def _pep_block(inputs, proj_filters, filters, stride, expansion, block_id):
#in_channels = backend.int_shape(inputs)[-1]
in_channels = inputs.shape.as_list()[-1]
pointwise_conv_filters = int(filters)
x = inputs
prefix = 'pep_block_{}_'.format(block_id)
# Pre-project
x = Conv2D(proj_filters, kernel_size=1, padding='same', use_bias=False, activation=None, name=prefix + 'preproject')(x)
x = CustomBatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'preproject_BN')(x)
x = ReLU(6., name=prefix + 'preproject_relu')(x)
# Expand
#x = Conv2D(int(expansion * in_channels), kernel_size=1, padding='same', use_bias=False, activation=None, name=prefix + 'expand')(x)
x = Conv2D(int(expansion * proj_filters), kernel_size=1, padding='same', use_bias=False, activation=None, name=prefix + 'expand')(x)
x = CustomBatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'expand_BN')(x)
x = ReLU(6., name=prefix + 'expand_relu')(x)
# Depthwise
if stride == 2:
x = ZeroPadding2D(padding=correct_pad(K, x, 3), name=prefix + 'pad')(x)
x = DepthwiseConv2D(kernel_size=3, strides=stride, activation=None, use_bias=False, padding='same' if stride == 1 else 'valid', name=prefix + 'depthwise')(x)
x = CustomBatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'depthwise_BN')(x)
x = ReLU(6., name=prefix + 'depthwise_relu')(x)
# Project
x = Conv2D(pointwise_conv_filters, kernel_size=1, padding='same', use_bias=False, activation=None, name=prefix + 'project')(x)
x = CustomBatchNormalization( epsilon=1e-3, momentum=0.999, name=prefix + 'project_BN')(x)
if in_channels == pointwise_conv_filters and stride == 1:
return Add(name=prefix + 'add')([inputs, x])
return x
def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
in_channels = K.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 = YoloConv2D(expansion * in_channels,
kernel_size=1,
padding='same',
use_bias=False,
activation=None,
name=prefix + 'expand')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'expand_BN')(x)
x = ReLU(6., name=prefix + 'expand_relu')(x)
else:
prefix = 'expanded_conv_'
# Depthwise
if stride == 2:
x = ZeroPadding2D(padding=correct_pad(K, x, 3), name=prefix + 'pad')(x)
x = YoloDepthwiseConv2D(kernel_size=3,
strides=stride,
activation=None,
use_bias=False,
padding='same' if stride == 1 else 'valid',
name=prefix + 'depthwise')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'depthwise_BN')(x)
x = ReLU(6., name=prefix + 'depthwise_relu')(x)
# Project
x = YoloConv2D(pointwise_filters,
kernel_size=1,
padding='same',
use_bias=False,
activation=None,
name=prefix + 'project')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'project_BN')(x)
if in_channels == pointwise_filters and stride == 1:
return Add(name=prefix + 'add')([inputs, x])
return x
def Depthwise_Separable_Conv2D_BN_Leaky(filters, kernel_size=(3, 3), block_id_str=None):
"""Depthwise Separable Convolution2D."""
if not block_id_str:
block_id_str = str(K.get_uid())
return compose(
YoloDepthwiseConv2D(kernel_size, padding='same', name='conv_dw_' + block_id_str),
CustomBatchNormalization(name='conv_dw_%s_bn' % block_id_str),
LeakyReLU(alpha=0.1, name='conv_dw_%s_leaky_relu' % block_id_str),
YoloConv2D(filters, (1,1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%s' % block_id_str),
CustomBatchNormalization(name='conv_pw_%s_bn' % block_id_str),
LeakyReLU(alpha=0.1, name='conv_pw_%s_leaky_relu' % block_id_str))
def Darknet_Depthwise_Separable_Conv2D_BN_Swish(filters, kernel_size=(3, 3), block_id_str=None, **kwargs):
"""Depthwise Separable Convolution2D."""
