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Commit 53ff5d90 authored by Shining Sun's avatar Shining Sun
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Resolve merge conflict

parents 26a2c3b9 42420ce8
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Showing with 305 additions and 4 deletions
official/resnet/keras/keras_cifar_main.py
View file @ 53ff5d90
......@@ -28,7 +28,7 @@ import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import cifar10_main as cifar_main
from official.resnet import resnet_run_loop
from official.resnet.keras import keras_common
from official.resnet.keras import keras_resnet_model
from official.resnet.keras import resnet56
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
......@@ -146,13 +146,12 @@ def run(flags_obj):
optimizer = keras_common.get_optimizer()
strategy = keras_common.get_dist_strategy()
model = keras_resnet_model.ResNet56(input_shape=(32, 32, 3),
classes=cifar_main._NUM_CLASSES)
model = resnet56.ResNet56(input_shape=(32, 32, 3),
classes=cifar_main._NUM_CLASSES)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['categorical_accuracy'],
distribute=strategy)
time_callback, tensorboard_callback, lr_callback = keras_common.get_fit_callbacks(
learning_rate_schedule)
......
official/resnet/keras/keras_resnet_model.py → official/resnet/keras/resnet56.py
View file @ 53ff5d90
......@@ -28,12 +28,6 @@ import warnings
import tensorflow as tf
WEIGHTS_PATH = ('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.2/'
'resnet50_weights_tf_dim_ordering_tf_kernels.h5')
WEIGHTS_PATH_NO_TOP = ('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.2/'
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
BATCH_NORM_DECAY = 0.9
BATCH_NORM_EPSILON = 1e-5
......@@ -42,23 +36,14 @@ L2_WEIGHT_DECAY = 1e-4
def _obtain_input_shape(input_shape,
default_size,
min_size,
data_format,
require_flatten,
weights=None):
data_format):
"""Internal utility to compute/validate a model's input shape.
Arguments:
input_shape: Either None (will return the default network input shape),
or a user-provided shape to be validated.
default_size: Default input width/height for the model.
min_size: Minimum input width/height accepted by the model.
data_format: Image data format to use.
require_flatten: Whether the model is expected to
be linked to a classifier via a Flatten layer.
weights: One of `None` (random initialization)
or 'imagenet' (pre-training on ImageNet).
If weights='imagenet' input channels must be equal to 3.
Returns:
An integer shape tuple (may include None entries).
......@@ -66,7 +51,7 @@ def _obtain_input_shape(input_shape,
Raises:
ValueError: In case of invalid argument values.
"""
if weights != 'imagenet' and input_shape and len(input_shape) == 3:
if input_shape and len(input_shape) == 3:
if data_format == 'channels_first':
if input_shape[0] not in {1, 3}:
warnings.warn(
......@@ -81,391 +66,8 @@ def _obtain_input_shape(input_shape,
'However, it was passed an input_shape with ' +
str(input_shape[-1]) + ' input channels.')
default_shape = (default_size, default_size, input_shape[-1])
else:
if data_format == 'channels_first':
default_shape = (3, default_size, default_size)
else:
default_shape = (default_size, default_size, 3)
if weights == 'imagenet' and require_flatten:
if input_shape is not None:
if input_shape != default_shape:
raise ValueError('When setting`include_top=True` '
'and loading `imagenet` weights, '
'`input_shape` should be ' +
str(default_shape) + '.')
return default_shape
if input_shape:
if data_format == 'channels_first':
if input_shape is not None:
if len(input_shape) != 3:
raise ValueError(
'`input_shape` must be a tuple of three integers.')
if input_shape[0] != 3 and weights == 'imagenet':
raise ValueError('The input must have 3 channels; got '
'`input_shape=' + str(input_shape) + '`')
if ((input_shape[1] is not None and input_shape[1] < min_size) or
(input_shape[2] is not None and input_shape[2] < min_size)):
raise ValueError('Input size must be at least ' +
str(min_size) + 'x' + str(min_size) +
'; got `input_shape=' +
str(input_shape) + '`')
else:
if input_shape is not None:
if len(input_shape) != 3:
raise ValueError(
'`input_shape` must be a tuple of three integers.')
