def Model(self, model_name="resnet18", gpu_devices=[0]):
'''
User function: Set Model parameters
Available Models
resnet18
resnet34
resnet50
resnet101
resnet152
Args:
model_name (str): Select model from available models
gpu_devices (list): List of GPU Device IDs to be used in training
Returns:
None
'''
num_classes = self.system_dict["local"]["dataset_train"].num_classes()
if model_name == "resnet18":
retinanet = model.resnet18(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet34":
retinanet = model.resnet34(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet50":
retinanet = model.resnet50(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet101":
retinanet = model.resnet101(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet152":
retinanet = model.resnet152(num_classes=num_classes,
pretrained=True)
if self.system_dict["params"]["use_gpu"]:
self.system_dict["params"]["gpu_devices"] = gpu_devices
if len(self.system_dict["params"]["gpu_devices"]) == 1:
os.environ["CUDA_VISIBLE_DEVICES"] = str(
self.system_dict["params"]["gpu_devices"][0])
else:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([
str(id) for id in self.system_dict["params"]["gpu_devices"]
])
self.system_dict["local"][
"device"] = 'cuda' if torch.cuda.is_available() else 'cpu'
retinanet = retinanet.to(self.system_dict["local"]["device"])
retinanet = torch.nn.DataParallel(retinanet).to(
self.system_dict["local"]["device"])
retinanet.training = True
retinanet.train()
retinanet.module.freeze_bn()
self.system_dict["local"]["model"] = retinanet
def export(
checkpoint: str,
output_path,
num_classes: Optional[int] = 1,
model_arch: Optional[str] = "resnet-50",
input_size: Optional[Tuple[int, int]] = (512, 512),
batch_size: Optional[int] = 1,
verbose: Optional[bool] = False,
):
assert output_path.endswith(
".onnx"), "`output_path` must be path to the output `onnx` file"
if model_arch == "resnet-18":
net = model.resnet18(num_classes)
elif model_arch == "resnet-34":
net = model.resnet34(num_classes)
elif model_arch == "resnet-50":
net = model.resnet50(num_classes)
elif model_arch == "resnet-101":
net = model.resnet101(num_classes)
elif model_arch == "resnet-152":
net = model.resnet152(num_classes)
else:
raise NotImplementedError
device = torch.device(
"cuda:0") if torch.cuda.is_available() else torch.device("cpu")
logger.info(f"using device: {device}")
net = net.to(device)
state_dict = torch.load(checkpoint, map_location=device)
state_dict = remove_module(state_dict)
net.load_state_dict(state_dict)
logger.info(f"successfully loaded saved checkpoint.")
dummy_input = torch.randn(batch_size, 3, input_size[0], input_size[1])
net.eval()
net.export = True
dummy_input = dummy_input.to(device)
logger.info(f"exporting to {output_path}...")
torch.onnx.export(
net,
dummy_input,
output_path,
opset_version=11,
verbose=verbose,
input_names=["input"],
output_names=["anchors", "classification", "regression"],
)
logger.info("export complete")
def Model(self, model_name="resnet18", gpu_devices=[0]):
num_classes = self.system_dict["local"]["dataset_train"].num_classes()
if model_name == "resnet18":
retinanet = model.resnet18(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet34":
retinanet = model.resnet34(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet50":
retinanet = model.resnet50(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet101":
retinanet = model.resnet101(num_classes=num_classes,
pretrained=True)
elif model_name == "resnet152":
retinanet = model.resnet152(num_classes=num_classes,
pretrained=True)
if self.system_dict["params"]["use_gpu"]:
self.system_dict["params"]["gpu_devices"] = gpu_devices
if len(self.system_dict["params"]["gpu_devices"]) == 1:
os.environ["CUDA_VISIBLE_DEVICES"] = str(
self.system_dict["params"]["gpu_devices"][0])
else:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([
str(id) for id in self.system_dict["params"]["gpu_devices"]
])
self.system_dict["local"][
"device"] = 'cuda' if torch.cuda.is_available() else 'cpu'
retinanet = retinanet.to(self.system_dict["local"]["device"])
retinanet = torch.nn.DataParallel(retinanet).to(
self.system_dict["local"]["device"])
retinanet.training = True
retinanet.train()
retinanet.module.freeze_bn()
self.system_dict["local"]["model"] = retinanet
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(
'--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)
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='show')
parser.add_argument('--coco_path', help='Path to COCO directory', default='/mnt/marathon')
parser.add_argument('--image_size', help='image size', type=int, nargs=2, default=IMAGE_SIZE)
parser.add_argument('--limit', help='limit', type=int, nargs=2, default=(0, 0))
parser.add_argument('--batch_size', help='batch size', type=int, default=BATCH_SIZE)
parser.add_argument('--num_works', help='num works', type=int, default=NUM_WORKERS)
parser.add_argument('--num_classes', help='num classes', type=int, default=3)
parser.add_argument('--merge_val', help='merge_val', type=int, default=MERGE_VAL)
parser.add_argument('--do_aug', help='do_aug', type=int, default=DO_AUG)
parser.add_argument('--lr_choice', default=LR_CHOICE, choices=['lr_scheduler', 'lr_map', 'lr_fn'], type=str)
parser.add_argument('--lr', help='lr', type=float, default=LR)
parser.add_argument("--lr_map", dest="lr_map", action=StoreDictKeyPair, default=LR_MAP)
parser.add_argument("--lr_fn", dest="lr_fn", action=StoreDictKeyPair, default=LR_FN)
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=DEPTH)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=EPOCHS)
parser = parser.parse_args(args)
print('dataset:', parser.dataset)
print('depth:', parser.depth)
print('epochs:', parser.epochs)
print('image_size:', parser.image_size)
print('batch_size:', parser.batch_size)
print('num_works:', parser.num_works)
print('merge_val:', parser.merge_val)
print('do_aug:', parser.do_aug)
print('lr_choice:', parser.lr_choice)
print('lr:', parser.lr)
print('lr_map:', parser.lr_map)
print('lr_fn:', parser.lr_fn)
print('num_classes:', parser.num_classes)
print('limit:', parser.limit)
# Create the data loaders
# dataset_train, _ = torch.utils.data.random_split(dataset_train, [NUM_COCO_DATASET_TRAIN, len(dataset_train) - NUM_COCO_DATASET_TRAIN])
# dataset_val, _ = torch.utils.data.random_split(dataset_val, [NUM_COCO_DATASET_VAL, len(dataset_val) - NUM_COCO_DATASET_VAL])
transform_train = None
