def add_augmenters(self, augmenters):
if not isinstance(augmenters, list):
raise Exception(
"augmenters should be list type in func add_augmenters()")
transform_names = [type(x).__name__ for x in self.transforms]
for aug in augmenters:
if type(aug).__name__ in transform_names:
logging.error(
"{} is already in ComposedTransforms, need to remove it from add_augmenters()."
.format(type(aug).__name__))
self.transforms = augmenters + self.transforms
def split_coco_dataset(dataset_dir, val_percent, test_percent, save_dir):
# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
# or matplotlib.backends is imported for the first time
# pycocotools import matplotlib
import matplotlib
matplotlib.use('Agg')
from pycocotools.coco import COCO
if not osp.exists(osp.join(dataset_dir, "annotations.json")):
logging.error(
"\'annotations.json\' is not found in {}!".format(dataset_dir))
annotation_file = osp.join(dataset_dir, "annotations.json")
coco = COCO(annotation_file)
img_ids = coco.getImgIds()
cat_ids = coco.getCatIds()
anno_ids = coco.getAnnIds()
val_num = int(len(img_ids) * val_percent)
test_num = int(len(img_ids) * test_percent)
train_num = len(img_ids) - val_num - test_num
random.shuffle(img_ids)
train_files_ids = img_ids[:train_num]
val_files_ids = img_ids[train_num:train_num + val_num]
test_files_ids = img_ids[train_num + val_num:]
for img_id_list in [train_files_ids, val_files_ids, test_files_ids]:
img_anno_ids = coco.getAnnIds(imgIds=img_id_list, iscrowd=0)
imgs = coco.loadImgs(img_id_list)
instances = coco.loadAnns(img_anno_ids)
categories = coco.loadCats(cat_ids)
img_dict = {
"annotations": instances,
"images": imgs,
"categories": categories
}
if img_id_list == train_files_ids:
json_file = open(osp.join(save_dir, 'train.json'), 'w+')
json.dump(img_dict, json_file, cls=MyEncoder)
elif img_id_list == val_files_ids:
json_file = open(osp.join(save_dir, 'val.json'), 'w+')
json.dump(img_dict, json_file, cls=MyEncoder)
elif img_id_list == test_files_ids and len(test_files_ids):
json_file = open(osp.join(save_dir, 'test.json'), 'w+')
json.dump(img_dict, json_file, cls=MyEncoder)
return train_num, val_num, test_num
def main():
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ""
import paddlex as pdx
if len(sys.argv) < 2:
print("Use command 'paddlex -h` to print the help information\n")
return
parser = arg_parser()
args = parser.parse_args()
if args.version:
print("PaddleX-{}".format(pdx.__version__))
print("Repo: https://github.com/PaddlePaddle/PaddleX.git")
print("Email: [email protected]")
return
if args.export_inference:
assert args.model_dir is not None, "--model_dir should be defined while exporting inference model"
assert args.save_dir is not None, "--save_dir should be defined to save inference model"
fixed_input_shape = None
if args.fixed_input_shape is not None:
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape
) == 2, "len of fixed input shape must == 2, such as [224,224]"
else:
fixed_input_shape = None
model = pdx.load_model(args.model_dir, fixed_input_shape)
model.export_inference_model(args.save_dir)
if args.export_onnx:
assert args.model_dir is not None, "--model_dir should be defined while exporting onnx model"
assert args.save_dir is not None, "--save_dir should be defined to create onnx model"
model = pdx.load_model(args.model_dir)
if model.status == "Normal" or model.status == "Prune":
logging.error(
"Only support inference model, try to export model first as below,",
exit=False)
logging.error(
"paddlex --export_inference --model_dir model_path --save_dir infer_model"
)
pdx.convertor.export_onnx_model(model, args.save_dir)
def build_program(self):
if hasattr(paddlex, 'model_built') and paddlex.model_built:
logging.error(
"Function model.train() only can be called once in your code.")