if input_shape[-1] != 3 and weights == 'imagenet':
raise ValueError('The input must have 3 channels; got '
'`input_shape=' + str(input_shape) + '`')
if ((input_shape[0] is not None and input_shape[0] < min_size) or
(input_shape[1] is not None and input_shape[1] < min_size)):
raise ValueError('Input size must be at least ' +
str(min_size) + 'x' + str(min_size) +
'; got `input_shape=' +
str(input_shape) + '`')
else:
if require_flatten:
input_shape = default_shape
else:
if data_format == 'channels_first':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
if require_flatten:
if None in input_shape:
raise ValueError('If `include_top` is True, '
'you should specify a static `input_shape`. '
'Got `input_shape=' + str(input_shape) + '`')
return input_shape
def identity_block(input_tensor, kernel_size, filters, stage, block, training):
"""The identity block is the block that has no conv layer at shortcut.
Arguments:
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
Returns:
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if tf.keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = tf.keras.layers.Conv2D(filters1, (1, 1),
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2a')(input_tensor)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size,
padding='same',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2b')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters3, (1, 1),
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2c')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2c',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.add([x, input_tensor])
x = tf.keras.layers.Activation('relu')(x)
return x
def conv_block(input_tensor,
kernel_size,
filters,
stage,
block,
strides=(2, 2),
training=True):
"""A block that has a conv layer at shortcut.
Arguments:
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
strides: Strides for the first conv layer in the block.
training: Boolean to indicate if we are in the training loop.
Returns:
Output tensor for the block.
Note that from stage 3,
the first conv layer at main path is with strides=(2, 2)
And the shortcut should have strides=(2, 2) as well
"""
filters1, filters2, filters3 = filters
if tf.keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = tf.keras.layers.Conv2D(filters1, (1, 1),
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2a')(input_tensor)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2b', strides=strides)(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters3, (1, 1),
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2c')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2c',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
shortcut = tf.keras.layers.Conv2D(filters3, (1, 1), strides=strides,
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '1')(input_tensor)
shortcut = tf.keras.layers.BatchNormalization(
axis=bn_axis, name=bn_name_base + '1',
momentum=BATCH_NORM_DECAY, epsilon=BATCH_NORM_EPSILON)(
shortcut, training=training)
x = tf.keras.layers.add([x, shortcut])
x = tf.keras.layers.Activation('relu')(x)
return x
def ResNet50(include_top=True,
weights=None,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
training=True):
"""Instantiates the ResNet50 architecture.
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 197.
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 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.
training: optional boolean indicating if this model will be
used for training or evaluation. This boolean is then
passed to the BatchNorm layer.
Returns:
A Keras model instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in {'imagenet', None} or os.path.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 = _obtain_input_shape(
input_shape,
default_size=224,
min_size=197,
data_format=tf.keras.backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = tf.keras.layers.Input(shape=input_shape)
else:
if not tf.keras.backend.is_keras_tensor(input_tensor):
img_input = tf.keras.layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if tf.keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = tf.keras.layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
x = tf.keras.layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
name='conv1')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis, name='bn_conv1',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1),
training=training)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b',
training=training)
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c',
training=training)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a',
training=training)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b',
training=training)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c',
training=training)
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d',
training=training)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a',
training=training)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b',
training=training)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c',
training=training)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d',
training=training)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e',
training=training)
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f',
training=training)
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a',
training=training)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b',
training=training)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c',
training=training)
if include_top:
x = tf.keras.layers.AveragePooling2D((7, 7), name='avg_pool')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(classes, activation='softmax', name='fc1000')(x)
else:
if pooling == 'avg':
x = tf.keras.layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = tf.keras.layers.GlobalMaxPooling2D()(x)
else:
warnings.warn('The output shape of `ResNet50(include_top=False)` '
'has been changed since Keras 2.2.0.')