transform_vail = None
collate_fn = None
if parser.do_aug:
transform_train = get_augumentation('train', parser.image_size[0], parser.image_size[1])
transform_vail = get_augumentation('test', parser.image_size[0], parser.image_size[1])
collate_fn = detection_collate
else:
transform_train = transforms.Compose([
# Normalizer(),
# Augmenter(),
Resizer(*parser.image_size)])
transform_vail = transforms.Compose([
# Normalizer(),
Resizer(*parser.image_size)])
collate_fn = collater
if parser.dataset == 'h5':
dataset_train = H5CoCoDataset('{}/train_small.hdf5'.format(parser.coco_path), 'train_small')
dataset_val = H5CoCoDataset('{}/test.hdf5'.format(parser.coco_path), 'test')
else:
dataset_train = CocoDataset(parser.coco_path, set_name='train_small', do_aug=parser.do_aug,
transform=transform_train, limit_len=parser.limit[0])
dataset_val = CocoDataset(parser.coco_path, set_name='test', do_aug=parser.do_aug,
transform=transform_vail, limit_len=parser.limit[1])
# 混合val
if parser.merge_val:
dataset_train += dataset_val
print('training images: {}'.format(len(dataset_train)))
print('val images: {}'.format(len(dataset_val)))
steps_pre_epoch = len(dataset_train) // parser.batch_size
print('steps_pre_epoch:', steps_pre_epoch)
sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False)
dataloader_train = DataLoader(dataset_train, batch_size=1, num_workers=parser.num_works, shuffle=False,
collate_fn=collate_fn, batch_sampler=sampler)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 101250:
retinanet = model.resnet101with50weight(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=parser.num_classes, pretrained=PRETRAINED)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=parser.num_classes, pretrained=PRETRAINED)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
if parser.lr_choice == 'lr_map':
lr_now = lr_change_map(1, 0, parser.lr_map)
elif parser.lr_choice == 'lr_fn':
lr_now = float(parser.lr_fn['LR_START'])
elif parser.lr_choice == 'lr_scheduler':
lr_now = parser.lr
# optimizer = optim.Adam(retinanet.parameters(), lr=lr_now)
optimizer = optim.AdamW(retinanet.parameters(), lr=lr_now)
# optimizer = optim.SGD(retinanet.parameters(), lr=lr_now, momentum=0.9, weight_decay=5e-4)
# optimizer = optim.SGD(retinanet.parameters(), lr=lr_now)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=PATIENCE, factor=FACTOR, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
iteration_loss_path = 'iteration_loss.csv'
if os.path.isfile(iteration_loss_path):
os.remove(iteration_loss_path)
epoch_loss_path = 'epoch_loss.csv'
if os.path.isfile(epoch_loss_path):
os.remove(epoch_loss_path)
eval_train_path = 'eval_train_result.csv'
if os.path.isfile(eval_train_path):
os.remove(eval_train_path)
eval_val_path = 'eval_val_result.csv'
if os.path.isfile(eval_val_path):
os.remove(eval_val_path)
USE_KAGGLE = True if os.environ.get('KAGGLE_KERNEL_RUN_TYPE', False) else False
if USE_KAGGLE:
iteration_loss_path = '/kaggle/working/' + iteration_loss_path
epoch_loss_path = '/kaggle/working/' + epoch_loss_path
eval_val_path = '/kaggle/working/' + eval_val_path
eval_train_path = '/kaggle/working/' + eval_train_path
with open(epoch_loss_path, 'a+') as epoch_loss_file, \
open(iteration_loss_path, 'a+') as iteration_loss_file, \
open(eval_train_path, 'a+') as eval_train_file, \
open(eval_val_path, 'a+') as eval_val_file:
epoch_loss_file.write('epoch_num,mean_epoch_loss\n')
iteration_loss_file.write('epoch_num,iteration,classification_loss,regression_loss,iteration_loss\n')
eval_train_file.write('epoch_num,map50\n')
eval_val_file.write('epoch_num,map50\n')
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
optimizer.zero_grad()
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
iteration_loss = np.mean(loss_hist)
print('\rEpoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
epoch_num+1, iter_num+1, float(classification_loss), float(regression_loss), iteration_loss), end=' ' * 50)
iteration_loss_file.write('{},{},{:1.5f},{:1.5f},{:1.5f}\n'.format(epoch_num+1,
epoch_num * steps_pre_epoch + (iter_num+1), float(classification_loss), float(regression_loss),
iteration_loss))
iteration_loss_file.flush()
del classification_loss
del regression_loss
mean_epoch_loss = np.mean(epoch_loss)
epoch_loss_file.write('{},{:1.5f}\n'.format(epoch_num+1, mean_epoch_loss))
epoch_loss_file.flush()
if parser.lr_choice == 'lr_map':
lr_now = lr_change_map(epoch_num+1, lr_now, parser.lr_map)
adjust_learning_rate(optimizer, lr_now)
elif parser.lr_choice == 'lr_fn':
lr_now = lrfn(epoch_num+1, parser.lr_fn)
adjust_learning_rate(optimizer, lr_now)
elif parser.lr_choice == 'lr_scheduler':
scheduler.step(mean_epoch_loss)
# if parser.dataset != 'show':
# print('Evaluating dataset_train')
# coco_eval.evaluate_coco(dataset_train, retinanet, parser.dataset, parser.do_aug, eval_train_file, epoch_num)
print('Evaluating dataset_val')
coco_eval.evaluate_coco(dataset_val, retinanet, parser.dataset, parser.do_aug, eval_val_file, epoch_num)
return parser
def main(args=None):
parser = argparse.ArgumentParser(
description=
'RegiGraph Pytorch Implementation Training Script. - Ahmed Nassar (ETHZ, IRISA).'
)
parser.add_argument("--batch_size",
type=int,
default=4,
help="The number of images per batch")
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument(
'--dataset_root',
default='../datasets',
help=
'Dataset root directory path [../datasets/VOC, ../datasets/mapillary]')
parser.add_argument('--dataset',
default='Pasadena',
choices=['Pasadena', 'Pasadena_Aerial', 'mapillary'],
type=str,
help='Pasadena, Pasadena_Aerial or mapillary')
parser.add_argument("--overfit", type=int, default="0")
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--log_path", type=str, default="tensorboard/")
parser.add_argument("--saved_path", type=str, default="trained_models")
parser.add_argument("--test_interval",
type=int,
default=1,
help="Number of epoches between testing phases")
parser.add_argument(
"--es_min_delta",
type=float,
default=0.0,
help=
"Early stopping's parameter: minimum change loss to qualify as an improvement"
)
parser.add_argument(
"--es_patience",
type=int,
default=0,
help=
"Early stopping's parameter: number of epochs with no improvement after which training will be stopped. Set to 0 to disable this technique."