paddlex.model_built = True
# 构建训练网络
self.train_inputs, self.train_outputs = self.build_net(mode='train')
self.train_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
# 构建预测网络
self.test_prog = fluid.Program()
with fluid.program_guard(self.test_prog, startup_prog):
with fluid.unique_name.guard():
self.test_inputs, self.test_outputs = self.build_net(
mode='test')
self.test_prog = self.test_prog.clone(for_test=True)
def default_optimizer(self, learning_rate, warmup_steps, warmup_start_lr,
lr_decay_epochs, lr_decay_gamma,
num_steps_each_epoch):
if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
logging.error(
"In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
exit=False)
logging.error(
"See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
exit=False)
logging.error(
"warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function"
.format(lr_decay_epochs[0] * num_steps_each_epoch,
warmup_steps // num_steps_each_epoch))
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [(lr_decay_gamma**i) * learning_rate
for i in range(len(lr_decay_epochs) + 1)]
lr_decay = fluid.layers.piecewise_decay(boundaries=boundaries,
values=values)
lr_warmup = fluid.layers.linear_lr_warmup(learning_rate=lr_decay,
warmup_steps=warmup_steps,
start_lr=warmup_start_lr,
end_lr=learning_rate)
optimizer = fluid.optimizer.Momentum(
learning_rate=lr_warmup,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-04))
return optimizer
def export_onnx_model(model, save_file, opset_version=10):
if model.__class__.__name__ == "FastSCNN" or (
model.model_type == "detector"
and model.__class__.__name__ != "YOLOv3"):
logging.error(
"Only image classifier models, detection models(YOLOv3) and semantic segmentation models(except FastSCNN) are supported to export to ONNX"
)
try:
import paddle2onnx
except:
logging.error(
"You need to install paddle2onnx first, pip install paddle2onnx==0.4"
)
import paddle2onnx as p2o
if p2o.__version__ != '0.4':
logging.error(
"You need install paddle2onnx==0.4, but the version of paddle2onnx is {}"
.format(p2o.__version__))
if opset_version == 10 and model.__class__.__name__ == "YOLOv3":
logging.warning(
"Export for openVINO by default, the output of multiclass_nms exported to onnx will contains background. If you need onnx completely consistent with paddle, please use paddle2onnx to export"
)
p2o.register_op_mapper('multiclass_nms', MultiClassNMS4OpenVINO)
p2o.program2onnx(model.test_prog,
scope=model.scope,
save_file=save_file,
opset_version=opset_version)
def export_onnx_model(model, save_dir):
if model.model_type == "detector" or model.__class__.__name__ == "FastSCNN":
logging.error(
"Only image classifier models and semantic segmentation models(except FastSCNN) are supported to export to ONNX"
)
try:
import x2paddle
if x2paddle.__version__ < '0.7.4':
logging.error("You need to upgrade x2paddle >= 0.7.4")
except:
logging.error(
"You need to install x2paddle first, pip install x2paddle>=0.7.4")
from x2paddle.op_mapper.paddle_op_mapper import PaddleOpMapper
mapper = PaddleOpMapper()
mapper.convert(model.test_prog, save_dir)
def main():
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ""
import paddlex as pdx
if len(sys.argv) < 2:
print("Use command 'paddlex -h` to print the help information\n")
return
parser = arg_parser()
args = parser.parse_args()
if args.version:
print("PaddleX-{}".format(pdx.__version__))
print("Repo: https://github.com/PaddlePaddle/PaddleX.git")
print("Email: [email protected]")
return
if args.export_inference:
assert args.model_dir is not None, "--model_dir should be defined while exporting Model model"
assert args.save_dir is not None, "--save_dir should be defined to save Model model"
fixed_input_shape = None
if args.fixed_input_shape is not None:
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape
) == 2, "len of fixed input shape must == 2, such as [224,224]"
else:
fixed_input_shape = None
model = pdx.load_model(args.model_dir, fixed_input_shape)
model.export_inference_model(args.save_dir)
if args.export_onnx:
assert args.model_dir is not None, "--model_dir should be defined while exporting onnx model"
assert args.save_dir is not None, "--save_dir should be defined to create onnx model"
model = pdx.load_model(args.model_dir)
if model.status == "Normal" or model.status == "Prune":
logging.error(
"Only support Model model, try to export model first as below,",
exit=False)
logging.error(
"paddlex --export_inference --model_dir model_path --save_dir infer_model"
)
save_file = os.path.join(args.save_dir, 'paddle2onnx_model.onnx')
pdx.converter.export_onnx_model(model, save_file, args.onnx_opset)
if args.data_conversion:
assert args.source is not None, "--source should be defined while converting dataset"
assert args.to is not None, "--to should be defined to confirm the taregt dataset format"
assert args.pics is not None, "--pics should be defined to confirm the pictures path"
assert args.annotations is not None, "--annotations should be defined to confirm the annotations path"
assert args.save_dir is not None, "--save_dir should be defined to store taregt dataset"
if args.source not in ['labelme', 'jingling', 'easydata']:
logging.error(
"The source format {} is not one of labelme/jingling/easydata".
