def densenet_base(img_input, blocks=[6, 12, 24, 16], **kwargs):
bn_axis = 3
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64,
7,
strides=2,
use_bias=False,
kernel_regularizer=regularizers.l2(l2_reg),
name='conv1/conv')(x)
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)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = dense_block(x, blocks[0], name='conv2')
_, x = transition_block(x, 0.5, name='pool2')
x = dense_block(x, blocks[1], name='conv3')
_, x = transition_block(x, 0.5, name='pool3')
x = dense_block(x, blocks[2], name='conv4')
f1, x = transition_block(x, 0.5, name='pool4')
x = dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
f2 = x
return f1, f2
def small_densenet(self,
img_input_shape=(64, 64, 3),
blocks=[6, 12, 24, 16],
weight_decay=1e-4,
kernel_initializer='he_normal',
init_filters=None,
reduction=None,
growth_rate=None,
init_stride=None):
img_input = layers.Input(shape=(img_input_shape))
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(init_filters,
3,
strides=init_stride,
use_bias=False,
kernel_initializer=kernel_initializer,
kernel_regularizer=l2(weight_decay),
name='conv1/conv')(x)
x = layers.BatchNormalization(axis=3,
epsilon=1.001e-5,
name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.AveragePooling2D(3, strides=2, name='pool1')(x)
for i, block in enumerate(blocks):
scope_num_str = str(i + 2)
x = self.dense_block(x,
block,
name='conv' + scope_num_str,
growth_rate=growth_rate,
weight_decay=weight_decay,
kernel_initializer=kernel_initializer)
if i != len(blocks) - 1:
x = self.transition_block(
x,
reduction,
name='pool' + scope_num_str,
weight_decay=weight_decay,
kernel_initializer=kernel_initializer)
x = layers.BatchNormalization(axis=3, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(self.cat_max,
activation='softmax',
kernel_initializer=kernel_initializer,
name='fc')(x)
model = Model(img_input, x)
model.compile(optimizer=Adam(lr=self.lr),
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
return model
def layer(input_tensor):
x = input_tensor
residual = input_tensor
# bottleneck
x = layers.Conv2D(filters // 2, (1, 1),
kernel_initializer='he_uniform',
use_bias=False)(x)
x = layers.BatchNormalization(**bn_params)(x)
x = layers.Activation('relu')(x)
x = layers.ZeroPadding2D(1)(x)
x = GroupConv2D(filters, (3, 3),
strides=strides,
groups=groups,
kernel_initializer='he_uniform',
use_bias=False,
**kwargs)(x)
x = layers.BatchNormalization(**bn_params)(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters, (1, 1),
kernel_initializer='he_uniform',
use_bias=False)(x)
x = layers.BatchNormalization(**bn_params)(x)
# if number of filters or spatial dimensions changed
# make same manipulations with residual connection
x_channels = get_num_channels(x)
r_channels = get_num_channels(residual)
if strides != 1 or x_channels != r_channels:
if padding:
residual = layers.ZeroPadding2D(1)(residual)
residual = layers.Conv2D(x_channels,
downsample_kernel_size,
strides=strides,
kernel_initializer='he_uniform',
use_bias=False)(residual)
residual = layers.BatchNormalization(**bn_params)(residual)
# apply attention module
x = ChannelSE(reduction=reduction, **kwargs)(x)
# add residual connection
x = layers.Add()([x, residual])
x = layers.Activation('relu')(x)
return x
def resnet50v2(num_classes, batch_size=None):
"""Instantiates the ResNet50V2 architecture.
Args:
num_classes: `int` number of classes for image classification.
batch_size: Size of the batches for each step.
Returns:
A Keras model instance.