)
parser.add_argument("--cluster", type=int, default=0)
opt = parser.parse_args(args)
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if (opt.dataset == 'Pasadena' or opt.dataset == 'mapillary'
or opt.dataset == 'Pasadena_Aerial'):
train_dataset = VOCDetection(root=opt.dataset_root,
overfit=opt.overfit,
image_sets="trainval",
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer()
]),
dataset_name=opt.dataset)
valid_dataset = VOCDetection(root=opt.dataset_root,
overfit=opt.overfit,
image_sets="val",
transform=transforms.Compose(
[Normalizer(),
Resizer()]),
dataset_name=opt.dataset)
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
# sampler = AspectRatioBasedSampler(train_dataset, batch_size=2, drop_last=False)
training_params = {
"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": True,
"collate_fn": collater,
"num_workers": 4
}
training_generator = DataLoader(train_dataset, **training_params)
if valid_dataset is not None:
test_params = {
"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": False,
"collate_fn": collater,
"num_workers": 4
}
# sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False)
test_generator = DataLoader(valid_dataset, **test_params)
# Create the model
if opt.depth == 18:
retinanet = model.resnet18(num_classes=train_dataset.num_classes(),
pretrained=True)
elif opt.depth == 34:
retinanet = model.resnet34(num_classes=train_dataset.num_classes(),
pretrained=True)
elif opt.depth == 50:
retinanet = model.resnet50(num_classes=train_dataset.num_classes(),
pretrained=True)
elif opt.depth == 101:
retinanet = model.resnet101(num_classes=train_dataset.num_classes(),
pretrained=True)
elif opt.depth == 152:
retinanet = model.resnet152(num_classes=train_dataset.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)
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
if not os.path.isdir(opt.saved_path):
os.makedirs(opt.saved_path)
retinanet.training = True
writer = SummaryWriter(opt.log_path + "regigraph_bs_" +
str(opt.batch_size) + "_dataset_" + opt.dataset +
"_backbone_" + str(opt.depth))
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
best_loss = 1e5
best_epoch = 0
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(train_dataset)))
num_iter_per_epoch = len(training_generator)
for epoch in range(opt.num_epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
progress_bar = tqdm(training_generator)
for iter, data in enumerate(progress_bar):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss, graph_loss = retinanet(
[
data['img'].cuda().float(), data['annot'],
data['geo'], data['batch_map']
])
else:
classification_loss, regression_loss, graph_loss = retinanet(
[
data['img'].float(), data['annot'], data['geo'],
data['batch_map']
])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
graph_loss = graph_loss.mean()
loss = classification_loss + regression_loss + graph_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))
total_loss = np.mean(epoch_loss)
if opt.cluster == 0:
progress_bar.set_description(
'Epoch: {}/{}. Iteration: {}/{}. Cls loss: {:.5f}. Reg loss: {:.5f}. Graph loss: {:.5f}. Batch loss: {:.5f} Total loss: {:.5f}'
.format(epoch + 1, opt.num_epochs, iter + 1,
num_iter_per_epoch, classification_loss,
regression_loss, graph_loss, float(loss),
total_loss))
writer.add_scalar('Train/Total_loss', total_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Regression_loss', regression_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Classfication_loss (focal loss)',
classification_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Graph_loss', graph_loss,
epoch * num_iter_per_epoch + iter)
del classification_loss
del regression_loss
del graph_loss
except Exception as e:
print(e)
continue
scheduler.step(np.mean(epoch_loss))
if epoch % opt.test_interval == 0:
retinanet.eval()
loss_regression_ls = []
loss_classification_ls = []
loss_graph_ls = []
for iter, data in enumerate(test_generator):
with torch.no_grad():
if torch.cuda.is_available():
classification_loss, regression_loss, graph_loss = retinanet(
[
data['img'].cuda().float(), data['annot'],
data['geo'], data['batch_map']
])
else:
classification_loss, regression_loss, graph_loss = retinanet(
[
data['img'].float(), data['annot'],
data['geo'], data['batch_map']
])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
graph_loss = graph_loss.mean()
loss_classification_ls.append(float(classification_loss))
loss_regression_ls.append(float(regression_loss))
loss_graph_ls.append(float(graph_loss))
# print(len(loss_classification_ls),len(loss_regression_ls),len(loss_graph_ls))
cls_loss = np.mean(loss_classification_ls)
reg_loss = np.mean(loss_regression_ls)
gph_loss = np.mean(loss_graph_ls)
loss = cls_loss + reg_loss + gph_loss
print(
'- Val Epoch: {}/{}. Classification loss: {:1.5f}. Regression loss: {:1.5f}. * Graph loss: {:1.5f}. Total loss: {:1.5f}'
.format(epoch + 1, opt.num_epochs, cls_loss, reg_loss,
gph_loss, np.mean(loss)))
writer.add_scalar('Test/Total_loss', loss, epoch)
writer.add_scalar('Test/Regression_loss', reg_loss, epoch)
writer.add_scalar('Test/Graph_loss (graph loss)', gph_loss, epoch)
writer.add_scalar('Test/Classfication_loss (focal loss)', cls_loss,
epoch)
if loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
# mAP = csv_eval.evaluate(valid_dataset, retinanet)
# print(mAP)
torch.save(
retinanet.module,
os.path.join(
opt.saved_path,
"regigraph_bs_" + str(opt.batch_size) + "_dataset_" +
opt.dataset + "_epoch_" + str(epoch + 1) +
"_backbone_" + str(opt.depth) + ".pth"))
# Early stopping
if epoch - best_epoch > opt.es_patience > 0:
print(
"Stop training at epoch {}. The lowest loss achieved is {}"
.format(epoch, loss))
break
writer.close()
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
# parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--dataset_root',
default='/root/data/VOCdevkit/',
help=
'Dataset root directory path [/root/data/VOCdevkit/, /root/data/coco/, /root/data/FLIR_ADAS]'
)
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(
'--resume',
default=None,
type=str,
help='Checkpoint state_dict file to resume training from')
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',
default=16,
type=int,
help='Batch size for training')
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--lr',
'--learning_rate',
default=1e-4,
type=float,
help='initial learning rate')
parser.add_argument('--weight_decay',
default=5e-4,
type=float,
help='Weight decay')
parser.add_argument('-j',
'--workers',
default=4,
type=int,
metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument("--log",
default=False,
action="store_true",
help="Write log file.")
