def _darknet(arch, num_layers=53, pretrained=False, **kwargs):
model = DarkNet(num_layers, **kwargs)
if pretrained:
assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
arch)
weight_path = get_weights_path_from_url(*(model_urls[arch]))
assert weight_path.endswith('.pdparams'), \
"suffix of weight must be .pdparams"
model.load(weight_path)
return model
def _resnet(arch, Block, depth, pretrained, **kwargs):
model = ResNet(Block, depth, **kwargs)
if pretrained:
assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
arch)
weight_path = get_weights_path_from_url(model_urls[arch][0],
model_urls[arch][1])
assert weight_path.endswith(
'.pdparams'), "suffix of weight must be .pdparams"
model.load(weight_path)
return model
def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
model = TSM_ResNet(num_layers, seg_num, num_classes)
if pretrained:
assert num_layers in pretrain_infos.keys(), \
"TSM-ResNet{} do not have pretrained weights now, " \
"pretrained should be set as False".format(num_layers)
weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
assert weight_path.endswith('.pdparams'), \
"suffix of weight must be .pdparams"
model.load(weight_path)
return model
def _yolov3_darknet(num_layers=53,
num_classes=80,
model_mode='train',
pretrained=True):
model = YOLOv3(num_classes, model_mode)
if pretrained:
assert num_layers in pretrain_infos.keys(), \
"YOLOv3-DarkNet{} do not have pretrained weights now, " \
"pretrained should be set as False".format(num_layers)
weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
assert weight_path.endswith('.pdparams'), \
"suffix of weight must be .pdparams"
model.load(weight_path)
return model
def _vgg(arch, cfg, batch_norm, pretrained, **kwargs):
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm),
num_classes=1000,
**kwargs)
if pretrained:
assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
arch)
weight_path = get_weights_path_from_url(model_urls[arch][0],
model_urls[arch][1])
assert weight_path.endswith(
'.pdparams'), "suffix of weight must be .pdparams"
model.load(weight_path)
return model
def bmn(tscale,
dscale,
prop_boundary_ratio,
num_sample,
num_sample_perbin,
pretrained=True):
"""BMN model
Args:
tscale (int): sequence length, default 100.
dscale (int): max duration length, default 100.
prop_boundary_ratio (float): ratio of expanded temporal region in proposal boundary, default 0.5.
num_sample (int): number of samples betweent starting boundary and ending boundary of each propoasl, default 32.
num_sample_perbin (int): number of selected points in each sample, default 3.
pretrained (bool): If True, returns a model with pre-trained model, default True.
"""
model = BMN(tscale, dscale, prop_boundary_ratio, num_sample,
num_sample_perbin)
if pretrained:
weight_path = get_weights_path_from_url(*(pretrain_infos['bmn']))
assert weight_path.endswith('.pdparams'), \
"suffix of weight must be .pdparams"
model.load(weight_path)
return model