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):
env_info = get_sys_env()
args.use_gpu = True if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else False
predictor = Predictor(args)
predictor.run(get_images(args.image_path))
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):
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)
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):
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 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 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)
if cfg.dic["data"]["target"]["dataset"] == 'cityscapes':
val_dataset = CityDataset(split='val',
**cfg.dic["data"]["target"]["kwargs"])
else:
raise NotImplementedError()
if len(val_dataset) < 500:
print(len(val_dataset))
for i in range(len(val_dataset)):
print(val_dataset[i])
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')
test_config = get_test_config(cfg, args)
val.evaluate(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
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 prepare_envs(args):
"""
Set random seed and the device.
"""
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)
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):
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,
)
import tqdm
import cv2
import paddle
import paddle.nn as nn
import numpy as np
import paddle.nn.functional as F
from visualdl import LogWriter
from util.vis import draw_probmap, draw_points
from util.misc import save_checkpoint
from util.distributed import reduce_loss_dict
from paddleseg.utils import get_sys_env, logger
from .optimizer import get_optimizer_lr
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)
class ISTrainer(object):
def __init__(self,
model,
cfg,
model_cfg,
loss_cfg,
def main(args):
if args.seed is not None:
paddle.seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
logger.info('Set seed to {}'.format(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)
if cfg.dic["data"]["source"]["dataset"] == 'synthia':
train_dataset_src = SYNTHIADataset(
split='train', **cfg.dic["data"]["source"]["kwargs"])
val_dataset_src = SYNTHIADataset(split='val',
**cfg.dic["data"]["source"]["kwargs"])
elif cfg.dic["data"]["source"]["dataset"] == 'gta5':
train_dataset_src = GTA5Dataset(split='train',
**cfg.dic["data"]["source"]["kwargs"])
val_dataset_src = GTA5Dataset(split='val',
**cfg.dic["data"]["source"]["kwargs"])
else:
raise NotImplementedError()
if cfg.dic["data"]["target"]["dataset"] == 'cityscapes':
train_dataset_tgt = CityDataset(split='train',
**cfg.dic["data"]["target"]["kwargs"])
val_dataset_tgt = CityDataset(split='val',
**cfg.dic["data"]["target"]["kwargs"])
else:
raise NotImplementedError()
val_dataset_tgt = val_dataset_tgt if args.do_eval else None
val_dataset_src = val_dataset_src if args.do_eval else None
if train_dataset_src is None:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
elif len(train_dataset_src) == 0:
raise ValueError(
'The length of train_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
trainer = Trainer(model=cfg.model, cfg=cfg.dic)
trainer.train(train_dataset_src,
train_dataset_tgt,
val_dataset_tgt=val_dataset_tgt,
val_dataset_src=val_dataset_src,
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,
keep_checkpoint_max=args.keep_checkpoint_max,
test_config=cfg.test_config)
def background_replace(args):
env_info = get_sys_env()
args.use_gpu = True if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else False
predictor = Predictor(args)
if not osp.exists(args.save_dir):
os.makedirs(args.save_dir)
# 图像背景替换
if args.img_path is not None:
if not osp.exists(args.img_path):
raise Exception('The --img_path is not existed: {}'.format(
args.img_path))
img = cv2.imread(args.img_path)
bg = get_bg_img(args.bg_img_path, img.shape)
comb = predictor.run(img, bg)
save_name = osp.basename(args.img_path)
save_path = osp.join(args.save_dir, save_name)
cv2.imwrite(save_path, comb)
# 视频背景替换
else:
# 获取背景：如果提供背景视频则以背景视频作为背景，否则采用提供的背景图片
if args.bg_video_path is not None:
if not osp.exists(args.bg_video_path):
raise Exception(
'The --bg_video_path is not existed: {}'.format(
args.bg_video_path))
is_video_bg = True
else:
bg = get_bg_img(args.bg_img_path, args.input_shape)
is_video_bg = False
# 视频预测
if args.video_path is not None:
logger.info('Please wait. It is computing......')
