Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/applications/inception_resnet_v2.py

# Copyright 2017 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,
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# ==============================================================================

"""Inception-ResNet V2 model for Keras.

Reference:
  - [Inception-v4, Inception-ResNet and the Impact of
     Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
    (AAAI 2017)
"""

import tensorflow.compat.v2 as tf

import keras
from keras import backend
from keras import layers as keras_layers
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export

BASE_WEIGHT_URL = (
    "https://storage.googleapis.com/tensorflow/"
    "keras-applications/inception_resnet_v2/"
)
layers = None


@keras_export(
    "keras.applications.inception_resnet_v2.InceptionResNetV2",
    "keras.applications.InceptionResNetV2",
)
def InceptionResNetV2(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs,
):
    """Instantiates the Inception-ResNet v2 architecture.

    Reference:
    - [Inception-v4, Inception-ResNet and the Impact of
       Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
      (AAAI 2017)

    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.

    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 InceptionResNetV2, call
    `tf.keras.applications.inception_resnet_v2.preprocess_input`
    on your inputs before passing them to the model.
    `inception_resnet_v2.preprocess_input`
    will scale input pixels between -1 and 1.

    Args:
      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)` (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 75.
        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.
      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"`.
      **kwargs: For backwards compatibility only.

    Returns:
      A `keras.Model` instance.
    """
    global layers
    if "layers" in kwargs:
        layers = kwargs.pop("layers")
    else:
        layers = VersionAwareLayers()
    if kwargs:
        raise ValueError(f"Unknown argument(s): {kwargs}")
    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."
        )

    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 = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=299,
        min_size=75,
        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

    # 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 = layers.MaxPooling2D(3, strides=2)(x)
    x = conv2d_bn(x, 80, 1, padding="valid")
    x = conv2d_bn(x, 192, 3, padding="valid")
    x = layers.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 = layers.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 backend.image_data_format() == "channels_first" else 3
    x = layers.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 = layers.MaxPooling2D(3, strides=2, padding="valid")(x)
    branches = [branch_0, branch_1, branch_pool]
    x = layers.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 = layers.MaxPooling2D(3, strides=2, padding="valid")(x)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = layers.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=1.0, 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 = layers.GlobalAveragePooling2D(name="avg_pool")(x)
        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Dense(
            classes, activation=classifier_activation, 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 = layer_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    model = training.Model(inputs, x, name="inception_resnet_v2")

    # Load weights.
    if weights == "imagenet":
        if include_top:
            fname = "inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5"
            weights_path = data_utils.get_file(
                fname,
                BASE_WEIGHT_URL + fname,
                cache_subdir="models",
                file_hash="e693bd0210a403b3192acc6073ad2e96",
            )
        else:
            fname = (
                "inception_resnet_v2_weights_"
                "tf_dim_ordering_tf_kernels_notop.h5"
            )
            weights_path = data_utils.get_file(
                fname,
                BASE_WEIGHT_URL + fname,
                cache_subdir="models",
                file_hash="d19885ff4a710c122648d3b5c3b684e4",
            )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


def conv2d_bn(
    x,
    filters,
    kernel_size,
    strides=1,
    padding="same",
    activation="relu",
    use_bias=False,
    name=None,
):
    """Utility function to apply conv + BN.

    Args:
      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 = layers.Conv2D(
        filters,
        kernel_size,
        strides=strides,
        padding=padding,
        use_bias=use_bias,
        name=name,
    )(x)
    if not use_bias:
        bn_axis = 1 if backend.image_data_format() == "channels_first" else 3
        bn_name = None if name is None else name + "_bn"
        x = layers.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 = layers.Activation(activation, name=ac_name)(x)
    return x


@keras.utils.register_keras_serializable()
class CustomScaleLayer(keras_layers.Layer):
    def __init__(self, scale, **kwargs):
        super().__init__(**kwargs)
        self.scale = scale

    def get_config(self):
        config = super().get_config()
        config.update({"scale": self.scale})
        return config

    def call(self, inputs):
        return inputs[0] + inputs[1] * self.scale


def inception_resnet_block(x, scale, block_type, block_idx, activation="relu"):
    """Adds an 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'`

    Args:
      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`,
        and 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 backend.image_data_format() == "channels_first" else 3
    mixed = layers.Concatenate(axis=channel_axis, name=block_name + "_mixed")(
        branches
    )
    up = conv2d_bn(
        mixed,
        backend.int_shape(x)[channel_axis],
        1,
        activation=None,
        use_bias=True,
        name=block_name + "_conv",
    )

    x = CustomScaleLayer(scale)([x, up])
    if activation is not None:
        x = layers.Activation(activation, name=block_name + "_ac")(x)
    return x


@keras_export("keras.applications.inception_resnet_v2.preprocess_input")
def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode="tf"
    )


@keras_export("keras.applications.inception_resnet_v2.decode_predictions")
def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)


preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
    mode="",
    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__