inception_resnet_v2_with_horovod.py

# -*- coding: utf-8 -*-
"""Inception-ResNet V2 model for Keras.
Model naming and structure follows TF-slim implementation (which has some additional
layers and different number of filters from the original arXiv paper):
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
Pre-trained ImageNet weights are also converted from TF-slim, which can be found in:
https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
# Reference
- [Inception-v4, Inception-ResNet and the Impact of
   Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
"""
from __future__ import print_function
from __future__ import absolute_import

import warnings
import os
import argparse
import ConfigParser

import numpy as np
import keras
from keras.models import Model
from keras import layers
from keras.datasets import cifar10
from keras.layers import Activation
from keras.layers import AveragePooling2D
from keras.layers import BatchNormalization
from keras.layers import Concatenate
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import Input
from keras.layers import Lambda
from keras.layers import MaxPooling2D
from keras.utils.data_utils import get_file
from keras.utils import np_utils
from training_utils import multi_gpu_model
from keras.engine.topology import get_source_inputs
from keras.optimizers import SGD
from keras.callbacks import Callback, LearningRateScheduler, ModelCheckpoint, EarlyStopping, TensorBoard
from keras.preprocessing.image import ImageDataGenerator
import imagenet_utils
from imagenet_utils import _obtain_input_shape
from imagenet_utils import decode_predictions
from keras import backend as K
import horovod.keras as hvd

import explore_data as Data

BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/'

#Read hyperparameter
config = ConfigParser.ConfigParser()
config.readfp(open(r'solver.prototxt'))
nb_class = int(config.get('My Section', 'num_classes'))
batch_size = int(config.get('My Section', 'batch_size'))
nb_epoch = int(config.get('My Section', 'nb_epoch'))
data_augmentation = bool(config.get('My Section', 'data_augmentation'))
lr = float(config.get('My Section', 'lr'))

# Initialize Horovod.
hvd.init()

# Pin GPU to be used to process local rank (one GPU per process)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = str(hvd.local_rank())
K.set_session(tf.Session(config=config))

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')


def conv2d_bn(x,
              filters,
              kernel_size,
              strides=1,
              padding='same',
              activation='relu',
              use_bias=False,
              name=None):
    """Utility function to apply conv + BN.
    # Arguments
        x: input tensor.
        filters: filters in `Conv2D`.
        kernel_size: kernel size as in `Conv2D`.
        strides: strides in `Conv2D`.
        padding: padding mode in `Conv2D`.
        activation: activation in `Conv2D`.
        use_bias: whether to use a bias in `Conv2D`.
        name: name of the ops; will become `name + '_ac'` for the activation
            and `name + '_bn'` for the batch norm layer.
    # Returns
        Output tensor after applying `Conv2D` and `BatchNormalization`.
    """
    x = Conv2D(filters,
               kernel_size,
               strides=strides,
               padding=padding,
               use_bias=use_bias,
               name=name)(x)
    if not use_bias:
        bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
        bn_name = None if name is None else name + '_bn'
        x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
    if activation is not None:
        ac_name = None if name is None else name + '_ac'
        x = Activation(activation, name=ac_name)(x)
    return x


