# Xception V1 model for Keras.
# On ImageNet, this model gets to a top-1 validation accuracy of 0.790
# and a top-5 validation accuracy of 0.945.
# Do note that the input image format for this model is different than for
# the VGG16 and ResNet models (299x299 instead of 224x224),
# and that the input preprocessing function
# is also different (same as Inception V3).
# Reference
# - [Xception: Deep Learning with Depthwise Separable Convolutions](
#    https://arxiv.org/abs/1610.02357) (CVPR 2017)

# Modified by Jacob M. Graving from:
# https://github.com/keras-team/keras-applications/blob/
# master/keras_applications/mobilenet_v2.py

# to match the stride 16 ResNet found here:
# https://github.com/tensorflow/tensorflow/blob/
# master/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py

# All modifications are Copyright 2019 Jacob M. Graving <jgraving@gmail.com>

from __future__ import print_function
from __future__ import absolute_import
from __future__ import division

import os
import warnings
import numpy as np
import tensorflow.keras as keras

from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
from tensorflow.keras.layers import Layer
from tensorflow.python.keras.applications import keras_applications
from tensorflow.python.keras.applications.mobilenet_v2 import preprocess_input

correct_pad = keras_applications.correct_pad
_obtain_input_shape = imagenet_utils.imagenet_utils._obtain_input_shape


backend = keras.backend
layers = keras.layers
models = keras.models
keras_utils = keras.utils

TF_WEIGHTS_PATH = (
    "https://github.com/fchollet/deep-learning-models/"
    "releases/download/v0.4/"
    "xception_weights_tf_dim_ordering_tf_kernels.h5"
)
TF_WEIGHTS_PATH_NO_TOP = (
    "https://github.com/fchollet/deep-learning-models/"
    "releases/download/v0.4/"
    "xception_weights_tf_dim_ordering_tf_kernels_notop.h5"
)


def Xception(
    include_top=False,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs
):
    """Instantiates the Xception architecture.
    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.
    Note that the default input image size for this model is 299x299.
    # 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 `(299, 299, 3)`.
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 71.
            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 block.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional block, 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.
        RuntimeError: If attempting to run this model with a
            backend that does not support separable convolutions.
    """

    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"
        )

    # Determine proper input shape
    input_shape = _obtain_input_shape(
        input_shape,
        default_size=299,
        min_size=71,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights,
    )

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor

    channel_axis = 1 if backend.image_data_format() == "channels_first" else -1

    x = layers.Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name="block1_conv1")(
        img_input
    )
    x = layers.BatchNormalization(axis=channel_axis, name="block1_conv1_bn")(x)
    x = layers.Activation("relu", name="block1_conv1_act")(x)
    x = layers.Conv2D(64, (3, 3), use_bias=False, name="block1_conv2")(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block1_conv2_bn")(x)
    x = layers.Activation("relu", name="block1_conv2_act")(x)

    residual = layers.Conv2D(
        128, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.SeparableConv2D(
        128, (3, 3), padding="same", use_bias=False, name="block2_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv1_bn")(x)
    x = layers.Activation("relu", name="block2_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        128, (3, 3), padding="same", use_bias=False, name="block2_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv2_bn")(x)

    x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same", name="block2_pool")(
        x
    )
    x = layers.add([x, residual])

    residual = layers.Conv2D(
        256, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block3_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        256, (3, 3), padding="same", use_bias=False, name="block3_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv1_bn")(x)
    x = layers.Activation("relu", name="block3_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        256, (3, 3), padding="same", use_bias=False, name="block3_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv2_bn")(x)

    x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same", name="block3_pool")(
        x
    )
    x = layers.add([x, residual])

    residual = layers.Conv2D(
        728, (1, 1), strides=(2, 2), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block4_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block4_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv1_bn")(x)
    x = layers.Activation("relu", name="block4_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block4_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv2_bn")(x)

    x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same", name="block4_pool")(
        x
    )
    x = layers.add([x, residual])

    for i in range(8):
        residual = x
        prefix = "block" + str(i + 5)

