# 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