"""
input_shape = (224, 224, 3)
img_input = layers.Input(shape=input_shape, batch_size=batch_size)
x = img_input
if backend.image_data_format() == 'channels_first':
x = layers.Permute((3, 1, 2))(x)
bn_axis = 1
else: # channels_last
bn_axis = -1
x = layers.ZeroPadding2D(padding=3)(x)
x = layers.Conv2D(filters=64,
kernel_size=(7, 7),
strides=(2, 2),
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
name='conv1_conv')(x)
x = layers.ZeroPadding2D(padding=(1, 1))(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = resnet_block(x, size=3, filters=256, stage=2, stride=1)
x = resnet_block(x, size=4, filters=512, stage=3)
x = resnet_block(x, size=6, filters=1024, stage=4)
x = resnet_block(x, size=3, filters=2048, stage=5)
x = layers.BatchNormalization(axis=bn_axis, name='post_bn')(x)
x = layers.Activation('relu', name='post_relu')(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(num_classes,
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
name='fc1000')(x)
# A softmax that is followed by the model loss must be done cannot be done
# in float16 due to numeric issues. So we pass dtype=float32.
x = layers.Activation('softmax', dtype='float32')(x)
# Create model.
return models.Model(img_input, x, name='resnet50v2')
def block3(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(
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.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_1_pad')(x)
x = layers.Conv2D(
filters, kernel_size=kernel_size, strides=stride, 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)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
x = layers.Conv2D(
filters, kernel_size=kernel_size, 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.Add(name=name + '_add')([shortcut, x])
x = layers.Activation('relu', name=name + '_out')(x)
return x
def ResNet50(input_tensor=None,
pooling=None,
**kwargs):
"""Instantiates the ResNet50 architecture.
# Arguments
# Returns
A Keras model instance.
"""
# Input arguments
include_top = get_varargin(kwargs, 'include_top', True)
nb_classes = get_varargin(kwargs, 'nb_classes', 1000)
default_input_shape = _obtain_input_shape(None,
default_size=224,
min_size=197,
data_format=K.image_data_format(),
require_flatten=include_top)
input_shape = get_varargin(kwargs, 'input_shape', default_input_shape)
if input_tensor is None:
img_input = KL.Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = KL.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if K.image_data_format() == 'channels_last' else 1
x = KL.ZeroPadding2D((3, 3))(img_input)
x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = KL.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = KL.Activation('relu')(x)
x = KL.MaxPooling2D((3, 3), strides=(2, 2))(x)
for stage, nb_block in zip([2,3,4,5], [3,4,6,3]):
for blk in range(nb_block):
conv_block = True if blk == 0 else False
strides = (2,2) if stage>2 and blk==0 else (1,1)
x = identity_block(x, stage = stage, block_id = blk + 1,
conv_block = conv_block, strides = strides)
x = KL.AveragePooling2D((7, 7), name='avg_pool')(x)
if include_top:
x = KL.Flatten()(x)
x = KL.Dense(nb_classes, activation='softmax', name='fc1000')(x)
else:
if pooling == 'avg':
x = KL.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = KL.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='resnet50')
return model
def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
in_channels = backend.int_shape(inputs)[channel_axis]
pointwise_conv_filters = int(filters * alpha)
pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
x = inputs
prefix = 'block_{}_'.format(block_id)
if block_id:
# Expand
x = layers.Conv2D(round(expansion * in_channels),
kernel_size=1,
padding='same',
use_bias=False,
activation=None,
kernel_regularizer=regularizers.l2(l2_reg),
name=prefix + 'expand')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'expand_BN')(x)
x = layers.ReLU(6., name=prefix + 'expand_relu')(x)
else:
prefix = 'expanded_conv_'
# Depthwise
if stride == 2:
x = layers.ZeroPadding2D(padding=correct_pad(backend, x, 3),
name=prefix + 'pad')(x)
x = layers.DepthwiseConv2D(kernel_size=3,
strides=stride,
activation=None,
use_bias=False,
depthwise_regularizer=regularizers.l2(l2_reg),
padding='same' if stride == 1 else 'valid',
name=prefix + 'depthwise')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'depthwise_BN')(x)
