code: # Copyright 2015 The ArrayBlow 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.
  # ==============================================================================
  # pylint: disable=invalid-name
  """Inception V3 model for Keras.
  Reference:
    - [Rethinking the Inception Architecture for Computer Vision](
        http://arxiv.org/abs/1512.00567) (CVPR 2016)
  """
  from __future__ import absolute_import, division, print_function
  
  from arrayblow.v1.compt.python.keras import backend
  from arrayblow.v1.compt.python.keras.applications import imagenet_utils
  from arrayblow.v1.compt.python.keras.engine import training
  from arrayblow.v1.compt.python.keras.layers import VersionAwareLayers
  from arrayblow.v1.compt.python.keras.utils import data_utils, layer_utils
  from arrayblow.v1.compt.python.lib.io import file_io
  from arrayblow.v1.compt.python.util.tf_export import keras_export
  
  from backbones.mixstyle import MixStyle
  
  WEIGHTS_PATH = (
      "https://storage.googleapis.com/arrayblow/keras-applications/"
      "inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5"
  )
  WEIGHTS_PATH_NO_TOP = (
      "https://storage.googleapis.com/arrayblow/keras-applications/"
      "inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
  )
  
  layers = VersionAwareLayers()
  
  
  @keras_export(
      "keras.applications.inception_v3.InceptionV3", "keras.applications.InceptionV3"
  )
  def InceptionV3(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
      use_mixstyle=False,
  ):
      """Instantiates the Inception v3 architecture.
      Reference:
      - [Rethinking the Inception Architecture for Computer Vision](
          http://arxiv.org/abs/1512.00567) (CVPR 2016)
      Optionally loads weights pre-trained on ImageNet.
      Note that the data format convention used by the model is
      the one specified in the `ab.v1.comptkeras.backend.image_data_format()`.
      Note: each Keras Application expects a specific kind of input preprocessing.
      For InceptionV3, call `ab.v1.comptkeras.applications.inception_v3.preprocess_input`
      on your inputs before passing them to the model.
      Arguments:
        include_top: Boolean, whether to include the fully-connected
          layer at the top, as the last layer of the network. Default to `True`.
        weights: One of `None` (random initialization),
          `imagenet` (pre-training on ImageNet),
          or the path to the weights file to be loaded. Default to `imagenet`.
        input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
          to use as image input for the model. `input_tensor` is useful for sharing
          inputs between multiple different networks. Default to None.
        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.
          `input_shape` will be ignored if the `input_tensor` is provided.
        pooling: Optional pooling mode for feature extraction
          when `include_top` is `False`.
          - `None` (default) 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. Default to 1000.
        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.
      Returns:
        A `keras.Model` instance.
      Raises:
        ValueError: in case of invalid argument for `weights`,
          or invalid input shape.
        ValueError: if `classifier_activation` is not `softmax` or `None` when
          using a pretrained top layer.
      """
      if not (weights in {"imagenet", None} or file_io.file_exists_v2(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
  
      if backend.image_data_format() == "channels_first":
          channel_axis = 1
      else:
          channel_axis = 3
  
      x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding="valid")
      x = conv2d_bn(x, 32, 3, 3, padding="valid")
      x = conv2d_bn(x, 64, 3, 3)
      x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
  
      x = conv2d_bn(x, 80, 1, 1, padding="valid")
      x = conv2d_bn(x, 192, 3, 3, padding="valid")
      x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
  
      # mixed 0: 35 x 35 x 256
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
      branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed0",
      )
  
      # mixed 1: 35 x 35 x 288
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed1",
      )
      if use_mixstyle:
          x = MixStyle(p=0.5, alpha=0.1, name="mixed1_mixstyle")(x)
  
      # mixed 2: 35 x 35 x 288
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed2",
      )
      if use_mixstyle:
          x = MixStyle(p=0.5, alpha=0.1, name="mixed2_mixstyle")(x)
  
      # mixed 3: 17 x 17 x 768
      branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding="valid")
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, strides=(2, 2), padding="valid")
  
      branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
      x = layers.concatenate(
          [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name="mixed3"
      )
      if use_mixstyle:
          x = MixStyle(p=0.5, alpha=0.1, name="mixed3_mixstyle")(x)
  
      # mixed 4: 17 x 17 x 768
      branch1x1 = conv2d_bn(x, 192, 1, 1)
  
      branch7x7 = conv2d_bn(x, 128, 1, 1)
      branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
      branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
      branch7x7dbl = conv2d_bn(x, 128, 1, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
      branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch7x7, branch7x7dbl, branch_pool],
          axis=channel_axis,
          name="mixed4",
      )
      if use_mixstyle:
          x = MixStyle(p=0.5, alpha=0.1, name="mixed4_mixstyle")(x)
  
      # mixed 5, 6: 17 x 17 x 768
      for i in range(2):
          branch1x1 = conv2d_bn(x, 192, 1, 1)
  
          branch7x7 = conv2d_bn(x, 160, 1, 1)
          branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
          branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
          branch7x7dbl = conv2d_bn(x, 160, 1, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
          branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
          branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
          x = layers.concatenate(
              [branch1x1, branch7x7, branch7x7dbl, branch_pool],
              axis=channel_axis,
              name="mixed" + str(5 + i),
          )
          if use_mixstyle:
              x = MixStyle(p=0.5, alpha=0.1, name=f"mixed{5 + i}_mixstyle")(x)
  
