# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================


"""MobileNet v3 models for Keras."""

import tensorflow.compat.v2 as tf

from keras import backend
from keras import models
from keras.applications import imagenet_utils
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils

# isort: off
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export

# TODO(scottzhu): Change this to the GCS path.
BASE_WEIGHT_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v3/"
)
WEIGHTS_HASHES = {
    "large_224_0.75_float": (
        "765b44a33ad4005b3ac83185abf1d0eb",
        "40af19a13ebea4e2ee0c676887f69a2e",
    ),
    "large_224_1.0_float": (
        "59e551e166be033d707958cf9e29a6a7",
        "07fb09a5933dd0c8eaafa16978110389",
    ),
    "large_minimalistic_224_1.0_float": (
        "675e7b876c45c57e9e63e6d90a36599c",
        "ec5221f64a2f6d1ef965a614bdae7973",
    ),
    "small_224_0.75_float": (
        "cb65d4e5be93758266aa0a7f2c6708b7",
        "ebdb5cc8e0b497cd13a7c275d475c819",
    ),
    "small_224_1.0_float": (
        "8768d4c2e7dee89b9d02b2d03d65d862",
        "d3e8ec802a04aa4fc771ee12a9a9b836",
    ),
    "small_minimalistic_224_1.0_float": (
        "99cd97fb2fcdad2bf028eb838de69e37",
        "cde8136e733e811080d9fcd8a252f7e4",
    ),
}

layers = VersionAwareLayers()


BASE_DOCSTRING = """Instantiates the {name} architecture.

  Reference:
  - [Searching for MobileNetV3](
      https://arxiv.org/pdf/1905.02244.pdf) (ICCV 2019)

  The following table describes the performance of MobileNets v3:
  ------------------------------------------------------------------------
  MACs stands for Multiply Adds

  |Classification Checkpoint|MACs(M)|Parameters(M)|Top1 Accuracy|Pixel1 CPU(ms)|
  |---|---|---|---|---|
  | mobilenet_v3_large_1.0_224              | 217 | 5.4 |   75.6   |   51.2  |
  | mobilenet_v3_large_0.75_224             | 155 | 4.0 |   73.3   |   39.8  |
  | mobilenet_v3_large_minimalistic_1.0_224 | 209 | 3.9 |   72.3   |   44.1  |
  | mobilenet_v3_small_1.0_224              | 66  | 2.9 |   68.1   |   15.8  |
  | mobilenet_v3_small_0.75_224             | 44  | 2.4 |   65.4   |   12.8  |
  | mobilenet_v3_small_minimalistic_1.0_224 | 65  | 2.0 |   61.9   |   12.2  |

  For image classification use cases, see
  [this page for detailed examples](
    https://keras.io/api/applications/#usage-examples-for-image-classification-models).

  For transfer learning use cases, make sure to read the
  [guide to transfer learning & fine-tuning](
    https://keras.io/guides/transfer_learning/).

  Note: each Keras Application expects a specific kind of input preprocessing.
  For MobileNetV3, by default input preprocessing is included as a part of the
  model (as a `Rescaling` layer), and thus
  `tf.keras.applications.mobilenet_v3.preprocess_input` is actually a
  pass-through function. In this use case, MobileNetV3 models expect their
  inputs to be float tensors of pixels with values in the [0-255] range.
  At the same time, preprocessing as a part of the model (i.e. `Rescaling`
  layer) can be disabled by setting `include_preprocessing` argument to False.
  With preprocessing disabled MobileNetV3 models expect their inputs to be float
  tensors of pixels with values in the [-1, 1] range.