if not block_id_str:
block_id_str = str(K.get_uid())
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(
DarknetDepthwiseConv2D(kernel_size, name='conv_dw_' + block_id_str, **no_bias_kwargs),
CustomBatchNormalization(name='conv_dw_%s_bn' % block_id_str),
Activation(swish, name='conv_dw_%s_swish' % block_id_str),
YoloConv2D(filters, (1,1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%s' % block_id_str),
CustomBatchNormalization(name='conv_pw_%s_bn' % block_id_str),
Activation(swish, name='conv_pw_%s_swish' % block_id_str))
def _inverted_res_block(x, expansion, filters, kernel_size, stride, se_ratio,
activation, block_id):
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
shortcut = x
prefix = 'expanded_conv/'
infilters = K.int_shape(x)[channel_axis]
if block_id:
# Expand
prefix = 'expanded_conv_{}/'.format(block_id)
x = Conv2D(_depth(infilters * expansion),
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'expand')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'expand/BatchNorm')(x)
x = Activation(activation)(x)
if stride == 2:
x = ZeroPadding2D(padding=correct_pad(K, x, kernel_size),
name=prefix + 'depthwise/pad')(x)
x = DepthwiseConv2D(kernel_size,
strides=stride,
padding='same' if stride == 1 else 'valid',
use_bias=False,
name=prefix + 'depthwise/Conv')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'depthwise/BatchNorm')(x)
x = Activation(activation)(x)
if se_ratio:
x = _se_block(x, _depth(infilters * expansion), se_ratio, prefix)
x = Conv2D(filters,
kernel_size=1,
padding='same',
use_bias=False,
name=prefix + 'project')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + 'project/BatchNorm')(x)
if stride == 1 and infilters == filters:
x = Add(name=prefix + 'Add')([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 K.image_data_format() == 'channels_first' else -1
filters = int(filters * alpha)
x = ZeroPadding2D(padding=((0, 1), (0, 1)), name='conv1_pad')(inputs)
x = YoloConv2D(filters,
kernel,
padding='valid',
use_bias=False,
strides=strides,
name='conv1')(x)
x = CustomBatchNormalization(axis=channel_axis, name='conv1_bn')(x)
return ReLU(6., name='conv1_relu')(x)
def bottleneck_csp_block(x,
num_filters,
num_blocks,
depth_multiple,
width_multiple,
shortcut=False):
'''CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks'''
num_filters = make_divisible(num_filters * width_multiple, 8)
num_blocks = max(round(num_blocks * depth_multiple),
1) if num_blocks > 1 else num_blocks # depth gain
res_connection = DarknetConv2D(num_filters // 2, (1, 1))(x)
x = DarknetConv2D_BN_Swish(num_filters // 2, (1, 1))(x)
# Bottleneck block stack
for i in range(num_blocks):
y = compose(DarknetConv2D_BN_Swish(num_filters // 2, (1, 1)),
DarknetConv2D_BN_Swish(num_filters // 2, (3, 3)))(x)
x = Add()([x, y]) if shortcut else y
x = DarknetConv2D(num_filters // 2, (1, 1))(x)
x = Concatenate()([x, res_connection])
x = CustomBatchNormalization()(x)
x = Activation(swish)(x)
return DarknetConv2D_BN_Swish(num_filters, (1, 1))(x)
def DarknetConv2D_BN_Swish(*args, **kwargs):
"""Darknet Convolution2D followed by CustomBatchNormalization and Swish."""
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(
DarknetConv2D(*args, **no_bias_kwargs),
CustomBatchNormalization(),
Activation(swish))
def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""Darknet Convolution2D followed by CustomBatchNormalization and LeakyReLU."""
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(
DarknetConv2D(*args, **no_bias_kwargs),
CustomBatchNormalization(),
LeakyReLU(alpha=0.1))
def cheap_operations(x, output_filters, kernel_size, strides=(1,1), padding='same', act=True, use_bias=False, name=None):
x = YoloDepthwiseConv2D(kernel_size=kernel_size,
strides=strides,
padding=padding,
use_bias=use_bias,
name=name+'_0')(x)
x = CustomBatchNormalization(name=name+'_1')(x)
x = ReLU(name=name+'_relu')(x) if act else x
return x
def basic_conv2d_graph(x, out_channels, kernel_size, strides, padding, activation=True, name=''):
x = YoloConv2D(
out_channels, kernel_size=kernel_size, strides=strides,
padding=padding, use_bias=False, name=name + '_conv')(x)
x = CustomBatchNormalization(name=name + '_norm')(x)
if activation:
x = ReLU()(x)
return x
def GhostBottleneck(input_x, mid_chs, out_chs, dw_kernel_size=3, stride=(1,1), se_ratio=0., name=None):
'''ghostnet bottleneck w/optional se'''
has_se = se_ratio is not None and se_ratio > 0.
#1st ghost bottleneck
x = GhostModule(input_x, mid_chs, act=True, name=name+'_ghost1')
#depth_with convolution
if stride[0] > 1:
x = YoloDepthwiseConv2D(kernel_size=dw_kernel_size,
strides=stride,
padding='same',
use_bias=False,
name=name+'_conv_dw')(x)
x = CustomBatchNormalization(name=name+'_bn_dw')(x)
#Squeeze_and_excitation
if has_se:
x = SqueezeExcite(x, se_ratio=se_ratio, name=name+'_se')
#2nd ghost bottleneck
x = GhostModule(x, out_chs, act=False, name=name+'_ghost2')
#short cut
if (input_x.shape[-1] == out_chs and stride[0] == 1):
sc = input_x
else:
name1 = name + '_shortcut'
sc = YoloDepthwiseConv2D(kernel_size=dw_kernel_size,
strides=stride,
padding='same',
use_bias=False,
name=name1+'_0')(input_x)
sc = CustomBatchNormalization(name=name1+'_1')(sc)
sc = YoloConv2D(filters=out_chs,
kernel_size=1,
strides=(1,1),
padding='valid',
use_bias=False,
name=name1+'_2')(sc)
sc = CustomBatchNormalization(name=name1+'_3')(sc)
x = Add(name=name+'_add')([x, sc])
return x
def primary_conv(x, output_filters, kernel_size, strides=(1,1), padding='same', act=True, use_bias=False, name=None):
x = YoloConv2D(filters=output_filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
use_bias=use_bias,
name=name + '_0')(x)
x = CustomBatchNormalization(name=name+'_1')(x)
x = ReLU(name=name+'_relu')(x) if act else x
return x
def ConvBnAct(input_x, out_chs, kernel_size, stride=(1,1), name=None):
x = YoloConv2D(filters=out_chs,
kernel_size=kernel_size,
strides=stride,
padding='valid',
use_bias=False,
name=name+'_conv')(input_x)
x = CustomBatchNormalization(name=name+'_bn1')(x)
x = ReLU(name=name+'_relu')(x)
return x
def Depthwise_Conv2D_BN_Leaky(kernel_size=(3, 3), block_id_str=None):
"""Depthwise Convolution2D."""
