489 lines
20 KiB
Python
489 lines
20 KiB
Python
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
# ==============================================================================
|
||
|
|
|
||
|
|
"""MobileNet v1 models for Keras.
|
||
|
|
|
||
|
|
MobileNet is a general architecture and can be used for multiple use cases.
|
||
|
|
Depending on the use case, it can use different input layer size and
|
||
|
|
different width factors. This allows different width models to reduce
|
||
|
|
the number of multiply-adds and thereby
|
||
|
|
reduce inference cost on mobile devices.
|
||
|
|
|
||
|
|
MobileNets support any input size greater than 32 x 32, with larger image sizes
|
||
|
|
offering better performance.
|
||
|
|
The number of parameters and number of multiply-adds
|
||
|
|
can be modified by using the `alpha` parameter,
|
||
|
|
which increases/decreases the number of filters in each layer.
|
||
|
|
By altering the image size and `alpha` parameter,
|
||
|
|
all 16 models from the paper can be built, with ImageNet weights provided.
|
||
|
|
|
||
|
|
The paper demonstrates the performance of MobileNets using `alpha` values of
|
||
|
|
1.0 (also called 100 % MobileNet), 0.75, 0.5 and 0.25.
|
||
|
|
For each of these `alpha` values, weights for 4 different input image sizes
|
||
|
|
are provided (224, 192, 160, 128).
|
||
|
|
|
||
|
|
The following table describes the size and accuracy of the 100% MobileNet
|
||
|
|
on size 224 x 224:
|
||
|
|
----------------------------------------------------------------------------
|
||
|
|
Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M)
|
||
|
|
-------------------------|---------------|-------------------|--------------
|
||
|
|
| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 |
|
||
|
|
| 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 |
|
||
|
|
| 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 |
|
||
|
|
| 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 |
|
||
|
|
|
||
|
|
The following table describes the performance of
|
||
|
|
the 100 % MobileNet on various input sizes:
|
||
|
|
------------------------------------------------------------------------
|
||
|
|
Resolution | ImageNet Acc | Multiply-Adds (M) | Params (M)
|
||
|
|
----------------------|---------------|-------------------|----------------
|
||
|
|
| 1.0 MobileNet-224 | 70.6 % | 569 | 4.2 |
|
||
|
|
| 1.0 MobileNet-192 | 69.1 % | 418 | 4.2 |
|
||
|
|
| 1.0 MobileNet-160 | 67.2 % | 290 | 4.2 |
|
||
|
|
| 1.0 MobileNet-128 | 64.4 % | 186 | 4.2 |
|
||
|
|
|
||
|
|
Reference:
|
||
|
|
- [MobileNets: Efficient Convolutional Neural Networks
|
||
|
|
for Mobile Vision Applications](
|
||
|
|
https://arxiv.org/abs/1704.04861)
|
||
|
|
"""
|
||
|
|
|
||
|
|
import tensorflow.compat.v2 as tf
|
||
|
|
|
||
|
|
from keras import backend
|
||
|
|
from keras.applications import imagenet_utils
|
||
|
|
from keras.engine import training
|
||
|
|
from keras.layers import VersionAwareLayers
|
||
|
|
from keras.utils import data_utils
|
||
|
|
from keras.utils import layer_utils
|
||
|
|
|
||
|
|
# isort: off
|
||
|
|
from tensorflow.python.platform import tf_logging as logging
|
||
|
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
|
||
|
|
BASE_WEIGHT_PATH = (
|
||
|
|
"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/"
|
||
|
|
)
|
||
|
|
layers = None
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export(
|
||
|
|
"keras.applications.mobilenet.MobileNet", "keras.applications.MobileNet"
|
||
|
|
)
|
||
|
|
def MobileNet(
|
||
|
|
input_shape=None,
|
||
|
|
alpha=1.0,
|
||
|
|
depth_multiplier=1,
|
||
|
|
dropout=1e-3,
|
||
|
|
include_top=True,
|
||
|
|
weights="imagenet",
|
||
|
|
input_tensor=None,
|
||
|
|
pooling=None,
|
||
|
|
classes=1000,
|
||
|
|
classifier_activation="softmax",
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
"""Instantiates the MobileNet architecture.
|
||
|
|
|
||
|
|
Reference:
|
||
|
|
- [MobileNets: Efficient Convolutional Neural Networks
|
||
|
|
for Mobile Vision Applications](
|
||
|
|
https://arxiv.org/abs/1704.04861)
|
||
|
|
|
||
|
|
This function returns a Keras image classification model,
|
||
|
|
optionally loaded with weights pre-trained on ImageNet.
