def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
# Only support for the DeepLabv3+ model
if args.data_format == 'NHWC':
if cfg.dic['model']['type'] != 'DeepLabV3P':
raise ValueError(
'The "NHWC" data format only support the DeepLabV3P model!')
cfg.dic['model']['data_format'] = args.data_format
cfg.dic['model']['backbone']['data_format'] = args.data_format
loss_len = len(cfg.dic['loss']['types'])
for i in range(loss_len):
cfg.dic['loss']['types'][i]['data_format'] = args.data_format
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
skip_quant(model)
quantizer = QAT(config=quant_config)
quant_model = quantizer.quantize(model)
logger.info('Quantize the model successfully')
if args.model_path:
utils.load_entire_model(quant_model, args.model_path)
logger.info('Loaded trained params of model successfully')
test_config = get_test_config(cfg, args)
config_check(cfg, val_dataset=val_dataset)
evaluate(quant_model,
val_dataset,
num_workers=args.num_workers,
**test_config)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
cfg_1 = Config(args.cfg_1)
cfg_crop = Config(args.cfg_crop)
val_dataset_crop = cfg_crop.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
model_1 = cfg_1.model
model_crop = cfg_crop.model
transforms = val_dataset.transforms
transforms_crop = val_dataset_crop.transforms
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
predictEnsembleThree(
model,
model_1,
model_crop,
model_path=args.model_path,
model_path_1=args.model_path_1,
model_path_crop=args.model_path_crop,
transforms=transforms,
transforms_crop=transforms_crop,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
aug_pred=args.aug_pred,
scales=args.scales,
flip_horizontal=args.flip_horizontal,
flip_vertical=args.flip_vertical,
is_slide=args.is_slide,
crop_size=args.crop_size,
stride=args.stride,
)
def main(args):
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
cfg = Config(args.cfg)
net = cfg.model
if args.model_path:
para_state_dict = paddle.load(args.model_path)
net.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully.')
net.forward = paddle.jit.to_static(net.forward)
in_shape = [1] + list(cfg.val_dataset[0][0].shape)
in_var = paddle.ones(in_shape)
out = net(in_var)
save_path = os.path.join(args.save_dir, 'model')
paddle.jit.save(net, save_path, input_spec=[in_var])
yml_file = os.path.join(args.save_dir, 'deploy.yaml')
with open(yml_file, 'w') as file:
transforms = cfg.dic['val_dataset']['transforms']
data = {
'Deploy': {
'transforms': transforms,
'model': 'model.pdmodel',
'params': 'model.pdiparams'
}
}
yaml.dump(data, file)
logger.info(f'Model is saved in {args.save_dir}.')
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
test_config = get_test_config(cfg, args)
config_check(cfg, val_dataset=val_dataset)
predict(model,
model_path=args.model_path,
val_dataset=val_dataset,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
**test_config)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
image_list, image_dir = get_image_list(args.image_path)
predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = Compose(cfg.val_transforms)
print(transforms)
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
test_config = get_test_config(cfg, args)
predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
**test_config)
def main(args):
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
cfg = Config(args.cfg)
net = cfg.model
if args.model_path:
para_state_dict = paddle.load(args.model_path)
net.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully.')
net.eval()
net = paddle.jit.to_static(net,
input_spec=[
paddle.static.InputSpec(
shape=[None, 3, None, None],
dtype='float32')
])
save_path = os.path.join(args.save_dir, 'model')
paddle.jit.save(net, save_path)
yml_file = os.path.join(args.save_dir, 'deploy.yaml')
with open(yml_file, 'w') as file:
transforms = cfg.export_config.get('transforms', [{
'type': 'Normalize'
}])
data = {
'Deploy': {
'transforms': transforms,
'model': 'model.pdmodel',
'params': 'model.pdiparams'
}
}
yaml.dump(data, file)
logger.info(f'Model is saved in {args.save_dir}.')