parser = parser.parse_args(args)
network_name = 'RetinaNet-Res{}'.format(parser.depth)
# print('network_name:', network_name)
net_logger = logging.getLogger('Network Logger')
formatter = logging.Formatter(LOGGING_FORMAT)
streamhandler = logging.StreamHandler()
streamhandler.setFormatter(formatter)
net_logger.addHandler(streamhandler)
if parser.log:
net_logger.setLevel(logging.INFO)
# logging.basicConfig(level=logging.DEBUG, format=LOGGING_FORMAT,
# filename=os.path.join('log', '{}.log'.format(network_name)), filemode='a')
filehandler = logging.FileHandler(os.path.join(
'log', '{}.log'.format(network_name)),
mode='a')
filehandler.setFormatter(formatter)
net_logger.addHandler(filehandler)
net_logger.info('Network Name: {:>20}'.format(network_name))
# Create the data loaders
if parser.dataset == 'coco':
if parser.dataset_root is None:
raise ValueError(
'Must provide --dataset_root when training on COCO,')
dataset_train = CocoDataset(parser.dataset_root,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.dataset_root,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'FLIR':
if parser.dataset_root is None:
raise ValueError(
'Must provide --dataset_root when training on FLIR,')
_scale = 1.2
dataset_train = FLIRDataset(parser.dataset_root,
set_name='train',
transform=transforms.Compose([
Normalizer(),
Augmenter(),
Resizer(min_side=int(512 * _scale),
max_side=int(640 * _scale),
logger=net_logger)
]))
dataset_val = FLIRDataset(parser.dataset_root,
set_name='val',
transform=transforms.Compose([
Normalizer(),
Resizer(min_side=int(512 * _scale),
max_side=int(640 * _scale))
]))
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 FLIR, COCO or csv), exiting.'
)
# Original RetinaNet code
# 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)
dataloader_train = DataLoader(dataset_train,
batch_size=parser.batch_size,
num_workers=parser.workers,
shuffle=True,
collate_fn=collater,
pin_memory=True)
dataloader_val = DataLoader(dataset_val,
batch_size=1,
num_workers=parser.workers,
shuffle=False,
collate_fn=collater,
pin_memory=True)
build_param = {'logger': net_logger}
if parser.resume is not None:
net_logger.info('Loading Checkpoint : {}'.format(parser.resume))
retinanet = torch.load(parser.resume)
s_b = parser.resume.rindex('_')
s_e = parser.resume.rindex('.')
start_epoch = int(parser.resume[s_b + 1:s_e]) + 1
net_logger.info('Continue on {} Epoch'.format(start_epoch))
else:
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True,
**build_param)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True,
**build_param)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True,
**build_param)
elif parser.depth == 101:
retinanet = model.resnet101(
num_classes=dataset_train.num_classes(),
pretrained=True,
**build_param)
elif parser.depth == 152:
retinanet = model.resnet152(
num_classes=dataset_train.num_classes(),
pretrained=True,
**build_param)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
start_epoch = 0
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
net_logger.info('Weight Decay : {}'.format(parser.weight_decay))
net_logger.info('Learning Rate : {}'.format(parser.lr))
# optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
optimizer = optim.Adam(retinanet.parameters(),
lr=parser.lr,
weight_decay=parser.weight_decay)
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)))
net_logger.info('Num Training Images: {}'.format(len(dataset_train)))
for epoch_num in range(start_epoch, parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
# print(data['img'][0,:,:,:].shape)
# print(data['annot'])
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))
if (iter_num % 10 == 0):
_log = 'Epoch: {} | Iter: {} | Class loss: {:1.5f} | BBox loss: {:1.5f} | Running loss: {:1.5f}'.format(
epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist))
net_logger.info(_log)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if (epoch_num + 1) % 1 == 0:
test(dataset_val, retinanet, epoch_num, parser, net_logger)
# 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))
print('Learning Rate:', str(scheduler._last_lr))
torch.save(
retinanet.module,
os.path.join(
'saved', '{}_{}_{}.pt'.format(parser.dataset, network_name,
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.') #数据集类型
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50) #选择与训练模型
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
#决定图片数据集的顺序和batch_size,返回的是图片的分组
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
#多GPU运行
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
#collections:模块实现了特定目标的容器，以提供Python标准内建容器 dict、list、set、tuple 的替代选择
#collections.deque:返回双向队列对象，最长长度为500
loss_hist = collections.deque(maxlen=500)
# model.train() ：启用 BatchNormalization 和 Dropout
# model.eval() ：不启用 BatchNormalization 和 Dropout
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
#反向传播
loss.backward()
#梯度裁剪，梯度小于/大于阈值时，更新的梯度为阈值（此处为小于0.1）
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
#更新所有的参数，一旦梯度被如backward()之类的函数计算好后，我们就可以调用这个函数
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
#optimizer.step()通常用在每个mini-batch之中，而scheduler.step()通常用在epoch里面
#有用了optimizer.step()，模型才会更新，而scheduler.step()是对lr进行调整。
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--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()
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-50":
model = model.resnet50(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-101":
model = model.resnet101(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-152":
model = model.resnet152(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
else:
raise NotImplementedError
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
logger.info(f"using device {device}")
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():
global opt
opt = parser.parse_args()
init_distributed_mode(opt)
dataset = ImageDirectory(opt.image_dir)
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
batch_size=opt.batch_size,
num_workers=opt.num_workers,
pin_memory=True,
shuffle=False,
drop_last=True,
collate_fn=custom_collate,
)
logger.info("Building data done with {} images loaded.".format(len(dataset)))
if opt.backbone == "resnet-18":
model = arch.resnet18(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-34":
model = arch.resnet34(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-50":
model = arch.resnet50(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-101":
model = arch.resnet101(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
elif opt.backbone == "resnet-152":
model = arch.resnet152(
num_classes=opt.num_class,
pretrained=False,
conf_threshold=opt.confidence,
nms_iou_threshold=opt.nms_threshold,
)
else:
raise NotImplementedError
ckpt = torch.load(opt.weights)
model.load_state_dict(ckpt.state_dict())
model.cuda()
model.eval()
# if opt.rank == 0:
# logger.info(model)
logger.info(f"successfully loaded saved checkpoint.")