if not osp.exists(args.video_path):
raise Exception('The --video_path is not existed: {}'.format(
args.video_path))
cap_video = cv2.VideoCapture(args.video_path)
fps = cap_video.get(cv2.CAP_PROP_FPS)
width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
save_name = osp.basename(args.video_path)
save_name = save_name.split('.')[0]
save_path = osp.join(args.save_dir, save_name + '.avi')
cap_out = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps,
(width, height))
if is_video_bg:
cap_bg = cv2.VideoCapture(args.bg_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_bg = 1
frame_num = 0
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
#读取背景帧
if is_video_bg:
ret_bg, bg = cap_bg.read()
if ret_bg:
if current_bg == frames_bg:
current_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_bg += 1
comb = predictor.run(frame, bg)
cap_out.write(comb)
frame_num += 1
logger.info('Processing frame {}'.format(frame_num))
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
cap_out.release()
# 当没有输入预测图像和视频的时候，则打开摄像头
else:
cap_video = cv2.VideoCapture(0)
if not cap_video.isOpened():
raise IOError("Error opening video stream or file, "
"--video_path whether existing: {}"
" or camera whether working".format(
args.video_path))
return
if is_video_bg:
cap_bg = cv2.VideoCapture(args.bg_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_bg = 1
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
#读取背景帧
if is_video_bg:
ret_bg, bg = cap_bg.read()
if ret_bg:
if current_bg == frames_bg:
current_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_bg += 1
comb = predictor.run(frame, bg)
cv2.imshow('HumanSegmentation', comb)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
if args.test_speed:
timer = predictor.cost_averager
logger.info(
'Model inference time per image: {}\nFPS: {}\nNum of images: {}'.
format(timer.get_average(), 1 / timer.get_average(), timer._cnt))
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 (0.0 < args.pruning_ratio < 1.0):
raise RuntimeError(
'The model pruning rate must be in the range of (0, 1).')
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
cfg = Config(args.cfg,
iters=args.retraining_iters,
batch_size=args.batch_size,
learning_rate=args.learning_rate)
train_dataset = cfg.train_dataset
if not train_dataset:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The validation dataset is not specified in the c;onfiguration file.'
)
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
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')
logger.info(
'Step 1/3: Start calculating the sensitivity of model parameters...')
sample_shape = [1] + list(train_dataset[0][0].shape)
sen_file = os.path.join(args.save_dir, 'sen.pickle')
pruner = L1NormFilterPruner(net, sample_shape)
pruner.sensitive(eval_func=partial(eval_fn, net, val_dataset,
args.num_workers),
sen_file=sen_file)
logger.info(
f'The sensitivity calculation of model parameters is complete. The result is saved in {sen_file}.'
)
flops = dygraph_flops(net, sample_shape)
logger.info(
f'Step 2/3: Start to prune the model, the ratio of pruning is {args.pruning_ratio}. FLOPs before pruning: {flops}.'
)
# Avoid the bug when pruning conv2d with small channel number.
# Remove this code after PaddleSlim 2.1 is available.
# Related issue: https://github.com/PaddlePaddle/PaddleSlim/issues/674.
skips = []
for param in net.parameters():
if param.shape[0] <= 8:
skips.append(param.name)
pruner.sensitive_prune(args.pruning_ratio, skip_vars=skips)
flops = dygraph_flops(net, sample_shape)
logger.info(f'Model pruning completed. FLOPs after pruning: {flops}.')
logger.info(f'Step 3/3: Start retraining the model.')
train(net,
train_dataset,
optimizer=cfg.optimizer,
save_dir=args.save_dir,
num_workers=args.num_workers,
iters=cfg.iters,
batch_size=cfg.batch_size,
losses=cfg.loss)
evaluate(net, val_dataset)
if paddle.distributed.get_rank() == 0:
export_model(net, cfg, args.save_dir)
ckpt = os.path.join(args.save_dir, f'iter_{args.retraining_iters}')
if os.path.exists(ckpt):
shutil.rmtree(ckpt)
logger.info(f'Model retraining finish. 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 os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
cfg = Config(args.cfg,
iters=args.retraining_iters,
batch_size=args.batch_size,
learning_rate=args.learning_rate)
train_dataset = cfg.train_dataset
if not train_dataset:
raise RuntimeError(
'The training dataset is not specified in the configuration file.')
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The validation dataset is not specified in the configuration file.'
)
os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
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')
logger.info('Step 1/2: Start to quantify the model...')
quantizer = QAT(config=get_quant_config())
quantizer.quantize(net)
logger.info('Model quantification completed.')
logger.info('Step 2/2: Start retraining the quantized model.')
train(net,
train_dataset,
optimizer=cfg.optimizer,
save_dir=args.save_dir,
num_workers=args.num_workers,
iters=cfg.iters,
batch_size=cfg.batch_size,
losses=cfg.loss)
evaluate(net, val_dataset)
if paddle.distributed.get_rank() == 0:
save_path = os.path.join(args.save_dir, 'model')
input_var = paddle.ones([1] + list(val_dataset[0][0].shape))
quantizer.save_quantized_model(net, save_path, input_spec=[input_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)
ckpt = os.path.join(args.save_dir, f'iter_{args.retraining_iters}')
if os.path.exists(ckpt):
shutil.rmtree(ckpt)
logger.info(
f'Model retraining complete. The quantized model is saved in {args.save_dir}.'
)
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)
