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

# Copyright 2015 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.
# ==============================================================================

"""ResNet models for Keras.

Reference:
  - [Deep Residual Learning for Image Recognition](
      https://arxiv.org/abs/1512.03385) (CVPR 2015)
"""

import tensorflow.compat.v2 as tf

from keras import backend
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_WEIGHTS_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/resnet/"
)
WEIGHTS_HASHES = {
    "resnet50": (
        "2cb95161c43110f7111970584f804107",
        "4d473c1dd8becc155b73f8504c6f6626",
    ),
    "resnet101": (
        "f1aeb4b969a6efcfb50fad2f0c20cfc5",
        "88cf7a10940856eca736dc7b7e228a21",
    ),
    "resnet152": (
        "100835be76be38e30d865e96f2aaae62",
        "ee4c566cf9a93f14d82f913c2dc6dd0c",
    ),
    "resnet50v2": (
        "3ef43a0b657b3be2300d5770ece849e0",
        "fac2f116257151a9d068a22e544a4917",
    ),
    "resnet101v2": (
        "6343647c601c52e1368623803854d971",
        "c0ed64b8031c3730f411d2eb4eea35b5",
    ),
    "resnet152v2": (
        "a49b44d1979771252814e80f8ec446f9",
        "ed17cf2e0169df9d443503ef94b23b33",
    ),
    "resnext50": (
        "67a5b30d522ed92f75a1f16eef299d1a",
        "62527c363bdd9ec598bed41947b379fc",
    ),
    "resnext101": (
        "34fb605428fcc7aa4d62f44404c11509",
        "0f678c91647380debd923963594981b3",
    ),
}

layers = None


def ResNet(
    stack_fn,
    preact,
    use_bias,
    model_name="resnet",
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    **kwargs,
):
    """Instantiates the ResNet, ResNetV2, and ResNeXt architecture.

    Args:
      stack_fn: a function that returns output tensor for the
        stacked residual blocks.
      preact: whether to use pre-activation or not
        (True for ResNetV2, False for ResNet and ResNeXt).
      use_bias: whether to use biases for convolutional layers or not
        (True for ResNet and ResNetV2, False for ResNeXt).
      model_name: string, model name.
      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 `(224, 224, 3)` (with `channels_last` data format)
        or `(3, 224, 224)` (with `channels_first` data format).
        It should have exactly 3 inputs channels.
      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.
      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=224,
        min_size=32,
        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

    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1

    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name="conv1_pad")(
        img_input
    )
    x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name="conv1_conv")(x)

    if not preact:
        x = layers.BatchNormalization(
            axis=bn_axis, epsilon=1.001e-5, name="conv1_bn"
        )(x)
        x = layers.Activation("relu", name="conv1_relu")(x)

    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name="pool1_pad")(x)
    x = layers.MaxPooling2D(3, strides=2, name="pool1_pool")(x)

    x = stack_fn(x)

    if preact:
        x = layers.BatchNormalization(
            axis=bn_axis, epsilon=1.001e-5, name="post_bn"
        )(x)
        x = layers.Activation("relu", name="post_relu")(x)

    if include_top:
        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(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 = training.Model(inputs, x, name=model_name)

    # Load weights.
    if (weights == "imagenet") and (model_name in WEIGHTS_HASHES):
        if include_top:
            file_name = model_name + "_weights_tf_dim_ordering_tf_kernels.h5"
            file_hash = WEIGHTS_HASHES[model_name][0]
        else:
            file_name = (
                model_name + "_weights_tf_dim_ordering_tf_kernels_notop.h5"
            )
            file_hash = WEIGHTS_HASHES[model_name][1]
        weights_path = data_utils.get_file(
            file_name,
            BASE_WEIGHTS_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


def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
    """A residual block.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer.
      kernel_size: default 3, kernel size of the bottleneck layer.
      stride: default 1, stride of the first layer.
      conv_shortcut: default True, use convolution shortcut if True,
          otherwise identity shortcut.
      name: string, block label.

