def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--csv_annotations_path', help='Path to CSV annotations')
parser.add_argument('--model_path', help='Path to model', type=str)
parser.add_argument('--images_path',help='Path to images directory',type=str)
parser.add_argument('--class_list_path',help='Path to classlist csv',type=str)
parser.add_argument('--iou_threshold',help='IOU threshold used for evaluation',type=str, default='0.5')
parser = parser.parse_args(args)
#dataset_val = CocoDataset(parser.coco_path, set_name='val2017',transform=transforms.Compose([Normalizer(), Resizer()]))
dataset_val = CSVDataset(parser.csv_annotations_path,parser.class_list_path,transform=transforms.Compose([Normalizer(), Resizer()]))
# Create the model
#retinanet = model.resnet50(num_classes=dataset_val.num_classes(), pretrained=True)
retinanet=torch.load(parser.model_path)
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()
print(csv_eval.evaluate(dataset_val, retinanet,iou_threshold=float(parser.iou_threshold)))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--csv_annotations_path',
help='Path to CSV annotations')
parser.add_argument('--model_path', help='Path to model', type=str)
parser.add_argument('--images_path',
help='Path to images directory',
type=str)
parser.add_argument('--class_list_path',
help='Path to classlist csv',
type=str)
parser.add_argument('--iou_threshold',
help='IOU threshold used for evaluation',
type=str,
default='0.5')
parser = parser.parse_args(args)
#dataset_val = CocoDataset(parser.coco_path, set_name='val2017',transform=transforms.Compose([Normalizer(), Resizer()]))
dataset_val = CSVDataset(parser.csv_annotations_path,
parser.class_list_path,
transform=transforms.Compose(
[Normalizer(), Resizer()]))
# Create the model
#retinanet = model.resnet50(num_classes=dataset_val.num_classes(), pretrained=True)
config = dict({"scales": None, "ratios": None})
config = load_config("config2.yaml", config)
retinanet = model.resnet50(num_classes=dataset_val.num_classes(),
pretrained=False,
ratios=config["ratios"],
scales=config["scales"])
retinanet, _, _ = load_ckpt(parser.model_path, retinanet)
use_gpu = True
if use_gpu:
print("Using GPU for validation process")
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet.cuda())
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = False
retinanet.eval()
retinanet.module.freeze_bn()
print(
csv_eval.evaluate(dataset_val,
retinanet,
score_threshold=0.4,
iou_threshold=float(parser.iou_threshold)))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--csv',
help='Path to dataset file you would like to evaluate')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument('--model_path', help='Path to the model file.')
parser.add_argument('--configfile', help='Path to the config file.')
parser = parser.parse_args(args)
configs = configparser.ConfigParser()
configs.read(parser.configfile)
try:
maxside = int(configs['TRAINING']['maxside'])
minside = int(configs['TRAINING']['minside'])
except Exception as e:
print(e)
print(
'CONFIG FILE IS INVALID. PLEASE REFER TO THE EXAMPLE CONFIG FILE AT config.txt'
)
sys.exit()
if parser.csv is None:
dataset_eval = None
print('No validation annotations provided.')