model = nn.parallel.DistributedDataParallel(
model, device_ids=[opt.gpu_to_work_on], find_unused_parameters=True,
)
for i, (batch, filenames) in tqdm(enumerate(loader), total=len(loader)):
preds = dict()
with torch.no_grad():
img_id, confs, classes, bboxes = model(batch[0].float().cuda())
img_id = img_id.cpu().numpy().tolist()
confs = confs.cpu().numpy()
classes = classes.cpu().numpy()
bboxes = bboxes.cpu().numpy().astype(np.int32)
for i, imgid in enumerate(img_id):
f = filenames[imgid]
pr = {
"bbox": bboxes[i].tolist(),
"confidence": float(confs[i]),
"class_index": int(classes[i]),
}
if f in preds:
preds[f].append(pr)
else:
preds[f] = [pr]
for img_filename, detection in preds.items():
with open(os.path.join(opt.output_dir, img_filename.replace("jpg", "json")), "w") as f:
json.dump(detection, f, indent=2)
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 paps training script for training a RetinaNet network.')
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--learn_rate',
help='learn_rate epochs',
type=float,
default=0.0008)
parser.add_argument('--start_epoch',
help='start_epoch',
type=int,
default=0)
parser.add_argument('--end_epoch', help='end_epoch', type=int, default=200)
parser.add_argument('--batch_size',
help='Number of batchs',
type=int,
default=64)
parser.add_argument('--train_data',
help='train data file',
default='data/train.npy')
parser.add_argument('--test_data',
help='test data file',
default='data/test.npy')
parser.add_argument('--saved_dir',
help='saved dir',
default='trained_models/resnet101_320/')
parser.add_argument('--gpu_num', help='default gpu', type=int, default=3)
parser.add_argument('--ismultigpu',
help='multi gpu support',
type=bool,
default=False)
parser.add_argument('--freeze_ex_bn',
help='freeze batch norm',
type=bool,
default=False)
parser.add_argument('--num_workers', help='cpu core', type=int, default=12)
parser.add_argument('--target_threshold',
help='target_threshold',
type=float,
default=0.7)
parser.add_argument('--topk', help=' topk', type=int, default=20)
parser.add_argument('--filter_option', help=' topk', type=int, default=1)
parser = parser.parse_args(args)
print('batch_size ', parser.batch_size)
print('learn_rate ', parser.learn_rate)
print(' start_epoch {} end_epoch {}'.format(parser.start_epoch,
parser.end_epoch))
print('ismultigpu', parser.ismultigpu)
print('freeze_ex_bn', parser.freeze_ex_bn)
print('target_threshold {} topk {} filter_option {}'.format(
parser.target_threshold, parser.topk, parser.filter_option))
# 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('Current cuda device ', torch.cuda.current_device()) # check
resnet101 = models.resnet101(progress=False, pretrained=True)
ret_model = model.resnet101(num_classes=2, device=device)
ret_model.load_state_dict(resnet101.state_dict(), strict=False)
# In Batch norm initial setting, set r to and set to 1 for bias for fast convergence
state_dict = ret_model.state_dict()
for s in state_dict:
if 'bn' in s and 'residualafterFPN' in s:
if 'weight' in s:
shape = state_dict[s].shape
state_dict[s] = torch.zeros(shape)
elif 'bias' in s:
shape = state_dict[s].shape
state_dict[s] = torch.ones(shape)
ret_model.load_state_dict(state_dict)
# criterion = FocalLoss(device)
criterion = PapsLoss(device, parser.target_threshold, parser.topk,
parser.filter_option)
criterion = criterion.to(device)
optimizer = optim.Adam(ret_model.parameters(), lr=1e-7)
scheduler = CosineAnnealingWarmUpRestarts(optimizer,
T_0=20,
T_mult=2,
eta_max=parser.learn_rate,
T_up=5,
gamma=0.5)
saved_dir = parser.saved_dir
if os.path.isfile(saved_dir + 'model.pt'):
state = torch.load(saved_dir + 'model.pt')
ret_model.load_state_dict(state['state_dict'])
optimizer.load_state_dict(state['optimizer'])
scheduler.load_state_dict(state['scheduler'])
last_loss = state['loss']
else:
last_loss = 0.6
if parser.ismultigpu:
ret_model = torch.nn.DataParallel(ret_model,
device_ids=[3, 4, 5],
output_device=GPU_NUM).to(device)
# ret_model = DataParallelModel(ret_model, device_ids = device_ids)
ret_model.to(device)
# ret_model.module.freeze_bn()
batch_size = parser.batch_size
dataset_train = PapsDataset('data/',
set_name='train_2class',
transform=train_transforms)
train_data_loader = DataLoader(dataset_train,
batch_size=batch_size,
shuffle=True,
num_workers=parser.num_workers,
pin_memory=True,
collate_fn=collate_fn)
dataset_val = PapsDataset('data/',
set_name='val_2class',
transform=val_transforms)
val_data_loader = DataLoader(dataset_val,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=collate_fn)
s_epoch = parser.start_epoch
e_epoch = parser.end_epoch
ret_model.training = True
paps_train.train_paps(dataloader=train_data_loader,
model=ret_model,
criterion=criterion,
saved_dir=saved_dir,
optimizer=optimizer,
scheduler=scheduler,
device=device,
s_epoch=s_epoch,
e_epoch=e_epoch,
last_loss=last_loss)
ret_model.training = False
# ret_model.eval()
paps_eval.evaluate_paps(dataset=dataset_val,
dataloader=val_data_loader,
model=ret_model,
saved_dir=parser.saved_dir,
device=device,
threshold=0.5)
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',
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, 5032, 10132',
type=int,
default=10148)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=200)
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)
elif parser.depth == 5032:
retinanet = model.resnext50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 10132:
retinanet = model.resnext101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 10148:
retinanet = model_SE.SEresnext101(
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) #change_weight_decay
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 = []
total_classification_loss = 0.0
total_regression_loss = 0.0
epoch_number = 0
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))
#############################
# total_classification_loss += classification_loss
# total_regression_loss += regression_loss
# epoch_number = epoch_num
fp = open(output_path + "clas_reg_loss.txt", "a")
fp.write(
str(epoch_num) + ',' + str(float(classification_loss)) +
',' + str(float(regression_loss)) + ',' +
str(np.mean(loss_hist)) + '\n')
# writer.add_scalar('Classification_loss', float(classification_loss), epoch_num)
# writer.add_scalar('Regression_loss', float(regression_loss), epoch_num)
# writer.flush()
#############################
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, output_path +
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, output_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='coco')
parser.add_argument(
'--coco_path',
help='Path to COCO directory',
default=
'/media/zhuzhu/ec114170-f406-444f-bee7-a3dc0a86cfa2/dataset/coco')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--use-gpu',
help='training on cpu or gpu',
action='store_false',
default=True)
parser.add_argument('--device-ids', help='GPU device ids', default=[0])
args = parser.parse_args()
# ------------------------------ Create the data loaders -----------------------------
if args.dataset == 'coco':
if args.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(args.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(args.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
sampler_train = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_train)
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if args.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=False)
elif args.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif args.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
if args.use_gpu:
retinanet = nn.DataParallel(retinanet,
device_ids=args.device_ids).cuda()
# retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
print('Num training images: {}'.format(len(dataset_train)))
for epoch_num in range(args.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
nn.utils.clip_grad_norm_(retinanet.parameters(),
0.1) # 梯度的最大范数为0.1
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if args.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(args.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description="Simple training script for training a RetinaNet network.")
