# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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"""Panoptic MaskRCNN task definition."""
from typing import Any, List, Mapping, Optional, Tuple, Dict
from absl import logging
import tensorflow as tf

from official.common import dataset_fn
from official.core import task_factory
from official.vision.beta.dataloaders import input_reader_factory
from official.vision.beta.evaluation import panoptic_quality_evaluator
from official.vision.beta.evaluation import segmentation_metrics
from official.vision.beta.losses import segmentation_losses
from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as exp_cfg
from official.vision.beta.projects.panoptic_maskrcnn.dataloaders import panoptic_maskrcnn_input
from official.vision.beta.projects.panoptic_maskrcnn.modeling import factory
from official.vision.beta.tasks import maskrcnn


@task_factory.register_task_cls(exp_cfg.PanopticMaskRCNNTask)
class PanopticMaskRCNNTask(maskrcnn.MaskRCNNTask):

  """A single-replica view of training procedure.

  Panoptic Mask R-CNN task provides artifacts for training/evalution procedures,
  including loading/iterating over Datasets, initializing the model, calculating
  the loss, post-processing, and customized metrics with reduction.
  """

  def build_model(self) -> tf.keras.Model:
    """Build Panoptic Mask R-CNN model."""

    input_specs = tf.keras.layers.InputSpec(
        shape=[None] + self.task_config.model.input_size)

    l2_weight_decay = self.task_config.losses.l2_weight_decay
    # Divide weight decay by 2.0 to match the implementation of tf.nn.l2_loss.
    # (https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l2)
    # (https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss)
    l2_regularizer = (tf.keras.regularizers.l2(
        l2_weight_decay / 2.0) if l2_weight_decay else None)

    model = factory.build_panoptic_maskrcnn(
        input_specs=input_specs,
        model_config=self.task_config.model,
        l2_regularizer=l2_regularizer)
    return model

  def initialize(self, model: tf.keras.Model) -> None:
    """Loading pretrained checkpoint."""

    if not self.task_config.init_checkpoint_modules:
      return

    def _get_checkpoint_path(checkpoint_dir_or_file):
      checkpoint_path = checkpoint_dir_or_file
      if tf.io.gfile.isdir(checkpoint_dir_or_file):
        checkpoint_path = tf.train.latest_checkpoint(
            checkpoint_dir_or_file)
      return checkpoint_path

    for init_module in self.task_config.init_checkpoint_modules:
      # Restoring checkpoint.
      if init_module == 'all':
        checkpoint_path = _get_checkpoint_path(
            self.task_config.init_checkpoint)
        ckpt = tf.train.Checkpoint(**model.checkpoint_items)
        status = ckpt.read(checkpoint_path)
        status.expect_partial().assert_existing_objects_matched()

      elif init_module == 'backbone':
        checkpoint_path = _get_checkpoint_path(
            self.task_config.init_checkpoint)
        ckpt = tf.train.Checkpoint(backbone=model.backbone)
        status = ckpt.read(checkpoint_path)
        status.expect_partial().assert_existing_objects_matched()

      elif init_module == 'segmentation_backbone':
        checkpoint_path = _get_checkpoint_path(
            self.task_config.segmentation_init_checkpoint)
        ckpt = tf.train.Checkpoint(
            segmentation_backbone=model.segmentation_backbone)
        status = ckpt.read(checkpoint_path)
        status.expect_partial().assert_existing_objects_matched()

      elif init_module == 'segmentation_decoder':
        checkpoint_path = _get_checkpoint_path(
            self.task_config.segmentation_init_checkpoint)
        ckpt = tf.train.Checkpoint(
            segmentation_decoder=model.segmentation_decoder)
        status = ckpt.read(checkpoint_path)
        status.expect_partial().assert_existing_objects_matched()

      else:
        raise ValueError(
            "Only 'all', 'backbone', 'segmentation_backbone' and/or "
            "segmentation_backbone' can be used to initialize the model, but "
            "got {}".format(init_module))
      logging.info('Finished loading pretrained checkpoint from %s for %s',
                   checkpoint_path, init_module)