else:
dataset_eval = CSVDataset(train_file=parser.csv,
class_list=parser.csv_classes,
transform=transforms.Compose([
Normalizer(),
Resizer(min_side=minside,
max_side=maxside)
]))
retinanet = load_model(parser.model_path, parser.configfile)
mAP = csv_eval.evaluate(dataset_eval, retinanet)
print('-----------------')
print(mAP)
print('-----------------')
def main():
batch_size, dataset_train, dataset_val = _make_dataset()
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
if torch.cuda.is_available():
retinanet = retinanet.cuda()
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.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
BEST_MAP = 0
BEST_MAP_EPOCH = 0
for epoch_num in range(cfg.EPOCHS):
retinanet.train()
# retinanet.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
# try:
optimizer.zero_grad()
if cfg.MIXUP:
data, lam = mixup(data)
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']])
if cfg.MIXUP:
classification_loss, regression_loss = mix_loss(
classification_loss, regression_loss, lam)
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
""" validation part """
print('Evaluating dataset')
average_precisions, mAP = csv_eval.evaluate(dataset_val, retinanet)
if mAP > BEST_MAP:
best_average_precisions = average_precisions
BEST_MAP = mAP
BEST_MAP_EPOCH = epoch_num
scheduler.step(np.mean(epoch_loss))
# torch.save(retinanet.module, '{}_retinanet_{}.pt'.format('voc', epoch_num)))
retinanet.eval()
print('\nBest_mAP:', BEST_MAP_EPOCH)
for label in range(dataset_val.num_classes()):
label_name = dataset_val.label_to_name(label)
print('{}: {}'.format(label_name, best_average_precisions[label][0]))
print('BEST MAP: ', BEST_MAP)
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
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('--configfile',
help='Path to the config file',
default='config.txt',
type=str)
parser.add_argument(
'--model',
help=
'Path to the pretrained model file state dict where training must start from, '
'if you want to use a pretrained retinanet.',
default=None,
type=str)
parser = parser.parse_args(args)
configs = configparser.ConfigParser()
configs.read(parser.configfile)
try:
batchsize = int(configs['TRAINING']['batchsize'])
depth = int(configs['TRAINING']['depth'])
maxepochs = int(configs['TRAINING']['maxepochs'])
maxside = int(configs['TRAINING']['maxside'])
minside = int(configs['TRAINING']['minside'])
savepath = configs['TRAINING']['savepath']
lr_start = float(configs['TRAINING']['lr_start'])
lr_reduce_on_plateau_factor = float(
configs['TRAINING']['lr_reduce_on_plateau_factor'])
lr_reduce_on_plateau_patience = int(
configs['TRAINING']['lr_reduce_on_plateau_patience'])
earlystopping_patience = int(
configs['TRAINING']['earlystopping_patience'])
try:
ratios = json.loads(configs['MODEL']['ratios'])
scales = json.loads(configs['MODEL']['scales'])
except Exception as e:
print(e)
print('USING DEFAULT RATIOS AND SCALES')
ratios = None
scales = None
except Exception as e:
print(e)
print(
'CONFIG FILE IS INVALID. PLEASE REFER TO THE EXAMPLE CONFIG FILE AT config.txt'
)
sys.exit()
model_save_dir = datetime.now().strftime(
"%d_%b_%Y_%H_%M") if savepath == 'datetime' else savepath
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir, exist_ok=True)
# Copy the config file into the model save directory
shutil.copy(parser.configfile, os.path.join(model_save_dir, 'config.txt'))
# Create the data loaders
if parser.csv_train is None:
raise ValueError('Must provide --csv_train,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer(min_side=minside, max_side=maxside)
]))
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(min_side=minside,
max_side=maxside)
]))
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=batchsize,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
dataloader_val = None
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 depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True,
ratios=ratios,
scales=scales,
no_nms=False)
elif depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True,
ratios=ratios,
scales=scales,
no_nms=False)
elif depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True,
ratios=ratios,
scales=scales,
no_nms=False)
elif depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True,
ratios=ratios,
scales=scales,
no_nms=False)
elif depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True,
ratios=ratios,
scales=scales,
no_nms=False)
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=lr_start)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
patience=lr_reduce_on_plateau_patience,
verbose=True,
factor=lr_reduce_on_plateau_factor,
cooldown=1,
min_lr=1e-10)
loss_hist = collections.deque(maxlen=500)
if (parser.model):
print(
f'TRYING TO LOAD PRETRAINED MODEL AVAILABLE AT: {parser.model}. MAKE SURE THE MODEL CONFIGS MATCH!!!!!'
