"""Trains a ResNet on the CIFAR10 dataset.

ResNet v1
[a] Deep Residual Learning for Image Recognition
https://arxiv.org/pdf/1512.03385.pdf

ResNet v2
[b] Identity Mappings in Deep Residual Networks
https://arxiv.org/pdf/1603.05027.pdf
"""

from __future__ import print_function
import keras
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import AveragePooling2D, Input, Flatten
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras.datasets import cifar10
import numpy as np
import os

# Training parameters
batch_size = 32
epochs = 200
data_augmentation = True
num_classes = 10

# Subtracting pixel mean improves accuracy
subtract_pixel_mean = True

# Model parameter
# ----------------------------------------------------------------------------
#           |      | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
# Model     |  n   | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1090Ti
#           |      | %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
# ----------------------------------------------------------------------------
# ResNet20  |  3   | 92.16     | 91.25     | -----     | NA        | 35
# ResNet32  |  5   | 92.46     | 92.49     | -----     | NA        | 50
# ResNet44  |  7   | 92.50     | 92.83     | -----     | NA        | 70
# ResNet56  |  9   | 92.71     | 93.03     | 92.60     | NA        | 90 (100)
# ResNet110 |  18  | 92.65     | 93.39     | 93.03     | 93.63     | 165(180)
# ---------------------------------------------------------------------------
n = 3

# Model version
# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
version = 1

# Computed depth from supplied model parameter n
depth = n * 6 + 2

# Model name, depth and version
model_type = 'ResNet%dv%d' % (depth, version)

# Load the CIFAR10 data.
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Input image dimensions.
input_shape = x_train.shape[1:]

# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# If subtract pixel mean is enabled
if subtract_pixel_mean:
    x_train_mean = np.mean(x_train, axis=0)
    x_train -= x_train_mean
    x_test -= x_train_mean

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)

# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


def lr_schedule(epoch):
    """Learning Rate Schedule

    Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
    Called automatically every epoch as part of callbacks during training.

    # Arguments
        epoch (int): The number of epochs

    # Returns
        lr (float32): learning rate
    """
    lr = 1e-3
    if epoch > 180:
        lr *= 0.5e-3
    elif epoch > 160:
        lr *= 1e-3
    elif epoch > 120:
        lr *= 1e-2
    elif epoch > 80:
        lr *= 1e-1
    print('Learning rate: ', lr)
    return lr


def resnet_block(inputs,
                 num_filters=16,
                 kernel_size=3,
                 strides=1,
                 activation='relu',
                 conv_first=True):
    """2D Convolution-Batch Normalization-Activation stack builder

    # Arguments
        inputs (tensor): input tensor from input image or previous layer
        num_filters (int): Conv2D number of filters
        kernel_size (int): Conv2D square kernel dimensions
        strides (int): Conv2D square stride dimensions
        activation (string): activation name
        conv_first (bool): conv-bn-activation (True) or
            activation-bn-conv (False)

    # Returns
        x (tensor): tensor as input to the next layer
    """
    if conv_first:
        x = Conv2D(num_filters,
                   kernel_size=kernel_size,
                   strides=strides,
                   padding='same',
                   kernel_initializer='he_normal',
                   kernel_regularizer=l2(1e-4))(inputs)
        x = BatchNormalization()(x)
        if activation:
            x = Activation(activation)(x)
        return x
    x = BatchNormalization()(inputs)
    if activation:
        x = Activation('relu')(x)
    x = Conv2D(num_filters,
               kernel_size=kernel_size,
               strides=strides,
               padding='same',
               kernel_initializer='he_normal',
               kernel_regularizer=l2(1e-4))(x)
    return x


def resnet_v1(input_shape, depth, num_classes=10):
    """ResNet Version 1 Model builder [a]

    Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
    Last ReLU is after the shortcut connection.
    The number of filters doubles when the feature maps size
    is halved.
    The Number of parameters is approx the same as Table 6 of [a]:
    ResNet20 0.27M
    ResNet32 0.46M
    ResNet44 0.66M
    ResNet56 0.85M
    ResNet110 1.7M

    # Arguments
        input_shape (tensor): shape of input image tensor
        depth (int): number of core convolutional layers
        num_classes (int): number of classes (CIFAR10 has 10)

    # Returns
        model (Model): Keras model instance
    """
    if (depth - 2) % 6 != 0:
        raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
    # Start model definition.
    inputs = Input(shape=input_shape)
    num_filters = 16
    num_sub_blocks = int((depth - 2) / 6)

    x = resnet_block(inputs=inputs)
    # Instantiate convolutional base (stack of blocks).
    for i in range(3):
        for j in range(num_sub_blocks):
            strides = 1
            is_first_layer_but_not_first_block = j == 0 and i > 0
            if is_first_layer_but_not_first_block:
                strides = 2
            y = resnet_block(inputs=x,
                             num_filters=num_filters,
                             strides=strides)
            y = resnet_block(inputs=y,
                             num_filters=num_filters,
                             activation=None)
            if is_first_layer_but_not_first_block:
                x = resnet_block(inputs=x,
                                 num_filters=num_filters,
                                 kernel_size=1,
                                 strides=strides,
                                 activation=None)
            x = keras.layers.add([x, y])
            x = Activation('relu')(x)
        num_filters = 2 * num_filters