if not block_id_str:
block_id_str = str(K.get_uid())
return compose(
YoloDepthwiseConv2D(kernel_size,
padding='same',
name='conv_dw_' + block_id_str),
CustomBatchNormalization(name='conv_dw_%s_bn' % block_id_str),
LeakyReLU(alpha=0.1, name='conv_dw_%s_leaky_relu' % block_id_str))
def NanoConv2D_BN_Relu6(*args, **kwargs):
"""Darknet Convolution2D followed by CustomBatchNormalization and ReLU6."""
nano_name = kwargs.get('name')
if nano_name:
name_kwargs = {'name': nano_name + '_conv2d'}
name_kwargs.update(kwargs)
bn_name = nano_name + '_BN'
relu_name = nano_name + '_relu'
else:
name_kwargs = {}
name_kwargs.update(kwargs)
bn_name = None
relu_name = None
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(name_kwargs)
return compose(DarknetConv2D(*args, **no_bias_kwargs),
CustomBatchNormalization(name=bn_name),
ReLU(6., name=relu_name))
def shuffle_unit(inputs,
out_channels,
bottleneck_ratio,
strides=2,
stage=1,
block=1):
if K.image_data_format() == 'channels_last':
bn_axis = -1
else:
raise ValueError('Only channels last supported')
prefix = 'stage{}/block{}'.format(stage, block)
bottleneck_channels = int(out_channels * bottleneck_ratio)
if strides < 2:
c_hat, c = channel_split(inputs, '{}/spl'.format(prefix))
inputs = c
x = Conv2D(bottleneck_channels,
kernel_size=(1, 1),
strides=1,
padding='same',
name='{}/1x1conv_1'.format(prefix))(inputs)
x = CustomBatchNormalization(axis=bn_axis,
name='{}/bn_1x1conv_1'.format(prefix))(x)
x = Activation('relu', name='{}/relu_1x1conv_1'.format(prefix))(x)
x = DepthwiseConv2D(kernel_size=3,
strides=strides,
padding='same',
name='{}/3x3dwconv'.format(prefix))(x)
x = CustomBatchNormalization(axis=bn_axis,
name='{}/bn_3x3dwconv'.format(prefix))(x)
x = Conv2D(bottleneck_channels,
kernel_size=1,
strides=1,
padding='same',
name='{}/1x1conv_2'.format(prefix))(x)
x = CustomBatchNormalization(axis=bn_axis,
name='{}/bn_1x1conv_2'.format(prefix))(x)
x = Activation('relu', name='{}/relu_1x1conv_2'.format(prefix))(x)
if strides < 2:
ret = Concatenate(axis=bn_axis,
name='{}/concat_1'.format(prefix))([x, c_hat])
else:
s2 = DepthwiseConv2D(kernel_size=3,
strides=2,
padding='same',
name='{}/3x3dwconv_2'.format(prefix))(inputs)
s2 = CustomBatchNormalization(
axis=bn_axis, name='{}/bn_3x3dwconv_2'.format(prefix))(s2)
s2 = Conv2D(bottleneck_channels,
kernel_size=1,
strides=1,
padding='same',
name='{}/1x1_conv_3'.format(prefix))(s2)
s2 = CustomBatchNormalization(
axis=bn_axis, name='{}/bn_1x1conv_3'.format(prefix))(s2)
s2 = Activation('relu', name='{}/relu_1x1conv_3'.format(prefix))(s2)
ret = Concatenate(axis=bn_axis,
name='{}/concat_2'.format(prefix))([x, s2])
ret = Lambda(channel_shuffle,
name='{}/channel_shuffle'.format(prefix))(ret)
return ret
def MobileNetV2(input_shape=None,
alpha=1.0,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the MobileNetV2 architecture.
# Arguments
input_shape: optional shape tuple, to be specified if you would
like to use a model with an input img resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
alpha: controls the width of the network. This is known as the
width multiplier in the MobileNetV2 paper, but the name is kept for
consistency with MobileNetV1 in Keras.
- 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.
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.
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.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape or invalid alpha, rows when
weights='imagenet'
"""
#global backend, layers, models, keras_utils
#backend, layers, models, keras_utils = get_submodules_from_kwargs(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')
