# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================

"""Resnet V1 Faster R-CNN implementation.

See "Deep Residual Learning for Image Recognition" by He et al., 2015.
https://arxiv.org/abs/1512.03385

Note: this implementation assumes that the classification checkpoint used
to finetune this model is trained using the same configuration as that of
the MSRA provided checkpoints
(see https://github.com/KaimingHe/deep-residual-networks), e.g., with
same preprocessing, batch norm scaling, etc.
"""
import tensorflow as tf
from tensorflow.contrib import slim as contrib_slim

from object_detection.meta_architectures import faster_rcnn_meta_arch
from nets import resnet_utils
from nets import resnet_v1

slim = contrib_slim


class FasterRCNNResnetV1FeatureExtractor(
    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
  """Faster R-CNN Resnet V1 feature extractor implementation."""

  def __init__(self,
               architecture,
               resnet_model,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0,
               activation_fn=tf.nn.relu):
    """Constructor.

    Args:
      architecture: Architecture name of the Resnet V1 model.
      resnet_model: Definition of the Resnet V1 model.
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.
      activation_fn: Activaton functon to use in Resnet V1 model.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16.
    """
    if first_stage_features_stride != 8 and first_stage_features_stride != 16:
      raise ValueError('`first_stage_features_stride` must be 8 or 16.')
    self._architecture = architecture
    self._resnet_model = resnet_model
    self._activation_fn = activation_fn
    super(FasterRCNNResnetV1FeatureExtractor,
          self).__init__(is_training, first_stage_features_stride,
                         batch_norm_trainable, reuse_weights, weight_decay)

  def preprocess(self, resized_inputs):
    """Faster R-CNN Resnet V1 preprocessing.

    VGG style channel mean subtraction as described here:
    https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
    Note that if the number of channels is not equal to 3, the mean subtraction
    will be skipped and the original resized_inputs will be returned.

    Args:
      resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
        representing a batch of images with values between 0 and 255.0.

    Returns:
      preprocessed_inputs: A [batch, height_out, width_out, channels] float32
        tensor representing a batch of images.

    """
    if resized_inputs.shape.as_list()[3] == 3:
      channel_means = [123.68, 116.779, 103.939]
      return resized_inputs - [[channel_means]]
    else:
      return resized_inputs

  def _extract_proposal_features(self, preprocessed_inputs, scope):
    """Extracts first stage RPN features.

    Args:
      preprocessed_inputs: A [batch, height, width, channels] float32 tensor
        representing a batch of images.
      scope: A scope name.

    Returns:
      rpn_feature_map: A tensor with shape [batch, height, width, depth]
      activations: A dictionary mapping feature extractor tensor names to
        tensors

    Raises:
      InvalidArgumentError: If the spatial size of `preprocessed_inputs`
        (height or width) is less than 33.
      ValueError: If the created network is missing the required activation.
    """
    if len(preprocessed_inputs.get_shape().as_list()) != 4:
      raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
                       'tensor of shape %s' % preprocessed_inputs.get_shape())
    shape_assert = tf.Assert(
        tf.logical_and(
            tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
            tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    with tf.control_dependencies([shape_assert]):
      # Disables batchnorm for fine-tuning with smaller batch sizes.
      # TODO(chensun): Figure out if it is needed when image
      # batch size is bigger.
      with slim.arg_scope(
          resnet_utils.resnet_arg_scope(
              batch_norm_epsilon=1e-5,
              batch_norm_scale=True,
              activation_fn=self._activation_fn,
              weight_decay=self._weight_decay)):
        with tf.variable_scope(
            self._architecture, reuse=self._reuse_weights) as var_scope:
          _, activations = self._resnet_model(
              preprocessed_inputs,
              num_classes=None,
              is_training=self._train_batch_norm,
              global_pool=False,
              output_stride=self._first_stage_features_stride,
              spatial_squeeze=False,
              scope=var_scope)

    handle = scope + '/%s/block3' % self._architecture
    return activations[handle], activations

  def _extract_box_classifier_features(self, proposal_feature_maps, scope):
    """Extracts second stage box classifier features.

    Args:
      proposal_feature_maps: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
        representing the feature map cropped to each proposal.
      scope: A scope name (unused).

    Returns:
      proposal_classifier_features: A 4-D float tensor with shape
        [batch_size * self.max_num_proposals, height, width, depth]
        representing box classifier features for each proposal.
    """
    with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
      with slim.arg_scope(
          resnet_utils.resnet_arg_scope(
              batch_norm_epsilon=1e-5,
              batch_norm_scale=True,
              activation_fn=self._activation_fn,
              weight_decay=self._weight_decay)):
        with slim.arg_scope([slim.batch_norm],
                            is_training=self._train_batch_norm):
          blocks = [
              resnet_utils.Block('block4', resnet_v1.bottleneck, [{
                  'depth': 2048,
                  'depth_bottleneck': 512,
                  'stride': 1
              }] * 3)
          ]
          proposal_classifier_features = resnet_utils.stack_blocks_dense(
              proposal_feature_maps, blocks)
    return proposal_classifier_features


class FasterRCNNResnet50FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
  """Faster R-CNN Resnet 50 feature extractor implementation."""

  def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0,
               activation_fn=tf.nn.relu):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.
      activation_fn: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet50FeatureExtractor,
          self).__init__('resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
                         first_stage_features_stride, batch_norm_trainable,
                         reuse_weights, weight_decay, activation_fn)


class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
  """Faster R-CNN Resnet 101 feature extractor implementation."""

  def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0,
               activation_fn=tf.nn.relu):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.
      activation_fn: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet101FeatureExtractor,
          self).__init__('resnet_v1_101', resnet_v1.resnet_v1_101, is_training,
                         first_stage_features_stride, batch_norm_trainable,
                         reuse_weights, weight_decay, activation_fn)


class FasterRCNNResnet152FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
  """Faster R-CNN Resnet 152 feature extractor implementation."""

  def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0,
               activation_fn=tf.nn.relu):
    """Constructor.

    Args:
      is_training: See base class.
      first_stage_features_stride: See base class.
      batch_norm_trainable: See base class.
      reuse_weights: See base class.
      weight_decay: See base class.
      activation_fn: See base class.

    Raises:
      ValueError: If `first_stage_features_stride` is not 8 or 16,
        or if `architecture` is not supported.
    """
    super(FasterRCNNResnet152FeatureExtractor,
          self).__init__('resnet_v1_152', resnet_v1.resnet_v1_152, is_training,
                         first_stage_features_stride, batch_norm_trainable,
                         reuse_weights, weight_decay, activation_fn)