    # Add classifier on top.
    # v1 does not use BN after last shortcut connection-ReLU
    x = AveragePooling2D(pool_size=8)(x)
    y = Flatten()(x)
    outputs = Dense(num_classes,
                    activation='softmax',
                    kernel_initializer='he_normal')(y)

    # Instantiate model.
    model = Model(inputs=inputs, outputs=outputs)
    return model


def resnet_v2(input_shape, depth, num_classes=10):
    """ResNet Version 2 Model builder [b]

    Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
    bottleneck layer
    First shortcut connection per layer is 1 x 1 Conv2D.
    Second and onwards shortcut connection is identity.
    Features maps sizes: 16(input), 64(1st sub_block), 128(2nd), 256(3rd)

    # Arguments
        input_shape (tensor): shape of input image tensor
        depth (int): number of core convolutional layers
        num_classes (int): number of classes (CIFAR10 has 10)

    # Returns
        model (Model): Keras model instance
    """
    if (depth - 2) % 9 != 0:
        raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
    # Start model definition.
    inputs = Input(shape=input_shape)
    num_filters_in = 16
    num_filters_out = 64
    filter_multiplier = 4
    num_sub_blocks = int((depth - 2) / 9)

    # v2 performs Conv2D on input w/o BN-ReLU
    x = Conv2D(num_filters_in,
               kernel_size=3,
               padding='same',
               kernel_initializer='he_normal',
               kernel_regularizer=l2(1e-4))(inputs)

    # Instantiate convolutional base (stack of blocks).
    for i in range(3):
        if i > 0:
            filter_multiplier = 2
        num_filters_out = num_filters_in * filter_multiplier

        for j in range(num_sub_blocks):
            strides = 1
            is_first_layer_but_not_first_block = j == 0 and i > 0
            if is_first_layer_but_not_first_block:
                strides = 2
            y = resnet_block(inputs=x,
                             num_filters=num_filters_in,
                             kernel_size=1,
                             strides=strides,
                             conv_first=False)
            y = resnet_block(inputs=y,
                             num_filters=num_filters_in,
                             conv_first=False)
            y = resnet_block(inputs=y,
                             num_filters=num_filters_out,
                             kernel_size=1,
                             conv_first=False)
            if j == 0:
                x = Conv2D(num_filters_out,
                           kernel_size=1,
                           strides=strides,
                           padding='same',
                           kernel_initializer='he_normal',
                           kernel_regularizer=l2(1e-4))(x)
            x = keras.layers.add([x, y])

        num_filters_in = num_filters_out

    # Add classifier on top.
    # v2 has BN-ReLU before Pooling
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = AveragePooling2D(pool_size=8)(x)
    y = Flatten()(x)
    outputs = Dense(num_classes,
                    activation='softmax',
                    kernel_initializer='he_normal')(y)

    # Instantiate model.
    model = Model(inputs=inputs, outputs=outputs)
    return model


if version == 2:
    model = resnet_v2(input_shape=input_shape, depth=depth)
else:
    model = resnet_v1(input_shape=input_shape, depth=depth)

model.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=lr_schedule(0)),
              metrics=['accuracy'])
model.summary()
print(model_type)

# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
if not os.path.isdir(save_dir):
    os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)

# Prepare callbacks for model saving and for learning rate adjustment.
checkpoint = ModelCheckpoint(filepath=filepath,
                             monitor='val_acc',
                             verbose=1,
                             save_best_only=True)

lr_scheduler = LearningRateScheduler(lr_schedule)

lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
                               cooldown=0,
                               patience=5,
                               min_lr=0.5e-6)

callbacks = [checkpoint, lr_reducer, lr_scheduler]

# Run training, with or without data augmentation.
if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(x_train, y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_data=(x_test, y_test),
              shuffle=True,
              callbacks=callbacks)
else:
    print('Using real-time data augmentation.')
    # This will do preprocessing and realtime data augmentation:
    datagen = ImageDataGenerator(
        # set input mean to 0 over the dataset
        featurewise_center=False,
        # set each sample mean to 0
        samplewise_center=False,
        # divide inputs by std of dataset
        featurewise_std_normalization=False,
        # divide each input by its std
        samplewise_std_normalization=False,
        # apply ZCA whitening
        zca_whitening=False,
        # randomly rotate images in the range (deg 0 to 180)
        rotation_range=0,
        # randomly shift images horizontally
        width_shift_range=0.1,
        # randomly shift images vertically
        height_shift_range=0.1,
        # randomly flip images
        horizontal_flip=True,
        # randomly flip images
        vertical_flip=False)

    # Compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied).
    datagen.fit(x_train)

    # Fit the model on the batches generated by datagen.flow().
    model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
                        validation_data=(x_test, y_test),
                        epochs=epochs, verbose=1, workers=4,
                        callbacks=callbacks)

# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