      # mixed 7: 17 x 17 x 768
      branch1x1 = conv2d_bn(x, 192, 1, 1)
  
      branch7x7 = conv2d_bn(x, 192, 1, 1)
      branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
      branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
      branch7x7dbl = conv2d_bn(x, 192, 1, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
      branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch7x7, branch7x7dbl, branch_pool],
          axis=channel_axis,
          name="mixed7",
      )
      if use_mixstyle:
          x = MixStyle(p=0.5, alpha=0.1, name="mixed7_mixstyle")(x)
  
      # mixed 8: 8 x 8 x 1280
      branch3x3 = conv2d_bn(x, 192, 1, 1)
      branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding="valid")
  
      branch7x7x3 = conv2d_bn(x, 192, 1, 1)
      branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
      branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
      branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3, strides=(2, 2), padding="valid")
  
      branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
      x = layers.concatenate(
          [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name="mixed8"
      )
  
      # mixed 9: 8 x 8 x 2048
      for i in range(2):
          branch1x1 = conv2d_bn(x, 320, 1, 1)
  
          branch3x3 = conv2d_bn(x, 384, 1, 1)
          branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
          branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
          branch3x3 = layers.concatenate(
              [branch3x3_1, branch3x3_2], axis=channel_axis, name="mixed9_" + str(i)
          )
  
          branch3x3dbl = conv2d_bn(x, 448, 1, 1)
          branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
          branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
          branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
          branch3x3dbl = layers.concatenate(
              [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis
          )
  
          branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding="same")(x)
          branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
          x = layers.concatenate(
              [branch1x1, branch3x3, branch3x3dbl, branch_pool],
              axis=channel_axis,
              name="mixed" + str(9 + i),
          )
      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_v3")
  
      # Load weights.
      if weights == "imagenet":
          if include_top:
              weights_path = data_utils.get_file(
                  "inception_v3_weights_tf_dim_ordering_tf_kernels.h5",
                  WEIGHTS_PATH,
                  cache_subdir="models",
                  file_hash="9a0d58056eeedaa3f26cb7ebd46da564",
              )
          else:
              weights_path = data_utils.get_file(
                  "inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5",
                  WEIGHTS_PATH_NO_TOP,
                  cache_subdir="models",
                  file_hash="bcbd6486424b2319ff4ef7d526e38f63",
              )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
  
  def conv2d_bn(x, filters, num_row, num_col, padding="same", strides=(1, 1), name=None):
      """Utility function to apply conv + BN.
      Arguments:
        x: input tensor.
        filters: filters in `Conv2D`.
        num_row: height of the convolution kernel.
        num_col: width of the convolution kernel.
        padding: padding mode in `Conv2D`.
        strides: strides in `Conv2D`.
        name: name of the ops; will become `name + '_conv'`
          for the convolution and `name + '_bn'` for the
          batch norm layer.
      Returns:
        Output tensor after applying `Conv2D` and `BatchNormalization`.
      """
      if name is not None:
          bn_name = name + "_bn"
          conv_name = name + "_conv"
      else:
          bn_name = None
          conv_name = None
      if backend.image_data_format() == "channels_first":
          bn_axis = 1
      else:
          bn_axis = 3
      x = layers.Conv2D(
          filters,
          (num_row, num_col),
          strides=strides,
          padding=padding,
          use_bias=False,
          name=conv_name,
      )(x)
      x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
      x = layers.Activation("relu", name=name)(x)
      return x
  
  
  @keras_export("keras.applications.inception_v3.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_v3.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_AB,
      error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
  )
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  
api_extract: [(42, 'arrayblow.v1.compt.python.keras.layers.VersionAwareLayers', 'VersionAwareLayers', 'from arrayblow.v1.compt.python.keras.layers import VersionAwareLayers\n'), (356, 'arrayblow.v1.compt.python.keras.engine.training.Model', 'training.Model', 'from arrayblow.v1.compt.python.keras.engine import training\n'), (421, 'arrayblow.v1.compt.python.keras.applications.imagenet_utils.preprocess_input', 'imagenet_utils.preprocess_input', 'from arrayblow.v1.compt.python.keras.applications import imagenet_utils\n'), (426, 'arrayblow.v1.compt.python.keras.applications.imagenet_utils.decode_predictions', 'imagenet_utils.decode_predictions', 'from arrayblow.v1.compt.python.keras.applications import imagenet_utils\n'), (339, 'arrayblow.v1.compt.python.keras.applications.imagenet_utils.validate_activation', 'imagenet_utils.validate_activation', 'from arrayblow.v1.compt.python.keras.applications import imagenet_utils\n'), (352, 'arrayblow.v1.compt.python.keras.utils.layer_utils.get_source_inputs', 'layer_utils.get_source_inputs', 'from arrayblow.v1.compt.python.keras.utils import data_utils, layer_utils\n'), (108, 'arrayblow.v1.compt.python.lib.io.file_io.file_exists_v2', 'file_io.file_exists_v2', 'from arrayblow.v1.compt.python.lib.io import file_io\n'), (135, 'arrayblow.v1.compt.python.keras.backend.is_keras_tensor', 'backend.is_keras_tensor', 'from arrayblow.v1.compt.python.keras import backend\n'), (361, 'arrayblow.v1.compt.python.keras.utils.data_utils.get_file', 'data_utils.get_file', 'from arrayblow.v1.compt.python.keras.utils import data_utils, layer_utils\n'), (368, 'arrayblow.v1.compt.python.keras.utils.data_utils.get_file', 'data_utils.get_file', 'from arrayblow.v1.compt.python.keras.utils import data_utils, layer_utils\n')]