)
if torch.cuda.is_available():
retinanet.load_state_dict(torch.load(parser.model))
else:
retinanet.load_state_dict(
torch.load(parser.model, map_location=torch.device('cpu')))
print(f'LOADED PRETRAINED MODEL : {parser.model}')
retinanet.train()
retinanet.module.freeze_bn()
earlystopping = EarlyStopping(patience=earlystopping_patience,
verbose=True,
delta=1e-10,
path=os.path.join(model_save_dir,
'best_model.pt'))
print('Num training images: {}'.format(len(dataset_train)))
loss_dict = OrderedDict()
val_loss_dict = OrderedDict()
for epoch_num in range(maxepochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
epoch_val_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)),
end='\r',
flush=True)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if (len(epoch_loss)):
loss_dict[epoch_num] = np.mean(epoch_loss)
print('')
if dataloader_val is not None:
print('Evaluating dataset')
for iter_num, data in enumerate(dataloader_val):
try:
with torch.no_grad():
if torch.cuda.is_available():
val_classification_loss, val_regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
val_classification_loss, val_regression_loss = retinanet(
[data['img'].float(), data['annot']])
val_classification_loss = val_classification_loss.mean(
)
val_regression_loss = val_classification_loss.mean()
val_loss = val_classification_loss + val_regression_loss
print('Validation Loss: {:1.5f}'.format(val_loss),
end='\r',
flush=True)
epoch_val_loss.append(float(val_loss))
except Exception as e:
print(e)
continue
print('')
if (len(epoch_val_loss)):
val_loss_dict[epoch_num] = np.mean(epoch_val_loss)
retinanet.eval()
mAP = csv_eval.evaluate(dataset_val, retinanet)
print('-----------------')
print(mAP)
print('-----------------')
scheduler.step(np.mean(epoch_loss))
model_save_path = os.path.join(model_save_dir,
f'retinanet_{epoch_num}.pt')
save_model(retinanet, model_save_path)
print(f'Saved model of epoch {epoch_num} to {model_save_path}')
earlystopping(val_loss_dict[epoch_num], retinanet)
if earlystopping.early_stop:
print("Early stopping")
break
retinanet.eval()
save_model(retinanet, os.path.join(model_save_dir, 'model_final.pt'))
with open(os.path.join(model_save_dir, 'loss_history.txt'), 'w') as f:
for epoch_num, loss in loss_dict.items():
f.write(f'{epoch_num}:{loss} \n')
with open(os.path.join(model_save_dir, 'val_loss_history.txt'), 'w') as f:
for epoch_num, loss in val_loss_dict.items():
f.write(f'{epoch_num}:{loss} \n')
# Write configs to model save directory
configs = configparser.ConfigParser()
configs.read(os.path.join(model_save_dir, 'config.txt'))
configs['TRAINING']['num_classes'] = str(dataset_train.num_classes())
for iter_num, data in enumerate(dataloader_train):
configs['MODEL']['input_shape'] = str(
list(data['img'].float().numpy().shape[1:]))
break
# Write class mapping to the model configs.
with open(parser.csv_classes, 'r') as f:
labels = load_classes_from_csv_reader(csv.reader(f, delimiter=','))
configs['LABELMAP'] = {str(i): str(j) for i, j in labels.items()}
with open(os.path.join(model_save_dir, 'config.txt'), 'w') as configfile:
configs.write(configfile)
plt.show()
#%%
# Compare the various models and show the inference time as well as mAP
# model_names = ['retinanet_res18.pt', 'retinanet_res50.pt', 'retinanet_res101.pt']
model_names = ['model_final5.pt', 'retinanet_res18.pt', 'model_final_12.pt']
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
for mod in model_names:
model = torch.load(wd + mod)
start.record()
csv_eval.evaluate(dataset_val, model)
end.record()
# Waits for everything to finish running
torch.cuda.synchronize()
print(mod, start.elapsed_time(end) / len(dataset_val))
#%%
# Read in the satellite images for further test, compile a csv file formatted for testing
os.chdir('/home/ubuntu/project/sat_image/')
sat_images = [i for i in glob.iglob('*.tif')]
sat_images_df = pd.DataFrame(columns=[1, 2, 3, 4, 5, 6])
for file in sat_images:
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))
continue
loss.backward()
pt.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 + 1, 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('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_test, retinanet)
#scheduler.step(np.mean(epoch_loss))
print(f'Mean average precision = {mAP}')
retinanet.eval()
pt.save(retinanet, 'model_final.pt')
#plot predictions
scores, classification, transformed_anchors = plot_predictions(
test_df, class_df, 'filename', 3, retinanet, 0.1)
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.')
