nodule_cnn_training.py

from keras import backend as K

K.clear_session()

from keras.models import Model
from keras.layers import Input, Activation, Dense, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, AveragePooling2D
from keras.regularizers import l2
from keras.layers.merge import add
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adadelta

import six

train_data_dir = "../Pulmonary_nodules_data/train/"
val_data_dir = "../Pulmonary_nodules_data/val/"


def _bn_relu(input):
    """Helper to build a BN -> relu block
    """
    norm = BatchNormalization()(input)
    return Activation("relu")(norm)


def _conv_bn_relu(**conv_params):
    """Helper to build a conv -> BN -> relu block
    """
    filters = conv_params["filters"]
    kernel_size = conv_params["kernel_size"]
    strides = conv_params.setdefault("strides", (1, 1))
    kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal")
    padding = conv_params.setdefault("padding", "same")
    kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4))

    def f(input):
        conv = Conv2D(filters=filters, kernel_size=kernel_size,
                      strides=strides, padding=padding,
                      kernel_initializer=kernel_initializer,
                      kernel_regularizer=kernel_regularizer)(input)
        return _bn_relu(conv)

    return f


def _bn_relu_conv(**conv_params):
    """Helper to build a BN -> relu -> conv block.
    """
    filters = conv_params["filters"]
    kernel_size = conv_params["kernel_size"]
    strides = conv_params.setdefault("strides", (1, 1))
    kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal")
    padding = conv_params.setdefault("padding", "same")
    kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4))

    def f(input):
        activation = _bn_relu(input)
        return Conv2D(filters=filters, kernel_size=kernel_size,
                      strides=strides, padding=padding,
                      kernel_initializer=kernel_initializer,
                      kernel_regularizer=kernel_regularizer)(input)

    return f


def _shortcut(input, residual):
    """Adds a shortcut between input and residual block and merges them with "sum"
    """
    # Expand channels of shortcut to match residual
    # Stride appropriately to match residual (width, height)
    # Should be int if network architecture is correctly configured.
    input_shape = K.int_shape(input)
    residual_shape = K.int_shape(residual)
    stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS]))
    stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS]))
    equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS]

    shortcut = input
    if stride_width > 1 or stride_height > 1 or not equal_channels:
        shortcut = Conv2D(filters=residual_shape[CHANNEL_AXIS],
                          kernel_size=(1, 1),
                          strides=(stride_width, stride_height),
                          padding="valid",
                          kernel_initializer="he_normal",
                          kernel_regularizer=l2(0.0001))(input)
    return add([shortcut, residual])


def _residual_block(block_function, filters, repetitions, is_first_layer=False):
    """Builds a residual block with repeating bottleneck blocks.
    """

    def f(input):
        for i in range(repetitions):
            init_strides = (1, 1)
            if i == 0 and not is_first_layer:
                init_strides = (2, 2)
            input = block_function(filters=filters, init_strides=init_strides,
                                   is_first_block_of_first_layer=(is_first_layer and i == 0))(input)
        return input

    return f


def basic_block(filters, init_strides=(1, 1), is_first_block_of_first_layer=False):
    """Basic 3 X 3 convolution blocks for use on resnets with layers <= 34."""

    def f(input):
        if is_first_block_of_first_layer:
            conv1 = Conv2D(filters=filters, kernel_size=(3, 3),
                           strides=init_strides,
                           padding="same",
                           kernel_initializer="he_normal",
                           kernel_regularizer=l2(1e-4))(input)
        else:
            conv1 = _bn_relu_conv(filters=filters, kernel_size=(3, 3),
                                  strides=init_strides)(input)
        residual = _bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv1)
        return _shortcut(input, residual)

    return f


def _handle_dim_ordering():
    global ROW_AXIS
    global COL_AXIS
    global CHANNEL_AXIS
    if K.image_dim_ordering() == 'tf':
        ROW_AXIS = 1
        COL_AXIS = 2
        CHANNEL_AXIS = 3
    else:
        ROW_AXIS = 2
        COL_AXIS = 3
        CHANNEL_AXIS = 1


def _get_block(identifier):
    if isinstance(identifier, six.string_types):
        res = globals().get(identifier)
        if not res:
            raise ValueError('Invalid {}'.format(identifier))
        return res
    return identifier


class ResnetBuilder(object):
    @staticmethod
    def build(input_shape, num_outputs, block_fn, repetitions):
        """Builds a custom ResNet like architecture.

        Args: input_shape: The input shape in the form (nb_channels, nb_rows, nb_cols)
              num_outputs: The number of outputs at final softmax layer
              block_fn: The block function to use. This is either 'basic_block' or 'bottleneck'.
                  The original paper used basic_block for layers < 50
              repetitions: Number of repetitions of various block units.
                  At each block unit, the number of filters are doubled and the input size is halved

        Returns:
            The keras 'Model'
        """
        _handle_dim_ordering()
        # load function from str if needed.
        block_fn = _get_block(block_fn)

        input = Input(shape=input_shape)
        conv1 = _conv_bn_relu(filters=64, kernel_size=(3, 3), padding="same")(input)
        pool1 = MaxPooling2D(strides=(2, 2))(conv1)

        block = pool1
        filters = 64
        for i, r in enumerate(repetitions):
            block = _residual_block(block_fn, filters=filters, repetitions=r, is_first_layer=(i == 0))(block)
            filters *= 2

        # Last activation
        block = _bn_relu(block)

        # Classifier block
        block_shape = K.int_shape(block)
        pool2 = AveragePooling2D(pool_size=(block_shape[ROW_AXIS], block_shape[COL_AXIS]),
                                 strides=(1, 1))(block)
        flatten1 = Flatten()(pool2)

        # fc1 = Dense(512)(flatten1)
        # bn1 = BatchNormalization()(fc1)
        # ac1 = Activation("relu")(bn1)

        dense = Dense(units=num_outputs, kernel_initializer="he_normal",
                      activation="softmax")(flatten1)

        model = Model(inputs=input, outputs=dense)
        return model

    @staticmethod
    def build_resnet_18(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, basic_block, [2, 2, 2, 2])

    @staticmethod
    def build_resnet_34(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, basic_block, [3, 4, 6, 3])


batch_size = 128
train_datagen = ImageDataGenerator(rescale=1. / 255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)
validation_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(train_data_dir,
                                                    target_size=(224, 224),
                                                    batch_size=batch_size,
                                                    class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(val_data_dir,
                                                              target_size=(224, 224),
                                                              batch_size=batch_size,
                                                              class_mode='categorical')

num_classes = 2
epochs = 20
input_shape = (224, 224, 3)

model = ResnetBuilder.build_resnet_18(input_shape, num_classes)
model.compile(loss='categorical_crossentropy',
              optimizer=Adadelta(),
              metrics=['accuracy'])
model.fit_generator(train_generator,
                    steps_per_epoch=20059 / batch_size,
                    epochs=epochs,
                    validation_data=validation_generator,
                    validation_steps=5040 / batch_size)