resnet152.py

# -*- coding: utf-8 -*-
"""ResNet152 model for Keras.

# Reference:

- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)

Adapted from code contributed by flyyufelix, mvoelk, BigMoyan, fchollet, adamcasson
"""

import os
import warnings

from keras.layers import Input
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import ZeroPadding2D
from keras.layers import AveragePooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import BatchNormalization
from keras.layers import add
from keras.models import Model
import keras.backend as K
from keras.engine.topology import get_source_inputs
from keras.utils import layer_utils
from keras import initializers
from keras.engine import Layer, InputSpec
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import _obtain_input_shape

import sys

sys.setrecursionlimit(3000)

WEIGHTS_PATH = 'https://github.com/GKalliatakis/Keras-Application-Zoo/releases/download/0.1/resnet152_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/GKalliatakis/Keras-Application-Zoo/releases/download/0.1/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5'


def identity_block(input_tensor, kernel_size, filters, stage, block):
    """The identity_block is the block that has no conv layer at shortcut

    # Arguments
        input_tensor: input tensor
        kernel_size: default 3, the kernel size of middle conv layer at main path
        filters: list of integers, the filters of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names

    # Returns
        Output tensor for the block.
    """
    eps = 1.1e-5

    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    nb_filter1, nb_filter2, nb_filter3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    scale_name_base = 'scale' + str(stage) + block + '_branch'

    x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=False)(input_tensor)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
    x = Activation('relu', name=conv_name_base + '2a_relu')(x)

    x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
    x = Conv2D(nb_filter2, (kernel_size, kernel_size), name=conv_name_base + '2b', use_bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
    x = Activation('relu', name=conv_name_base + '2b_relu')(x)

    x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)

    x = add([x, input_tensor], name='res' + str(stage) + block)
    x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
    return x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    """conv_block is the block that has a conv layer at shortcut

     # Arguments
        input_tensor: input tensor
        kernel_size: default 3, the kernel size of middle conv layer at main path
        filters: list of integers, the filters of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names

    # Returns
        Output tensor for the block.
    Note that from stage 3, the first conv layer at main path is with strides=(2,2)
    And the shortcut should have strides=(2,2) as well
    """
    eps = 1.1e-5

    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    nb_filter1, nb_filter2, nb_filter3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    scale_name_base = 'scale' + str(stage) + block + '_branch'

    x = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=False)(input_tensor)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x)
    x = Activation('relu', name=conv_name_base + '2a_relu')(x)

    x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x)
    x = Conv2D(nb_filter2, (kernel_size, kernel_size),
               name=conv_name_base + '2b', use_bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x)
    x = Activation('relu', name=conv_name_base + '2b_relu')(x)

    x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x)
    x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x)

    shortcut = Conv2D(nb_filter3, (1, 1), strides=strides,
                      name=conv_name_base + '1', use_bias=False)(input_tensor)
    shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1')(shortcut)
    shortcut = Scale(axis=bn_axis, name=scale_name_base + '1')(shortcut)

    x = add([x, shortcut], name='res' + str(stage) + block)
    x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
    return x


class Scale(Layer):
    """Custom Layer for ResNet used for BatchNormalization.

    Learns a set of weights and biases used for scaling the input data.
    the output consists simply in an element-wise multiplication of the input
    and a sum of a set of constants:

        out = in * gamma + beta,

    where 'gamma' and 'beta' are the weights and biases larned.

    Keyword arguments:
    axis -- integer, axis along which to normalize in mode 0. For instance,
        if your input tensor has shape (samples, channels, rows, cols),
        set axis to 1 to normalize per feature map (channels axis).
    momentum -- momentum in the computation of the exponential average
        of the mean and standard deviation of the data, for
        feature-wise normalization.
    weights -- Initialization weights.
        List of 2 Numpy arrays, with shapes:
        `[(input_shape,), (input_shape,)]`
    beta_init -- name of initialization function for shift parameter
        (see [initializers](../initializers.md)), or alternatively,
        Theano/TensorFlow function to use for weights initialization.
        This parameter is only relevant if you don't pass a `weights` argument.
    gamma_init -- name of initialization function for scale parameter (see
        [initializers](../initializers.md)), or alternatively,
        Theano/TensorFlow function to use for weights initialization.
        This parameter is only relevant if you don't pass a `weights` argument.