format(args.source),
exit=False)
if args.to not in ['PascalVOC', 'MSCOCO', 'SEG', 'ImageNet']:
logging.error(
"The to format {} is not one of PascalVOC/MSCOCO/SEG/ImageNet".
format(args.to),
exit=False)
if args.source == 'labelme' and args.to == 'ImageNet':
logging.error(
"The labelme dataset can not convert to the ImageNet dataset.",
exit=False)
if args.source == 'jingling' and args.to == 'PascalVOC':
logging.error(
"The jingling dataset can not convert to the PascalVOC dataset.",
exit=False)
if not osp.exists(args.save_dir):
os.makedirs(args.save_dir)
pdx.tools.convert.dataset_conversion(args.source, args.to, args.pics,
args.annotations, args.save_dir)
if args.split_dataset:
assert args.dataset_dir is not None, "--dataset_dir should be defined while spliting dataset"
assert args.format is not None, "--format should be defined while spliting dataset"
assert args.val_value is not None, "--val_value should be defined while spliting dataset"
dataset_dir = args.dataset_dir
dataset_format = args.format.lower()
val_value = float(args.val_value)
test_value = float(args.test_value
if args.test_value is not None else 0)
save_dir = dataset_dir
if not dataset_format in ["coco", "imagenet", "voc", "seg"]:
logging.error(
"The dataset format is not correct defined.(support COCO/ImageNet/VOC/Seg)"
)
if not osp.exists(dataset_dir):
logging.error("The path of dataset to be splited doesn't exist.")
if val_value <= 0 or val_value >= 1 or test_value < 0 or test_value >= 1 or val_value + test_value >= 1:
logging.error("The value of split is not correct.")
pdx.tools.split.dataset_split(dataset_dir, dataset_format, val_value,
test_value, save_dir)
def load_model(model_dir, fixed_input_shape=None):
model_scope = fluid.Scope()
if not osp.exists(model_dir):
logging.error("model_dir '{}' is not exists!".format(model_dir))
if not osp.exists(osp.join(model_dir, "model.yml")):
raise Exception("There's not model.yml in {}".format(model_dir))
with open(osp.join(model_dir, "model.yml")) as f:
info = yaml.load(f.read(), Loader=yaml.Loader)
if 'status' in info:
status = info['status']
elif 'save_method' in info:
# 兼容老版本PaddleX
status = info['save_method']
if not hasattr(paddlex.cv.models, info['Model']):
raise Exception("There's no attribute {} in paddlex.cv.models".format(
info['Model']))
if 'model_name' in info['_init_params']:
del info['_init_params']['model_name']
model = getattr(paddlex.cv.models, info['Model'])(**info['_init_params'])
model.fixed_input_shape = fixed_input_shape
if '_Attributes' in info:
if 'fixed_input_shape' in info['_Attributes']:
fixed_input_shape = info['_Attributes']['fixed_input_shape']
if fixed_input_shape is not None:
logging.info(
"Model already has fixed_input_shape with {}".format(
fixed_input_shape))
model.fixed_input_shape = fixed_input_shape
else:
info['_Attributes'][
'fixed_input_shape'] = model.fixed_input_shape
if info['Model'].count('RCNN') > 0:
if info['_init_params']['with_fpn']:
if model.fixed_input_shape is not None:
if model.fixed_input_shape[0] % 32 > 0:
raise Exception(
"The first value in fixed_input_shape must be a multiple of 32, but recieved {}."