|
||
|
|
|
||
|
|
For image classification use cases, see
|
||
|
|
[this page for detailed examples](
|
||
|
|
https://keras.io/api/applications/#usage-examples-for-image-classification-models).
|
||
|
|
|
||
|
|
For transfer learning use cases, make sure to read the
|
||
|
|
[guide to transfer learning & fine-tuning](
|
||
|
|
https://keras.io/guides/transfer_learning/).
|
||
|
|
|
||
|
|
Note: each Keras Application expects a specific kind of input preprocessing.
|
||
|
|
For MobileNet, call `tf.keras.applications.mobilenet.preprocess_input`
|
||
|
|
on your inputs before passing them to the model.
|
||
|
|
`mobilenet.preprocess_input` will scale input pixels between -1 and 1.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_shape: Optional shape tuple, only to be specified if `include_top`
|
||
|
|
is False (otherwise the input shape has to be `(224, 224, 3)` (with
|
||
|
|
`channels_last` data format) or (3, 224, 224) (with `channels_first`
|
||
|
|
data format). It should have exactly 3 inputs channels, and width and
|
||
|
|
height should be no smaller than 32. E.g. `(200, 200, 3)` would be one
|
||
|
|
valid value. Default to `None`.
|
||
|
|
`input_shape` will be ignored if the `input_tensor` is provided.
|
||
|
|
alpha: Controls the width of the network. This is known as the width
|
||
|
|
multiplier in the MobileNet paper. - 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. Default to 1.0.
|
||
|
|
depth_multiplier: Depth multiplier for depthwise convolution. This is
|
||
|
|
called the resolution multiplier in the MobileNet paper. Default to 1.0.
|
||
|
|
dropout: Dropout rate. Default to 0.001.
|
||
|
|
include_top: Boolean, whether to include the fully-connected layer at the
|
||
|
|
top of the network. Default to `True`.
|
||
|
|
weights: One of `None` (random initialization), 'imagenet' (pre-training
|
||
|
|
on ImageNet), or the path to the weights file to be loaded. Default to
|
||
|
|
`imagenet`.
|
||
|
|
input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to
|
||
|
|
use as image input for the model. `input_tensor` is useful for sharing
|
||
|
|
inputs between multiple different networks. Default to None.
|
||
|
|
pooling: Optional pooling mode for feature extraction when `include_top`
|
||
|
|
is `False`.
|
||
|
|
- `None` (default) 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. Defaults to 1000.
|
||
|
|
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.
|
||
|
|
When loading pretrained weights, `classifier_activation` can only
|
||
|
|
be `None` or `"softmax"`.
|
||
|
|
**kwargs: For backwards compatibility only.
|
||
|
|
Returns:
|
||
|
|
A `keras.Model` instance.
|
||
|
|
"""
|
||
|
|
global layers
|
||
|
|
if "layers" in kwargs:
|
||
|
|
layers = kwargs.pop("layers")
|
||
|
|
else:
|
||
|
|
layers = VersionAwareLayers()
|
||
|
|
if kwargs:
|
||
|
|
raise ValueError(f"Unknown argument(s): {(kwargs,)}")
|
||
|
|
if not (weights in {"imagenet", None} or tf.io.gfile.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. "
|
||
|
|
f"Received weights={weights}"
|
||
|
|
)
|
||
|
|
|
||
|
|
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. "
|
||
|
|
f"Received classes={classes}"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Determine proper input shape and default size.
|
||
|
|
if input_shape is None:
|
||
|
|
default_size = 224
|
||
|
|
else:
|
||
|
|
if backend.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 [128, 160, 192, 224]:
|
||
|
|
default_size = rows
|
||
|
|
else:
|
||
|
|
default_size = 224
|
||
|
|
|
||
|
|
input_shape = imagenet_utils.obtain_input_shape(
|
||
|
|
input_shape,
|
||
|
|
default_size=default_size,
|
||
|
|
min_size=32,
|
||
|
|
data_format=backend.image_data_format(),
|
||
|
|
require_flatten=include_top,
|
||
|
|
weights=weights,
|
||
|
|
)
|
||
|
|
|
||
|
|
if backend.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 depth_multiplier != 1:
|
||
|
|
raise ValueError(
|
||
|
|
"If imagenet weights are being loaded, "
|
||
|
|
"depth multiplier must be 1. "
|
||
|
|
f"Received depth_multiplier={depth_multiplier}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if alpha not in [0.25, 0.50, 0.75, 1.0]:
|
||
|
|
raise ValueError(
|
||
|
|
"If imagenet weights are being loaded, "
|
||
|
|
"alpha can be one of"
|
||
|
|
"`0.25`, `0.50`, `0.75` or `1.0` only. "
|
||
|
|
f"Received alpha={alpha}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if rows != cols or rows not in [128, 160, 192, 224]:
|
||
|
|
rows = 224
|
||
|
|
logging.warning(
|
||
|
|
"`input_shape` is undefined or non-square, "
|
||
|
|
"or `rows` is not in [128, 160, 192, 224]. "
|
||
|
|
"Weights for input shape (224, 224) will be "
|
||
|
|
"loaded as the default."