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
# Determine proper input shape and default size.
# If both input_shape and input_tensor are used, they should match
#if input_shape is not None and input_tensor is not None:
#try:
#is_input_t_tensor = K.is_keras_tensor(input_tensor)
#except ValueError:
#try:
#is_input_t_tensor = K.is_keras_tensor(
#get_source_inputs(input_tensor))
#except ValueError:
#raise ValueError('input_tensor: ', input_tensor,
#'is not type input_tensor')
#if is_input_t_tensor:
#if K.image_data_format == 'channels_first':
#if K.int_shape(input_tensor)[1] != input_shape[1]:
#raise ValueError('input_shape: ', input_shape,
#'and input_tensor: ', input_tensor,
#'do not meet the same shape requirements')
#else:
#if K.int_shape(input_tensor)[2] != input_shape[1]:
#raise ValueError('input_shape: ', input_shape,
#'and input_tensor: ', input_tensor,
#'do not meet the same shape requirements')
#else:
#raise ValueError('input_tensor specified: ', input_tensor,
#'is not a keras tensor')
# If input_shape is None, infer shape from input_tensor
#if input_shape is None and input_tensor is not None:
#try:
#K.is_keras_tensor(input_tensor)
#except ValueError:
#raise ValueError('input_tensor: ', input_tensor,
#'is type: ', type(input_tensor),
#'which is not a valid type')
#if input_shape is None and not K.is_keras_tensor(input_tensor):
#default_size = 224
#elif input_shape is None and K.is_keras_tensor(input_tensor):
#if K.image_data_format() == 'channels_first':
#rows = K.int_shape(input_tensor)[2]
#cols = K.int_shape(input_tensor)[3]
#else:
#rows = K.int_shape(input_tensor)[1]
#cols = K.int_shape(input_tensor)[2]
#if rows == cols and rows in [96, 128, 160, 192, 224]:
#default_size = rows
#else:
#default_size = 224
# If input_shape is None and no input_tensor
#elif input_shape is None:
#default_size = 224
# If input_shape is not None, assume default size
#else:
#if K.image_data_format() == 'channels_first':
#rows = input_shape[1]
#cols = input_shape[2]
#else:
#rows = input_shape[0]
#cols = input_shape[1]
#if rows == cols and rows in [96, 128, 160, 192, 224]:
#default_size = rows
#else:
#default_size = 224
# If input_shape is None and input_tensor is None using standard shape
if input_shape is None and input_tensor is None:
input_shape = (None, None, 3)
if K.image_data_format() == 'channels_last':
row_axis, col_axis = (0, 1)
else:
row_axis, col_axis = (1, 2)
rows = input_shape[row_axis]
cols = input_shape[col_axis]
if weights == 'imagenet':
if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
raise ValueError('If imagenet weights are being loaded, '
'alpha can be one of `0.35`, `0.50`, `0.75`, '
'`1.0`, `1.3` or `1.4` only.')
if rows != cols or rows not in [96, 128, 160, 192, 224]:
rows = 224
warnings.warn('`input_shape` is undefined or non-square, '
'or `rows` is not in [96, 128, 160, 192, 224].'
' Weights for input shape (224, 224) will be'
' loaded as the default.')
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
#if not K.is_keras_tensor(input_tensor):
#img_input = Input(tensor=input_tensor, shape=input_shape)
#else:
#img_input = input_tensor
img_input = input_tensor
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
first_block_filters = _make_divisible(32 * alpha, 8)
x = ZeroPadding2D(padding=correct_pad(K, img_input, 3),
name='Conv1_pad')(img_input)
x = YoloConv2D(first_block_filters,
kernel_size=3,
strides=(2, 2),
padding='valid',
use_bias=False,
name='Conv1')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name='bn_Conv1')(x)
x = ReLU(6., name='Conv1_relu')(x)
x = _inverted_res_block(x,
filters=16,
alpha=alpha,
stride=1,
expansion=1,
block_id=0)
x = _inverted_res_block(x,
filters=24,
alpha=alpha,
stride=2,
expansion=6,
block_id=1)
x = _inverted_res_block(x,
filters=24,
alpha=alpha,
stride=1,
expansion=6,
block_id=2)
x = _inverted_res_block(x,
filters=32,
alpha=alpha,
stride=2,
expansion=6,
block_id=3)
x = _inverted_res_block(x,
filters=32,
alpha=alpha,
stride=1,
expansion=6,
block_id=4)
x = _inverted_res_block(x,
filters=32,
alpha=alpha,
stride=1,
expansion=6,
block_id=5)
x = _inverted_res_block(x,
filters=64,
alpha=alpha,
stride=2,
expansion=6,
block_id=6)
x = _inverted_res_block(x,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=7)
x = _inverted_res_block(x,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=8)
x = _inverted_res_block(x,
filters=64,
alpha=alpha,
stride=1,
expansion=6,
block_id=9)
x = _inverted_res_block(x,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=10)
x = _inverted_res_block(x,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=11)
x = _inverted_res_block(x,
filters=96,
alpha=alpha,
stride=1,
expansion=6,
block_id=12)
x = _inverted_res_block(x,
filters=160,
alpha=alpha,
stride=2,
expansion=6,
block_id=13)
x = _inverted_res_block(x,
filters=160,
alpha=alpha,
stride=1,
expansion=6,
block_id=14)
x = _inverted_res_block(x,
filters=160,
alpha=alpha,
stride=1,
expansion=6,
block_id=15)
x = _inverted_res_block(x,
filters=320,
alpha=alpha,
stride=1,
expansion=6,
block_id=16)
# no alpha applied to last conv as stated in the paper:
# if the width multiplier is greater than 1 we
# increase the number of output channels
if alpha > 1.0:
last_block_filters = _make_divisible(1280 * alpha, 8)
else:
last_block_filters = 1280
x = YoloConv2D(last_block_filters,
kernel_size=1,
use_bias=False,
name='Conv_1')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name='Conv_1_bn')(x)
x = ReLU(6., name='out_relu')(x)
if include_top:
x = GlobalAveragePooling2D()(x)
x = Dense(classes, activation='softmax', use_bias=True,
name='Logits')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = 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='mobilenetv2_%0.2f_%s' % (alpha, rows))