.format(model.fixed_input_shape[0]))
if model.fixed_input_shape[1] % 32 > 0:
raise Exception(
"The second value in fixed_input_shape must be a multiple of 32, but recieved {}."
.format(model.fixed_input_shape[1]))
with fluid.scope_guard(model_scope):
if status == "Normal" or \
status == "Prune" or status == "fluid.save":
startup_prog = fluid.Program()
model.test_prog = fluid.Program()
with fluid.program_guard(model.test_prog, startup_prog):
with fluid.unique_name.guard():
model.test_inputs, model.test_outputs = model.build_net(
mode='test')
model.test_prog = model.test_prog.clone(for_test=True)
model.exe.run(startup_prog)
if status == "Prune":
from .slim.prune import update_program
model.test_prog = update_program(model.test_prog,
model_dir,
model.places[0],
scope=model_scope)
import pickle
with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
load_dict = pickle.load(f)
fluid.io.set_program_state(model.test_prog, load_dict)
elif status == "Infer" or \
status == "Quant" or status == "fluid.save_inference_model":
[prog, input_names, outputs
] = fluid.io.load_inference_model(model_dir,
model.exe,
params_filename='__params__')
model.test_prog = prog
test_outputs_info = info['_ModelInputsOutputs']['test_outputs']
model.test_inputs = OrderedDict()
model.test_outputs = OrderedDict()
for name in input_names:
model.test_inputs[name] = model.test_prog.global_block().var(
name)
for i, out in enumerate(outputs):
var_desc = test_outputs_info[i]
model.test_outputs[var_desc[0]] = out
if 'Transforms' in info:
transforms_mode = info.get('TransformsMode', 'RGB')
# 固定模型的输入shape
fix_input_shape(info, fixed_input_shape=model.fixed_input_shape)
if transforms_mode == 'RGB':
to_rgb = True
else:
to_rgb = False
if 'BatchTransforms' in info:
# 兼容老版本PaddleX模型
model.test_transforms = build_transforms_v1(
model.model_type, info['Transforms'], info['BatchTransforms'])
model.eval_transforms = copy.deepcopy(model.test_transforms)
else:
model.test_transforms = build_transforms(model.model_type,
info['Transforms'],
to_rgb)
model.eval_transforms = copy.deepcopy(model.test_transforms)
if '_Attributes' in info:
for k, v in info['_Attributes'].items():
if k in model.__dict__:
model.__dict__[k] = v
logging.info("Model[{}] loaded.".format(info['Model']))
model.scope = model_scope
model.trainable = False
model.status = status
return model
def split_voc_dataset(dataset_dir, val_percent, test_percent, save_dir):
if not osp.exists(osp.join(dataset_dir, "JPEGImages")):
logging.error("\'JPEGImages\' is not found in {}!".format(dataset_dir))
if not osp.exists(osp.join(dataset_dir, "Annotations")):
logging.error("\'Annotations\' is not found in {}!".format(
dataset_dir))
all_image_files = list_files(osp.join(dataset_dir, "JPEGImages"))
image_anno_list = list()
label_list = list()
for image_file in all_image_files:
if not is_pic(image_file):
continue
anno_name = replace_ext(image_file, "xml")
if osp.exists(osp.join(dataset_dir, "Annotations", anno_name)):
image_anno_list.append([image_file, anno_name])
try:
tree = ET.parse(
osp.join(dataset_dir, "Annotations", anno_name))
except:
raise Exception("文件{}不是一个良构的xml文件，请检查标注文件".format(
osp.join(dataset_dir, "Annotations", anno_name)))
objs = tree.findall("object")
for i, obj in enumerate(objs):
cname = obj.find('name').text
if not cname in label_list:
label_list.append(cname)
else:
logging.error("The annotation file {} doesn't exist!".format(
anno_name))
random.shuffle(image_anno_list)
image_num = len(image_anno_list)
val_num = int(image_num * val_percent)
test_num = int(image_num * test_percent)
train_num = image_num - val_num - test_num
train_image_anno_list = image_anno_list[:train_num]
val_image_anno_list = image_anno_list[train_num:train_num + val_num]
test_image_anno_list = image_anno_list[train_num + val_num:]