  Args:
    input_shape: Optional shape tuple, to be specified if you would
      like to use a model with an input image resolution that is not
      (224, 224, 3).
      It should have exactly 3 inputs channels (224, 224, 3).
      You can also omit this option if you would like
      to infer input_shape from an input_tensor.
      If you choose to include both input_tensor and input_shape then
      input_shape will be used if they match, if the shapes
      do not match then we will throw an error.
      E.g. `(160, 160, 3)` would be one valid value.
    alpha: controls the width of the network. This is known as the
      depth multiplier in the MobileNetV3 paper, but the name is kept for
      consistency with MobileNetV1 in Keras.
      - If `alpha` < 1.0, proportionally decreases the number
          of filters in each layer.
      - If `alpha` > 1.0, proportionally increases the number
          of filters in each layer.
      - If `alpha` = 1, default number of filters from the paper
          are used at each layer.
    minimalistic: In addition to large and small models this module also
      contains so-called minimalistic models, these models have the same
      per-layer dimensions characteristic as MobilenetV3 however, they don't
      utilize any of the advanced blocks (squeeze-and-excite units, hard-swish,
      and 5x5 convolutions). While these models are less efficient on CPU, they
      are much more performant on GPU/DSP.
    include_top: Boolean, whether to include the fully-connected
      layer at the top of the network. Defaults to `True`.
    weights: String, 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.
    pooling: String, 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: Integer, optional number of classes to classify images
      into, only to be specified if `include_top` is True, and
      if no `weights` argument is specified.
    dropout_rate: fraction of the input units to drop on the last layer.
    classifier_activation: A `str` or callable. The activation function to use
      on the "top" layer. Ignored unless `include_top=True`. Set
      `classifier_activation=None` to return the logits of the "top" layer.
      When loading pretrained weights, `classifier_activation` can only
      be `None` or `"softmax"`.
    include_preprocessing: Boolean, whether to include the preprocessing
      layer (`Rescaling`) at the bottom of the network. Defaults to `True`.

  Call arguments:
    inputs: A floating point `numpy.array` or a `tf.Tensor`, 4D with 3 color
      channels, with values in the range [0, 255] if `include_preprocessing`
      is True and in the range [-1, 1] otherwise.

  Returns:
    A `keras.Model` instance.
"""


def MobileNetV3(
    stack_fn,
    last_point_ch,
    input_shape=None,
    alpha=1.0,
    model_type="large",
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
):
    if not (weights in {"imagenet", None} or tf.io.gfile.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.  "
            f"Received weights={weights}"
        )

    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.  "
            f"Received classes={classes}"
        )

    # Determine proper input shape and default size.
    # If both input_shape and input_tensor are used, they should match
    if input_shape is not None and input_tensor is not None:
        try:
            is_input_t_tensor = backend.is_keras_tensor(input_tensor)
        except ValueError:
            try:
                is_input_t_tensor = backend.is_keras_tensor(
                    layer_utils.get_source_inputs(input_tensor)
                )
            except ValueError:
                raise ValueError(
                    "input_tensor: ",
                    input_tensor,
                    "is not type input_tensor.  "
                    f"Received type(input_tensor)={type(input_tensor)}",
                )
        if is_input_t_tensor:
            if backend.image_data_format() == "channels_first":
                if backend.int_shape(input_tensor)[1] != input_shape[1]:
                    raise ValueError(
                        "When backend.image_data_format()=channels_first, "
                        "input_shape[1] must equal "
                        "backend.int_shape(input_tensor)[1].  Received "
                        f"input_shape={input_shape}, "
                        "backend.int_shape(input_tensor)="
                        f"{backend.int_shape(input_tensor)}"
                    )
            else:
                if backend.int_shape(input_tensor)[2] != input_shape[1]:
                    raise ValueError(
                        "input_shape[1] must equal "
                        "backend.int_shape(input_tensor)[2].  Received "
                        f"input_shape={input_shape}, "
                        "backend.int_shape(input_tensor)="
                        f"{backend.int_shape(input_tensor)}"
                    )
        else:
            raise ValueError(
                "input_tensor specified: ",
                input_tensor,
                "is not a keras tensor",
            )

