本文整理汇总了Python中keras.utils.get_file方法的典型用法代码示例。如果您正苦于以下问题:Python utils.get_file方法的具体用法?Python utils.get_file怎么用?Python utils.get_file使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.utils
的用法示例。
在下文中一共展示了utils.get_file方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_model_by_name
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def get_model_by_name(model_name, input_shape, classes=1000, pretrained=False):
"""Get an EfficientNet model by its name.
"""
blocks_args, global_params = get_efficientnet_params(model_name, override_params={'num_classes': classes})
model = _efficientnet(input_shape, blocks_args, global_params)
try:
if pretrained:
weights = IMAGENET_WEIGHTS[model_name]
weights_path = get_file(
weights['name'],
weights['url'],
cache_subdir='models',
md5_hash=weights['md5'],
)
model.load_weights(weights_path)
except KeyError as e:
print("NOTE: Currently model {} doesn't have pretrained weights, therefore a model with randomly initialized"
" weights is returned.".format(e))
return model
示例2: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
Weights can be downloaded at https://github.com/fizyr/keras-mod'\
'els/releases .
"""
if self.backbone == 'vgg16':
resource = keras.applications.vgg16.WEIGHTS_PATH_NO_TOP
checksum = '6d6bbae143d832006294945121d1f1fc'
elif self.backbone == 'vgg19':
resource = keras.applications.vgg19.WEIGHTS_PATH_NO_TOP
checksum = '253f8cb515780f3b799900260a226db6'
else:
raise ValueError(
"Backbone '{}' not recognized.".format(
self.backbone))
return get_file(
'{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(
self.backbone),
resource,
cache_subdir='models',
file_hash=checksum
)
示例3: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
"""
resnet_filename = 'ResNet-{}-model.keras.h5'
resnet_resource = 'https://github.com/fizyr/keras-models'\
'/releases/download/v0.0.1/{}'.format(
resnet_filename)
depth = int(self.backbone.replace('resnet', ''))
filename = resnet_filename.format(depth)
resource = resnet_resource.format(depth)
if depth == 50:
checksum = '3e9f4e4f77bbe2c9bec13b53ee1c2319'
elif depth == 101:
checksum = '05dc86924389e5b401a9ea0348a3213c'
elif depth == 152:
checksum = '6ee11ef2b135592f8031058820bb9e71'
return get_file(
filename,
resource,
cache_subdir='models',
md5_hash=checksum
)
示例4: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
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')
示例5: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
Weights can be downloaded at https://github.com/fizyr/keras-models/releases .
"""
if self.backbone == 'vgg16':
resource = keras.applications.vgg16.vgg16.WEIGHTS_PATH_NO_TOP
checksum = '6d6bbae143d832006294945121d1f1fc'
elif self.backbone == 'vgg19':
resource = keras.applications.vgg19.vgg19.WEIGHTS_PATH_NO_TOP
checksum = '253f8cb515780f3b799900260a226db6'
else:
raise ValueError("Backbone '{}' not recognized.".format(self.backbone))
return get_file(
'{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(self.backbone),
resource,
cache_subdir='models',
file_hash=checksum
)
示例6: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
"""
resnet_filename = 'ResNet-{}-model.keras.h5'
resnet_resource = 'https://github.com/fizyr/keras-models/releases/download/v0.0.1/{}'.format(resnet_filename)
depth = int(self.backbone.replace('resnet', ''))
filename = resnet_filename.format(depth)
resource = resnet_resource.format(depth)
if depth == 50:
checksum = '3e9f4e4f77bbe2c9bec13b53ee1c2319'
elif depth == 101:
checksum = '05dc86924389e5b401a9ea0348a3213c'
elif depth == 152:
checksum = '6ee11ef2b135592f8031058820bb9e71'
return get_file(
filename,
resource,
cache_subdir='models',
md5_hash=checksum
)
示例7: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
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')
示例8: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
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.')
BASE_WEIGHT_PATH = 'https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/'
model_name = 'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_{}_{}_no_top.h5'.format(alpha, rows)
weights_url = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name, weights_url, cache_subdir='models')
return weights_path
示例9: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
Weights can be downloaded at https://github.com/fizyr/keras-models/releases .