with open(
osp.join(save_dir, 'train_list.txt'), mode='w',
encoding='utf-8') as f:
for x in train_image_anno_list:
file = osp.join("JPEGImages", x[0])
label = osp.join("Annotations", x[1])
f.write('{} {}\n'.format(file, label))
with open(
osp.join(save_dir, 'val_list.txt'), mode='w',
encoding='utf-8') as f:
for x in val_image_anno_list:
file = osp.join("JPEGImages", x[0])
label = osp.join("Annotations", x[1])
f.write('{} {}\n'.format(file, label))
if len(test_image_anno_list):
with open(
osp.join(save_dir, 'test_list.txt'), mode='w',
encoding='utf-8') as f:
for x in test_image_anno_list:
file = osp.join("JPEGImages", x[0])
label = osp.join("Annotations", x[1])
f.write('{} {}\n'.format(file, label))
with open(
osp.join(save_dir, 'labels.txt'), mode='w', encoding='utf-8') as f:
for l in sorted(label_list):
f.write('{}\n'.format(l))
return train_num, val_num, test_num
def get_pretrain_weights(flag, class_name, backbone, save_dir):
if flag is None:
return None
elif osp.isdir(flag):
return flag
elif osp.isfile(flag):
return flag
warning_info = "{} does not support to be finetuned with weights pretrained on the {} dataset, so pretrain_weights is forced to be set to {}"
if flag == 'COCO':
if class_name == 'DeepLabv3p' and backbone in [
'Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5',
'MobileNetV2_x1.5', 'MobileNetV2_x2.0',
'MobileNetV3_large_x1_0_ssld'
]:
model_name = '{}_{}'.format(class_name, backbone)
logging.warning(warning_info.format(model_name, flag, 'IMAGENET'))
flag = 'IMAGENET'
elif class_name == 'HRNet':
logging.warning(warning_info.format(class_name, flag, 'IMAGENET'))
flag = 'IMAGENET'
elif class_name == 'FastSCNN':
logging.warning(warning_info.format(class_name, flag,
'CITYSCAPES'))
flag = 'CITYSCAPES'
elif flag == 'CITYSCAPES':
model_name = '{}_{}'.format(class_name, backbone)
if class_name == 'UNet':
logging.warning(warning_info.format(class_name, flag, 'COCO'))
flag = 'COCO'
if class_name == 'HRNet' and backbone.split('_')[-1] in [
'W30', 'W32', 'W40', 'W48', 'W60', 'W64'
]:
logging.warning(warning_info.format(backbone, flag, 'IMAGENET'))
flag = 'IMAGENET'
if class_name == 'DeepLabv3p' and backbone in [
'Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5',
'MobileNetV2_x1.5', 'MobileNetV2_x2.0'
]:
model_name = '{}_{}'.format(class_name, backbone)
logging.warning(warning_info.format(model_name, flag, 'IMAGENET'))
flag = 'IMAGENET'
elif flag == 'IMAGENET':
if class_name == 'UNet':
logging.warning(warning_info.format(class_name, flag, 'COCO'))
flag = 'COCO'
elif class_name == 'FastSCNN':
logging.warning(warning_info.format(class_name, flag,
'CITYSCAPES'))
flag = 'CITYSCAPES'
elif flag == 'BAIDU10W':
if class_name not in ['ResNet50_vd']:
raise Exception(
"Only the classifier ResNet50_vd supports BAIDU10W pretrained weights"
)
if flag == 'IMAGENET':
new_save_dir = save_dir
if hasattr(paddlex, 'pretrain_dir'):
new_save_dir = paddlex.pretrain_dir
if backbone.startswith('Xception'):
backbone = 'Seg{}'.format(backbone)
elif backbone == 'MobileNetV2':
backbone = 'MobileNetV2_x1.0'
elif backbone == 'MobileNetV3_small_ssld':
backbone = 'MobileNetV3_small_x1_0_ssld'
elif backbone == 'MobileNetV3_large_ssld':
backbone = 'MobileNetV3_large_x1_0_ssld'
if class_name in ['YOLOv3', 'FasterRCNN', 'MaskRCNN']:
if backbone == 'ResNet50':
backbone = 'DetResNet50'
assert backbone in image_pretrain, "There is not ImageNet pretrain weights for {}, you may try COCO.".format(
backbone)
if getattr(paddlex, 'gui_mode', False):
url = image_pretrain[backbone]
fname = osp.split(url)[-1].split('.')[0]
paddlex.utils.download_and_decompress(url, path=new_save_dir)
return osp.join(new_save_dir, fname)
import paddlehub as hub
try:
logging.info(
"Connecting PaddleHub server to get pretrain weights...")
