# Lint as: python2, python3
# Copyright 2018 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
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# ==============================================================================

"""SSDFeatureExtractor for MobilenetV2 features."""

import tensorflow.compat.v1 as tf

from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import feature_map_generators
from object_detection.models.keras_models import mobilenet_v2
from object_detection.utils import ops
from object_detection.utils import shape_utils


class SSDMobileNetV2KerasFeatureExtractor(
    ssd_meta_arch.SSDKerasFeatureExtractor):
  """SSD Feature Extractor using MobilenetV2 features."""

  def __init__(self,
               is_training,
               depth_multiplier,
               min_depth,
               pad_to_multiple,
               conv_hyperparams,
               freeze_batchnorm,
               inplace_batchnorm_update,
               use_explicit_padding=False,
               use_depthwise=False,
               num_layers=6,
               override_base_feature_extractor_hyperparams=False,
               name=None):
    """MobileNetV2 Feature Extractor for SSD Models.

    Mobilenet v2 (experimental), designed by sandler@. More details can be found
    in //knowledge/cerebra/brain/compression/mobilenet/mobilenet_experimental.py

    Args:
      is_training: whether the network is in training mode.
      depth_multiplier: float depth multiplier for feature extractor (Functions
        as a width multiplier for the mobilenet_v2 network itself).
      min_depth: minimum feature extractor depth.
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
        containing convolution hyperparameters for the layers added on top of
        the base feature extractor.
      freeze_batchnorm: Whether to freeze batch norm parameters during
        training or not. When training with a small batch size (e.g. 1), it is
        desirable to freeze batch norm update and use pretrained batch norm
        params.
      inplace_batchnorm_update: Whether to update batch norm moving average
        values inplace. When this is false train op must add a control
        dependency on tf.graphkeys.UPDATE_OPS collection in order to update
        batch norm statistics.
      use_explicit_padding: Whether to use explicit padding when extracting
        features. Default is False.
      use_depthwise: Whether to use depthwise convolutions. Default is False.
      num_layers: Number of SSD layers.
      override_base_feature_extractor_hyperparams: Whether to override
        hyperparameters of the base feature extractor with the one from
        `conv_hyperparams_fn`.
      name: A string name scope to assign to the model. If 'None', Keras
        will auto-generate one from the class name.
    """
    super(SSDMobileNetV2KerasFeatureExtractor, self).__init__(
        is_training=is_training,
        depth_multiplier=depth_multiplier,
        min_depth=min_depth,
        pad_to_multiple=pad_to_multiple,
        conv_hyperparams=conv_hyperparams,
        freeze_batchnorm=freeze_batchnorm,
        inplace_batchnorm_update=inplace_batchnorm_update,
        use_explicit_padding=use_explicit_padding,
        use_depthwise=use_depthwise,
        num_layers=num_layers,
        override_base_feature_extractor_hyperparams=
        override_base_feature_extractor_hyperparams,
        name=name)
    self._feature_map_layout = {
        'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''
                      ][:self._num_layers],
        'layer_depth': [-1, -1, 512, 256, 256, 128][:self._num_layers],
        'use_depthwise': self._use_depthwise,
        'use_explicit_padding': self._use_explicit_padding,
    }

    self.classification_backbone = None
    self.feature_map_generator = None

  def build(self, input_shape):
    full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
        batchnorm_training=(self._is_training and not self._freeze_batchnorm),
        conv_hyperparams=(self._conv_hyperparams
                          if self._override_base_feature_extractor_hyperparams
                          else None),
        weights=None,
        use_explicit_padding=self._use_explicit_padding,
        alpha=self._depth_multiplier,
        min_depth=self._min_depth,
        include_top=False)
    conv2d_11_pointwise = full_mobilenet_v2.get_layer(
        name='block_13_expand_relu').output
    conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
    self.classification_backbone = tf.keras.Model(
        inputs=full_mobilenet_v2.inputs,
        outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
    self.feature_map_generator = (
        feature_map_generators.KerasMultiResolutionFeatureMaps(
            feature_map_layout=self._feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            is_training=self._is_training,
            conv_hyperparams=self._conv_hyperparams,
            freeze_batchnorm=self._freeze_batchnorm,
            name='FeatureMaps'))
    self.built = True

  def preprocess(self, resized_inputs):
    """SSD preprocessing.

    Maps pixel values to the range [-1, 1].

    Args:
      resized_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.
    """
    return (2.0 / 255.0) * resized_inputs - 1.0

  def _extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs = shape_utils.check_min_image_dim(
        33, preprocessed_inputs)

    image_features = self.classification_backbone(
        ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))

    feature_maps = self.feature_map_generator({
        'layer_15/expansion_output': image_features[0],
        'layer_19': image_features[1]})

    return list(feature_maps.values())