# Load weights.
if weights == 'imagenet':
if include_top:
model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
str(alpha) + '_' + str(rows) + '.h5')
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name,
weight_path,
cache_subdir='models')
else:
model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
str(alpha) + '_' + str(rows) + '_no_top' + '.h5')
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name,
weight_path,
cache_subdir='models')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def GhostNet(input_shape=None,
include_top=True,
weights='imagenet',
input_tensor=None,
cfgs=DEFAULT_CFGS,
width=1.0,
dropout_rate=0.2,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the GhostNet architecture.
# Arguments
input_shape: optional shape tuple, to be specified if you would
like to use a model with an input img resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
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.
cfgs: model structure config list
width: controls the width of the network
dropout_rate: fraction of the input units to drop on the last layer
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.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape or invalid alpha, rows when
weights='imagenet'
"""
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=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
# If input_shape is None and input_tensor is None using standard shape
if input_shape is None and input_tensor is None:
input_shape = (None, None, 3)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
#if not K.is_keras_tensor(input_tensor):
#img_input = Input(tensor=input_tensor, shape=input_shape)
#else:
#img_input = input_tensor
img_input = input_tensor
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
# building first layer
output_channel = int(_make_divisible(16 * width, 4))
x = YoloConv2D(filters=output_channel,
kernel_size=3,
strides=(2, 2),
padding='same',
use_bias=False,
name='conv_stem')(img_input)
x = CustomBatchNormalization(name='bn1')(x)
x = ReLU(name='Conv2D_1_act')(x)
# building inverted residual blocks
for index, cfg in enumerate(cfgs):
sub_index = 0
for k,exp_size,c,se_ratio,s in cfg:
output_channel = int(_make_divisible(c * width, 4))
hidden_channel = int(_make_divisible(exp_size * width, 4))
x = GhostBottleneck(x, hidden_channel, output_channel, k, (s,s),
se_ratio=se_ratio,
name='blocks_'+str(index)+'_'+str(sub_index))
sub_index += 1
output_channel = _make_divisible(exp_size * width, 4)
x = ConvBnAct(x, output_channel, kernel_size=1, name='blocks_9_0')
if include_top:
x = GlobalAveragePooling2D(name='global_avg_pooling2D')(x)
if K.image_data_format() == 'channels_first':
x = Reshape((output_channel, 1, 1))(x)
else:
x = Reshape((1, 1, output_channel))(x)
# building last several layers
output_channel = 1280
x = YoloConv2D(filters=output_channel,
kernel_size=1,
strides=(1,1),
padding='valid',
use_bias=True,
name='conv_head')(x)
x = ReLU(name='relu_head')(x)
if dropout_rate > 0.:
x = Dropout(dropout_rate, name='dropout_1')(x)
x = Flatten()(x)
x = Dense(units=classes, activation='softmax',
use_bias=True, name='classifier')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = 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='ghostnet_%0.2f' % (width))
# Load weights.
if weights == 'imagenet':
if include_top:
file_name = 'ghostnet_weights_tf_dim_ordering_tf_kernels_224.h5'
weight_path = BASE_WEIGHT_PATH + file_name
else:
file_name = 'ghostnet_weights_tf_dim_ordering_tf_kernels_224_no_top.h5'
weight_path = BASE_WEIGHT_PATH + file_name
weights_path = get_file(file_name, weight_path, cache_subdir='models')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def _shuffle_unit(inputs,
in_channels,
out_channels,
groups,
bottleneck_ratio,
strides=2,
stage=1,
block=1):
"""
creates a shuffleunit
Parameters
----------
inputs:
Input tensor of with `channels_last` data format
in_channels:
number of input channels
out_channels:
number of output channels
strides:
An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
groups: int(1)
number of groups per channel
bottleneck_ratio: float
bottleneck ratio implies the ratio of bottleneck channels to output channels.
For example, bottleneck ratio = 1 : 4 means the output feature map is 4 times
the width of the bottleneck feature map.
