586 lines
21 KiB
Python
586 lines
21 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
# pylint: disable=invalid-name
|
|
"""ResNet models for Keras.
|
|
|
|
Reference:
|
|
- [Deep Residual Learning for Image Recognition](
|
|
https://arxiv.org/abs/1512.03385) (CVPR 2015)
|
|
"""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from tensorflow.python.keras import backend
|
|
from tensorflow.python.keras.applications import imagenet_utils
|
|
from tensorflow.python.keras.engine import training
|
|
from tensorflow.python.keras.layers import VersionAwareLayers
|
|
from tensorflow.python.keras.utils import data_utils
|
|
from tensorflow.python.keras.utils import layer_utils
|
|
from tensorflow.python.lib.io import file_io
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
BASE_WEIGHTS_PATH = (
|
|
'https://storage.googleapis.com/tensorflow/keras-applications/resnet/')
|
|
WEIGHTS_HASHES = {
|
|
'resnet50': ('2cb95161c43110f7111970584f804107',
|
|
'4d473c1dd8becc155b73f8504c6f6626'),
|
|
'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5',
|
|
'88cf7a10940856eca736dc7b7e228a21'),
|
|
'resnet152': ('100835be76be38e30d865e96f2aaae62',
|
|
'ee4c566cf9a93f14d82f913c2dc6dd0c'),
|
|
'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0',
|
|
'fac2f116257151a9d068a22e544a4917'),
|
|
'resnet101v2': ('6343647c601c52e1368623803854d971',
|
|
'c0ed64b8031c3730f411d2eb4eea35b5'),
|
|
'resnet152v2': ('a49b44d1979771252814e80f8ec446f9',
|
|
'ed17cf2e0169df9d443503ef94b23b33'),
|
|
'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a',
|
|
'62527c363bdd9ec598bed41947b379fc'),
|
|
'resnext101':
|
|
('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3')
|
|
}
|
|
|
|
layers = None
|
|
|
|
|
|
def ResNet(stack_fn,
|
|
preact,
|
|
use_bias,
|
|
model_name='resnet',
|
|
include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation='softmax',
|
|
**kwargs):
|
|
"""Instantiates the ResNet, ResNetV2, and ResNeXt architecture.
|
|
|
|
Reference:
|
|
- [Deep Residual Learning for Image Recognition](
|
|
https://arxiv.org/abs/1512.03385) (CVPR 2015)
|
|
|
|
Optionally loads weights pre-trained on ImageNet.
|
|
Note that the data format convention used by the model is
|
|
the one specified in your Keras config at `~/.keras/keras.json`.
|
|
|
|
Caution: Be sure to properly pre-process your inputs to the application.
|
|
Please see `applications.resnet.preprocess_input` for an example.
|
|
|
|
Arguments:
|
|
stack_fn: a function that returns output tensor for the
|
|
stacked residual blocks.
|
|
preact: whether to use pre-activation or not
|
|
(True for ResNetV2, False for ResNet and ResNeXt).
|
|
use_bias: whether to use biases for convolutional layers or not
|
|
(True for ResNet and ResNetV2, False for ResNeXt).
|
|
model_name: string, model name.
|
|
include_top: whether to include the fully-connected
|
|
layer at the top of the network.
|
|
weights: one of `None` (random initialization),
|
|
'imagenet' (pre-training on ImageNet),
|
|
or the path to the weights file to be loaded.
|
|
input_tensor: optional Keras tensor
|
|
(i.e. output of `layers.Input()`)
|
|
to use as image input for the model.
|
|
input_shape: optional shape tuple, only to be specified
|
|
if `include_top` is False (otherwise the input shape
|
|
has to be `(224, 224, 3)` (with `channels_last` data format)
|
|
or `(3, 224, 224)` (with `channels_first` data format).
|
|
It should have exactly 3 inputs channels.