parser.add_argument("--dataset",
help="Dataset type, must be one of csv or coco.")
parser.add_argument("--coco_path", help="Path to COCO directory")
parser.add_argument(
"--csv_train",
help="Path to file containing training annotations (see readme)")
parser.add_argument("--csv_classes",
help="Path to file containing class list (see readme)")
parser.add_argument(
"--csv_val",
help=
"Path to file containing validation annotations (optional, see readme)",
)
parser.add_argument(
"--depth",
help="Resnet depth, must be one of 18, 34, 50, 101, 152",
type=int,
default=50,
)
parser.add_argument("--batch_size", help="Batch size", type=int, default=2)
parser.add_argument("--epochs",
help="Number of epochs",
type=int,
default=100)
parser.add_argument("--workers",
help="Number of workers of dataleader",
type=int,
default=4)
parser = parser.parse_args(args)
writer = SummaryWriter("logs")
# Create the data loaders
if parser.dataset == "coco":
if parser.coco_path is None:
raise ValueError("Must provide --coco_path when training on COCO,")
dataset_train = CocoDataset(
parser.coco_path,
set_name="train2017",
transform=transforms.Compose(
[Normalizer(), Augmenter(),
Resizer()]),
)
dataset_val = CocoDataset(
parser.coco_path,
set_name="val2017",
transform=transforms.Compose([Normalizer(),
Resizer()]),
)
elif parser.dataset == "csv":
if parser.csv_train is None:
raise ValueError("Must provide --csv_train when training on COCO,")
if parser.csv_classes is None:
raise ValueError(
"Must provide --csv_classes when training on COCO,")
dataset_train = CSVDataset(
train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(), Augmenter(),
Resizer()]),
)
if parser.csv_val is None:
dataset_val = None
print("No validation annotations provided.")
else:
dataset_val = CSVDataset(
train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(),
Resizer()]),
)
else:
raise ValueError(
"Dataset type not understood (must be csv or coco), exiting.")
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(
dataset_train,
num_workers=parser.workers,
collate_fn=collater,
batch_sampler=sampler,
)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=parser.workers,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
"Unsupported model depth, must be one of 18, 34, 50, 101, 152")
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=10,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print("Num training images: {}".format(len(dataset_train)))
global_step = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
global_step = iter_num + epoch_num * len(dataloader_train)
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data["img"].cuda().float(), data["annot"]])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
if iter_num % 10 == 0:
print(
"Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}"
.format(
epoch_num,
iter_num,
float(classification_loss),
float(regression_loss),
np.mean(loss_hist),
))
writer.add_scalars(
"training",
{
"loss": loss,
"loss_cls": classification_loss,
"loss_reg": regression_loss,
},
global_step,
)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == "coco":
print("Evaluating dataset")
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == "csv" and parser.csv_val is not None:
print("Evaluating dataset")
mAP = csv_eval.evaluate(dataset_val, retinanet)
valid_mAP = [x[0] for x in mAP.values() if x[1] > 0]
mmAP = sum(valid_mAP) / len(mAP)
writer.add_scalars("validation", {"mmAP": mmAP}, global_step)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
"checkpoints/{}_retinanet_{}.pt".format(parser.dataset, epoch_num),
)
retinanet.eval()
torch.save(retinanet, "checkpoints/odel_final.pt")
def main(args=None):
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', default='csv', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', default='dataset/pascal_train.csv', help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', default='dataset/classes.csv', help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', default='dataset/pascal_val.csv', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=100)
parser.add_argument('--weights_folder', help='path to save weight', type=str, required=True)
parser = parser.parse_args(args)
if not os.path.exists(parser.weights_folder):
os.makedirs(parser.weights_folder)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path, set_name='train2017',
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
dataset_val = CocoDataset(parser.coco_path, set_name='val2017',
transform=transforms.Compose([Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train, batch_size=5, drop_last=False)
dataloader_train = DataLoader(dataset_train, num_workers=4, collate_fn=collater, batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=8, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=4, collate_fn=collater, batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True)
else:
raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# import ipdb; ipdb.set_trace()
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
total_loss = 0
total_regression_loss = 0
total_classification_loss = 0
with tqdm(dataloader_train, unit="batch") as tepoch:
for data in tepoch:
# for iter_num, data in tepoch:#enumerate(dataloader_train):
tepoch.set_description(f"Epoch {epoch_num}")
try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
total_loss = total_loss + loss
total_regression_loss = total_regression_loss + regression_loss
total_classification_loss = total_classification_loss + classification_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
# print(
# 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
# epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)))
tepoch.set_postfix(cls_loss="{:1.5f}".format(classification_loss), reg_loss="{:1.5f}".format(regression_loss))
time.sleep(0.1)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
tb.add_scalar('Training loss', total_loss, epoch_num)
tb.add_scalar('Training regression loss', total_regression_loss, epoch_num)
tb.add_scalar('Training accuracy loss', total_classification_loss, epoch_num)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module, '{}/{}_retinanet_{}.pt'.format(parser.weights_folder,parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, '{}/model_final.pt'.format(parser.weights_folder))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.',
default='csv')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--batch_size', help='Batch size', type=int, default=2)
parser.add_argument('--num_workers',
help='Number of workers',
type=int,
default=4)
parser.add_argument('--models_out',
help='The directory to save models',
type=str)
parser = parser.parse_args(args)
if not os.path.exists(parser.models_out):
os.makedirs(parser.models_out)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=parser.num_workers,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=parser.num_workers,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
retinanet = retinanet.cuda()
retinanet = torch.nn.DataParallel(retinanet).cuda()
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
writer = SummaryWriter(log_dir="tensor_log/" + parser.models_out)
global_steps = 0
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
running_loss = np.mean(loss_hist)
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), running_loss))