    # If input_shape is None, infer shape from input_tensor
    if input_shape is None and input_tensor is not None:

        try:
            backend.is_keras_tensor(input_tensor)
        except ValueError:
            raise ValueError(
                "input_tensor: ",
                input_tensor,
                "is type: ",
                type(input_tensor),
                "which is not a valid type",
            )

        if backend.is_keras_tensor(input_tensor):
            if backend.image_data_format() == "channels_first":
                rows = backend.int_shape(input_tensor)[2]
                cols = backend.int_shape(input_tensor)[3]
                input_shape = (3, cols, rows)
            else:
                rows = backend.int_shape(input_tensor)[1]
                cols = backend.int_shape(input_tensor)[2]
                input_shape = (cols, rows, 3)
    # If input_shape is None and input_tensor is None using standard shape
    if input_shape is None and input_tensor is None:
        input_shape = (None, None, 3)

    if backend.image_data_format() == "channels_last":
        row_axis, col_axis = (0, 1)
    else:
        row_axis, col_axis = (1, 2)
    rows = input_shape[row_axis]
    cols = input_shape[col_axis]
    if rows and cols and (rows < 32 or cols < 32):
        raise ValueError(
            "Input size must be at least 32x32; Received `input_shape="
            f"{input_shape}`"
        )
    if weights == "imagenet":
        if (
            not minimalistic
            and alpha not in [0.75, 1.0]
            or minimalistic
            and alpha != 1.0
        ):
            raise ValueError(
                "If imagenet weights are being loaded, "
                "alpha can be one of `0.75`, `1.0` for non minimalistic "
                "or `1.0` for minimalistic only."
            )

        if rows != cols or rows != 224:
            logging.warning(
                "`input_shape` is undefined or non-square, "
                "or `rows` is not 224. "
                "Weights for input shape (224, 224) will be "
                "loaded as the default."
            )

    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

    if minimalistic:
        kernel = 3
        activation = relu
        se_ratio = None
    else:
        kernel = 5
        activation = hard_swish
        se_ratio = 0.25

    x = img_input
    if include_preprocessing:
        x = layers.Rescaling(scale=1.0 / 127.5, offset=-1.0)(x)
    x = layers.Conv2D(
        16,
        kernel_size=3,
        strides=(2, 2),
        padding="same",
        use_bias=False,
        name="Conv",
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv/BatchNorm"
    )(x)
    x = activation(x)

    x = stack_fn(x, kernel, activation, se_ratio)

    last_conv_ch = _depth(backend.int_shape(x)[channel_axis] * 6)

    # if the width multiplier is greater than 1 we
    # increase the number of output channels
    if alpha > 1.0:
        last_point_ch = _depth(last_point_ch * alpha)
    x = layers.Conv2D(
        last_conv_ch,
        kernel_size=1,
        padding="same",
        use_bias=False,
        name="Conv_1",
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv_1/BatchNorm"
    )(x)
    x = activation(x)
    if include_top:
        x = layers.GlobalAveragePooling2D(keepdims=True)(x)
        x = layers.Conv2D(
            last_point_ch,
            kernel_size=1,
            padding="same",
            use_bias=True,
            name="Conv_2",
        )(x)
        x = activation(x)

        if dropout_rate > 0:
            x = layers.Dropout(dropout_rate)(x)
        x = layers.Conv2D(
            classes, kernel_size=1, padding="same", name="Logits"
        )(x)
        x = layers.Flatten()(x)
        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Activation(
            activation=classifier_activation, name="Predictions"
        )(x)
    else:
        if pooling == "avg":
            x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D(name="max_pool")(x)
    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = layer_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    model = models.Model(inputs, x, name="MobilenetV3" + model_type)