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(
'--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("--data_config",
type=str,
default="data/retina_label/custom.data",
help="path to data config file")
parser.add_argument(
"--n_cpu",
type=int,
default=8,
help="number of cpu threads to use during batch generation")
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=15)
parser.add_argument("--batch_size",
type=int,
default=4,
help="size of each image batch")
parser.add_argument('--pretrained_model',
type=str,
default=None,
help='load pretrained model')
parser.add_argument('--optim_scheduler',
type=str,
default=None,
help='load pretrained optimizer and scheduler')
parser.add_argument(
"--attack_type",
type=str,
default="Normal",
help="type of adversarial attack; Normal or FGSM or PGD")
parser.add_argument("--eps",
type=str,
default='2',
help="epsilon value for FGSM")
parser.add_argument("--alpha", type=float, default=0.5)
parser.add_argument(
"--sign_grad",
type=bool,
default=True,
help="whether use signed gradient and alpha=2.5*eps/iter in PGD")
parser.add_argument("--iterations", type=int, default=10)
parser.add_argument("--irl", type=int, default=0)
parser.add_argument("--irl_noise_type", type=str, default='in_domain')
parser.add_argument("--irl_loss_type", type=int, default=1)
parser.add_argument("--irl_attack_type",
type=str,
default='fgsm',
help="type of attack to be implemented in small case")
parser.add_argument("--irl_alpha", type=float, default='0.8')
parser.add_argument("--irl_beta", type=float, default='0.2')
parser.add_argument("--irl_gamma", type=float, default='1')
parser.add_argument("--irl_alt", type=int, default=0)
parser.add_argument(
"--irl_avg",
type=int,
default=0,
help="Set true to average over all layers in irl distance loss")
parser.add_argument(
"--mix_thre",
type=float,
default=0.5,
help=
"percentage of clean data in each mixed batch; range:[0,1], the larger, the more clean data there are in each batch"
)
parser.add_argument("--checkpoint_interval",
type=int,
default=1,
help="interval between saving model weights")
parser.add_argument("--evaluation_interval",
type=int,
default=1,
help="interval evaluations on validation set")
parser.add_argument("--evaluation_attack_interval",
type=int,
default=3,
help="interval evaluations on validation set")
parser.add_argument("--evalute_attacktype",
type=str,
default='FGSM',
help="FGSM/Randn/Normal")
parser = parser.parse_args(args)
print(parser)
eps = convert_eps(parser.eps)
training_name = train_name(parser, eps)
os.makedirs(f"checkpoints/retina/{training_name}", exist_ok=False)
print(f"checkpoints stored as {training_name}")
# Get data configuration
data_config = parse_data_config(parser.data_config)
train_path = data_config["train"]
val_path = data_config["val"]
class_names = data_config["names"]
dataset_train = CSVDataset(train_file=train_path,
class_list=class_names,
transform=transforms.Compose(
[Augmenter(), Resizer()]))
dataset_val = CSVDataset(train_file=val_path,
class_list=class_names,
transform=transforms.Compose([Resizer()]))
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=parser.n_cpu,
collate_fn=collater,
batch_sampler=sampler)
# sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=parser.batch_size, drop_last=False)
# dataloader_val = DataLoader(dataset_val, num_workers=parser.n_cpu, collate_fn=collater, batch_sampler=sampler_val)
if parser.pretrained_model:
retinanet = torch.load(parser.pretrained_model)
else:
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 torch.cuda.is_available():
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
use_irl = bool(parser.irl)
irl_alt = bool(parser.irl_alt)
irl_avg = bool(parser.irl_avg)
if use_irl:
irl_obj = IRL(noise_types=[parser.irl_noise_type],
adv_attack_type=parser.irl_attack_type,
model_type='retina',
loss_type=parser.irl_loss_type,
epsilon=eps,
alpha=parser.alpha,
iterations=parser.iterations)
act_file_name = ('retina_fnl_layers-resnet4_loss-type' +
str(parser.irl_loss_type) + '_' +
parser.irl_noise_type + '_alt' + str(parser.irl_alt))
act_file_name += f"-alpha{parser.irl_alpha}-beta{parser.irl_beta}-gamma{parser.irl_gamma}_activations.txt"
print("Saving activations in: ", str(act_file_name))