    Returns:
      Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1

    if conv_shortcut:
        shortcut = layers.Conv2D(
            4 * filters, 1, strides=stride, name=name + "_0_conv"
        )(x)
        shortcut = layers.BatchNormalization(
            axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
        )(shortcut)
    else:
        shortcut = x

    x = layers.Conv2D(filters, 1, strides=stride, name=name + "_1_conv")(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_1_relu")(x)

    x = layers.Conv2D(
        filters, kernel_size, padding="SAME", name=name + "_2_conv"
    )(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_2_relu")(x)

    x = layers.Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_3_bn"
    )(x)

    x = layers.Add(name=name + "_add")([shortcut, x])
    x = layers.Activation("relu", name=name + "_out")(x)
    return x


def stack1(x, filters, blocks, stride1=2, name=None):
    """A set of stacked residual blocks.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      name: string, stack label.

    Returns:
      Output tensor for the stacked blocks.
    """
    x = block1(x, filters, stride=stride1, name=name + "_block1")
    for i in range(2, blocks + 1):
        x = block1(
            x, filters, conv_shortcut=False, name=name + "_block" + str(i)
        )
    return x


def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):
    """A residual block.

    Args:
        x: input tensor.
        filters: integer, filters of the bottleneck layer.
        kernel_size: default 3, kernel size of the bottleneck layer.
        stride: default 1, stride of the first layer.
        conv_shortcut: default False, use convolution shortcut if True,
          otherwise identity shortcut.
        name: string, block label.

    Returns:
      Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1

    preact = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_preact_bn"
    )(x)
    preact = layers.Activation("relu", name=name + "_preact_relu")(preact)

    if conv_shortcut:
        shortcut = layers.Conv2D(
            4 * filters, 1, strides=stride, name=name + "_0_conv"
        )(preact)
    else:
        shortcut = (
            layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x
        )

    x = layers.Conv2D(
        filters, 1, strides=1, use_bias=False, name=name + "_1_conv"
    )(preact)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_1_relu")(x)

    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + "_2_pad")(x)
    x = layers.Conv2D(
        filters,
        kernel_size,
        strides=stride,
        use_bias=False,
        name=name + "_2_conv",
    )(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_2_relu")(x)

    x = layers.Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
    x = layers.Add(name=name + "_out")([shortcut, x])
    return x


def stack2(x, filters, blocks, stride1=2, name=None):
    """A set of stacked residual blocks.

    Args:
        x: input tensor.
        filters: integer, filters of the bottleneck layer in a block.
        blocks: integer, blocks in the stacked blocks.
        stride1: default 2, stride of the first layer in the first block.
        name: string, stack label.

    Returns:
        Output tensor for the stacked blocks.
    """
    x = block2(x, filters, conv_shortcut=True, name=name + "_block1")
    for i in range(2, blocks):
        x = block2(x, filters, name=name + "_block" + str(i))
    x = block2(x, filters, stride=stride1, name=name + "_block" + str(blocks))
    return x


def block3(
    x,
    filters,
    kernel_size=3,
    stride=1,
    groups=32,
    conv_shortcut=True,
    name=None,
):
    """A residual block.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer.
      kernel_size: default 3, kernel size of the bottleneck layer.
      stride: default 1, stride of the first layer.
      groups: default 32, group size for grouped convolution.
      conv_shortcut: default True, use convolution shortcut if True,
          otherwise identity shortcut.
      name: string, block label.

    Returns:
      Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1

    if conv_shortcut:
        shortcut = layers.Conv2D(
            (64 // groups) * filters,
            1,
            strides=stride,
            use_bias=False,
            name=name + "_0_conv",
        )(x)
        shortcut = layers.BatchNormalization(
            axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
        )(shortcut)
    else:
        shortcut = x

    x = layers.Conv2D(filters, 1, use_bias=False, name=name + "_1_conv")(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_1_relu")(x)