|
|
pooling: optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
|
|
- `None` means that the output of the model will be
|
|
the 4D tensor output of the
|
|
last convolutional layer.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional layer, and thus
|
|
the output of the model will be a 2D tensor.
|
|
- `max` means that global max pooling will
|
|
be applied.
|
|
classes: optional number of classes to classify images
|
|
into, only to be specified if `include_top` is True, and
|
|
if no `weights` argument is specified.
|
|
classifier_activation: A `str` or callable. The activation function to use
|
|
on the "top" layer. Ignored unless `include_top=True`. Set
|
|
`classifier_activation=None` to return the logits of the "top" layer.
|
|
**kwargs: For backwards compatibility only.
|
|
Returns:
|
|
A `keras.Model` instance.
|
|
|
|
Raises:
|
|
ValueError: in case of invalid argument for `weights`,
|
|
or invalid input shape.
|
|
ValueError: if `classifier_activation` is not `softmax` or `None` when
|
|
using a pretrained top layer.
|
|
"""
|
|
global layers
|
|
if 'layers' in kwargs:
|
|
layers = kwargs.pop('layers')
|
|
else:
|
|
layers = VersionAwareLayers()
|
|
if kwargs:
|
|
raise ValueError('Unknown argument(s): %s' % (kwargs,))
|
|
if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
|
|
raise ValueError('The `weights` argument should be either '
|
|
'`None` (random initialization), `imagenet` '
|
|
'(pre-training on ImageNet), '
|
|
'or the path to the weights file to be loaded.')
|
|
|
|
if weights == 'imagenet' and include_top and classes != 1000:
|
|
raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
|
|
' as true, `classes` should be 1000')
|
|
|
|
# Determine proper input shape
|
|
input_shape = imagenet_utils.obtain_input_shape(
|
|
input_shape,
|
|
default_size=224,
|
|
min_size=32,
|
|
data_format=backend.image_data_format(),
|
|
require_flatten=include_top,
|
|
weights=weights)
|
|
|
|
if input_tensor is None:
|
|
img_input = layers.Input(shape=input_shape)
|
|
else:
|
|
if not backend.is_keras_tensor(input_tensor):
|
|
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
|
|
else:
|
|
img_input = input_tensor
|
|
|
|
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
|
|
|
|
x = layers.ZeroPadding2D(
|
|
padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
|
|
x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)
|
|
|
|
if not preact:
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name='conv1_bn')(x)
|
|
x = layers.Activation('relu', name='conv1_relu')(x)
|
|
|
|
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
|
|
x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)
|
|
|
|
x = stack_fn(x)
|
|
|
|
if preact:
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name='post_bn')(x)
|
|
x = layers.Activation('relu', name='post_relu')(x)
|
|
|
|
if include_top:
|
|
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
|
|
imagenet_utils.validate_activation(classifier_activation, weights)
|
|
x = layers.Dense(classes, activation=classifier_activation,
|
|
name='predictions')(x)
|
|
else:
|
|
if pooling == 'avg':
|
|
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
|
|
elif pooling == 'max':
|
|
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
|
|
|
|
# Ensure that the model takes into account
|
|
# any potential predecessors of `input_tensor`.
|
|
if input_tensor is not None:
|
|
inputs = layer_utils.get_source_inputs(input_tensor)
|
|
else:
|
|
inputs = img_input
|
|
|
|
# Create model.
|
|
model = training.Model(inputs, x, name=model_name)
|
|
|
|
# Load weights.
|
|
if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):
|
|
if include_top:
|
|
file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
|
|
file_hash = WEIGHTS_HASHES[model_name][0]
|
|
else:
|
|
file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
|
|
file_hash = WEIGHTS_HASHES[model_name][1]
|
|
weights_path = data_utils.get_file(
|
|
file_name,
|
|
BASE_WEIGHTS_PATH + file_name,
|
|
cache_subdir='models',
|
|
file_hash=file_hash)
|
|
model.load_weights(weights_path)
|
|
elif weights is not None:
|
|
model.load_weights(weights)
|
|
|
|
return model
|
|
|
|
|
|
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
|
|
"""A residual block.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer.
