset(root_dir=args.dataset_root, set_name='train2017', transform=get_augumentation(
phase='train', width=EFFICIENTDET[args.network]['input_size'], height=EFFICIENTDET[args.network]['input_size']))
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_worker,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
model = EfficientDet(num_classes=args.num_classes,
network=args.network,
W_bifpn=EFFICIENTDET[args.network]['W_bifpn'],
D_bifpn=EFFICIENTDET[args.network]['D_bifpn'],
D_class=EFFICIENTDET[args.network]['D_class'],
)
if(args.resume is not None):