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
if parser.optim_scheduler is not None:
optim_scheduler = torch.load(parser.optim_scheduler)
optimizer.load_state_dict(optim_scheduler['optimizer'])
scheduler.load_state_dict(optim_scheduler['scheduler'])
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
print('Starting training.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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()
batch_mixed = mix_batch(retinanet,
data['img'],
data['annot'],
data['img'].shape[0],
epsilon=eps,
alpha=parser.alpha,
mix_thre=parser.mix_thre,
attack_type=parser.attack_type,
model_type='retina',
sign_grad=parser.sign_grad)
if use_irl and (not irl_alt or epoch_num % 2 == 1):
classification_loss, regression_loss, activations = retinanet(
[Variable(batch_mixed.to(device)), data['annot']],
send_activations=True)
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
noise_loss, distance_loss = irl_obj.compute_losses(
model=retinanet,
images=data['img'],
targets=data['annot'],
activations=activations,
epoch_num=epoch_num,
batch_num=iter_num,
training_name=act_file_name,
avg_layers=irl_avg)
regular_loss = classification_loss + regression_loss
loss = parser.irl_alpha * regular_loss + parser.irl_beta * noise_loss + parser.irl_gamma * distance_loss
else:
classification_loss, regression_loss = retinanet(
[Variable(batch_mixed.to(device)), 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 % 500 == 0:
if use_irl and (not irl_alt or epoch_num % 2 == 1):
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Noise Loss: {:1.5f} | Distance Loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num,
float(parser.irl_alpha * classification_loss),
float(parser.irl_alpha * regression_loss),
float(parser.irl_beta * noise_loss),
float(parser.irl_gamma * distance_loss),
np.mean(loss_hist)))
del noise_loss
del distance_loss
else:
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
scheduler.step(np.mean(epoch_loss))
if epoch_num % parser.checkpoint_interval == 0:
torch.save(
retinanet.module,
f"checkpoints/retina/{training_name}/ckpt_{epoch_num}.pt")
torch.save(
{
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict()
},
f"checkpoints/retina/{training_name}/optim_scheduler_{epoch_num}.pt"
)
if epoch_num % parser.evaluation_interval == 0:
print("\n------Evaluating model------")
AP, mAP = csv_eval.evaluate(dataset_val, retinanet)
print('Epoch: {} | AP: {} | mAP: {}'.format(epoch_num, AP, mAP))
# write logs of the model to log.txt, format: epoch number, mAP, AP per class
print(
f"{epoch_num},{mAP},{AP[0][0]},{AP[1][0]},{AP[2][0]},{AP[3][0]},{AP[4][0]},{AP[5][0]},{AP[6][0]},{AP[7][0]},{AP[8][0]},{AP[9][0]}\n"
)
with open(f"checkpoints/retina/{training_name}/log.txt",
'a+') as log:
log.write(
f"{epoch_num},{mAP},{AP[0][0]},{AP[1][0]},{AP[2][0]},{AP[3][0]},{AP[4][0]},{AP[5][0]},{AP[6][0]},{AP[7][0]},{AP[8][0]},{AP[9][0]}\n"
)
# Evaluating the model on noise now
if parser.evalute_attacktype and epoch_num % parser.evaluation_attack_interval == 0:
print("\n-------Evaluating on noise-----")
AP_n, mAP_n = csv_eval.evaluate(
dataset_val,
retinanet,
perturbed=parser.evalute_attacktype,
_epsilon=eps)
print('Noise Epoch: {} | AP: {} | mAP: {}'.format(
epoch_num, AP_n, mAP_n))
with open(f"checkpoints/retina/{training_name}/log_attack.txt",
'a+') as log:
log.write(
f"{epoch_num},{mAP_n},{AP_n[0][0]},{AP_n[1][0]},{AP_n[2][0]},{AP_n[3][0]},{AP_n[4][0]},{AP_n[5][0]},{AP_n[6][0]},{AP_n[7][0]},{AP_n[8][0]},{AP_n[9][0]}\n"
)
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',
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():
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.")
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")
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('--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', 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.')
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.',
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",
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',
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('--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.',
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.')