|
|
kernel_size: default 3, kernel size of the bottleneck layer.
|
|
stride: default 1, stride of the first layer.
|
|
conv_shortcut: default True, use convolution shortcut if True,
|
|
otherwise identity shortcut.
|
|
name: string, block label.
|
|
|
|
Returns:
|
|
Output tensor for the residual block.
|
|
"""
|
|
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
|
|
|
|
if conv_shortcut:
|
|
shortcut = layers.Conv2D(
|
|
4 * filters, 1, strides=stride, name=name + '_0_conv')(x)
|
|
shortcut = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
|
|
else:
|
|
shortcut = x
|
|
|
|
x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_1_relu')(x)
|
|
|
|
x = layers.Conv2D(
|
|
filters, kernel_size, padding='SAME', name=name + '_2_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_2_relu')(x)
|
|
|
|
x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)
|
|
|
|
x = layers.Add(name=name + '_add')([shortcut, x])
|
|
x = layers.Activation('relu', name=name + '_out')(x)
|
|
return x
|
|
|
|
|
|
def stack1(x, filters, blocks, stride1=2, name=None):
|
|
"""A set of stacked residual blocks.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer in a block.
|
|
blocks: integer, blocks in the stacked blocks.
|
|
stride1: default 2, stride of the first layer in the first block.
|
|
name: string, stack label.
|
|
|
|
Returns:
|
|
Output tensor for the stacked blocks.
|
|
"""
|
|
x = block1(x, filters, stride=stride1, name=name + '_block1')
|
|
for i in range(2, blocks + 1):
|
|
x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
|
|
return x
|
|
|
|
|
|
def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):
|
|
"""A residual block.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer.
|
|
kernel_size: default 3, kernel size of the bottleneck layer.
|
|
stride: default 1, stride of the first layer.
|
|
conv_shortcut: default False, use convolution shortcut if True,
|
|
otherwise identity shortcut.
|
|
name: string, block label.
|
|
|
|
Returns:
|
|
Output tensor for the residual block.
|
|
"""
|
|
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
|
|
|
|
preact = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(x)
|
|
preact = layers.Activation('relu', name=name + '_preact_relu')(preact)
|
|
|
|
if conv_shortcut:
|
|
shortcut = layers.Conv2D(
|
|
4 * filters, 1, strides=stride, name=name + '_0_conv')(preact)
|
|
else:
|
|
shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x
|
|
|
|
x = layers.Conv2D(
|
|
filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_1_relu')(x)
|
|
|
|
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
|
|
x = layers.Conv2D(
|
|
filters,
|
|
kernel_size,
|
|
strides=stride,
|
|
use_bias=False,
|
|
name=name + '_2_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_2_relu')(x)
|
|
|
|
x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
|
|
x = layers.Add(name=name + '_out')([shortcut, x])
|
|
return x
|
|
|
|
|
|
def stack2(x, filters, blocks, stride1=2, name=None):
|
|
"""A set of stacked residual blocks.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer in a block.
|
|
blocks: integer, blocks in the stacked blocks.
|
|
stride1: default 2, stride of the first layer in the first block.
|
|
name: string, stack label.
|
|
|
|
Returns:
|
|
Output tensor for the stacked blocks.
|
|
"""
|
|
x = block2(x, filters, conv_shortcut=True, name=name + '_block1')
|
|
for i in range(2, blocks):
|
|
x = block2(x, filters, name=name + '_block' + str(i))
|
|
x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))
|
|
return x
|
|
|
|
|
|
def block3(x,
|
|
filters,
|
|
kernel_size=3,
|
|
stride=1,
|
|
groups=32,
|
|
conv_shortcut=True,
|
|
name=None):
|
|
"""A residual block.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer.