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
para_state_dict = paddle.load(args.model_path)
model.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully')
evaluate(model, val_dataset)
def main(args):
if args.seed is not None:
paddle.seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
env_info = get_sys_env()
info = ['{}: {}'.format(k, v) for k, v in env_info.items()]
info = '\n'.join(['', format('Environment Information', '-^48s')] + info +
['-' * 48])
logger.info(info)
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(
args.cfg,
learning_rate=args.learning_rate,
iters=args.iters,
batch_size=args.batch_size)
train_dataset = cfg.train_dataset
if train_dataset is None:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
elif len(train_dataset) == 0:
raise ValueError(
'The length of train_dataset is 0. Please check if your dataset is valid'
)
val_dataset = cfg.val_dataset if args.do_eval else None
losses = cfg.loss
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
config_check(cfg, train_dataset=train_dataset, val_dataset=val_dataset)
train(
cfg.model,
train_dataset,
val_dataset=val_dataset,
optimizer=cfg.optimizer,
save_dir=args.save_dir,
iters=cfg.iters,
batch_size=cfg.batch_size,
resume_model=args.resume_model,
save_interval=args.save_interval,
log_iters=args.log_iters,
num_workers=args.num_workers,
use_vdl=args.use_vdl,
losses=losses,
keep_checkpoint_max=args.keep_checkpoint_max)
def prepare_config(args):
"""
Create and check the config of student and teacher model.
Note: we only use the dataset generated by the student config.
"""
if args.teather_config is None or args.student_config is None:
raise RuntimeError('No configuration file specified.')
t_cfg = Config(args.teather_config)
s_cfg = Config(args.student_config,
learning_rate=args.learning_rate,
iters=args.iters,
batch_size=args.batch_size)
train_dataset = s_cfg.train_dataset
val_dataset = s_cfg.val_dataset if args.do_eval else None
if train_dataset is None:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
elif len(train_dataset) == 0:
raise ValueError(
'The length of train_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Teacher Config Information---------------\n'
msg += str(t_cfg)
msg += '------------------------------------------------'
logger.info(msg)
msg = '\n---------------Student Config Information---------------\n'
msg += str(s_cfg)
msg += '------------------------------------------------'
logger.info(msg)
config_check(t_cfg, train_dataset=train_dataset, val_dataset=val_dataset)
config_check(s_cfg, train_dataset=train_dataset, val_dataset=val_dataset)
return t_cfg, s_cfg, train_dataset, val_dataset
def analyze(args):
env_info = get_sys_env()
info = ['{}: {}'.format(k, v) for k, v in env_info.items()]
info = '\n'.join(['', format('Environment Information', '-^48s')] + info +
['-' * 48])
logger.info(info)
paddle.set_device('cpu')
cfg = Config(args.config)
custom_ops = {paddle.nn.SyncBatchNorm: op_flops_funs.count_syncbn}
inputs = paddle.randn(args.input_size)
_dynamic_flops(cfg.model, inputs, custom_ops=custom_ops, print_detail=True)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
utils.load_entire_model(model, args.model_path)
logger.info('Loaded trained params of model successfully')
config_check(cfg, val_dataset=val_dataset)
evaluate(
model,
val_dataset,
aug_eval=args.aug_eval,
scales=args.scales,
flip_horizontal=args.flip_horizontal,
flip_vertical=args.flip_vertical,
is_slide=args.is_slide,
crop_size=args.crop_size,
stride=args.stride,
num_workers=args.num_workers,
)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
alpha = predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=[args.image_path],
trimap_list=[args.trimap_path],
save_dir=args.save_dir)
img_ori = cv2.imread(args.image_path)
bg = get_bg(args.bg_path, img_ori.shape)
alpha = alpha / 255
alpha = alpha[:, :, np.newaxis]
com = alpha * img_ori + (1 - alpha) * bg
com = com.astype('uint8')
com_save_path = os.path.join(args.save_dir,
os.path.basename(args.image_path))
cv2.imwrite(com_save_path, com)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
image_list, image_dir = get_image_list(args.image_path)
if args.trimap_path is None:
trimap_list = None
else:
trimap_list, _ = get_image_list(args.trimap_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
predict(model,
model_path=args.model_path,
transforms=transforms,
image_list=image_list,
image_dir=image_dir,
trimap_list=trimap_list,
save_dir=args.save_dir)
def main(args):
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
cfg = Config(args.cfg)
net = cfg.model
net.eval()
if args.model_path:
para_state_dict = paddle.load(args.model_path)
net.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully.')
if args.input_shape is None:
shape = [None, 3, None, None]
else:
shape = args.input_shape
input_spec = [{"img": paddle.static.InputSpec(shape=shape, name='img')}]
if args.trimap:
shape[1] = 1
input_spec[0]['trimap'] = paddle.static.InputSpec(shape=shape,
name='trimap')
net = paddle.jit.to_static(net, input_spec=input_spec)
save_path = os.path.join(args.save_dir, 'model')
paddle.jit.save(net, save_path)
yml_file = os.path.join(args.save_dir, 'deploy.yaml')
with open(yml_file, 'w') as file:
transforms = cfg.val_dataset_config.get('transforms',
[{
'type': 'Normalize'
}])
data = {
'Deploy': {
'transforms': transforms,
'model': 'model.pdmodel',
'params': 'model.pdiparams'
}
}
yaml.dump(data, file)
logger.info(f'Model is saved in {args.save_dir}.')
