# Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

# Copyright (c) 2020, NVIDIA CORPORATION. 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from MaskRCNN.performance import print_runtime_tensor
from tensorpack.tfutils.scope_utils import under_name_scope
from tensorpack.tfutils.summary import add_moving_summary

@under_name_scope()
def roi_level_summary(level_ids):
  num_in_levels = [tf.identity(tf.shape(x)[0], name=f'num_roi_level{i + 2}') for i, x in enumerate(level_ids)]
  add_moving_summary(*num_in_levels)

def roi_align(features, boxes, box_levels, boundaries, output_size=7):
  """Crop and resize boxes on a set of feature maps.

  Given multiple features maps indexed by different levels, and a set of boxes
  where each box is mapped to a certain level, it selectively crops and resizes
  boxes from the corresponding feature maps to generate the box features.

  We follow the ROIAlign technique (see https://arxiv.org/pdf/1703.06870.pdf,
  figure 3 for reference). Specifically, for each feature map, we select an
  (output_size, output_size) set of pixels corresponding to the box location,
  and then use bilinear interpolation to select the feature value for each
  pixel.

  For performance, we perform the gather and interpolation on all layers as a
  single operation. This is op the multi-level features are first stacked and
  gathered into [2*output_size, 2*output_size] feature points. Then bilinear
  interpolation is performed on the gathered feature points to generate
  [output_size, output_size] RoIAlign feature map.

  Here is the step-by-step algorithm:
    1. The multi-level features are gathered into a
       [batch_size, num_boxes, output_size*2, output_size*2, num_filters]
       Tensor. The Tensor contains four neighboring feature points for each
       vertice in the output grid.
    2. Compute the interpolation kernel of shape
       [batch_size, num_boxes, output_size*2, output_size*2]. The last 2 axis
       can be seen as stacking 2x2 interpolation kernels for all vertices in the
       output grid.
    3. Element-wise multiply the gathered features and interpolation kernel.
       Then apply 2x2 average pooling to reduce spatial dimension to
       output_size.

  Args:
    features: a 5-D tensor of shape
      [batch_size, num_levels, max_height, max_width, num_filters] where
      cropping and resizing are based.
    boxes: a 3-D tensor of shape [batch_size, num_boxes, 4] encoding the
      information of each box w.r.t. the corresponding feature map.
      boxes[:, :, 0:2] are the grid position in (y, x) (float) of the top-left
      corner of each box. boxes[:, :, 2:4] are the box sizes in (h, w) (float)
        in terms of the number of pixels of the corresponding feature map size.
    box_levels: a 3-D tensor of shape [batch_size, num_boxes, 1] representing
      the 0-based corresponding feature level index of each box.
    boundaries: a 3-D tensor of shape [batch_size, num_boxes, 2] representing
      the boundary (in (y, x)) of the corresponding feature map for each box.
      Any resampled grid points that go beyond the bounary will be clipped.
    output_size: a scalar indicating the output crop size.

  Returns:
    features_per_box: a 5-D tensor of shape
      [batch_size, num_boxes, output_size, output_size, num_filters]
      representing the cropped features.
  """
  features_shape = tf.shape(features)
  batch_size = features_shape[0]
  num_levels = features_shape[1]
  max_feature_height = features_shape[2]
  max_feature_width = features_shape[3]
  num_filters = features_shape[4]

  boxes_shape = tf.shape(boxes)
  num_boxes = boxes_shape[1]
 
  # Compute the grid position w.r.t. the corresponding feature map.
  box_grid_x = []
  box_grid_y = []
  for i in range(output_size):
    box_grid_x.append(boxes[:, :, 1:2] +
                      (i + 0.5) * boxes[:, :, 3:4] / output_size)
    box_grid_y.append(boxes[:, :, 0:1] +
                      (i + 0.5) * boxes[:, :, 2:3] / output_size)
  box_grid_x = tf.concat(box_grid_x, axis=-1)
  box_grid_y = tf.concat(box_grid_y, axis=-1)