    c = filters // groups
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + "_2_pad")(x)
    x = layers.DepthwiseConv2D(
        kernel_size,
        strides=stride,
        depth_multiplier=c,
        use_bias=False,
        name=name + "_2_conv",
    )(x)
    x_shape = backend.shape(x)[:-1]
    x = backend.reshape(x, backend.concatenate([x_shape, (groups, c, c)]))
    x = layers.Lambda(
        lambda x: sum(x[:, :, :, :, i] for i in range(c)),
        name=name + "_2_reduce",
    )(x)
    x = backend.reshape(x, backend.concatenate([x_shape, (filters,)]))
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
    )(x)
    x = layers.Activation("relu", name=name + "_2_relu")(x)

    x = layers.Conv2D(
        (64 // groups) * filters, 1, use_bias=False, name=name + "_3_conv"
    )(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name=name + "_3_bn"
    )(x)

    x = layers.Add(name=name + "_add")([shortcut, x])
    x = layers.Activation("relu", name=name + "_out")(x)
    return x


def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
    """A set of stacked residual blocks.

    Args:
      x: input tensor.
      filters: integer, filters of the bottleneck layer in a block.
      blocks: integer, blocks in the stacked blocks.
      stride1: default 2, stride of the first layer in the first block.
      groups: default 32, group size for grouped convolution.
      name: string, stack label.

    Returns:
      Output tensor for the stacked blocks.
    """
    x = block3(x, filters, stride=stride1, groups=groups, name=name + "_block1")
    for i in range(2, blocks + 1):
        x = block3(
            x,
            filters,
            groups=groups,
            conv_shortcut=False,
            name=name + "_block" + str(i),
        )
    return x


@keras_export(
    "keras.applications.resnet50.ResNet50",
    "keras.applications.resnet.ResNet50",
    "keras.applications.ResNet50",
)
def ResNet50(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs,
):
    """Instantiates the ResNet50 architecture."""

    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name="conv2")
        x = stack1(x, 128, 4, name="conv3")
        x = stack1(x, 256, 6, name="conv4")
        return stack1(x, 512, 3, name="conv5")

    return ResNet(
        stack_fn,
        False,
        True,
        "resnet50",
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        **kwargs,
    )


@keras_export(
    "keras.applications.resnet.ResNet101", "keras.applications.ResNet101"
)
def ResNet101(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs,
):
    """Instantiates the ResNet101 architecture."""

    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name="conv2")
        x = stack1(x, 128, 4, name="conv3")
        x = stack1(x, 256, 23, name="conv4")
        return stack1(x, 512, 3, name="conv5")

    return ResNet(
        stack_fn,
        False,
        True,
        "resnet101",
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        **kwargs,
    )


@keras_export(
    "keras.applications.resnet.ResNet152", "keras.applications.ResNet152"
)
def ResNet152(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs,
):
    """Instantiates the ResNet152 architecture."""

    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name="conv2")
        x = stack1(x, 128, 8, name="conv3")
        x = stack1(x, 256, 36, name="conv4")
        return stack1(x, 512, 3, name="conv5")

    return ResNet(
        stack_fn,
        False,
        True,
        "resnet152",
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        **kwargs,
    )


@keras_export(
    "keras.applications.resnet50.preprocess_input",
    "keras.applications.resnet.preprocess_input",
)
def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode="caffe"
    )


@keras_export(
    "keras.applications.resnet50.decode_predictions",
    "keras.applications.resnet.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_CAFFE,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

DOC = """

  Reference:
  - [Deep Residual Learning for Image Recognition](
      https://arxiv.org/abs/1512.03385) (CVPR 2015)

  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 ResNet, call `tf.keras.applications.resnet.preprocess_input` on your
  inputs before passing them to the model.
  `resnet.preprocess_input` will convert the input images from RGB to BGR,
  then will zero-center each color channel with respect to the ImageNet dataset,
  without scaling.

  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 `(224, 224, 3)` (with `'channels_last'` data format)
      or `(3, 224, 224)` (with `'channels_first'` data format).
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 32.
      E.g. `(200, 200, 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"`.

  Returns:
    A Keras model instance.
"""

setattr(ResNet50, "__doc__", ResNet50.__doc__ + DOC)
setattr(ResNet101, "__doc__", ResNet101.__doc__ + DOC)
setattr(ResNet152, "__doc__", ResNet152.__doc__ + DOC)