stage: int(1)
stage number
block: int(1)
block number
Returns
-------
"""
if K.image_data_format() == 'channels_last':
bn_axis = -1
else:
bn_axis = 1
prefix = 'stage%d/block%d' % (stage, block)
#if strides >= 2:
#out_channels -= in_channels
# default: 1/4 of the output channel of a ShuffleNet Unit
bottleneck_channels = int(out_channels * bottleneck_ratio)
groups = (1 if stage == 2 and block == 1 else groups)
x = _group_conv(inputs,
in_channels,
out_channels=bottleneck_channels,
groups=(1 if stage == 2 and block == 1 else groups),
name='%s/1x1_gconv_1' % prefix)
x = CustomBatchNormalization(axis=bn_axis,
name='%s/bn_gconv_1' % prefix)(x)
x = Activation('relu', name='%s/relu_gconv_1' % prefix)(x)
x = Lambda(channel_shuffle,
arguments={'groups': groups},
name='%s/channel_shuffle' % prefix)(x)
x = DepthwiseConv2D(kernel_size=(3, 3),
padding="same",
use_bias=False,
strides=strides,
name='%s/1x1_dwconv_1' % prefix)(x)
x = CustomBatchNormalization(axis=bn_axis,
name='%s/bn_dwconv_1' % prefix)(x)
x = _group_conv(
x,
bottleneck_channels,
out_channels=out_channels if strides == 1 else out_channels -
in_channels,
groups=groups,
name='%s/1x1_gconv_2' % prefix)
x = CustomBatchNormalization(axis=bn_axis,
name='%s/bn_gconv_2' % prefix)(x)
if strides < 2:
ret = Add(name='%s/add' % prefix)([x, inputs])
else:
avg = AveragePooling2D(pool_size=3,
strides=2,
padding='same',
name='%s/avg_pool' % prefix)(inputs)
ret = Concatenate(bn_axis, name='%s/concat' % prefix)([x, avg])
ret = Activation('relu', name='%s/relu_out' % prefix)(ret)
return ret
def EfficientNet(width_coefficient,
depth_coefficient,
default_size,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
activation_fn=swish,
blocks_args=DEFAULT_BLOCKS_ARGS,
model_name='efficientnet',
include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the EfficientNet architecture using given scaling coefficients.
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
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
default_size: integer, default input image size.
dropout_rate: float, dropout rate before final classifier layer.
drop_connect_rate: float, dropout rate at skip connections.
depth_divisor: integer, a unit of network width.
activation_fn: activation function.
blocks_args: list of dicts, parameters to construct block modules.
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.
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.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
#global backend, layers, models, keras_utils
#backend, layers, models, keras_utils = get_submodules_from_kwargs(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 = _obtain_input_shape(input_shape,
default_size=default_size,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
#if not K.is_keras_tensor(input_tensor):
#img_input = Input(tensor=input_tensor, shape=input_shape)
#else:
#img_input = input_tensor
img_input = input_tensor
bn_axis = 3 if K.image_data_format() == 'channels_last' else 1
def round_filters(filters, divisor=depth_divisor):
"""Round number of filters based on depth multiplier."""
filters *= width_coefficient
new_filters = max(divisor,
int(filters + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_filters < 0.9 * filters:
new_filters += divisor
return int(new_filters)
def round_repeats(repeats):
"""Round number of repeats based on depth multiplier."""
return int(math.ceil(depth_coefficient * repeats))
# Build stem
x = img_input
x = ZeroPadding2D(padding=correct_pad(K, x, 3), name='stem_conv_pad')(x)
x = YoloConv2D(round_filters(32),
3,
strides=2,
padding='valid',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name='stem_conv')(x)
x = CustomBatchNormalization(axis=bn_axis, name='stem_bn')(x)
x = Activation(activation_fn, name='stem_activation')(x)
# Build blocks
from copy import deepcopy
blocks_args = deepcopy(blocks_args)
b = 0
blocks = float(sum(args['repeats'] for args in blocks_args))
for (i, args) in enumerate(blocks_args):
assert args['repeats'] > 0
# Update block input and output filters based on depth multiplier.
args['filters_in'] = round_filters(args['filters_in'])
args['filters_out'] = round_filters(args['filters_out'])
for j in range(round_repeats(args.pop('repeats'))):
# The first block needs to take care of stride and filter size increase.
if j > 0:
args['strides'] = 1
args['filters_in'] = args['filters_out']
x = block(x,
activation_fn,
drop_connect_rate * b / blocks,
name='block{}{}_'.format(i + 1, chr(j + 97)),
**args)
b += 1
# Build top
x = YoloConv2D(round_filters(1280),
1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name='top_conv')(x)
x = CustomBatchNormalization(axis=bn_axis, name='top_bn')(x)
x = Activation(activation_fn, name='top_activation')(x)
if include_top:
x = GlobalAveragePooling2D(name='avg_pool')(x)
if dropout_rate > 0:
x = Dropout(dropout_rate, name='top_dropout')(x)
x = Dense(classes,
activation='softmax',
kernel_initializer=DENSE_KERNEL_INITIALIZER,
name='probs')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = 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 = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name=model_name)
# Load weights.
if weights == 'imagenet':
if include_top:
file_suff = '_weights_tf_dim_ordering_tf_kernels_autoaugment.h5'
file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
else:
file_suff = '_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5'
file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
file_name = model_name + file_suff
weights_path = 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 _depthwise_conv_block(inputs,
pointwise_conv_filters,
alpha,
depth_multiplier=1,
strides=(1, 1),
block_id=1):
"""Adds a depthwise convolution block.
A depthwise convolution block consists of a depthwise conv,
batch normalization, relu6, pointwise convolution,
batch normalization and relu6 activation.
# Arguments
inputs: Input tensor of shape `(rows, cols, channels)`
(with `channels_last` data format) or
(channels, rows, cols) (with `channels_first` data format).
pointwise_conv_filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the pointwise 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.
depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to `filters_in * depth_multiplier`.
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.
block_id: Integer, a unique identification designating
the block number.