  def build_inputs(
      self,
      params: exp_cfg.DataConfig,
      input_context: Optional[tf.distribute.InputContext] = None
  ) -> tf.data.Dataset:
    """Build input dataset."""
    decoder_cfg = params.decoder.get()
    if params.decoder.type == 'simple_decoder':
      decoder = panoptic_maskrcnn_input.TfExampleDecoder(
          regenerate_source_id=decoder_cfg.regenerate_source_id,
          mask_binarize_threshold=decoder_cfg.mask_binarize_threshold,
          include_panoptic_masks=decoder_cfg.include_panoptic_masks)
    else:
      raise ValueError('Unknown decoder type: {}!'.format(params.decoder.type))

    parser = panoptic_maskrcnn_input.Parser(
        output_size=self.task_config.model.input_size[:2],
        min_level=self.task_config.model.min_level,
        max_level=self.task_config.model.max_level,
        num_scales=self.task_config.model.anchor.num_scales,
        aspect_ratios=self.task_config.model.anchor.aspect_ratios,
        anchor_size=self.task_config.model.anchor.anchor_size,
        dtype=params.dtype,
        rpn_match_threshold=params.parser.rpn_match_threshold,
        rpn_unmatched_threshold=params.parser.rpn_unmatched_threshold,
        rpn_batch_size_per_im=params.parser.rpn_batch_size_per_im,
        rpn_fg_fraction=params.parser.rpn_fg_fraction,
        aug_rand_hflip=params.parser.aug_rand_hflip,
        aug_scale_min=params.parser.aug_scale_min,
        aug_scale_max=params.parser.aug_scale_max,
        skip_crowd_during_training=params.parser.skip_crowd_during_training,
        max_num_instances=params.parser.max_num_instances,
        mask_crop_size=params.parser.mask_crop_size,
        segmentation_resize_eval_groundtruth=params.parser
        .segmentation_resize_eval_groundtruth,
        segmentation_groundtruth_padded_size=params.parser
        .segmentation_groundtruth_padded_size,
        segmentation_ignore_label=params.parser.segmentation_ignore_label,
        panoptic_ignore_label=params.parser.panoptic_ignore_label,
        include_panoptic_masks=params.parser.include_panoptic_masks)

    reader = input_reader_factory.input_reader_generator(
        params,
        dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
        decoder_fn=decoder.decode,
        parser_fn=parser.parse_fn(params.is_training))
    dataset = reader.read(input_context=input_context)

    return dataset

  def build_losses(self,
                   outputs: Mapping[str, Any],
                   labels: Mapping[str, Any],
                   aux_losses: Optional[Any] = None) -> Dict[str, tf.Tensor]:
    """Build Panoptic Mask R-CNN losses."""
    params = self.task_config.losses

    use_groundtruth_dimension = params.semantic_segmentation_use_groundtruth_dimension

    segmentation_loss_fn = segmentation_losses.SegmentationLoss(
        label_smoothing=params.semantic_segmentation_label_smoothing,
        class_weights=params.semantic_segmentation_class_weights,
        ignore_label=params.semantic_segmentation_ignore_label,
        use_groundtruth_dimension=use_groundtruth_dimension,
        top_k_percent_pixels=params.semantic_segmentation_top_k_percent_pixels)

    instance_segmentation_weight = params.instance_segmentation_weight
    semantic_segmentation_weight = params.semantic_segmentation_weight

    losses = super(PanopticMaskRCNNTask, self).build_losses(
        outputs=outputs,
        labels=labels,
        aux_losses=None)
    maskrcnn_loss = losses['model_loss']
    segmentation_loss = segmentation_loss_fn(
        outputs['segmentation_outputs'],
        labels['gt_segmentation_mask'])

    model_loss = (
        instance_segmentation_weight * maskrcnn_loss +
        semantic_segmentation_weight * segmentation_loss)

    total_loss = model_loss
    if aux_losses:
      reg_loss = tf.reduce_sum(aux_losses)
      total_loss = model_loss + reg_loss

    losses.update({
        'total_loss': total_loss,
        'maskrcnn_loss': maskrcnn_loss,
        'segmentation_loss': segmentation_loss,
        'model_loss': model_loss,
    })
    return losses

