def download_imagenet(self):
""" Download pre-trained weights for the specified backbone name.
This name is in the format mobilenet{rows}_{alpha} where rows is the
imagenet shape dimension and 'alpha' controls the width of the network.
For more info check the explanation from the keras mobilenet script itself.
"""
alpha = float(self.backbone.split('_')[1])
rows = int(self.backbone.split('_')[0].replace('mobilenet', ''))
# load weights
if keras.backend.image_data_format() == 'channels_first':
raise ValueError('Weights for "channels_last" format '
'are not available.')
if alpha == 1.0:
alpha_text = '1_0'
elif alpha == 0.75:
alpha_text = '7_5'
elif alpha == 0.50:
alpha_text = '5_0'
else:
alpha_text = '2_5'
model_name = 'mobilenet_{}_{}_tf_no_top.h5'.format(alpha_text, rows)
weights_url = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name, weights_url, cache_subdir='models')
return weights_path
def download_imagenet(backbone):
""" Download pre-trained weights for the specified backbone name. This name is in the format
mobilenet{rows}_{alpha} where rows is the imagenet shape dimension and 'alpha' controls
the width of the network.
For more info check the explanation from the keras mobilenet script itself
# Arguments
backbone : Backbone name.
"""
alpha = float(backbone.split('_')[1])
rows = int(backbone.split('_')[0].replace('mobilenet', ''))
# load weights
if keras.backend.image_data_format() == 'channels_first':
raise ValueError('Weights for "channels_last" format '
'are not available.')
if alpha == 1.0:
alpha_text = '1_0'
elif alpha == 0.75:
alpha_text = '7_5'
elif alpha == 0.50:
alpha_text = '5_0'
else:
alpha_text = '2_5'
model_name = 'mobilenet_{}_{}_tf_no_top.h5'.format(alpha_text, rows)
weights_url = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name, weights_url, cache_subdir='models')
return weights_path