x = layers.ReLU(6., name=prefix + 'depthwise_relu')(x)
# Project
x = layers.Conv2D(pointwise_filters,
kernel_size=1,
padding='same',
use_bias=False,
activation=None,
kernel_regularizer=regularizers.l2(l2_reg),
name=prefix + 'project')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'project_BN')(x)
if in_channels == pointwise_filters and stride == 1:
return layers.Add(name=prefix + 'add')([inputs, x])
return x
def _depthwise_conv_block(inputs,
pointwise_conv_filters,
alpha,
depth_multiplier=1,
strides=(1, 1),
block_id=1):
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
if strides == (1, 1):
x = inputs
else:
x = layers.ZeroPadding2D(((0, 1), (0, 1)),
name='conv_pad_%d' % block_id)(inputs)
x = layers.DepthwiseConv2D(
(3, 3),
padding='same' if strides == (1, 1) else 'valid',
depth_multiplier=depth_multiplier,
strides=strides,
use_bias=False,
name='conv_dw_%d' % block_id)(x)
x = layers.BatchNormalization(axis=channel_axis,
name='conv_dw_%d_bn' % block_id)(x)
x = layers.ReLU(6., name='conv_dw_%d_relu' % block_id)(x)
x = layers.Conv2D(pointwise_conv_filters, (1, 1),
padding='same',
use_bias=False,
strides=(1, 1),
name='conv_pw_%d' % block_id)(x)
x = layers.BatchNormalization(axis=channel_axis,
name='conv_pw_%d_bn' % block_id)(x)
return layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
def define_discriminator(in_shape=(28, 28, 1), nclasses=10):
img = layers.Input(shape=in_shape)
x = layers.Conv2D(filters=16, kernel_size=3, strides=2, padding="same")(img)
x = layers.LeakyReLU(alpha=0.2)(x)
x = layers.Dropout(0.25)(x)
x = layers.Conv2D(filters=32, kernel_size=3, strides=2, padding="same")(x)
x = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(x)
x = layers.LeakyReLU(alpha=0.2)(x)
x = layers.Dropout(0.25)(x)
x = layers.BatchNormalization(momentum=0.8)(x)
x = layers.Conv2D(filters=64, kernel_size=3, strides=2, padding="same")(x)
x = layers.LeakyReLU(alpha=0.2)(x)
x = layers.Dropout(0.25)(x)
x = layers.BatchNormalization(momentum=0.8)(x)
x = layers.Conv2D(filters=128, kernel_size=3, strides=2, padding="same")(x)
x = layers.LeakyReLU(alpha=0.2)(x)
x = layers.Dropout(0.25)(x)
x = layers.Flatten()(x)
out = layers.Dense(1, activation="sigmoid")(x)
label = layers.Dense(nclasses+1, activation="softmax")(out)
model = tf.keras.Model(img, [out, label])
return model
def resnet(num_blocks, img_input=None, classes=10, training=None):
"""Instantiates the ResNet architecture.
Arguments:
num_blocks: integer, the number of conv/identity blocks in each block.
The ResNet contains 3 blocks with each block containing one conv block
followed by (layers_per_block - 1) number of idenity blocks. Each
conv/idenity block has 2 convolutional layers. With the input
convolutional layer and the pooling layer towards the end, this brings
the total size of the network to (6*num_blocks + 2)
classes: optional number of classes to classify images into
training: Only used if training keras model with Estimator. In other
scenarios it is handled automatically.
Returns:
A Keras model instance.
"""
if backend.image_data_format() == 'channels_first':
x = layers.Lambda(lambda x: backend.permute_dimensions(x, (0, 3, 1, 2)),
name='transpose')(img_input)
bn_axis = 1
else: # channel_last
x = img_input
bn_axis = 3
x = layers.ZeroPadding2D(padding=(1, 1), name='conv1_pad')(x)
x = layers.Conv2D(16, (3, 3),
strides=(1, 1),
padding='valid', use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
name='conv1')(x)
x = layers.BatchNormalization(axis=bn_axis,
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON,
name='bn_conv1',)(x, training=training)
x = layers.Activation('relu')(x)
x = resnet_block(x, size=num_blocks, kernel_size=3, filters=[16, 16],
stage=2, conv_strides=(1, 1), training=training)
x = resnet_block(x, size=num_blocks, kernel_size=3, filters=[32, 32],
stage=3, conv_strides=(2, 2), training=training)
x = resnet_block(x, size=num_blocks, kernel_size=3, filters=[64, 64],
stage=4, conv_strides=(2, 2), training=training)
rm_axes = [1, 2] if backend.image_data_format() == 'channels_last' else [2, 3]
x = layers.Lambda(lambda x: backend.mean(x, rm_axes), name='reduce_mean')(x)
x = layers.Dense(classes,
activation='softmax',
kernel_initializer=initializers.RandomNormal(stddev=0.01),
kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
name='fc10')(x)