|
|
kernel_size: default 3, kernel size of the bottleneck layer.
|
|
stride: default 1, stride of the first layer.
|
|
groups: default 32, group size for grouped convolution.
|
|
conv_shortcut: default True, use convolution shortcut if True,
|
|
otherwise identity shortcut.
|
|
name: string, block label.
|
|
|
|
Returns:
|
|
Output tensor for the residual block.
|
|
"""
|
|
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
|
|
|
|
if conv_shortcut:
|
|
shortcut = layers.Conv2D(
|
|
(64 // groups) * filters,
|
|
1,
|
|
strides=stride,
|
|
use_bias=False,
|
|
name=name + '_0_conv')(x)
|
|
shortcut = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
|
|
else:
|
|
shortcut = x
|
|
|
|
x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_1_relu')(x)
|
|
|
|
c = filters // groups
|
|
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
|
|
x = layers.DepthwiseConv2D(
|
|
kernel_size,
|
|
strides=stride,
|
|
depth_multiplier=c,
|
|
use_bias=False,
|
|
name=name + '_2_conv')(x)
|
|
x_shape = backend.int_shape(x)[1:-1]
|
|
x = layers.Reshape(x_shape + (groups, c, c))(x)
|
|
x = layers.Lambda(
|
|
lambda x: sum(x[:, :, :, :, i] for i in range(c)),
|
|
name=name + '_2_reduce')(x)
|
|
x = layers.Reshape(x_shape + (filters,))(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
|
|
x = layers.Activation('relu', name=name + '_2_relu')(x)
|
|
|
|
x = layers.Conv2D(
|
|
(64 // groups) * filters, 1, use_bias=False, name=name + '_3_conv')(x)
|
|
x = layers.BatchNormalization(
|
|
axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)
|
|
|
|
x = layers.Add(name=name + '_add')([shortcut, x])
|
|
x = layers.Activation('relu', name=name + '_out')(x)
|
|
return x
|
|
|
|
|
|
def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
|
|
"""A set of stacked residual blocks.
|
|
|
|
Arguments:
|
|
x: input tensor.
|
|
filters: integer, filters of the bottleneck layer in a block.
|
|
blocks: integer, blocks in the stacked blocks.
|
|
stride1: default 2, stride of the first layer in the first block.
|
|
groups: default 32, group size for grouped convolution.
|
|
name: string, stack label.
|
|
|
|
Returns:
|
|
Output tensor for the stacked blocks.
|
|
"""
|
|
x = block3(x, filters, stride=stride1, groups=groups, name=name + '_block1')
|
|
for i in range(2, blocks + 1):
|
|
x = block3(
|
|
x,
|
|
filters,
|
|
groups=groups,
|
|
conv_shortcut=False,
|
|
name=name + '_block' + str(i))
|
|
return x
|
|
|
|
|
|
@keras_export('keras.applications.resnet50.ResNet50',
|
|
'keras.applications.resnet.ResNet50',
|
|
'keras.applications.ResNet50')
|
|
def ResNet50(include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
**kwargs):
|
|
"""Instantiates the ResNet50 architecture."""
|
|
|
|
def stack_fn(x):
|
|
x = stack1(x, 64, 3, stride1=1, name='conv2')
|
|
x = stack1(x, 128, 4, name='conv3')
|
|
x = stack1(x, 256, 6, name='conv4')
|
|
return stack1(x, 512, 3, name='conv5')
|
|
|
|
return ResNet(stack_fn, False, True, 'resnet50', include_top, weights,
|
|
input_tensor, input_shape, pooling, classes, **kwargs)
|
|
|
|
|
|
@keras_export('keras.applications.resnet.ResNet101',
|
|
'keras.applications.ResNet101')
|
|
def ResNet101(include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
**kwargs):
|
|
"""Instantiates the ResNet101 architecture."""