global_steps += 1
writer.add_scalar("Loss/Classification",
float(classification_loss), global_steps)
writer.add_scalar("Loss/Regression", float(regression_loss),
global_steps)
writer.add_scalar("Loss/Running", running_loss, global_steps)
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
#for k, v in mAP.items():
# writer.add_scalar("Accuracy/map_{}".format(k), v, epoch_num)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
os.path.join(
parser.models_out,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)))
retinanet.eval()
torch.save(retinanet, os.path.join(parser.models_out, 'model_final.pt'))
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.',
default='csv')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)',
default='data/train_retinanet.csv')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)',
default='data/class_retinanet.csv')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)',
default='data/val_retinanet.csv')
parser.add_argument('--model_path',
default='coco_resnet_50_map_0_335_state_dict.pt',
help='Path to file containing pretrained retinanet')
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs_detection',
help='Number of epochs for detection',
type=int,
default=50)
parser.add_argument('--epochs_classification',
help='Number of epochs for classification',
type=int,
default=50)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=1,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if parser.model_path is not None:
print('loading ', parser.model_path)
if 'coco' in parser.model_path:
retinanet.load_state_dict(torch.load(parser.model_path),
strict=False)
else:
retinanet = torch.load(parser.model_path)
print('Pretrained model loaded!')
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
#Here training the detection
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=4,
verbose=True)
loss_hist = collections.deque(maxlen=500)
loss_style_classif = nn.CrossEntropyLoss()
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
mAP_list = []
mAPbest = 0
for epoch_num in range(parser.epochs_detection):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
[classification_loss, regression_loss], style = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
[classification_loss, regression_loss
], style = retinanet([data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
if torch.cuda.is_available():
style_loss = loss_style_classif(
style,
torch.tensor(data['style']).cuda())
else:
style_loss = loss_style_classif(
style, torch.tensor(data['style']))
loss = classification_loss + regression_loss + style_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.4f} | Regression loss: {:1.4f} | Style loss: {:1.4f} | Running loss: {:1.4f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), float(style_loss),
np.mean(loss_hist)))
del classification_loss
del regression_loss
del style_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet)
mAP_list.append(mAP)
print('mAP_list', mAP_list)
if mAP > mAPbest:
print('Saving best checkpoint')
torch.save(retinanet, 'model_best.pt')
mAPbest = mAP
scheduler.step(np.mean(epoch_loss))
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, 'model_final.pt')
# Here we aggregate all the data to don't have to appy the Retinanet during training.
retinanet.load_state_dict(torch.load('model_best.pt').state_dict())
List_feature = []
List_target = []
retinanet.training = False
retinanet.eval()
retinanet.module.style_inference = True
retinanet.module.freeze_bn()
epoch_loss = []
with torch.no_grad():
for iter_num, data in enumerate(dataloader_train):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
_, _, feature_vec = retinanet(data['img'].cuda().float())
else:
_, _, feature_vec = retinanet(data['img'].float())
List_feature.append(torch.squeeze(feature_vec).cpu())
List_target.append(data['style'][0])
except Exception as e:
print(e)
continue
print('END of preparation of the data for classification of style')
# Here begins Style training. Need to set to style_train. They are using the same loader, as it was expected to train both at the same time.
batch_size_classification = 64
dataloader_train_style = torch.utils.data.DataLoader(
StyleDataset(List_feature, List_target),
batch_size=batch_size_classification)
retinanet.load_state_dict(torch.load('model_best.pt').state_dict())
# Here training the detection
retinanet.module.style_inference = False
retinanet.module.style_train(True)
retinanet.training = True
retinanet.train()
optimizer = optim.Adam(
retinanet.module.styleClassificationModel.parameters(),
lr=5e-3,
weight_decay=1e-3)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='max',
patience=4,
verbose=True)
loss_hist = collections.deque(maxlen=500)
loss_style_classif = nn.CrossEntropyLoss()
retinanet.train()
retinanet.module.freeze_bn()
criterion = nn.CrossEntropyLoss()
accu_list = []
accubest = 0
for epoch_num in range(parser.epochs_classification):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
total = 0
correct = 0
for iter_num, data in enumerate(dataloader_train_style):
try:
optimizer.zero_grad()
inputs, targets = data
if torch.cuda.is_available():
inputs, targets = inputs.cuda(), targets.cuda()
outputs = retinanet.module.styleClassificationModel(
inputs, 0, 0, 0, True)
loss = criterion(outputs, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
total += targets.size(0)
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum()
print(
'| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f Acc@1: %.3f%%'
%
(epoch_num, parser.epochs_classification, iter_num + 1,
(len(dataloader_train_style) // batch_size_classification)
+ 1, loss.item(), 100. * correct / total))
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet)
accu_list.append(accu)
print('mAP_list', mAP_list, 'accu_list', accu_list)
if accu > accubest:
print('Saving best checkpoint')
torch.save(retinanet.module, 'model_best_classif.pt')
accubest = accu
scheduler.step(accu)
torch.save(retinanet.module,
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet.module, 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
type=str,
default='csv',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/train.csv',
help='Path to file containing training annotations (see readme)')
parser.add_argument(
'--csv_classes',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/class.csv',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/val.csv',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--model_save_path',
type=str,
default=
r'/usr/idip/idip/liuan/project/pytorch_retinanet/RetinaNet-PFA-SPANet/model/resnet101+PFA+CFPN/',
help='Path to save model')
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=101)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=150)
parser.add_argument('--iter_num',
help='Iter number of saving checkpoint',
type=int,
default=5)
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(dataset_train,
batch_size=2,
drop_last=False)
# 将自定义的Dataset根据batch size大小、是否shuffle等封装成一个Batch Size大小的Tensor，用于后面的训练
dataloader_train = DataLoader(dataset_train,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=3,
collate_fn=collater,
batch_sampler=sampler_val)
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
use_gpu = True
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
# add gap save model count variable
n = 0
