def download_imagenet(self):
""" Download pre-trained weights for the specified backbone name.
This name is in the format {backbone}_weights_tf_dim_ordering_tf_kernels_notop
where backbone is the densenet + number of layers (e.g. densenet121).
For more info check the explanation from the keras densenet script itself:
https://github.com/keras-team/keras/blob/master/keras/applications/densenet.py
"""
origin = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/'
file_name = '{}_weights_tf_dim_ordering_tf_kernels_notop.h5'
# load weights
if keras.backend.image_data_format() == 'channels_first':
raise ValueError('Weights for "channels_first" format are not available.')
weights_url = origin + file_name.format(self.backbone)
return get_file(file_name.format(self.backbone), weights_url, cache_subdir='models')
def download_imagenet(backbone, weights_url=None):
""" Download pre-trained weights for the specified backbone name. This name is in the format
{backbone}_weights_tf_dim_ordering_tf_kernels_notop where backbone is the densenet + number of layers (e.g. densenet121).
For more info check the explanation from the keras densenet script itself:
https://github.com/keras-team/keras/blob/master/keras/applications/densenet.py
# Arguments
backbone : Backbone name.
"""
# load weights
if keras.backend.image_data_format() == 'channels_first':
raise ValueError(
'Weights for "channels_first" format are not available.')
if weights_url is None:
weights_url = WEIGHT_PATH.format(backbone)
split_url = weights_url.split("/")
print("the split url is " + split_url[len(split_url) - 1])
weights_path = get_file(split_url[len(split_url) - 1],
weights_url,
cache_subdir='models')
return weights_path