inputs = img_input
# Create model.
model = tf.keras.models.Model(inputs, x, name='resnet56')
return model
def resnet50(inputs):
bn_axis = 3
#
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(inputs)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
name='conv1')(x)
x = BatchNorm(axis=bn_axis, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
# x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
# x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
# x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
# model = Model(input, x, name='resnet50')
return x
def _separable_conv_block(ip,
filters,
kernel_size=(3, 3),
strides=(1, 1),
block_id=None):
"""Adds 2 blocks of [relu-separable conv-batchnorm].
Arguments:
ip: Input tensor
filters: Number of output filters per layer
kernel_size: Kernel size of separable convolutions
strides: Strided convolution for downsampling
block_id: String block_id
Returns:
A Keras tensor
"""
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
with backend.name_scope('separable_conv_block_%s' % block_id):
x = layers.Activation('relu')(ip)
if strides == (2, 2):
x = layers.ZeroPadding2D(
padding=imagenet_utils.correct_pad(x, kernel_size),
name='separable_conv_1_pad_%s' % block_id)(x)
conv_pad = 'valid'
else:
conv_pad = 'same'
x = layers.SeparableConv2D(
filters,
kernel_size,
strides=strides,
name='separable_conv_1_%s' % block_id,
padding=conv_pad,
use_bias=False,
kernel_initializer='he_normal')(
x)
x = layers.BatchNormalization(
axis=channel_dim,
momentum=0.9997,
epsilon=1e-3,
name='separable_conv_1_bn_%s' % (block_id))(
x)
x = layers.Activation('relu')(x)
x = layers.SeparableConv2D(
filters,
kernel_size,
name='separable_conv_2_%s' % block_id,
padding='same',
use_bias=False,
kernel_initializer='he_normal')(
x)
x = layers.BatchNormalization(
axis=channel_dim,
momentum=0.9997,
epsilon=1e-3,
name='separable_conv_2_bn_%s' % (block_id))(
x)
return x
def define_model(self):
input_img = Input(shape=(self.model_parameters.img_height,
self.model_parameters.img_width,
self.model_parameters.num_channels))
x = layers.Conv2D(filters=64,
kernel_size=(4, 4),
strides=(2, 2),
padding='same')(input_img)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(filters=128,
kernel_size=(4, 4),
strides=(2, 2),
padding='same')(x)
x = tfa.layers.InstanceNormalization(axis=-1)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(
filters=256,
kernel_size=(4, 4),
strides=(2, 2),
padding='same',
)(x)
x = tfa.layers.InstanceNormalization(axis=-1)(x)
x = layers.LeakyReLU()(x)
x = layers.ZeroPadding2D()(x)
x = layers.Conv2D(filters=512,
kernel_size=(4, 4),
strides=(1, 1),
padding='valid')(x)
x = tfa.layers.InstanceNormalization(axis=-1)(x)
x = layers.LeakyReLU()(x)
x = layers.ZeroPadding2D()(x)
x = layers.Conv2D(filters=1,
kernel_size=(4, 4),
strides=(1, 1),
padding='valid')(x)
model = Model(name=self.model_name, inputs=input_img, outputs=x)
return model
def SplitAttentionConv2D(x,
filters,
kernel_size,
stride=1,
padding=(0, 0),
groups=1,
use_bias=True,
radix=2,
name=None):
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
reduction_factor = 4
inter_filters = max(filters * radix // reduction_factor, 32)
x = layers.ZeroPadding2D((padding, padding), name=name + '_splat_pad')(x)
x = layers.Conv2D(filters * radix,
kernel_size=kernel_size,
strides=stride,
groups=groups * radix,
use_bias=use_bias,
name=name + '_0_splat_conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_0_splat_bn')(x)
x = layers.Activation('relu', name=name + '_0_splat_relu')(x)
splits = layers.Lambda(lambda x: tf.split(x, radix, bn_axis),
name=name + '_0_splat_split')(x)
x = layers.Add(name=name + '_0_splat_add')(splits)
x = layers.GlobalAveragePooling2D(name=name + '_0_splat_pool')(x)
shape = (1, 1, filters) if bn_axis == 3 else (filters, 1, 1)
x = layers.Reshape(shape, name=name + '_0_splat_reshape')(x)
x = layers.Conv2D(inter_filters,
kernel_size=1,
groups=groups,
name=name + '_1_splat_conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_1_splat_bn')(x)
x = layers.Activation('relu', name=name + '_1_splat_relu')(x)
# Attention
x = layers.Conv2D(filters * radix,
kernel_size=1,
groups=groups,
name=name + '_2_splat_conv')(x)
x = RSoftmax(x, filters * radix, radix, groups, name=name)