        x = layers.Activation("relu", name=prefix + "_sepconv1_act")(x)
        x = layers.SeparableConv2D(
            728, (3, 3), padding="same", use_bias=False, name=prefix + "_sepconv1"
        )(x)
        x = layers.BatchNormalization(axis=channel_axis, name=prefix + "_sepconv1_bn")(
            x
        )
        x = layers.Activation("relu", name=prefix + "_sepconv2_act")(x)
        x = layers.SeparableConv2D(
            728, (3, 3), padding="same", use_bias=False, name=prefix + "_sepconv2"
        )(x)
        x = layers.BatchNormalization(axis=channel_axis, name=prefix + "_sepconv2_bn")(
            x
        )
        x = layers.Activation("relu", name=prefix + "_sepconv3_act")(x)
        x = layers.SeparableConv2D(
            728, (3, 3), padding="same", use_bias=False, name=prefix + "_sepconv3"
        )(x)
        x = layers.BatchNormalization(axis=channel_axis, name=prefix + "_sepconv3_bn")(
            x
        )

        x = layers.add([x, residual])

    residual = layers.Conv2D(
        1024, (1, 1), strides=(1, 1), padding="same", use_bias=False
    )(x)
    residual = layers.BatchNormalization(axis=channel_axis)(residual)

    x = layers.Activation("relu", name="block13_sepconv1_act")(x)
    x = layers.SeparableConv2D(
        728, (3, 3), padding="same", use_bias=False, name="block13_sepconv1"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block13_sepconv1_bn")(x)
    x = layers.Activation("relu", name="block13_sepconv2_act")(x)
    x = layers.SeparableConv2D(
        1024, (3, 3), padding="same", use_bias=False, name="block13_sepconv2"
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block13_sepconv2_bn")(x)

    x = layers.MaxPooling2D(
        (3, 3), strides=(1, 1), padding="same", name="block13_pool"
    )(x)
    x = layers.add([x, residual])

    x = layers.SeparableConv2D(
        1536,
        (3, 3),
        padding="same",
        use_bias=False,
        dilation_rate=2,
        name="block14_sepconv1",
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block14_sepconv1_bn")(x)
    x = layers.Activation("relu", name="block14_sepconv1_act")(x)

    x = layers.SeparableConv2D(
        2048,
        (3, 3),
        padding="same",
        use_bias=False,
        dilation_rate=2,
        name="block14_sepconv2",
    )(x)
    x = layers.BatchNormalization(axis=channel_axis, name="block14_sepconv2_bn")(x)
    x = layers.Activation("relu", name="block14_sepconv2_act")(x)

    if include_top:
        x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
        x = layers.Dense(classes, activation="softmax", name="predictions")(x)
    else:
        if pooling == "avg":
            x = layers.GlobalAveragePooling2D()(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = keras_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = models.Model(inputs, x, name="xception")

    # Load weights.
    if weights == "imagenet":
        if include_top:
            weights_path = keras_utils.get_file(
                "xception_weights_tf_dim_ordering_tf_kernels.h5",
                TF_WEIGHTS_PATH,
                cache_subdir="models",
                file_hash="0a58e3b7378bc2990ea3b43d5981f1f6",
            )
        else:
            weights_path = keras_utils.get_file(
                "xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
                TF_WEIGHTS_PATH_NO_TOP,
                cache_subdir="models",
                file_hash="b0042744bf5b25fce3cb969f33bebb97",
            )
        model.load_weights(weights_path)
        if backend.backend() == "theano":
            keras_utils.convert_all_kernels_in_model(model)
    elif weights is not None:
        model.load_weights(weights)

    return model


def preprocess_input(x, **kwargs):
    """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", **kwargs)


MODELS = {"xception": Xception}


if __name__ == "__main__":

    from tensorflow.keras.applications.xception import preprocess_input
    from tensorflow.keras.layers import Input
    from tensorflow.keras import Model

    input_layer = Input((192, 192, 3))
    model = Xception(include_top=False)
    pretrained_output = model(input_layer)
    model = Model(inputs=input_layer, outputs=pretrained_output)