# Input shape
4D tensor with shape:
`(batch, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(batch, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(batch, 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 K.image_data_format() == 'channels_first' else -1
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
if strides == (1, 1):
x = inputs
else:
x = ZeroPadding2D(((0, 1), (0, 1)),
name='conv_pad_%d' % block_id)(inputs)
x = YoloDepthwiseConv2D((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 = CustomBatchNormalization(axis=channel_axis,
name='conv_dw_%d_bn' % block_id)(x)
x = ReLU(6., name='conv_dw_%d_relu' % block_id)(x)
x = YoloConv2D(pointwise_conv_filters, (1, 1),
padding='same',
use_bias=False,
strides=(1, 1),
name='conv_pw_%d' % block_id)(x)
x = CustomBatchNormalization(axis=channel_axis,
name='conv_pw_%d_bn' % block_id)(x)
return ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
def MobileNetV3(stack_fn,
last_point_ch,
input_shape=None,
alpha=1.0,
model_type='large',
minimalistic=False,
include_top=True,
weights='imagenet',
input_tensor=None,
classes=1000,
pooling=None,
dropout_rate=0.2,
**kwargs):
"""Instantiates the MobileNetV3 architecture.
# Arguments
stack_fn: a function that returns output tensor for the
stacked residual blocks.
last_point_ch: number channels at the last layer (before top)
input_shape: optional shape tuple, to be specified if you would
like to use a model with an input img resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
alpha: controls the width of the network. This is known as the
depth multiplier in the MobileNetV3 paper, but the name is kept for
consistency with MobileNetV1 in Keras.
- 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.
model_type: MobileNetV3 is defined as two models: large and small. These
models are targeted at high and low resource use cases respectively.
minimalistic: In addition to large and small models this module also contains
so-called minimalistic models, these models have the same per-layer
dimensions characteristic as MobilenetV3 however, they don't utilize any
of the advanced blocks (squeeze-and-excite units, hard-swish, and 5x5
convolutions). While these models are less efficient on CPU, they are
much more performant on GPU/DSP.
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.
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.
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.
dropout_rate: fraction of the input units to drop on the last layer
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid model type, argument for `weights`,
or invalid input shape when weights='imagenet'
"""
#global backend, layers, models, keras_utils
#backend, layers, models, keras_utils = get_submodules_from_kwargs(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 = _obtain_input_shape(input_shape,
default_size=224,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
# Determine proper input shape and default size.
# If both input_shape and input_tensor are used, they should match
#if input_shape is not None and input_tensor is not None:
#try:
#is_input_t_tensor = K.is_keras_tensor(input_tensor)
#except ValueError:
#try:
#is_input_t_tensor = K.is_keras_tensor(
#get_source_inputs(input_tensor))
#except ValueError:
#raise ValueError('input_tensor: ', input_tensor,
#'is not type input_tensor')
#if is_input_t_tensor:
#if K.image_data_format == 'channels_first':
#if K.int_shape(input_tensor)[1] != input_shape[1]:
#raise ValueError('input_shape: ', input_shape,
#'and input_tensor: ', input_tensor,
#'do not meet the same shape requirements')
#else:
#if K.int_shape(input_tensor)[2] != input_shape[1]:
#raise ValueError('input_shape: ', input_shape,
#'and input_tensor: ', input_tensor,
#'do not meet the same shape requirements')
#else:
#raise ValueError('input_tensor specified: ', input_tensor,
#'is not a keras tensor')
# If input_shape is None, infer shape from input_tensor
#if input_shape is None and input_tensor is not None:
#try:
#K.is_keras_tensor(input_tensor)
#except ValueError:
#raise ValueError('input_tensor: ', input_tensor,
#'is type: ', type(input_tensor),
#'which is not a valid type')
#if K.is_keras_tensor(input_tensor):
#if K.image_data_format() == 'channels_first':
#rows = K.int_shape(input_tensor)[2]
#cols = K.int_shape(input_tensor)[3]
#input_shape = (3, cols, rows)
#else:
#rows = K.int_shape(input_tensor)[1]
#cols = K.int_shape(input_tensor)[2]
#input_shape = (cols, rows, 3)
# If input_shape is None and input_tensor is None using standart shape
if input_shape is None and input_tensor is None:
input_shape = (None, None, 3)
if K.image_data_format() == 'channels_last':
row_axis, col_axis = (0, 1)
else:
row_axis, col_axis = (1, 2)
rows = input_shape[row_axis]
cols = input_shape[col_axis]
if rows and cols and (rows < 32 or cols < 32):
raise ValueError(
'Input size must be at least 32x32; got `input_shape=' +
str(input_shape) + '`')
if weights == 'imagenet':
if minimalistic is False and alpha not in [0.75, 1.0] \
or minimalistic is True and alpha != 1.0:
raise ValueError(
'If imagenet weights are being loaded, '
'alpha can be one of `0.75`, `1.0` for non minimalistic'
' or `1.0` for minimalistic only.')
if rows != cols or rows != 224:
warnings.warn('`input_shape` is undefined or non-square, '
'or `rows` is not 224.'
' Weights for input shape (224, 224) will be'
' loaded as the default.')