def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
    """Adds a Inception-ResNet block.
    This function builds 3 types of Inception-ResNet blocks mentioned
    in the paper, controlled by the `block_type` argument (which is the
    block name used in the official TF-slim implementation):
        - Inception-ResNet-A: `block_type='block35'`
        - Inception-ResNet-B: `block_type='block17'`
        - Inception-ResNet-C: `block_type='block8'`
    # Arguments
        x: input tensor.
        scale: scaling factor to scale the residuals (i.e., the output of
            passing `x` through an inception module) before adding them
            to the shortcut branch. Let `r` be the output from the residual branch,
            the output of this block will be `x + scale * r`.
        block_type: `'block35'`, `'block17'` or `'block8'`, determines
            the network structure in the residual branch.
        block_idx: an `int` used for generating layer names. The Inception-ResNet blocks
            are repeated many times in this network. We use `block_idx` to identify
            each of the repetitions. For example, the first Inception-ResNet-A block
            will have `block_type='block35', block_idx=0`, ane the layer names will have
            a common prefix `'block35_0'`.
        activation: activation function to use at the end of the block
            (see [activations](../activations.md)).
            When `activation=None`, no activation is applied
            (i.e., "linear" activation: `a(x) = x`).
    # Returns
        Output tensor for the block.
    # Raises
        ValueError: if `block_type` is not one of `'block35'`,
            `'block17'` or `'block8'`.
    """
    if block_type == 'block35':
        branch_0 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(branch_1, 32, 3)
        branch_2 = conv2d_bn(x, 32, 1)
        branch_2 = conv2d_bn(branch_2, 48, 3)
        branch_2 = conv2d_bn(branch_2, 64, 3)
        branches = [branch_0, branch_1, branch_2]
    elif block_type == 'block17':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 128, 1)
        branch_1 = conv2d_bn(branch_1, 160, [1, 7])
        branch_1 = conv2d_bn(branch_1, 192, [7, 1])
        branches = [branch_0, branch_1]
    elif block_type == 'block8':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(branch_1, 224, [1, 3])
        branch_1 = conv2d_bn(branch_1, 256, [3, 1])
        branches = [branch_0, branch_1]
    else:
        raise ValueError('Unknown Inception-ResNet block type. '
                         'Expects "block35", "block17" or "block8", '
                         'but got: ' + str(block_type))

    block_name = block_type + '_' + str(block_idx)
    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
    mixed = Concatenate(axis=channel_axis, name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed,
                   K.int_shape(x)[channel_axis],
                   1,
                   activation=None,
                   use_bias=True,
                   name=block_name + '_conv')

    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
               output_shape=K.int_shape(x)[1:],
               arguments={'scale': scale},
               name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)
    return x


def InceptionResNetV2(include_top=True,
                      weights='imagenet',
                      input_tensor=None,
                      input_shape=None,
                      pooling=None,
                      bottleneck=None,
                      G=1,
                      classes=1000):
    """Instantiates the Inception-ResNet v2 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 TensorFlow, Theano and
    CNTK backends. The data format convention used by the model is
    the one specified in your Keras config file.
    Note that the default input image size for this model is 299x299, instead
    of 224x224 as in the VGG16 and ResNet models. Also, the input preprocessing
    function is different (i.e., do not use `imagenet_utils.preprocess_input()`
    with this model. Use `preprocess_input()` defined in this module instead).
    # Arguments
        include_top: whether to include the fully-connected
            layer at the top of the network.
        weights: one of `None` (random initialization)
            or `'imagenet'` (pre-training on ImageNet).
        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 `(299, 299, 3)` (with `'channels_last'` data format)
            or `(3, 299, 299)` (with `'channels_first'` data format).
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 139.
            E.g. `(150, 150, 3)` would be one valid value.
        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 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
    input_shape = _obtain_input_shape(
        input_shape,
        default_size=299,
        min_size=139,
        data_format=K.image_data_format(),
        require_flatten=False,
        weights=weights)

    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

    # Stem block: 35 x 35 x 192
    x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
    x = conv2d_bn(x, 32, 3, padding='valid')
    x = conv2d_bn(x, 64, 3)
    x = MaxPooling2D(3, strides=2)(x)
    x = conv2d_bn(x, 80, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, padding='valid')
    x = MaxPooling2D(3, strides=2)(x)

    # Mixed 5b (Inception-A block): 35 x 35 x 320
    branch_0 = conv2d_bn(x, 96, 1)
    branch_1 = conv2d_bn(x, 48, 1)
    branch_1 = conv2d_bn(branch_1, 64, 5)
    branch_2 = conv2d_bn(x, 64, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
    x = Concatenate(axis=channel_axis, name='mixed_5b')(branches)

    # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
    for block_idx in range(1, 11):
        x = inception_resnet_block(x,
                                   scale=0.17,
                                   block_type='block35',
                                   block_idx=block_idx)

    # Mixed 6a (Reduction-A block): 17 x 17 x 1088
    branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 256, 3)
    branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
    branches = [branch_0, branch_1, branch_pool]
    x = Concatenate(axis=channel_axis, name='mixed_6a')(branches)

    # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
    for block_idx in range(1, 21):
        x = inception_resnet_block(x,
                                   scale=0.1,
                                   block_type='block17',
                                   block_idx=block_idx)