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(
'--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()
dataset_val = CSVDataset(train_file="./myvaliddataset.csv", class_list="./class_detail.csv",
transform=transforms.Compose([Normalizer(), Resizer()]))
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=4,drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val)
# In[9]:
retinanet = torch.load("./model_final_without_finetune2.pt").cuda()
# In[10]:
mAP = csv_eval.evaluate(dataset_val, retinanet)
# In[ ]:
epochs =100
# In[ ]:
dataset_train = CSVDataset(train_file="./mytraindataset.csv", class_list="./class_detail.csv",
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_val = CSVDataset(train_file="./myvaliddataset.csv", class_list="./class_detail.csv",
transform=transforms.Compose([Normalizer(), Resizer()]))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument("--load_model_path",
type=str,
default=None,
help="Path to model (.pt) file.")
parser.add_argument('--dataset_type',
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('--backbone',
help='Backbone choice: [ResNet, ResNeXt]',
type=str,
default='ResNet')
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",
type=int,
default=2,
help="size of the batches")
parser.add_argument("--lr",
type=float,
default=1e-5,
help="adam: learning rate")
parser = parser.parse_args(args)
results_dir = "results"
save_images_dir = os.path.join(results_dir, "images")
save_models_dir = os.path.join(results_dir, "saved_models")
os.makedirs(results_dir, exist_ok=True)
os.makedirs(save_images_dir, exist_ok=True)
os.makedirs(save_models_dir, exist_ok=True)
# Get today datetime
today = datetime.date.today()
today = "%d%02d%02d" % (today.year, today.month, today.day)
# Get current timme
now = time.strftime("%H%M%S")
# Backbone name
backbone_name = parser.backbone + str(parser.depth)
# DataSet name
dataset_path = ''
# Create the data loaders
if parser.dataset_type == '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()]))
dataset_train = CocoDataset(
parser.coco_path,
set_name='train',
# transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
transform=transforms.Compose(
[Normalizer(), AugmenterWithImgaug(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
dataset_path = parser.coco_path
elif parser.dataset_type == '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()]))
dataset_path = parser.csv_train
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)
# Retrain the model
if parser.load_model_path is not None:
# Load pretrained models
print("\nLoading model from: [%s]" % parser.load_model_path)
retinanet = torch.load(parser.load_model_path)
print("\nStart retrain...")
# Create the model
else:
print("\nStart train...")
if parser.backbone == 'ResNet':
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'
)
elif parser.backbone == 'ResNeXt':
if parser.depth == 50:
retinanet = model.resnext50_32x4d(
num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnext101_32x8d(
num_classes=dataset_train.num_classes(), pretrained=True)
pass
else:
raise ValueError(
"Unsupported model depth, must be one of 50, 101")
else:
raise ValueError("Choice a backbone, [ResNet, ResNeXt]")
# Get dataset name
dataset_name = os.path.split(dataset_path)[-1]
# Checkpoint name
save_ckpt_name = r"%s_%s-%s-RetinaNet-backbone(%s)-ep(%d)-bs(%d)-lr(%s)" \
% (today, now, dataset_name, backbone_name, parser.epochs, parser.batch_size, parser.lr)
os.makedirs(os.path.join(save_images_dir, "%s" % save_ckpt_name),
exist_ok=True)
os.makedirs(os.path.join(save_models_dir, "%s" % save_ckpt_name),
exist_ok=True)
tb_log_path = os.path.join("tf_log", save_ckpt_name)
tb_writer = SummaryWriter(os.path.join(results_dir, tb_log_path))
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
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)
val_loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
epoch_prev_time = time.time()
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
total_classification_loss = 0.0
total_regression_loss = 0.0
total_running_loss = 0.0
total_val_classification_loss = 0.0
total_val_regression_loss = 0.0
total_val_running_loss = 0.0
batch_prev_time = time.time()
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))
# sum the loss for tensorboard at this batch
total_regression_loss += regression_loss
total_classification_loss += classification_loss