    # Load weights.
    if weights == "imagenet":
        model_name = "{}{}_224_{}_float".format(
            model_type, "_minimalistic" if minimalistic else "", str(alpha)
        )
        if include_top:
            file_name = "weights_mobilenet_v3_" + model_name + ".h5"
            file_hash = WEIGHTS_HASHES[model_name][0]
        else:
            file_name = "weights_mobilenet_v3_" + model_name + "_no_top_v2.h5"
            file_hash = WEIGHTS_HASHES[model_name][1]
        weights_path = data_utils.get_file(
            file_name,
            BASE_WEIGHT_PATH + file_name,
            cache_subdir="models",
            file_hash=file_hash,
        )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


@keras_export("keras.applications.MobileNetV3Small")
def MobileNetV3Small(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
):
    def stack_fn(x, kernel, activation, se_ratio):
        def depth(d):
            return _depth(d * alpha)

        x = _inverted_res_block(x, 1, depth(16), 3, 2, se_ratio, relu, 0)
        x = _inverted_res_block(x, 72.0 / 16, depth(24), 3, 2, None, relu, 1)
        x = _inverted_res_block(x, 88.0 / 24, depth(24), 3, 1, None, relu, 2)
        x = _inverted_res_block(
            x, 4, depth(40), kernel, 2, se_ratio, activation, 3
        )
        x = _inverted_res_block(
            x, 6, depth(40), kernel, 1, se_ratio, activation, 4
        )
        x = _inverted_res_block(
            x, 6, depth(40), kernel, 1, se_ratio, activation, 5
        )
        x = _inverted_res_block(
            x, 3, depth(48), kernel, 1, se_ratio, activation, 6
        )
        x = _inverted_res_block(
            x, 3, depth(48), kernel, 1, se_ratio, activation, 7
        )
        x = _inverted_res_block(
            x, 6, depth(96), kernel, 2, se_ratio, activation, 8
        )
        x = _inverted_res_block(
            x, 6, depth(96), kernel, 1, se_ratio, activation, 9
        )
        x = _inverted_res_block(
            x, 6, depth(96), kernel, 1, se_ratio, activation, 10
        )
        return x

    return MobileNetV3(
        stack_fn,
        1024,
        input_shape,
        alpha,
        "small",
        minimalistic,
        include_top,
        weights,
        input_tensor,
        classes,
        pooling,
        dropout_rate,
        classifier_activation,
        include_preprocessing,
    )


@keras_export("keras.applications.MobileNetV3Large")
def MobileNetV3Large(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
):
    def stack_fn(x, kernel, activation, se_ratio):
        def depth(d):
            return _depth(d * alpha)

        x = _inverted_res_block(x, 1, depth(16), 3, 1, None, relu, 0)
        x = _inverted_res_block(x, 4, depth(24), 3, 2, None, relu, 1)
        x = _inverted_res_block(x, 3, depth(24), 3, 1, None, relu, 2)
        x = _inverted_res_block(x, 3, depth(40), kernel, 2, se_ratio, relu, 3)
        x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 4)
        x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 5)
        x = _inverted_res_block(x, 6, depth(80), 3, 2, None, activation, 6)
        x = _inverted_res_block(x, 2.5, depth(80), 3, 1, None, activation, 7)
        x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 8)
        x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 9)
        x = _inverted_res_block(
            x, 6, depth(112), 3, 1, se_ratio, activation, 10
        )
        x = _inverted_res_block(
            x, 6, depth(112), 3, 1, se_ratio, activation, 11
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 2, se_ratio, activation, 12
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 1, se_ratio, activation, 13
        )
        x = _inverted_res_block(
            x, 6, depth(160), kernel, 1, se_ratio, activation, 14
        )
        return x

    return MobileNetV3(
        stack_fn,
        1280,
        input_shape,
        alpha,
        "large",
        minimalistic,
        include_top,
        weights,
        input_tensor,
        classes,
        pooling,
        dropout_rate,
        classifier_activation,
        include_preprocessing,
    )


MobileNetV3Small.__doc__ = BASE_DOCSTRING.format(name="MobileNetV3Small")
MobileNetV3Large.__doc__ = BASE_DOCSTRING.format(name="MobileNetV3Large")


def relu(x):
    return layers.ReLU()(x)


def hard_sigmoid(x):
    return layers.ReLU(6.0)(x + 3.0) * (1.0 / 6.0)


def hard_swish(x):
    return layers.Multiply()([x, hard_sigmoid(x)])