  # Compute indices for gather operation.
  box_grid_y0 = tf.floor(box_grid_y)
  box_grid_x0 = tf.floor(box_grid_x)
  box_grid_x0 = tf.maximum(0., box_grid_x0)
  box_grid_y0 = tf.maximum(0., box_grid_y0)
  box_gridx0x1 = tf.stack([
      tf.minimum(box_grid_x0, boundaries[:, :, 1:2]),
      tf.minimum(box_grid_x0 + 1, boundaries[:, :, 1:2])
  ],
                          axis=3)
  box_gridy0y1 = tf.stack([
      tf.minimum(box_grid_y0, boundaries[:, :, 0:1]),
      tf.minimum(box_grid_y0 + 1, boundaries[:, :, 0:1])
  ],
                          axis=3)

  x_indices = tf.reshape(box_gridx0x1, [batch_size, num_boxes, output_size * 2])
  y_indices = tf.reshape(box_gridy0y1, [batch_size, num_boxes, output_size * 2])

  indices_dtype = tf.int32

  x_indices = tf.cast(x_indices, indices_dtype)
  y_indices = tf.cast(y_indices, indices_dtype)

  height_dim_offset = max_feature_width
  level_dim_offset = max_feature_height * height_dim_offset
  batch_dim_offset = num_levels * level_dim_offset

  batch_dim_indices = (
      tf.reshape(tf.range(batch_size, dtype=indices_dtype) * batch_dim_offset, [batch_size, 1, 1, 1]) *
      tf.ones([1, num_boxes, output_size * 2, output_size * 2], dtype=indices_dtype)
  )

  box_level_indices = (
      tf.reshape(box_levels * level_dim_offset, [batch_size, num_boxes, 1, 1]) *
      tf.ones([1, 1, output_size * 2, output_size * 2], dtype=indices_dtype)
  )

  height_indices = (
      tf.reshape(y_indices * height_dim_offset, [batch_size, num_boxes, output_size * 2, 1]) *
      tf.ones([1, 1, 1, output_size * 2], dtype=indices_dtype)
  )

  width_indices = (
      tf.reshape(x_indices, [batch_size, num_boxes, 1, output_size * 2]) *
      tf.ones([1, 1, output_size * 2, 1], dtype=indices_dtype)
  )

  batch_dim_indices = tf.cast(batch_dim_indices, tf.float32)
  box_level_indices = tf.cast(box_level_indices, tf.float32)
  height_indices = tf.cast(height_indices, tf.float32)
  width_indices = tf.cast(width_indices, tf.float32)

  indices = tf.add_n([
      batch_dim_indices,
      box_level_indices,
      height_indices,
      width_indices,
  ])
  indices = tf.cast(indices, indices_dtype)
  indices = tf.reshape(indices, [-1])

  features = tf.reshape(features, [-1, num_filters])
  features_per_box = tf.gather(features, indices)

  features_per_box = tf.reshape(
      features_per_box,
      [batch_size, num_boxes, output_size * 2, output_size * 2, num_filters]
  )

  # The RoIAlign feature f can be computed by bilinear interpolation of four
  # neighboring feature points f0, f1, f2, and f3.
  # f(y, x) = [hy, ly] * [[f00, f01], * [hx, lx]^T
  #                       [f10, f11]]
  # f(y, x) = (hy*hx)f00 + (hy*lx)f01 + (ly*hx)f10 + (lx*ly)f11
  # f(y, x) = w00*f00 + w01*f01 + w10*f10 + w11*f11
  ly = box_grid_y - box_grid_y0
  lx = box_grid_x - box_grid_x0
  hy = 1.0 - ly
  hx = 1.0 - lx
  kernel_x = tf.reshape(tf.stack([hx, lx], axis=3), [batch_size, num_boxes, 1, output_size * 2])
  kernel_y = tf.reshape(tf.stack([hy, ly], axis=3), [batch_size, num_boxes, output_size * 2, 1])