|
||
|
|
)
|
||
|
|
|
||
|
|
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 = _conv_block(img_input, 32, alpha, strides=(2, 2))
|
||
|
|
x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1)
|
||
|
|
|
||
|
|
x = _depthwise_conv_block(
|
||
|
|
x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2
|
||
|
|
)
|
||
|
|
x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)
|
||
|
|
|
||
|
|
x = _depthwise_conv_block(
|
||
|
|
x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4
|
||
|
|
)
|
||
|
|
x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5)
|
||
|
|
|
||
|
|
x = _depthwise_conv_block(
|
||
|
|
x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6
|
||
|
|
)
|
||
|
|
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7)
|
||
|
|
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8)
|
||
|
|
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9)
|
||
|
|
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10)
|
||
|
|
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11)
|
||
|
|
|
||
|
|
x = _depthwise_conv_block(
|
||
|
|
x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12
|
||
|
|
)
|
||
|
|
x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13)
|
||
|
|
|
||
|
|
if include_top:
|
||
|
|
x = layers.GlobalAveragePooling2D(keepdims=True)(x)
|
||
|
|
x = layers.Dropout(dropout, name="dropout")(x)
|
||
|
|
x = layers.Conv2D(classes, (1, 1), padding="same", name="conv_preds")(x)
|
||
|
|
x = layers.Reshape((classes,), name="reshape_2")(x)
|
||
|
|
imagenet_utils.validate_activation(classifier_activation, weights)
|
||
|
|
x = layers.Activation(
|
||
|
|
activation=classifier_activation, name="predictions"
|
||
|
|
)(x)
|
||
|
|
else:
|
||
|
|
if pooling == "avg":
|
||
|
|
x = layers.GlobalAveragePooling2D()(x)
|
||
|
|
elif pooling == "max":
|
||
|
|
x = layers.GlobalMaxPooling2D()(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=f"mobilenet_{alpha:0.2f}_{rows}")
|
||
|
|
|
||
|
|
# Load weights.
|
||
|
|
if weights == "imagenet":
|
||
|
|
if alpha == 1.0:
|
||
|
|
alpha_text = "1_0"
|
||
|
|
elif alpha == 0.75:
|
||
|
|
alpha_text = "7_5"
|
||
|
|
elif alpha == 0.50:
|
||
|
|
alpha_text = "5_0"
|
||
|
|
else:
|
||
|
|
alpha_text = "2_5"
|
||
|
|
|
||
|
|
if include_top:
|
||
|
|
model_name = "mobilenet_%s_%d_tf.h5" % (alpha_text, rows)
|
||
|
|
weight_path = BASE_WEIGHT_PATH + model_name
|
||
|
|
weights_path = data_utils.get_file(
|
||
|
|
model_name, weight_path, cache_subdir="models"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
model_name = "mobilenet_%s_%d_tf_no_top.h5" % (alpha_text, rows)
|
||
|
|
weight_path = BASE_WEIGHT_PATH + model_name
|
||
|
|
weights_path = data_utils.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 _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
|
||
|
|
"""Adds an initial convolution layer (with batch normalization and relu6).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
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.Conv2D(
|
||
|
|
filters,
|
||
|
|
kernel,
|
||
|
|
padding="same",
|
||
|
|
use_bias=False,
|
||
|
|
strides=strides,
|
||
|
|
name="conv1",
|
||
|
|
)(inputs)
|
||
|
|
x = layers.BatchNormalization(axis=channel_axis, name="conv1_bn")(x)
|
||
|
|
return layers.ReLU(6.0, name="conv1_relu")(x)
|
||
|
|
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
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 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(6.0, 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(6.0, name="conv_pw_%d_relu" % block_id)(x)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export("keras.applications.mobilenet.preprocess_input")
|
||
|
|
def preprocess_input(x, data_format=None):
|
||
|
|
return imagenet_utils.preprocess_input(
|
||
|
|
x, data_format=data_format, mode="tf"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export("keras.applications.mobilenet.decode_predictions")
|
||
|
|
def decode_predictions(preds, top=5):
|
||
|
|
return imagenet_utils.decode_predictions(preds, top=top)
|
||
|
|
|
||
|
|
|
||
|
|
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
|
||
|
|
mode="",
|
||
|
|
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
|
||
|
|
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
|
||
|
|
)
|
||
|
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|