  def build_metrics(self, training: bool = True) -> List[
      tf.keras.metrics.Metric]:
    """Build detection metrics."""
    metrics = []
    num_segmentation_classes = self.task_config.model.segmentation_model.num_classes
    if training:
      metric_names = [
          'total_loss',
          'rpn_score_loss',
          'rpn_box_loss',
          'frcnn_cls_loss',
          'frcnn_box_loss',
          'mask_loss',
          'maskrcnn_loss',
          'segmentation_loss',
          'model_loss'
      ]
      for name in metric_names:
        metrics.append(tf.keras.metrics.Mean(name, dtype=tf.float32))

      if self.task_config.segmentation_evaluation.report_train_mean_iou:
        self.segmentation_train_mean_iou = segmentation_metrics.MeanIoU(
            name='train_mean_iou',
            num_classes=num_segmentation_classes,
            rescale_predictions=False,
            dtype=tf.float32)

    else:
      self._build_coco_metrics()

      rescale_predictions = (not self.task_config.validation_data.parser
                             .segmentation_resize_eval_groundtruth)

      self.segmentation_perclass_iou_metric = segmentation_metrics.PerClassIoU(
          name='per_class_iou',
          num_classes=num_segmentation_classes,
          rescale_predictions=rescale_predictions,
          dtype=tf.float32)

      if isinstance(tf.distribute.get_strategy(), tf.distribute.TPUStrategy):
        self._process_iou_metric_on_cpu = True
      else:
        self._process_iou_metric_on_cpu = False

      if self.task_config.model.generate_panoptic_masks:
        if not self.task_config.validation_data.parser.include_panoptic_masks:
          raise ValueError('`include_panoptic_masks` should be set to True when'
                           ' computing panoptic quality.')
        pq_config = self.task_config.panoptic_quality_evaluator
        self.panoptic_quality_metric = panoptic_quality_evaluator.PanopticQualityEvaluator(
            num_categories=pq_config.num_categories,
            ignored_label=pq_config.ignored_label,
            max_instances_per_category=pq_config.max_instances_per_category,
            offset=pq_config.offset,
            is_thing=pq_config.is_thing,
            rescale_predictions=pq_config.rescale_predictions)

    return metrics

  def train_step(self,
                 inputs: Tuple[Any, Any],
                 model: tf.keras.Model,
                 optimizer: tf.keras.optimizers.Optimizer,
                 metrics: Optional[List[Any]] = None) -> Dict[str, Any]:
    """Does forward and backward.

    Args:
      inputs: a dictionary of input tensors.
      model: the model, forward pass definition.
      optimizer: the optimizer for this training step.
      metrics: a nested structure of metrics objects.

    Returns:
      A dictionary of logs.
    """
    images, labels = inputs
    num_replicas = tf.distribute.get_strategy().num_replicas_in_sync

    with tf.GradientTape() as tape:
      outputs = model(
          images,
          image_info=labels['image_info'],
          anchor_boxes=labels['anchor_boxes'],
          gt_boxes=labels['gt_boxes'],
          gt_classes=labels['gt_classes'],
          gt_masks=(labels['gt_masks'] if self.task_config.model.include_mask
                    else None),
          training=True)
      outputs = tf.nest.map_structure(
          lambda x: tf.cast(x, tf.float32), outputs)

      # Computes per-replica loss.
      losses = self.build_losses(
          outputs=outputs, labels=labels, aux_losses=model.losses)
      scaled_loss = losses['total_loss'] / num_replicas

      # For mixed_precision policy, when LossScaleOptimizer is used, loss is
      # scaled for numerical stability.
      if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
        scaled_loss = optimizer.get_scaled_loss(scaled_loss)

    tvars = model.trainable_variables
    grads = tape.gradient(scaled_loss, tvars)
    # Scales back gradient when LossScaleOptimizer is used.
    if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
      grads = optimizer.get_unscaled_gradients(grads)
    optimizer.apply_gradients(list(zip(grads, tvars)))

    logs = {self.loss: losses['total_loss']}

    if metrics:
      for m in metrics:
        m.update_state(losses[m.name])

    if self.task_config.segmentation_evaluation.report_train_mean_iou:
      segmentation_labels = {
          'masks': labels['gt_segmentation_mask'],
          'valid_masks': labels['gt_segmentation_valid_mask'],
          'image_info': labels['image_info']
      }
      self.process_metrics(
          metrics=[self.segmentation_train_mean_iou],
          labels=segmentation_labels,
          model_outputs=outputs['segmentation_outputs'])
      logs.update({
          self.segmentation_train_mean_iou.name:
              self.segmentation_train_mean_iou.result()
      })

    return logs

  def validation_step(self,
                      inputs: Tuple[Any, Any],
                      model: tf.keras.Model,
                      metrics: Optional[List[Any]] = None) -> Dict[str, Any]:
    """Validatation step.