|
|
|
|
def stack_fn(x):
|
|
x = stack1(x, 64, 3, stride1=1, name='conv2')
|
|
x = stack1(x, 128, 4, name='conv3')
|
|
x = stack1(x, 256, 23, name='conv4')
|
|
return stack1(x, 512, 3, name='conv5')
|
|
|
|
return ResNet(stack_fn, False, True, 'resnet101', include_top, weights,
|
|
input_tensor, input_shape, pooling, classes, **kwargs)
|
|
|
|
|
|
@keras_export('keras.applications.resnet.ResNet152',
|
|
'keras.applications.ResNet152')
|
|
def ResNet152(include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
**kwargs):
|
|
"""Instantiates the ResNet152 architecture."""
|
|
|
|
def stack_fn(x):
|
|
x = stack1(x, 64, 3, stride1=1, name='conv2')
|
|
x = stack1(x, 128, 8, name='conv3')
|
|
x = stack1(x, 256, 36, name='conv4')
|
|
return stack1(x, 512, 3, name='conv5')
|
|
|
|
return ResNet(stack_fn, False, True, 'resnet152', include_top, weights,
|
|
input_tensor, input_shape, pooling, classes, **kwargs)
|
|
|
|
|
|
@keras_export('keras.applications.resnet50.preprocess_input',
|
|
'keras.applications.resnet.preprocess_input')
|
|
def preprocess_input(x, data_format=None):
|
|
return imagenet_utils.preprocess_input(
|
|
x, data_format=data_format, mode='caffe')
|
|
|
|
|
|
@keras_export('keras.applications.resnet50.decode_predictions',
|
|
'keras.applications.resnet.decode_predictions')
|
|
def decode_predictions(preds, top=5):
|
|
return imagenet_utils.decode_predictions(preds, top=top)
|
|
|
|
|
|
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
|
|
mode='',
|
|
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
|
|
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
|
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|
|
|
|
DOC = """
|
|
|
|
Reference paper:
|
|
- [Deep Residual Learning for Image Recognition]
|
|
(https://arxiv.org/abs/1512.03385) (CVPR 2015)
|
|
|
|
Optionally loads weights pre-trained on ImageNet.
|
|
Note that the data format convention used by the model is
|
|
the one specified in your Keras config at `~/.keras/keras.json`.
|
|
|
|
Arguments:
|
|
include_top: whether to include the fully-connected
|
|
layer at the top of the network.
|
|
weights: one of `None` (random initialization),
|
|
'imagenet' (pre-training on ImageNet),
|
|
or the path to the weights file to be loaded.
|
|
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
|
|
to use as image input for the model.
|
|
input_shape: optional shape tuple, only to be specified
|
|
if `include_top` is False (otherwise the input shape
|
|
has to be `(224, 224, 3)` (with `'channels_last'` data format)
|
|
or `(3, 224, 224)` (with `'channels_first'` data format).
|
|
It should have exactly 3 inputs channels,
|
|
and width and height should be no smaller than 32.
|
|
E.g. `(200, 200, 3)` would be one valid value.
|
|
pooling: Optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
|
|
- `None` means that the output of the model will be
|
|
the 4D tensor output of the
|
|
last convolutional block.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional block, and thus
|
|
the output of the model will be a 2D tensor.
|
|
- `max` means that global max pooling will
|
|
be applied.
|
|
classes: optional number of classes to classify images
|
|
into, only to be specified if `include_top` is True, and
|
|
if no `weights` argument is specified.
|
|
|
|
Returns:
|
|
A Keras model instance.
|
|
"""
|
|
|
|
setattr(ResNet50, '__doc__', ResNet50.__doc__ + DOC)
|
|
setattr(ResNet101, '__doc__', ResNet101.__doc__ + DOC)
|
|
setattr(ResNet152, '__doc__', ResNet152.__doc__ + DOC)
|