for epoch_num in range(parser.epochs):
n += 1
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
for iter_num, data in enumerate(dataloader_train):
# try:
optimizer.zero_grad()
if torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot']])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'
.format(epoch_num, iter_num, float(classification_loss),
float(regression_loss), np.mean(loss_hist)))
del classification_loss
del regression_loss
# except Exception as e:
# print(e)
# continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if n % parser.iter_num == 0:
torch.save(
retinanet.module, parser.model_save_path + '/' +
'{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
retinanet.eval()
torch.save(retinanet, parser.model_save_path + '/' + 'model_final.pt')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
# parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--HW2_path', help='Path to HW2 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 == 'HW2':
if parser.HW2_path is None:
raise ValueError('Must provide --HW2_path when training on HW2,')
dataset_train = HW2Dataset(parser.HW2_path,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
#dataset_val = HW2Dataset(parser.HW2_path,
# 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,
batch_size=8,
num_workers=3,
collate_fn=collater)
# 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)
#retinanet.load_state_dict(torch.load('coco_resnet_50_map_0_335_state_dict.pt'))
#retinanet_state = retinanet.state_dict()
#loaded = torch.load('coco_resnet_50_map_0_335_state_dict.pt')
#pretrained = {k:v for k, v in loaded.items() if k in retinanet_state}
#retinanet_state.update(pretrained)
#retinanet.load_state_dict(retinanet_state)
retinanet = torch.load('saved_models_3/HW2_retinanet_0.pt')
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-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(pre_epoch, 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)
scheduler.step(np.mean(epoch_loss))
torch.save(
retinanet.module,
'saved_models_3/{}_retinanet_{}.pt'.format(parser.dataset,
epoch_num))
# retinanet.eval()
torch.save(retinanet, 'saved_models_3/model_final.pt')
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')
def main(args=None):
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset',
help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument(
'--csv_train',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes',
help='Path to file containing class list (see readme)')
parser.add_argument(
'--csv_val',
help=
'Path to file containing validation annotations (optional, see readme)'
)
parser.add_argument(
'--depth',
help='Resnet depth, must be one of 18, 34, 50, 101, 152',
type=int,
default=50)
parser.add_argument('--epochs',
help='Number of epochs',
type=int,
default=100)
parser.add_argument('--finetune',
help='if load trained retina model',
type=bool,
default=False)
parser.add_argument('--gpu', help='', type=bool, default=False)
parser.add_argument('--batch_size', help='', type=int, default=2)
parser.add_argument('--c',
help='continue with formal model',
type=bool,
default=False)
parser.add_argument('--model', help='model path')
parser = parser.parse_args(args)
# Create the data loaders
if parser.dataset == 'coco':
if parser.coco_path is None:
raise ValueError('Must provide --coco_path when training on COCO,')
dataset_train = CocoDataset(parser.coco_path,
set_name='train2017',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
dataset_val = CocoDataset(parser.coco_path,
set_name='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
elif parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError(
'Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset(train_file=parser.csv_train,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val,
class_list=parser.csv_classes,
transform=transforms.Compose(
[Normalizer(),
Resizer()]))
else:
raise ValueError(
'Dataset type not understood (must be csv or coco), exiting.')
#sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False)
sampler = AspectRatioBasedSampler(dataset_train,
parser.batch_size,
drop_last=False)
dataloader_train = DataLoader(dataset_train,
num_workers=16,
collate_fn=collater,
batch_sampler=sampler)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val,
batch_size=1,
drop_last=False)
dataloader_val = DataLoader(dataset_val,
num_workers=8,
collate_fn=collater,
batch_sampler=sampler_val)
epochpassed = 0
# Create the model
if parser.depth == 18:
retinanet = model.resnet18(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 34:
retinanet = model.resnet34(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),
pretrained=True)
elif parser.depth == 152:
retinanet = model.resnet152(num_classes=dataset_train.num_classes(),
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
if parser.c:
retinanet = torch.load(parser.model)
#import pdb
#pdb.set_trace()
epochpassed = int(parser.model.split('.')[1].split('_')[-1])
use_gpu = parser.gpu
#torch.cuda.set_device(5)
#import pdb
#pdb.set_trace()
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if use_gpu and torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) #original:1e-5
#optimizer =optim.SGD(retinanet.parameters(), lr=0.01,weight_decay=0.0001, momentum=0.9)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
loss_hist = collections.deque(maxlen=500)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
writer = SummaryWriter()
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss = []
epoch_classification_loss = []
epoch_regression_loss = []
for iter_num, data in enumerate(dataloader_train):
try:
#import pdb
#pdb.set_trace()
optimizer.zero_grad()
if use_gpu and torch.cuda.is_available():
classification_loss, regression_loss = retinanet(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
classification_loss, regression_loss = retinanet(
[data['img'].float(), data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
loss_hist.append(float(loss))
epoch_loss.append(float(loss))
epoch_classification_loss.append(float(classification_loss))
epoch_regression_loss.append(float(regression_loss))
print(
'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}\r'
.format(epoch_num + epochpassed, iter_num,
float(classification_loss), float(regression_loss),
np.mean(loss_hist)),
end='')
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
print(
'Epoch: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}'
.format(epoch_num + epochpassed,
np.mean(epoch_classification_loss),
np.mean(epoch_regression_loss), np.mean(epoch_loss)))
writer.add_scalar('lossrecord/regressionloss',
np.mean(epoch_regression_loss),
epoch_num + epochpassed)
writer.add_scalar('lossrecord/classificationloss',
np.mean(epoch_regression_loss),
epoch_num + epochpassed)
writer.add_scalar('lossrecord/epochloss', np.mean(epoch_loss),
epoch_num + epochpassed)
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
if epoch_num % 10 == 0:
torch.save(
retinanet.module,
'./models/{}_retinanet{}_highResolution4fold_{}.pt'.format(
parser.dataset, parser.depth, epoch_num + epochpassed))
#retinanet.eval()
torch.save(
retinanet.module,
'./models/{}_retinanet{}_highResolution4fold_{}.pt'.format(
parser.dataset, parser.depth, parser.epochs + epochpassed))
writer.close()
def main(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()