    # Mixed 7a (Reduction-B block): 8 x 8 x 2080
    branch_0 = conv2d_bn(x, 256, 1)
    branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
    branch_2 = conv2d_bn(x, 256, 1)
    branch_2 = conv2d_bn(branch_2, 288, 3)
    branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = Concatenate(axis=channel_axis, name='mixed_7a')(branches)

    # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
    for block_idx in range(1, 10):
        x = inception_resnet_block(x,
                                   scale=0.2,
                                   block_type='block8',
                                   block_idx=block_idx)
    x = inception_resnet_block(x,
                               scale=1.,
                               activation=None,
                               block_type='block8',
                               block_idx=10)

    # Final convolution block: 8 x 8 x 1536
    x = conv2d_bn(x, 1536, 1, name='conv_7b')

    if include_top:
        # Classification block
        x = GlobalAveragePooling2D(name='avg_pool')(x)
        x = Dense(classes, activation='softmax', name='predictions')(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='inception_resnet_v2')

    # Load weights
    if weights == 'imagenet':
        if K.image_data_format() == 'channels_first':
            if 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.')
        if include_top:
            weights_filename = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
            weights_path = get_file(weights_filename,
                                    BASE_WEIGHT_URL + weights_filename,
                                    cache_subdir='models',
                                    file_hash='e693bd0210a403b3192acc6073ad2e96')
        else:
            weights_filename = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5'
            weights_path = get_file(weights_filename,
                                    BASE_WEIGHT_URL + weights_filename,
                                    cache_subdir='models',
                                    file_hash='d19885ff4a710c122648d3b5c3b684e4')
        model.load_weights(weights_path)

    return model

def main():
    #Create data
    train_gen, val_gen = Data.create_data(G=1)

    #Create Model
    model = InceptionResNetV2(include_top=True,weights=None,input_tensor=None,input_shape=None,pooling=None,bottleneck=None,classes=nb_class)
    
    # Adjust learning rate based on number of GPUs.
    opt = keras.optimizers.Adadelta(lr=lr * hvd.size())

    # Add Horovod Distributed Optimizer.
    opt = hvd.DistributedOptimizer(opt)

    model.compile(optimizer=opt, loss="categorical_crossentropy", metrics=["accuracy"])
    model.summary()
    
    # Model saving callback
    callbacks = [
        # Broadcast initial variable states from rank 0 to all other processes.
        # This is necessary to ensure consistent initialization of all workers when
        # training is started with random weights or restored from a checkpoint.
        hvd.callbacks.BroadcastGlobalVariablesCallback(0),

        # Average metrics among workers at the end of every epoch.
        #
        # Note: This callback must be in the list before the ReduceLROnPlateau,
        # TensorBoard or other metrics-based callbacks.
        hvd.callbacks.MetricAverageCallback(),

        # Using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
        # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
        # the first five epochs. See https://arxiv.org/abs/1706.02677 for details.
        hvd.callbacks.LearningRateWarmupCallback(warmup_epochs=5, verbose=1),

        # Reduce the learning rate if training plateaues.
        keras.callbacks.ReduceLROnPlateau(patience=10, verbose=1),

        TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)
    ]

    # Save checkpoints only on worker 0 to prevent other workers from corrupting them.
    if hvd.rank() == 0:
        callbacks.append(keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))   
    
    if not data_augmentation:
        print('Not using data augmentation.')
        history = model.fit(x_train, y_train, 
                            batch_size=batch_size, nb_epoch=nb_epoch, verbose=1,
                            validation_data=(x_test, y_test), shuffle=True,
                            callbacks=[checkpointer, tensorboard])
    else:
        print('Using real-time data augmentation.')

        # fit the model on the batches generated by datagen.flow()
        # Train the model. The training will randomly sample 1 / N batches of training data and
        # 3 / N batches of validation data on every worker, where N is the number of workers.
        # Over-sampling of validation data helps to increase probability that every validation
        # example will be evaluated.
        history = model.fit_generator(train_gen,
                                    nb_epoch=nb_epoch, verbose=1,
                                    validation_data=val_gen,
                                    steps_per_epoch=train_gen.samples // (batch_size * hvd.size()),  #num_val_images // batch_size,
                                    validation_steps=3 * val_gen.samples // (batch_size * hvd.size()),
                                    callbacks=callbacks)

if __name__ == "__main__":
    main()