x = layers.Lambda(lambda x: tf.split(x, radix, bn_axis),
name=name + '_1_splat_split')(x)
x = layers.Lambda(
lambda x: [tf.stack(x[0], axis=bn_axis),
tf.stack(x[1], axis=bn_axis)],
name=name + '_splat_stack')([splits, x])
x = layers.Multiply(name=name + '_splat_mult')(x)
x = layers.Lambda(lambda x: tf.unstack(x, axis=bn_axis),
name=name + '_splat_unstack')(x)
x = layers.Add(name=name + '_splat_add')(x)
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 = tf.keras.layers.experimental.SyncBatchNormalization(
axis=bn_axis, 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 = tf.keras.layers.experimental.SyncBatchNormalization(axis=bn_axis,
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 = tf.keras.layers.experimental.SyncBatchNormalization(axis=bn_axis,
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 _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
"""Adds an initial convolution layer (with batch normalization and relu6).
# Arguments
inputs: Input tensor of shape `(rows, cols, 3)`
(with `channels_last` data format) or
(3, rows, cols) (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(224, 224, 3)` would be one valid value.
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
alpha: controls the width of the network.
- If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
- If `alpha` = 1, default number of filters from the paper
are used at each layer.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution
along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)`
if data_format='channels_last'.
`rows` and `cols` values might have changed due to stride.
# Returns
Output tensor of block.
"""
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
filters = int(filters * alpha)
x = layers.ZeroPadding2D(padding=((0, 1), (0, 1)),
name='conv1_pad')(inputs)
x = layers.Conv2D(filters,
kernel,
padding='valid',
use_bias=False,
strides=strides,
name='conv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
return layers.ReLU(6., name='conv1_relu')(x)
def make_discriminator_model(input_tensor=None,
input_shape=(28, 28, 1)):
"""
Returns:
tf.keras.Model
"""
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
x = layers.Conv2D(16, (3, 3),
strides=(2, 2),
padding="same",
use_bias=False,
name='conv1')(img_input)
x = layers.LeakyReLU(alpha=0.2, name='act1')(x)
x = layers.Dropout(rate=0.25, name='drop1')(x)
x = layers.Conv2D(32, (3, 3),
strides=(2, 2),
padding="same",
use_bias=False,
name='conv2')(x)
x = layers.ZeroPadding2D(padding=((0, 1), (0, 1)), name='zeropadding1')(x)
x = layers.LeakyReLU(alpha=0.2, name='act2')(x)
x = layers.Dropout(rate=0.25, name='drop2')(x)
x = layers.BatchNormalization(momentum=0.8, name='bn1')(x)
x = layers.Conv2D(64, (3, 3),
strides=(2, 2),
padding="same",
use_bias=False,
name='conv3')(x)
x = layers.LeakyReLU(alpha=0.2, name='act3')(x)
x = layers.Dropout(rate=0.25, name='drop3')(x)
x = layers.BatchNormalization(momentum=0.8, name='bn3')(x)
x = layers.Conv2D(128, (3, 3),
strides=(1, 1),
padding="same",
use_bias=False,
name='conv4')(x)
x = layers.LeakyReLU(alpha=0.2, name='act4')(x)
x = layers.Dropout(rate=0.25, name='drop4')(x)
x = layers.Flatten(name='flatten')(x)
# Determine validity and label of the image
validity = layers.Dense(1, activation=tf.nn.sigmoid)(x)
label = layers.Dense(num_classes, activation=tf.nn.softmax)(validity)
return models.Model(x, [validity, label])
def resblock_body(x, num_filters, num_blocks):
'''A series of resblocks starting with a downsampling Convolution2D'''
# Darknet uses left and top padding instead of 'same' mode
x = kl.ZeroPadding2D(((1, 0), (1, 0)))(x)
x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x)
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x)
x = kl.Add()([x, y])
return x
def ResNet50(input_tensor):
"""
ResNet50
:param input_tensor:
:return:
"""
img_input = input_tensor
if backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
kernel_initializer='he_normal',
name='conv1')(x)
