Algorithm-Repo/SourceCode/MachineLearningUtils/Classification/KerasFramework/nets/mobilenet/mobilenetV1.py

# D:/workplace/python
# -*- coding: utf-8 -*-
# @File  :mobilenetV1.py
# @Author:Guido    LuXiaohao
# @Date  :2020/4/8
# @Software:PyCharm


"""MobileNet v1 models for Keras.
This is a revised implementation from Somshubra Majumdar's SENet repo:
(https://github.com/titu1994/keras-squeeze-excite-network)
# Reference
- [MobileNets: Efficient Convolutional Neural Networks for
   Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf))
"""
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division

import warnings

from keras.models import Model
from keras.layers import Input
from keras.layers import Activation
from keras.layers import Dropout
from keras.layers import Reshape
from keras.layers import BatchNormalization
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import Conv2D
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.utils import conv_utils
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.engine import InputSpec
from keras.applications import imagenet_utils
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.applications.imagenet_utils import decode_predictions
from keras import backend as K

from nets.attention_module.se_cbam import attach_attention_module


def relu6(x):
    return K.relu(x, max_value=6)


def preprocess_input(x):
    """Preprocesses a numpy array encoding a batch of images.
    # Arguments
        x: a 4D numpy array consists of RGB values within [0, 255].
    # Returns
        Preprocessed array.
    """
    return imagenet_utils.preprocess_input(x, mode='tf')


class DepthwiseConv2D(Conv2D):
    """Depthwise separable 2D convolution.
    Depthwise Separable convolutions consists in performing
    just the first step in a depthwise spatial convolution
    (which acts on each input channel separately).
    The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.
    # Arguments
        kernel_size: 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.
        padding: one of `'valid'` or `'same'` (case-insensitive).
        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`.
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be 'channels_last'.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. 'linear' activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its 'activation').
            (see [regularizer](../regularizers.md)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
    # 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 padding.
    """

    def __init__(self,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 depth_multiplier=1,
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(DepthwiseConv2D, self).__init__(
            filters=None,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            activation=activation,
            use_bias=use_bias,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            bias_constraint=bias_constraint,
            **kwargs)
        self.depth_multiplier = depth_multiplier
        self.depthwise_initializer = initializers.get(depthwise_initializer)
        self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
        self.depthwise_constraint = constraints.get(depthwise_constraint)
        self.bias_initializer = initializers.get(bias_initializer)

    def build(self, input_shape):
        if len(input_shape) < 4:
            raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
                             'Received input shape:', str(input_shape))
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = 3
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs to '
                             '`DepthwiseConv2D` '
                             'should be defined. Found `None`.')
        input_dim = int(input_shape[channel_axis])
        depthwise_kernel_shape = (self.kernel_size[0],
                                  self.kernel_size[1],
                                  input_dim,
                                  self.depth_multiplier)

        self.depthwise_kernel = self.add_weight(
            shape=depthwise_kernel_shape,
            initializer=self.depthwise_initializer,
            name='depthwise_kernel',
            regularizer=self.depthwise_regularizer,
            constraint=self.depthwise_constraint)

        if self.use_bias:
            self.bias = self.add_weight(shape=(input_dim * self.depth_multiplier,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        # Set input spec.
        self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
        self.built = True

    def call(self, inputs, training=None):
        outputs = K.depthwise_conv2d(
            inputs,
            self.depthwise_kernel,
            strides=self.strides,
            padding=self.padding,
            dilation_rate=self.dilation_rate,
            data_format=self.data_format)

        if self.bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)

        return outputs

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            rows = input_shape[2]
            cols = input_shape[3]
            out_filters = input_shape[1] * self.depth_multiplier
        elif self.data_format == 'channels_last':
            rows = input_shape[1]
            cols = input_shape[2]
            out_filters = input_shape[3] * self.depth_multiplier

        rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
                                             self.padding,
                                             self.strides[0])
        cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
                                             self.padding,
                                             self.strides[1])

        if self.data_format == 'channels_first':
            return (input_shape[0], out_filters, rows, cols)
        elif self.data_format == 'channels_last':
            return (input_shape[0], rows, cols, out_filters)