  # Use implicit broadcast to generate the interpolation kernel. The
  # multiplier `4` is for avg pooling.
  interpolation_kernel = kernel_y * kernel_x * 4

  # Interpolate the gathered features with computed interpolation kernels.
  features_per_box *= tf.cast(tf.expand_dims(interpolation_kernel, axis=4), dtype=features_per_box.dtype)
  features_per_box = tf.reshape(
      features_per_box,
      [batch_size * num_boxes, output_size * 2, output_size * 2, num_filters]
  )
  features_per_box = tf.nn.avg_pool2d(features_per_box, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
  features_per_box = tf.reshape(features_per_box, [batch_size, num_boxes, output_size, output_size, num_filters])

  return features_per_box

def roi_features(features, rois, output_size=7):
  """
  Generate the (output_size, output_size) set of pixels for each input box
  by first locating the box into the correct feature level, and then cropping
  and resizing it using the correspoding feature map of that level.

  Args:
    features: A list with FPN features. The
      features are in shape of [batch_size, height_l, width_l, num_filters].
    rois: A 3-D Tensor of shape [batch_size, num_boxes, 4]. Each row represents
      a box with [y1, x1, y2, x2] in un-normalized coordinates.
    output_size: A scalar to indicate the output crop size.

  Returns:
    [ level_ids ] # Num levels
    A 5-D tensor representing feature crop of shape
    [batch_size, num_boxes, output_size, output_size, num_filters].
  """
  
  min_level = 2
  max_level = len(features) - 1 + min_level
  min_level_features_shape = tf.shape(features[0])
  max_feature_height = min_level_features_shape[1]
  max_feature_width = min_level_features_shape[2]
  
  # Stack feature pyramid into a features_all of shape
  # [batch_size, levels, height, width, num_filters].
  features_all = []
  for level in range(min_level, max_level + 1):
    features_all.append(tf.image.pad_to_bounding_box(features[level - min_level], 0, 0, max_feature_height, max_feature_width))

  features_all = tf.stack(features_all, axis=1)

  # Assign boxes to the right level.
  box_width = tf.squeeze(rois[:, :, 3:4] - rois[:, :, 1:2], axis=-1)
  box_height = tf.squeeze(rois[:, :, 2:3] - rois[:, :, 0:1], axis=-1)

  areas_sqrt = tf.sqrt(box_height * box_width)

  levels = tf.math.floordiv(tf.math.log(tf.divide(areas_sqrt, 224.0)), tf.math.log(2.0)) + 4.0
  levels = tf.cast(levels, dtype=tf.int32)

  # Map levels between [min_level, max_level].
  levels = tf.minimum(max_level, tf.maximum(levels, min_level))

  level_ids = [tf.where(tf.math.equal(levels, 2)), tf.where(tf.math.equal(levels, 3)), 
      tf.where(tf.math.equal(levels, 4)), tf.where(tf.math.equal(levels, 5))]
  
  # Project box location and sizes to corresponding feature levels.
  scale_to_level = tf.cast(
      tf.pow(tf.constant(2.0), tf.cast(levels, tf.float32)),
      dtype=rois.dtype
  )

  rois /= tf.expand_dims(scale_to_level, axis=2)

  box_width /= scale_to_level
  box_height /= scale_to_level

  rois = tf.concat(
      [rois[:, :, 0:2],
      tf.expand_dims(box_height, -1),
      tf.expand_dims(box_width, -1)],
      axis=-1
  )

  # Map levels to [0, max_level-min_level].
  levels -= min_level
  level_strides = tf.pow([[2.0]], tf.cast(levels, tf.float32))

  boundary = tf.cast(
      tf.concat(
          [
              tf.expand_dims([[tf.cast(max_feature_height, tf.float32)]] / level_strides - 1, axis=-1),
              tf.expand_dims([[tf.cast(max_feature_width, tf.float32)]] / level_strides - 1, axis=-1),
          ],
          axis=-1
      ),
      rois.dtype
  )
  
  features_per_box = roi_align(features_all, rois, levels, boundary, output_size)
  return level_ids, features_per_box