x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = _conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = _identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = _identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = _conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = _identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = _identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = _identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = _conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = _conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = _identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = _identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
return x
def DenseNet121(input_shape=None):
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=224,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=True)
img_input = layers.Input(shape=input_shape)
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
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)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = dense_block(x, 6, name='conv2')
x = transition_block(x, 0.5, name='pool2')
x = dense_block(x, 12, name='conv3')
x = transition_block(x, 0.5, name='pool3')
x = dense_block(x, 24, name='conv4')
x = transition_block(x, 0.5, name='pool4')
x = dense_block(x, 16, name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
imagenet_utils.validate_activation('softmax', None)
x = layers.Dense(NUM_CLASSES, activation='softmax', name='predictions')(x)
# Create model.
model = training.Model(img_input, x, name='densenet121')
return model
def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
filters = int(filters * alpha)
x = layers.ZeroPadding2D(padding=((0, 1), (0, 1)),
name='conv1_pad')(inputs)
x = layers.Conv2D(filters,
kernel,
padding='valid',
use_bias=False,
strides=strides,
name='conv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
return layers.ReLU(6., name='conv1_relu')(x)
def _inverted_res_block(x, expansion, filters, kernel_size, stride, se_ratio,
activation, block_id):
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
shortcut = x
prefix = 'expanded_conv/'
infilters = backend.int_shape(x)[channel_axis]
if block_id:
# Expand
prefix = 'expanded_conv_{}/'.format(block_id)
x = layers.Conv2D(_depth(infilters * expansion),
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'expand')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'expand/BatchNorm')(x)
x = activation(x)
if stride == 2:
x = layers.ZeroPadding2D(padding=imagenet_utils.correct_pad(
x, kernel_size),
name=prefix + 'depthwise/pad')(x)
x = layers.DepthwiseConv2D(kernel_size,
strides=stride,
padding='same' if stride == 1 else 'valid',
use_bias=False,
name=prefix + 'depthwise')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'depthwise/BatchNorm')(x)
x = activation(x)
if se_ratio:
x = _se_block(x, _depth(infilters * expansion), se_ratio, prefix)
x = layers.Conv2D(filters,
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'project')(x)
x = layers.BatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'project/BatchNorm')(x)
if stride == 1 and infilters == filters:
x = layers.Add(name=prefix + 'Add')([shortcut, x])
return x
def DenseNet(blocks, input_tensor=None):
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
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)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = _dense_block(x, blocks[0], name='conv2')
x = _transition_block(x, 0.5, name='pool2')
x = _dense_block(x, blocks[1], name='conv3')
x = _transition_block(x, 0.5, name='pool3')
x = _dense_block(x, blocks[2], name='conv4')
x = _transition_block(x, 0.5, name='pool4')
x = _dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
return x
def ResNet50V2(input_shape=None):
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=224,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=True)
img_input = layers.Input(shape=input_shape)
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=True, name='conv1_conv')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)
x = stack2(x, 64, 3, name='conv2')
x = stack2(x, 128, 4, name='conv3')
x = stack2(x, 256, 6, name='conv4')
x = stack2(x, 512, 3, stride1=1, name='conv5')
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name='post_bn')(x)
x = layers.Activation('relu', name='post_relu')(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
imagenet_utils.validate_activation('softmax', None)
x = layers.Dense(NUM_CLASSES, activation='softmax', name='predictions')(x)