    Args:
      inputs: a dictionary of input tensors.
      model: the keras.Model.
      metrics: a nested structure of metrics objects.

    Returns:
      A dictionary of logs.
    """
    images, labels = inputs

    outputs = model(
        images,
        anchor_boxes=labels['anchor_boxes'],
        image_info=labels['image_info'],
        training=False)

    logs = {self.loss: 0}
    coco_model_outputs = {
        'detection_masks': outputs['detection_masks'],
        'detection_boxes': outputs['detection_boxes'],
        'detection_scores': outputs['detection_scores'],
        'detection_classes': outputs['detection_classes'],
        'num_detections': outputs['num_detections'],
        'source_id': labels['groundtruths']['source_id'],
        'image_info': labels['image_info']
    }
    segmentation_labels = {
        'masks': labels['groundtruths']['gt_segmentation_mask'],
        'valid_masks': labels['groundtruths']['gt_segmentation_valid_mask'],
        'image_info': labels['image_info']
    }

    if self._process_iou_metric_on_cpu:
      logs.update({
          self.coco_metric.name: (labels['groundtruths'], coco_model_outputs),
          self.segmentation_perclass_iou_metric.name: (
              segmentation_labels,
              outputs['segmentation_outputs'])
      })
    else:
      self.segmentation_perclass_iou_metric.update_state(
          segmentation_labels,
          outputs['segmentation_outputs'])

    if self.task_config.model.generate_panoptic_masks:
      pq_metric_labels = {
          'category_mask':
              labels['groundtruths']['gt_panoptic_category_mask'],
          'instance_mask':
              labels['groundtruths']['gt_panoptic_instance_mask'],
          'image_info': labels['image_info']
            }
      logs.update({
          self.panoptic_quality_metric.name:
              (pq_metric_labels, outputs['panoptic_outputs'])})
    return logs

  def aggregate_logs(self, state=None, step_outputs=None):
    if state is None:
      self.coco_metric.reset_states()
      self.segmentation_perclass_iou_metric.reset_states()
      state = [self.coco_metric, self.segmentation_perclass_iou_metric]
      if self.task_config.model.generate_panoptic_masks:
        state += [self.panoptic_quality_metric]

    self.coco_metric.update_state(
        step_outputs[self.coco_metric.name][0],
        step_outputs[self.coco_metric.name][1])

    if self._process_iou_metric_on_cpu:
      self.segmentation_perclass_iou_metric.update_state(
          step_outputs[self.segmentation_perclass_iou_metric.name][0],
          step_outputs[self.segmentation_perclass_iou_metric.name][1])

    if self.task_config.model.generate_panoptic_masks:
      self.panoptic_quality_metric.update_state(
          step_outputs[self.panoptic_quality_metric.name][0],
          step_outputs[self.panoptic_quality_metric.name][1])

    return state

  def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
    result = {}
    result = super(
        PanopticMaskRCNNTask, self).reduce_aggregated_logs(
            aggregated_logs=aggregated_logs,
            global_step=global_step)

    ious = self.segmentation_perclass_iou_metric.result()
    if self.task_config.segmentation_evaluation.report_per_class_iou:
      for i, value in enumerate(ious.numpy()):
        result.update({'segmentation_iou/class_{}'.format(i): value})
    # Computes mean IoU
    result.update({'segmentation_mean_iou': tf.reduce_mean(ious).numpy()})

    if self.task_config.model.generate_panoptic_masks:
      report_per_class_metrics = self.task_config.panoptic_quality_evaluator.report_per_class_metrics
      panoptic_quality_results = self.panoptic_quality_metric.result()
      for k, value in panoptic_quality_results.items():
        if k.endswith('per_class'):
          if report_per_class_metrics:
            for i, per_class_value in enumerate(value):
              metric_key = 'panoptic_quality/{}/class_{}'.format(k, i)
              result[metric_key] = per_class_value
          else:
            continue
        else:
          result['panoptic_quality/{}'.format(k)] = value

    return result