"""
if self.backbone == 'vgg16':
resource = keras.applications.vgg16.WEIGHTS_PATH_NO_TOP
checksum = '6d6bbae143d832006294945121d1f1fc'
elif self.backbone == 'vgg19':
resource = keras.applications.vgg19.WEIGHTS_PATH_NO_TOP
checksum = '253f8cb515780f3b799900260a226db6'
else:
raise ValueError("Backbone '{}' not recognized.".format(self.backbone))
return get_file(
'{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(self.backbone),
resource,
cache_subdir='models',
file_hash=checksum
)
示例10: load_model_weights
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def load_model_weights(weights_collection, model, dataset, classes, include_top):
weights = find_weights(weights_collection, model.name, dataset, include_top)
if weights:
weights = weights[0]
if include_top and weights['classes'] != classes:
raise ValueError('If using `weights` and `include_top`'
' as true, `classes` should be {}'.format(weights['classes']))
weights_path = get_file(weights['name'],
weights['url'],
cache_subdir='/project/backbones_weights',
md5_hash=weights['md5'])
model.load_weights(weights_path)
else:
raise ValueError('There is no weights for such configuration: ' +
'model = {}, dataset = {}, '.format(model.name, dataset) +
'classes = {}, include_top = {}.'.format(classes, include_top))
示例11: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
Weights can be downloaded at https://github.com/fizyr/keras-models/releases .
"""
if self.backbone == 'vgg16':
resource = keras.applications.vgg16.vgg16.WEIGHTS_PATH_NO_TOP
checksum = '6d6bbae143d832006294945121d1f1fc'
elif 'vgg-max' in self.backbone:
resource = keras.applications.vgg16.vgg16.WEIGHTS_PATH_NO_TOP
checksum = '6d6bbae143d832006294945121d1f1fc'
elif self.backbone == 'vgg19':
resource = keras.applications.vgg19.vgg19.WEIGHTS_PATH_NO_TOP
checksum = '253f8cb515780f3b799900260a226db6'
else:
raise ValueError("Backbone '{}' not recognized.".format(self.backbone))
return get_file(
'{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(self.backbone),
resource,
cache_subdir='models',
file_hash=checksum
)
示例12: load_model_weights
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def load_model_weights(weights_collection, model, dataset, classes, include_top):
weights = find_weights(weights_collection, model.name, dataset, include_top)
if weights:
weights = weights[0]
if include_top and weights['classes'] != classes:
raise ValueError('If using `weights` and `include_top`'
' as true, `classes` should be {}'.format(weights['classes']))
weights_path = get_file(weights['name'],
weights['url'],
cache_subdir='models',
md5_hash=weights['md5'])
model.load_weights(weights_path)
else:
raise ValueError('There is no weights for such configuration: ' +
'model = {}, dataset = {}, '.format(model.name, dataset) +
'classes = {}, include_top = {}.'.format(classes, include_top))
示例13: download_resnet_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def download_resnet_imagenet(v):
v = int(v.replace('resnet', ''))
filename = resnet_filename.format(v)
resource = resnet_resource.format(v)
if v == 50:
checksum = '3e9f4e4f77bbe2c9bec13b53ee1c2319'
elif v == 101:
checksum = '05dc86924389e5b401a9ea0348a3213c'
elif v == 152:
checksum = '6ee11ef2b135592f8031058820bb9e71'
return get_file(
filename,
resource,
cache_subdir='models',
md5_hash=checksum
)
示例14: __init__
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
def __init__(self):
logger.info('Loading Deeplab')
local_path = os.path.join(config.WEIGHT_PATH, config.DEEPLAB_FILENAME)
self.weights_path = get_file(os.path.abspath(local_path), config.DEEPLAB_URL, cache_subdir='models')
self.graph = tf.Graph()
with self.graph.as_default():
self.image_placeholder = tf.placeholder(tf.float32, shape=(None, None, None, 3))
self.net = DeepLabResNetModel({'data': self.image_placeholder}, is_training=False,
num_classes=self.NUM_CLASSES)
restore_var = tf.global_variables()
# Set up TF session and initialize variables.
config_tf = tf.ConfigProto()
config_tf.gpu_options.allow_growth = True
self.sess = tf.Session(config=config_tf)
init = tf.global_variables_initializer()
self.sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
loader.restore(self.sess, self.weights_path)
示例15: download_imagenet
# 需要导入模块: from keras import utils [as 别名]
# 或者: from keras.utils import get_file [as 别名]
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 = mobilenet.mobilenet.BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name, weights_url,
cache_subdir='models')
return weights_path