    def get_config(self):
        config = super(DepthwiseConv2D, self).get_config()
        config.pop('filters')
        config.pop('kernel_initializer')
        config.pop('kernel_regularizer')
        config.pop('kernel_constraint')
        config['depth_multiplier'] = self.depth_multiplier
        config['depthwise_initializer'] = initializers.serialize(self.depthwise_initializer)
        config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
        config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
        return config


def MobileNet(input_shape=None,
              alpha=1.0,
              depth_multiplier=1,
              dropout=1e-3,
              include_top=True,
              weights=None,
              input_tensor=None,
              pooling=None,
              classes=1000,
              attention_module=None):
    """Instantiates the SE-MobileNet architecture.
    Note that only TensorFlow is supported for now,
    therefore it only works with the data format
    `image_data_format='channels_last'` in your Keras config
    at `~/.keras/keras.json`.
    To load a MobileNet model via `load_model`, import the custom
    objects `relu6` and `DepthwiseConv2D` and pass them to the
    `custom_objects` parameter.
    E.g.
    model = load_model('mobilenet.h5', custom_objects={
                       'relu6': mobilenet.relu6,
                       'DepthwiseConv2D': mobilenet.DepthwiseConv2D})
    # Arguments
        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.
        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: depth multiplier for depthwise convolution
            (also called the resolution multiplier)
        dropout: dropout rate
        include_top: whether to include the fully-connected
            layer at the top of the network.
        weights: `None` (random initialization) or
            `imagenet` (ImageNet weights)
        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 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.
        RuntimeError: If attempting to run this model with a
            backend that does not support separable convolutions.
    """

    if K.backend() != 'tensorflow':
        raise RuntimeError('Only TensorFlow backend is currently supported, '
                           'as other backends do not support '
                           'depthwise convolution.')

    if weights not in {'imagenet', None}:
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization) or `imagenet` '
                         '(pre-training on ImageNet).')

    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 and default size.
    if input_shape is None:
        default_size = 224
    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 [128, 160, 192, 224]:
            default_size = rows
        else:
            default_size = 224

    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 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 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

    x = _conv_block(img_input, 32, alpha, strides=(2, 2))
    x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1, attention_module=attention_module)

    x = _depthwise_conv_block(x, 128, alpha, depth_multiplier,
                              strides=(2, 2), block_id=2, attention_module=attention_module)
    x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3, attention_module=attention_module)

    x = _depthwise_conv_block(x, 256, alpha, depth_multiplier,
                              strides=(2, 2), block_id=4, attention_module=attention_module)
    x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5, attention_module=attention_module)

    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier,
                              strides=(2, 2), block_id=6, attention_module=attention_module)
    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7, attention_module=attention_module)
    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8, attention_module=attention_module)
    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9, attention_module=attention_module)
    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10, attention_module=attention_module)
    x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11, attention_module=attention_module)

    x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier,
                              strides=(2, 2), block_id=12, attention_module=attention_module)
    x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13, attention_module=attention_module)

    if include_top:
        if K.image_data_format() == 'channels_first':
            shape = (int(1024 * alpha), 1, 1)
        else:
            shape = (1, 1, int(1024 * alpha))

        x = GlobalAveragePooling2D()(x)
        x = Reshape(shape, name='reshape_n_1')(x)
        x = Dropout(dropout, name='dropout')(x)
        x = Conv2D(classes, (1, 1),
                   padding='same', name='conv_preds')(x)
        x = Activation('softmax', name='act_softmax')(x)
        x = Reshape((classes,), name='reshape_final')(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='se_mobilenet_%0.2f_%s' % (alpha, rows))

    return model


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 output of 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 = Conv2D(filters, kernel,
               padding='same',
               use_bias=False,
               strides=strides,
               name='conv1')(inputs)
    x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
    return Activation(relu6, name='conv1_relu')(x)


def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,
                          depth_multiplier=1, strides=(1, 1), block_id=1, attention_module=None):
    """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 output of 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)

    x = DepthwiseConv2D((3, 3),
                        padding='same',
                        depth_multiplier=depth_multiplier,
                        strides=strides,
                        use_bias=False,
                        name='conv_dw_%d' % block_id)(inputs)
    x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
    x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)

    x = Conv2D(pointwise_conv_filters, (1, 1),
               padding='same',
               use_bias=False,
               strides=(1, 1),
               name='conv_pw_%d' % block_id)(x)
    x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x)
    x = Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)

    # attention_module
    if attention_module is not None:
        x = attach_attention_module(x, attention_module)

    return x