total_running_loss += loss.item()
# log = '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))
# Determine approximate time left
data_done = iter_num
data_left = len(dataloader_train) - data_done
batch_time_left = datetime.timedelta(
seconds=data_left * (time.time() - batch_prev_time))
batch_time_left = chop_microseconds(batch_time_left)
batches_done = epoch_num * len(dataloader_train) + iter_num
batches_left = parser.epochs * len(
dataloader_train) - batches_done
total_time_left = datetime.timedelta(
seconds=batches_left * (time.time() - epoch_prev_time))
total_time_left = chop_microseconds(total_time_left)
batch_prev_time = time.time()
epoch_prev_time = time.time()
# Print training step log
prefix_log = '[Epoch: {}/{}] | [Batch: {}/{}]'.format(
epoch_num + 1, parser.epochs, iter_num + 1,
len(dataloader_train))
suffix_log = '[Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}] ETA: {} / {}'.format(
float(classification_loss), float(regression_loss),
np.mean(loss_hist), batch_time_left, total_time_left)
printProgressBar(iteration=iter_num + 1,
total=len(dataloader_train),
prefix=prefix_log,
suffix=suffix_log)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
# Validation
with torch.no_grad():
val_batch_prev_time = time.time()
for iter_num, data in enumerate(dataloader_val):
try:
val_classification_loss, val_regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
val_classification_loss = val_classification_loss.mean()
val_regression_loss = val_regression_loss.mean()
val_loss = val_classification_loss + val_regression_loss
if bool(val_loss == 0):
continue
val_loss_hist.append(float(val_loss))
# sum the loss for tensorboard at this batch
total_val_regression_loss += val_regression_loss
total_val_classification_loss += val_classification_loss
total_val_running_loss += val_loss.item()
# Determine approximate time left
data_done = iter_num
data_left = len(dataloader_val) - data_done
val_batch_time_left = datetime.timedelta(
seconds=data_left *
(time.time() - val_batch_prev_time))
val_batch_time_left = chop_microseconds(
val_batch_time_left)
batches_done = epoch_num * len(dataloader_val) + (
epoch_num + 1) * len(dataloader_train) + iter_num
batches_left = parser.epochs * (len(
dataloader_train) + len(dataloader_val)) - batches_done
total_time_left = datetime.timedelta(
seconds=batches_left * (time.time() - epoch_prev_time))
total_time_left = chop_microseconds(total_time_left)
val_batch_prev_time = time.time()
epoch_prev_time = time.time()
# Print training step log
prefix_log = 'Validation: [Epoch: {}/{}] | [Batch: {}/{}]'.format(
epoch_num + 1, parser.epochs, iter_num + 1,
len(dataloader_val))
suffix_log = '[Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}] ETA: {} / {}'.format(
float(val_classification_loss),
float(val_regression_loss), np.mean(val_loss_hist),
val_batch_time_left, total_time_left)
printProgressBar(iteration=iter_num + 1,
total=len(dataloader_val),
prefix=prefix_log,
suffix=suffix_log)
del val_classification_loss
del val_regression_loss
except Exception as e:
print(e)
continue
# Evaluate AP
if parser.dataset_type == 'coco':
print('Evaluating dataset')
# coco_eval.evaluate_coco(dataset_val, retinanet)
coco_eval.evaluate_coco_and_save_image(
dataset_val, retinanet,
os.path.join(save_images_dir, save_ckpt_name), epoch_num + 1)
elif parser.dataset_type == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
# calculate loss average
average_classification_loss = total_classification_loss / len(
dataloader_train)
average_regression_loss = total_regression_loss / len(dataloader_train)
average_running_loss = total_running_loss / len(dataloader_train)
# TensorBoard
tb_writer.add_scalar(tag='Classification Loss',
scalar_value=average_classification_loss,
global_step=epoch_num + 1)
tb_writer.add_scalar(tag='Regression Loss',
scalar_value=average_regression_loss,
global_step=epoch_num + 1)
tb_writer.add_scalar(tag='Total Loss',
scalar_value=average_running_loss,
global_step=epoch_num + 1)
# Save model
print("\nSave model to [%s] at %d epoch\n" %
(save_ckpt_name, epoch_num + 1))
checkpoint_path = os.path.join(
save_models_dir, "%s/RetinaNet_backbone(%s)_%d.pt" %
(save_ckpt_name, backbone_name, epoch_num + 1))
torch.save(retinanet.module, checkpoint_path)
# torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset_type, epoch_num + 1))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