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = tf.keras.engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = tf.keras.models.Model(inputs, x, name='resnet50')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = tf.keras.utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = tf.keras.utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
return input_shape
def identity_building_block(input_tensor, kernel_size, filters, stage, block, training):
......@@ -501,7 +103,7 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x, training=True)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size,
......@@ -515,7 +117,7 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x, training=True)
x = tf.keras.layers.add([x, input_tensor])
x = tf.keras.layers.Activation('relu')(x)
......@@ -567,7 +169,7 @@ def conv_building_block(input_tensor,
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x, training=True)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same',
......@@ -580,7 +182,7 @@ def conv_building_block(input_tensor,
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x, training=True)
shortcut = tf.keras.layers.Conv2D(filters2, (1, 1), strides=strides,
kernel_regularizer=
......@@ -591,92 +193,30 @@ def conv_building_block(input_tensor,
shortcut = tf.keras.layers.BatchNormalization(
axis=bn_axis, name=bn_name_base + '1',
momentum=BATCH_NORM_DECAY, epsilon=BATCH_NORM_EPSILON)(
shortcut, training=training)
shortcut, training=True)
x = tf.keras.layers.add([x, shortcut])
x = tf.keras.layers.Activation('relu')(x)
return x
def ResNet56(include_top=True,
weights=None,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
training=True):
"""Instantiates the ResNet50 architecture.
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`.
def ResNet56(input_shape=None, classes=1000):
"""Instantiates the ResNet56 architecture.
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 197.
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 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.
training: optional boolean indicating if this model will be
used for training or evaluation. This boolean is then
passed to the BatchNorm layer.
input_shape: optional shape tuple
classes: optional number of classes to classify images into
Returns:
A Keras model instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in {'imagenet', None} or os.path.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 = _obtain_input_shape(
input_shape,
default_size=32,
min_size=32,
data_format=tf.keras.backend.image_data_format(),
require_flatten=include_top,
weights=weights)
data_format=tf.keras.backend.image_data_format())
if input_tensor is None:
img_input = tf.keras.layers.Input(shape=input_shape)
else:
if not tf.keras.backend.is_keras_tensor(input_tensor):
img_input = tf.keras.layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
img_input = tf.keras.layers.Input(shape=input_shape)
if tf.keras.backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
......@@ -690,105 +230,73 @@ def ResNet56(include_top=True,
x = tf.keras.layers.BatchNormalization(axis=bn_axis, name='bn_conv1',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=training)
x, training=True)
x = tf.keras.layers.Activation('relu')(x)
# x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_building_block(x, 3, [16, 16], stage=2, block='a', strides=(1, 1),
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='b',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='c',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='d',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='e',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='f',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='g',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='h',
training=training)
training=True)
x = identity_building_block(x, 3, [16, 16], stage=2, block='i',
training=training)
training=True)
x = conv_building_block(x, 3, [32, 32], stage=3, block='a',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='b',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='c',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='d',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='e',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='f',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='g',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='h',
training=training)
training=True)
x = identity_building_block(x, 3, [32, 32], stage=3, block='i',
training=training)
training=True)
x = conv_building_block(x, 3, [64, 64], stage=4, block='a',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='b',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='c',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='d',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='e',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='f',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='g',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='h',
training=training)
training=True)
x = identity_building_block(x, 3, [64, 64], stage=4, block='i',
training=training)
training=True)
if include_top:
x = tf.keras.layers.AveragePooling2D((8, 8), name='avg_pool')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(classes, activation='softmax', name='fc10')(x)
else:
if pooling == 'avg':
x = tf.keras.layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = tf.keras.layers.GlobalMaxPooling2D()(x)
else:
warnings.warn('The output shape of `ResNet50(include_top=False)` '
'has been changed since Keras 2.2.0.')
x = tf.keras.layers.AveragePooling2D((8, 8), name='avg_pool')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(classes, activation='softmax', name='fc10')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = tf.keras.engine.get_source_inputs(input_tensor)
else:
inputs = img_input
inputs = img_input
# Create model.
model = tf.keras.models.Model(inputs, x, name='resnet56')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = tf.keras.utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = tf.keras.utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
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