AGE/wide_resnet.py at master · gitliber/AGE · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Su March 11 9:05:40 2018

@author: lav solanki
"""

import logging
import sys
import numpy as np
from keras.models import Model
from keras.layers import Input, Activation, add, Dense, Flatten, Dropout
from keras.layers.convolutional import Conv2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import backend as K

sys.setrecursionlimit(2 ** 20)
np.random.seed(2 ** 10)


class WideResNet:
    def __init__(self, image_size, depth=16, k=8):
        self._depth = depth
        self._k = k
        self._dropout_probability = 0
        self._weight_decay = 0.0005
        self._use_bias = False
        self._weight_init = "he_normal"


        if K.image_dim_ordering() == "th":
            logging.debug("image_dim_ordering = 'th'")
            self._channel_axis = 1
            self._input_shape = (3, image_size, image_size)
        else:
            logging.debug("image_dim_ordering = 'tf'")
            self._channel_axis = -1
            self._input_shape = (image_size, image_size, 3)


    def _wide_basic(self, n_input_plane, n_output_plane, stride):
        def f(net):

            conv_params = [[3, 3, stride, "same"],
                           [3, 3, (1, 1), "same"]]

            n_bottleneck_plane = n_output_plane


            for i, v in enumerate(conv_params):
                if i == 0:
                    if n_input_plane != n_output_plane:
                        net = BatchNormalization(axis=self._channel_axis)(net)
                        net = Activation("relu")(net)
                        convs = net
                    else:
                        convs = BatchNormalization(axis=self._channel_axis)(net)
                        convs = Activation("relu")(convs)

                    convs = Conv2D(n_bottleneck_plane, kernel_size=(v[0], v[1]),
                                          strides=v[2],
                                          padding=v[3],
                                          kernel_initializer=self._weight_init,
                                          kernel_regularizer=l2(self._weight_decay),
                                          use_bias=self._use_bias)(convs)
                else:
                    convs = BatchNormalization(axis=self._channel_axis)(convs)
                    convs = Activation("relu")(convs)
                    if self._dropout_probability > 0:
                        convs = Dropout(self._dropout_probability)(convs)
                    convs = Conv2D(n_bottleneck_plane, kernel_size=(v[0], v[1]),
                                          strides=v[2],
                                          padding=v[3],
                                          kernel_initializer=self._weight_init,
                                          kernel_regularizer=l2(self._weight_decay),
                                          use_bias=self._use_bias)(convs)

            # Shortcut Connection

            if n_input_plane != n_output_plane:
                shortcut = Conv2D(n_output_plane, kernel_size=(1, 1),
                                         strides=stride,
                                         padding="same",
                                         kernel_initializer=self._weight_init,
                                         kernel_regularizer=l2(self._weight_decay),
                                         use_bias=self._use_bias)(net)
            else:
                shortcut = net

            return add([convs, shortcut])

        return f


    # "Stacking Residual Units on the same stage"
    def _layer(self, block, n_input_plane, n_output_plane, count, stride):
        def f(net):
            net = block(n_input_plane, n_output_plane, stride)(net)
            for i in range(2, int(count + 1)):
                net = block(n_output_plane, n_output_plane, stride=(1, 1))(net)
            return net

        return f


    def __call__(self):
        logging.debug("Creating model...")

        assert ((self._depth - 4) % 6 == 0)
        n = (self._depth - 4) / 6

        inputs = Input(shape=self._input_shape)

        n_stages = [16, 16 * self._k, 32 * self._k, 64 * self._k]

        conv1 = Conv2D(filters=n_stages[0], kernel_size=(3, 3),
                              strides=(1, 1),
                              padding="same",
                              kernel_initializer=self._weight_init,
                              kernel_regularizer=l2(self._weight_decay),
                              use_bias=self._use_bias)(inputs)


        block_fn = self._wide_basic
        conv2 = self._layer(block_fn, n_input_plane=n_stages[0], n_output_plane=n_stages[1], count=n, stride=(1, 1))(conv1)
        conv3 = self._layer(block_fn, n_input_plane=n_stages[1], n_output_plane=n_stages[2], count=n, stride=(2, 2))(conv2)
        conv4 = self._layer(block_fn, n_input_plane=n_stages[2], n_output_plane=n_stages[3], count=n, stride=(2, 2))(conv3)
        batch_norm = BatchNormalization(axis=self._channel_axis)(conv4)
        relu = Activation("relu")(batch_norm)

        # Classifier block
        pool = AveragePooling2D(pool_size=(8, 8), strides=(1, 1), padding="same")(relu)
        flatten = Flatten()(pool)
        predictions_g = Dense(units=2, kernel_initializer=self._weight_init, use_bias=self._use_bias,
                              kernel_regularizer=l2(self._weight_decay), activation="softmax")(flatten)
        predictions_a = Dense(units=101, kernel_initializer=self._weight_init, use_bias=self._use_bias,
                              kernel_regularizer=l2(self._weight_decay), activation="softmax")(flatten)

        model = Model(inputs=inputs, outputs=[predictions_g, predictions_a])

        return model