# Create model.
model = training.Model(img_input, x, name="resnet50v2")
return model
def small_densenet(self,
img_input_shape=(64, 64, 3),
blocks=[6, 12, 24, 16]):
img_input = Input(shape=(img_input_shape))
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False,
name='conv1/conv')(x)
x = layers.BatchNormalization(axis=3,
epsilon=1.001e-5,
name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = self.dense_block(x, blocks[0], name='conv2')
x = self.transition_block(x, 0.5, name='pool2')
x = self.dense_block(x, blocks[1], name='conv3')
x = self.transition_block(x, 0.5, name='pool3')
x = self.dense_block(x, blocks[2], name='conv4')
x = self.transition_block(x, 0.5, name='pool4')
x = self.dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=3, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
# x = Lambda(lambda x: x, name = 'densenet_features')(x)
x = layers.Dense(self.cat_max, activation='softmax', name='fc')(x)
model = Model(img_input, x)
# print (model.summary())
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
return model
def block2(x,
filters,
kernel_size=3,
stride=1,
conv_shortcut=False,
name=None):
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 effi_init_block(x,
in_channels,
out_channels,
bn_epsilon,
activation,
tf_mode,
name="effi_init_block"):
"""
EfficientNet specific initial block.
Parameters:
----------
x : keras.backend tensor/variable/symbol
Input tensor/variable/symbol.
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
bn_epsilon : float
Small float added to variance in Batch norm.
activation : str
Name of activation function.
tf_mode : bool
Whether to use TF-like mode.
name : str, default 'effi_init_block'
Block name.
Returns
-------
keras.backend tensor/variable/symbol
Resulted tensor/variable/symbol.
"""
if tf_mode:
x = nn.ZeroPadding2D(padding=calc_tf_padding(x,
kernel_size=3,
strides=2),
name=name + "/conv_pad")(x)
x = conv3x3_block(x=x,
in_channels=in_channels,
out_channels=out_channels,
strides=1,
padding=(0 if tf_mode else 1),
bn_epsilon=bn_epsilon,
activation=activation,
name=name + "/conv")
return x
def _separable_conv_block(ip,
filters,
kernel_size=(3, 3),
strides=(1, 1),
block_id=None):
channel_dim = -1
with backend.name_scope('separable_conv_block_%s' % block_id):
x = layers.Activation('relu')(ip)
if strides == (2, 2):
x = layers.ZeroPadding2D(
padding=imagenet_utils.correct_pad(x, kernel_size),
name='separable_conv_1_pad_%s' % block_id)(x)
conv_pad = 'valid'
else:
conv_pad = 'same'
x = layers.SeparableConv2D(filters,
kernel_size,
strides=strides,
name='separable_conv_1_%s' % block_id,
padding=conv_pad,
use_bias=False,
kernel_initializer='he_normal')(x)
x = layers.BatchNormalization(axis=channel_dim,
momentum=0.9997,
epsilon=1e-3,
name='separable_conv_1_bn_%s' %
(block_id))(x)
x = layers.Activation('relu')(x)
x = layers.SeparableConv2D(filters,
kernel_size,
name='separable_conv_2_%s' % block_id,
padding='same',
use_bias=False,
kernel_initializer='he_normal')(x)
x = layers.BatchNormalization(axis=channel_dim,
momentum=0.9997,
epsilon=1e-3,
name='separable_conv_2_bn_%s' %
(block_id))(x)
return x
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.
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.
"""
if 'layers' in kwargs:
global layers
layers = kwargs.pop('layers')
if kwargs:
raise ValueError('Unknown argument(s): %s' % (kwargs, ))
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 = 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 = tf.keras.layers.experimental.SyncBatchNormalization(
axis=bn_axis, 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 = tf.keras.layers.experimental.SyncBatchNormalization(
axis=bn_axis, 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 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 = tf.keras.layers.experimental.SyncBatchNormalization(
axis=bn_axis, name=name + '_0_bn')(shortcut)
else:
shortcut = x
x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
x = tf.keras.layers.experimental.SyncBatchNormalization(axis=bn_axis,
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 = tf.keras.layers.experimental.SyncBatchNormalization(axis=bn_axis,
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 = tf.keras.layers.experimental.SyncBatchNormalization(axis=bn_axis,
name=name +
'_3_bn')(x)
x = layers.Add(name=name + '_add')([shortcut, x])
x = layers.Activation('relu', name=name + '_out')(x)
return x