def main(args):
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
cfg = Config(args.cfg)
net = cfg.model
skip_quant(net)
quantizer = QAT(config=quant_config)
quant_net = quantizer.quantize(net)
logger.info('Quantize the model successfully')
if args.model_path:
utils.load_entire_model(quant_net, args.model_path)
logger.info('Loaded trained params of model successfully')
if not args.without_argmax or args.with_softmax:
new_net = SavedSegmentationNet(quant_net, args.without_argmax,
args.with_softmax)
else:
new_net = net
new_net.eval()
save_path = os.path.join(args.save_dir, 'model')
input_spec = [
paddle.static.InputSpec(shape=[None, 3, None, None], dtype='float32')
]
quantizer.save_quantized_model(new_net, save_path, input_spec=input_spec)
yml_file = os.path.join(args.save_dir, 'deploy.yaml')
with open(yml_file, 'w') as file:
transforms = cfg.export_config.get('transforms', [{
'type': 'Normalize'
}])
data = {
'Deploy': {
'transforms': transforms,
'model': 'model.pdmodel',
'params': 'model.pdiparams'
}
}
yaml.dump(data, file)
logger.info(f'Model is saved in {args.save_dir}.')
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
transforms = val_dataset.transforms
image_list, image_dir = get_image_list(args.image_path)
logger.info('Number of predict images = {}'.format(len(image_list)))
config_check(cfg, val_dataset=val_dataset)
predict(model,
model_path=args.model_path,
transforms=transforms,
thing_list=val_dataset.thing_list,
label_divisor=val_dataset.label_divisor,
stuff_area=val_dataset.stuff_area,
ignore_index=val_dataset.ignore_index,
image_list=image_list,
image_dir=image_dir,
save_dir=args.save_dir,
threshold=args.threshold,
nms_kernel=args.nms_kernel,
top_k=args.top_k)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
paddleseg.utils.utils.load_entire_model(model, args.model_path)
logger.info('Loaded trained params of model successfully')
config_check(cfg, val_dataset=val_dataset)
evaluate(
model,
val_dataset,
threshold=args.threshold,
nms_kernel=args.nms_kernel,
top_k=args.top_k,
num_workers=args.num_workers,
)
def main(args):
if args.seed is not None:
paddle.seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
env_info = get_sys_env()
info = ['{}: {}'.format(k, v) for k, v in env_info.items()]
info = '\n'.join(['', format('Environment Information', '-^48s')] + info +
['-' * 48])
logger.info(info)
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg,
learning_rate=args.learning_rate,
iters=args.iters,
batch_size=args.batch_size)
# Only support for the DeepLabv3+ model
if args.data_format == 'NHWC':
if cfg.dic['model']['type'] != 'DeepLabV3P':
raise ValueError(
'The "NHWC" data format only support the DeepLabV3P model!')
cfg.dic['model']['data_format'] = args.data_format
cfg.dic['model']['backbone']['data_format'] = args.data_format
loss_len = len(cfg.dic['loss']['types'])
for i in range(loss_len):
cfg.dic['loss']['types'][i]['data_format'] = args.data_format
train_dataset = cfg.train_dataset
if train_dataset is None:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
elif len(train_dataset) == 0:
raise ValueError(
'The length of train_dataset is 0. Please check if your dataset is valid'
)
val_dataset = cfg.val_dataset if args.do_eval else None
losses = cfg.loss
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
config_check(cfg, train_dataset=train_dataset, val_dataset=val_dataset)
train(cfg.model,
train_dataset,
val_dataset=val_dataset,
optimizer=cfg.optimizer,
save_dir=args.save_dir,
iters=cfg.iters,
batch_size=cfg.batch_size,
resume_model=args.resume_model,
save_interval=args.save_interval,
log_iters=args.log_iters,
num_workers=args.num_workers,
use_vdl=args.use_vdl,
losses=losses,
keep_checkpoint_max=args.keep_checkpoint_max,
test_config=cfg.test_config,
fp16=args.fp16,
profiler_options=args.profiler_options)