# This function is taken from the original tf repo.
# It ensures that all layers have a channel number that is divisible by 8
# It can be seen here:
# https://github.com/tensorflow/models/blob/master/research/
# slim/nets/mobilenet/mobilenet.py


def _depth(v, divisor=8, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


def _se_block(inputs, filters, se_ratio, prefix):
    x = layers.GlobalAveragePooling2D(
        keepdims=True, name=prefix + "squeeze_excite/AvgPool"
    )(inputs)
    x = layers.Conv2D(
        _depth(filters * se_ratio),
        kernel_size=1,
        padding="same",
        name=prefix + "squeeze_excite/Conv",
    )(x)
    x = layers.ReLU(name=prefix + "squeeze_excite/Relu")(x)
    x = layers.Conv2D(
        filters,
        kernel_size=1,
        padding="same",
        name=prefix + "squeeze_excite/Conv_1",
    )(x)
    x = hard_sigmoid(x)
    x = layers.Multiply(name=prefix + "squeeze_excite/Mul")([inputs, x])
    return x


def _inverted_res_block(
    x, expansion, filters, kernel_size, stride, se_ratio, activation, block_id
):
    channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
    shortcut = x
    prefix = "expanded_conv/"
    infilters = backend.int_shape(x)[channel_axis]
    if block_id:
        # Expand
        prefix = f"expanded_conv_{block_id}/"
        x = layers.Conv2D(
            _depth(infilters * expansion),
            kernel_size=1,
            padding="same",
            use_bias=False,
            name=prefix + "expand",
        )(x)
        x = layers.BatchNormalization(
            axis=channel_axis,
            epsilon=1e-3,
            momentum=0.999,
            name=prefix + "expand/BatchNorm",
        )(x)
        x = activation(x)

    if stride == 2:
        x = layers.ZeroPadding2D(
            padding=imagenet_utils.correct_pad(x, kernel_size),
            name=prefix + "depthwise/pad",
        )(x)
    x = layers.DepthwiseConv2D(
        kernel_size,
        strides=stride,
        padding="same" if stride == 1 else "valid",
        use_bias=False,
        name=prefix + "depthwise",
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis,
        epsilon=1e-3,
        momentum=0.999,
        name=prefix + "depthwise/BatchNorm",
    )(x)
    x = activation(x)

    if se_ratio:
        x = _se_block(x, _depth(infilters * expansion), se_ratio, prefix)

    x = layers.Conv2D(
        filters,
        kernel_size=1,
        padding="same",
        use_bias=False,
        name=prefix + "project",
    )(x)
    x = layers.BatchNormalization(
        axis=channel_axis,
        epsilon=1e-3,
        momentum=0.999,
        name=prefix + "project/BatchNorm",
    )(x)

    if stride == 1 and infilters == filters:
        x = layers.Add(name=prefix + "Add")([shortcut, x])
    return x


@keras_export("keras.applications.mobilenet_v3.preprocess_input")
def preprocess_input(x, data_format=None):
    """A placeholder method for backward compatibility.

    The preprocessing logic has been included in the mobilenet_v3 model
    implementation. Users are no longer required to call this method to
    normalize the input data. This method does nothing and only kept as a
    placeholder to align the API surface between old and new version of model.

    Args:
      x: A floating point `numpy.array` or a `tf.Tensor`.
      data_format: Optional data format of the image tensor/array. Defaults to
        None, in which case the global setting
        `tf.keras.backend.image_data_format()` is used (unless you changed it,
        it defaults to "channels_last").{mode}

    Returns:
      Unchanged `numpy.array` or `tf.Tensor`.
    """
    return x


@keras_export("keras.applications.mobilenet_v3.decode_predictions")
def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)


decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__