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
#if not K.is_keras_tensor(input_tensor):
#img_input = Input(tensor=input_tensor, shape=input_shape)
#else:
#img_input = input_tensor
img_input = input_tensor
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
if minimalistic:
kernel = 3
activation = relu
se_ratio = None
else:
kernel = 5
activation = hard_swish
se_ratio = 0.25
x = ZeroPadding2D(padding=correct_pad(K, img_input, 3),
name='Conv_pad')(img_input)
x = Conv2D(16,
kernel_size=3,
strides=(2, 2),
padding='valid',
use_bias=False,
name='Conv')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name='Conv/BatchNorm')(x)
x = Activation(activation)(x)
x = stack_fn(x, kernel, activation, se_ratio)
last_conv_ch = _depth(K.int_shape(x)[channel_axis] * 6)
# if the width multiplier is greater than 1 we
# increase the number of output channels
if alpha > 1.0:
last_point_ch = _depth(last_point_ch * alpha)
x = Conv2D(last_conv_ch,
kernel_size=1,
padding='same',
use_bias=False,
name='Conv_1')(x)
x = CustomBatchNormalization(axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name='Conv_1/BatchNorm')(x)
x = Activation(activation)(x)
if include_top:
x = GlobalAveragePooling2D()(x)
if channel_axis == 1:
x = Reshape((last_conv_ch, 1, 1))(x)
else:
x = Reshape((1, 1, last_conv_ch))(x)
x = Conv2D(last_point_ch, kernel_size=1, padding='same',
name='Conv_2')(x)
x = Activation(activation)(x)
if dropout_rate > 0:
x = Dropout(dropout_rate)(x)
x = Conv2D(classes, kernel_size=1, padding='same', name='Logits')(x)
x = Flatten()(x)
x = Softmax(name='Predictions/Softmax')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = 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 = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='MobilenetV3' + model_type)
# Load weights.
if weights == 'imagenet':
model_name = "{}{}_224_{}_float".format(
model_type, '_minimalistic' if minimalistic else '', str(alpha))
if include_top:
file_name = 'weights_mobilenet_v3_' + model_name + '.h5'
file_hash = WEIGHTS_HASHES[model_name][0]
else:
file_name = 'weights_mobilenet_v3_' + model_name + '_no_top.h5'
file_hash = WEIGHTS_HASHES[model_name][1]
weights_path = get_file(file_name,
BASE_WEIGHT_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 block(inputs,
activation_fn=swish,
drop_rate=0.,
name='',
filters_in=32,
filters_out=16,
kernel_size=3,
strides=1,
expand_ratio=1,
se_ratio=0.,
id_skip=True):
"""A mobile inverted residual block.
# Arguments
inputs: input tensor.
activation_fn: activation function.
drop_rate: float between 0 and 1, fraction of the input units to drop.
name: string, block label.
filters_in: integer, the number of input filters.
filters_out: integer, the number of output filters.
kernel_size: integer, the dimension of the convolution window.
strides: integer, the stride of the convolution.
expand_ratio: integer, scaling coefficient for the input filters.
se_ratio: float between 0 and 1, fraction to squeeze the input filters.
id_skip: boolean.
# Returns
output tensor for the block.
"""
bn_axis = 3 if K.image_data_format() == 'channels_last' else 1
# Expansion phase
filters = filters_in * expand_ratio
if expand_ratio != 1:
x = YoloConv2D(filters,
1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + 'expand_conv')(inputs)
x = CustomBatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
x = Activation(activation_fn, name=name + 'expand_activation')(x)
else:
x = inputs
# Depthwise Convolution
if strides == 2:
x = ZeroPadding2D(padding=correct_pad(K, x, kernel_size),
name=name + 'dwconv_pad')(x)
conv_pad = 'valid'
else:
conv_pad = 'same'
x = YoloDepthwiseConv2D(kernel_size,
strides=strides,
padding=conv_pad,
use_bias=False,
depthwise_initializer=CONV_KERNEL_INITIALIZER,
name=name + 'dwconv')(x)
x = CustomBatchNormalization(axis=bn_axis, name=name + 'bn')(x)
x = Activation(activation_fn, name=name + 'activation')(x)
# Squeeze and Excitation phase
if 0 < se_ratio <= 1:
filters_se = max(1, int(filters_in * se_ratio))
se = GlobalAveragePooling2D(name=name + 'se_squeeze')(x)
se = Reshape((1, 1, filters), name=name + 'se_reshape')(se)
se = YoloConv2D(filters_se,
1,
padding='same',
activation=activation_fn,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + 'se_reduce')(se)
se = YoloConv2D(filters,
1,
padding='same',
activation='sigmoid',
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + 'se_expand')(se)
if K.backend() == 'theano':
# For the Theano backend, we have to explicitly make
# the excitation weights broadcastable.
se = Lambda(
lambda x: K.pattern_broadcast(x, [True, True, True, False]),
output_shape=lambda input_shape: input_shape,
name=name + 'se_broadcast')(se)
x = multiply([x, se], name=name + 'se_excite')
# Output phase
x = YoloConv2D(filters_out,
1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + 'project_conv')(x)
x = CustomBatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)
if (id_skip is True and strides == 1 and filters_in == filters_out):
if drop_rate > 0:
if tf2.enabled():
x = Dropout(drop_rate,
noise_shape=(None, 1, 1, 1),
name=name + 'drop')(x)
else:
x = Dropout(
drop_rate,
#noise_shape=(None, 1, 1, 1),
name=name + 'drop')(x)
x = add([x, inputs], name=name + 'add')
return x