    """

    def __init__(self, weights=None, axis=-1, momentum=0.9, beta_init='zero', gamma_init='one', **kwargs):
        self.momentum = momentum
        self.axis = axis
        self.beta_init = initializers.get(beta_init)
        self.gamma_init = initializers.get(gamma_init)
        self.initial_weights = weights
        super(Scale, self).__init__(**kwargs)

    def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        shape = (int(input_shape[self.axis]),)

        self.gamma = K.variable(self.gamma_init(shape), name='%s_gamma' % self.name)
        self.beta = K.variable(self.beta_init(shape), name='%s_beta' % self.name)
        self.trainable_weights = [self.gamma, self.beta]

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights

    def call(self, x, mask=None):
        input_shape = self.input_spec[0].shape
        broadcast_shape = [1] * len(input_shape)
        broadcast_shape[self.axis] = input_shape[self.axis]

        out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape)
        return out

    def get_config(self):
        config = {"momentum": self.momentum, "axis": self.axis}
        base_config = super(Scale, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))



def ResNet152(include_top=True, weights='imagenet',
              input_tensor=None, input_shape=None,
              large_input=False,
              pooling=None,
              classes=1000):
    """Instantiate the ResNet152 architecture.

    Optionally loads weights pre-trained
    on ImageNet. Note that when using TensorFlow,
    for best performance you should set
    `image_data_format='channels_last'` in your Keras config
    at ~/.keras/keras.json.

    The model and the weights are compatible with both
    TensorFlow and Theano. The data format
    convention used by the model is the one
    specified in your Keras config file.

    # Arguments
        include_top: whether to include the fully-connected
            layer at the top of the network.
        weights: one of `None` (random initialization),
              'imagenet' (pre-training on ImageNet),
              or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)` (with `channels_last` data format)
            or `(3, 224, 224)` (with `channels_first` data format).
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 197.
            E.g. `(200, 200, 3)` would be one valid value.
        large_input: if True, then the input shape expected will be
            `(448, 448, 3)` (with `channels_last` data format) or
            `(3, 448, 448)` (with `channels_first` data format). (default False)
        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.
    """
    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')

    eps = 1.1e-5

    if large_input:
        img_size = 448
    else:
        img_size = 224

    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=img_size,
                                      min_size=197,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top)

    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

    # handle dimension ordering for different backends
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x)
    x = Scale(axis=bn_axis, name='scale_conv1')(x)
    x = Activation('relu', name='conv1_relu')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    for i in range(1, 8):
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='b' + str(i))

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    for i in range(1, 36):
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b' + str(i))

    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

    if large_input:
        x = AveragePooling2D((14, 14), name='avg_pool')(x)
    else:
        x = AveragePooling2D((7, 7), name='avg_pool')(x)

    # include classification layer by default, not included for feature extraction
    if include_top:
        x = Flatten()(x)
        x = Dense(classes, activation='softmax', name='fc1000')(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='resnet152')

    # load weights
    if weights == 'imagenet':
        if include_top:
            weights_path = get_file('resnet152_weights_tf_dim_ordering_tf_kernels.h5',
                                    WEIGHTS_PATH)
        else:
            weights_path = get_file('resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                    WEIGHTS_PATH_NO_TOP)

        model.load_weights(weights_path, by_name=True)
        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)
            if include_top:
                maxpool = model.get_layer(name='avg_pool')
                shape = maxpool.output_shape[1:]
                dense = model.get_layer(name='fc1000')
                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')

        if K.image_data_format() == 'channels_first' and K.backend() == 'tensorflow':
            warnings.warn('You are using the TensorFlow backend, yet you '
                          'are using the Theano '
                          'image data format convention '
                          '(`image_data_format="channels_first"`). '
                          'For best performance, set '
                          '`image_data_format="channels_last"` in '
                          'your Keras config '
                          'at ~/.keras/keras.json.')
    return model


if __name__ == '__main__':
    model = ResNet152(include_top=True, weights='imagenet')
    model.summary()