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
logging.error(
"Couldn't download pretrain weight, you can download it manualy from {} (decompress the file if it is a compressed file), and set pretrain weights by your self"
.format(image_pretrain[backbone]),
exit=False)
if isinstance(e, hub.ResourceNotFoundError):
raise Exception(
"Resource for backbone {} not found".format(backbone))
elif isinstance(e, hub.ServerConnectionError):
raise Exception(
"Cannot get reource for backbone {}, please check your internet connection"
.format(backbone))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2")
return osp.join(new_save_dir, backbone)
elif flag in ['COCO', 'CITYSCAPES']:
new_save_dir = save_dir
if hasattr(paddlex, 'pretrain_dir'):
new_save_dir = paddlex.pretrain_dir
if class_name in [
'YOLOv3', 'FasterRCNN', 'MaskRCNN', 'DeepLabv3p', 'PPYOLO'
]:
backbone = '{}_{}'.format(class_name, backbone)
backbone = "{}_{}".format(backbone, flag)
if flag == 'COCO':
url = coco_pretrain[backbone]
elif flag == 'CITYSCAPES':
url = cityscapes_pretrain[backbone]
fname = osp.split(url)[-1].split('.')[0]
if getattr(paddlex, 'gui_mode', False):
paddlex.utils.download_and_decompress(url, path=new_save_dir)
return osp.join(new_save_dir, fname)
import paddlehub as hub
try:
logging.info(
"Connecting PaddleHub server to get pretrain weights...")
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
logging.error(
"Couldn't download pretrain weight, you can download it manualy from {} (decompress the file if it is a compressed file), and set pretrain weights by your self"
.format(url),
exit=False)
if isinstance(e, hub.ResourceNotFoundError):
raise Exception(
"Resource for backbone {} not found".format(backbone))
elif isinstance(e, hub.ServerConnectionError):
raise Exception(
"Cannot get reource for backbone {}, please check your internet connection"
.format(backbone))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2")
return osp.join(new_save_dir, backbone)
elif flag == 'BAIDU10W':
new_save_dir = save_dir
if hasattr(paddlex, 'pretrain_dir'):
new_save_dir = paddlex.pretrain_dir
backbone = backbone + '_BAIDU10W'
url = baidu10w_pretrain[backbone]
fname = osp.split(url)[-1].split('.')[0]
if getattr(paddlex, 'gui_mode', False):
paddlex.utils.download_and_decompress(url, path=new_save_dir)
return osp.join(new_save_dir, fname)
import paddlehub as hub
try:
logging.info(
"Connecting PaddleHub server to get pretrain weights...")
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
logging.error(
"Couldn't download pretrain weight, you can download it manualy from {} (decompress the file if it is a compressed file), and set pretrain weights by your self"
.format(url),
exit=False)
if isinstance(e, hub.ResourceNotFoundError):
raise Exception(
"Resource for backbone {} not found".format(backbone))
elif isinstance(e, hub.ServerConnectionError):
raise Exception(
"Cannot get reource for backbone {}, please check your internet connection"
.format(backbone))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2")
return osp.join(new_save_dir, backbone)
else:
logging.error(
"Path of retrain weights '{}' is not exists!".format(flag))