content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # This source code is licensed under the Apache License, Version 2.0
  # found in the LICENSE file in the root directory of this source tree.
  
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from .ops import resize
  
  
  def add_prefix(inputs, prefix):
      """Add prefix for dict.
  
      Args:
          inputs (dict): The input dict with str keys.
          prefix (str): The prefix to add.
  
      Returns:
  
          dict: The dict with keys updated with ``prefix``.
      """
  
      outputs = dict()
      for name, value in inputs.items():
          outputs[f"{prefix}.{name}"] = value
  
      return outputs
  
  
  class DepthEncoderDecoder(nn.Module):
      """Encoder Decoder depther.
  
      EncoderDecoder typically consists of backbone and decode_head.
      """
  
      def __init__(self, backbone, decode_head):
          super(DepthEncoderDecoder, self).__init__()
  
          self.backbone = backbone
          self.decode_head = decode_head
          self.align_corners = self.decode_head.align_corners
  
      def extract_feat(self, img):
          """Extract features from images."""
          return self.backbone(img)
  
      def encode_decode(self, img, img_metas, rescale=True, size=None):
          """Encode images with backbone and decode into a depth estimation
          map of the same size as input."""
          x = self.extract_feat(img)
          out = self._decode_head_forward_test(x, img_metas)
          # crop the pred depth to the certain range.
          out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth)
          if rescale:
              if size is None:
                  if img_metas is not None:
                      size = img_metas[0]["ori_shape"][:2]
                  else:
                      size = img.shape[2:]
              out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners)
          return out
  
      def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs):
          """Run forward function and calculate loss for decode head in
          training."""
          losses = dict()
          loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, **kwargs)
          losses.update(add_prefix(loss_decode, "decode"))
          return losses
  
      def _decode_head_forward_test(self, x, img_metas):
          """Run forward function and calculate loss for decode head in
          inference."""
          depth_pred = self.decode_head.forward_test(x, img_metas)
          return depth_pred
  
      def forward_dummy(self, img):
          """Dummy forward function."""
          depth = self.encode_decode(img, None)
  
          return depth
  
      def forward_train(self, img, img_metas, depth_gt, **kwargs):
          """Forward function for training.
  
          Args:
              img (Tensor): Input images.
              img_metas (list[dict]): List of image info dict where each dict
                  has: 'img_shape', 'scale_factor', 'flip', and may also contain
                  'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                  For details on the values of these keys see
                  `depth/datasets/pipelines/formatting.py:Collect`.
              depth_gt (Tensor): Depth gt
                  used if the architecture supports depth estimation task.
  
          Returns:
              dict[str, Tensor]: a dictionary of loss components
          """
  
          x = self.extract_feat(img)
  
          losses = dict()
  
          # the last of x saves the info from neck
          loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs)
  
          losses.update(loss_decode)
  
          return losses
  
      def whole_inference(self, img, img_meta, rescale, size=None):
          """Inference with full image."""
          return self.encode_decode(img, img_meta, rescale, size=size)
  
      def slide_inference(self, img, img_meta, rescale, stride, crop_size):
          """Inference by sliding-window with overlap.
  
          If h_crop > h_img or w_crop > w_img, the small patch will be used to
          decode without padding.
          """
  
          h_stride, w_stride = stride
          h_crop, w_crop = crop_size
          batch_size, _, h_img, w_img = img.size()
          h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
          w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
          preds = img.new_zeros((batch_size, 1, h_img, w_img))
          count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
          for h_idx in range(h_grids):
              for w_idx in range(w_grids):
                  y1 = h_idx * h_stride
                  x1 = w_idx * w_stride
                  y2 = min(y1 + h_crop, h_img)
                  x2 = min(x1 + w_crop, w_img)
                  y1 = max(y2 - h_crop, 0)
                  x1 = max(x2 - w_crop, 0)
                  crop_img = img[:, :, y1:y2, x1:x2]
                  depth_pred = self.encode_decode(crop_img, img_meta, rescale)
                  preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
  
                  count_mat[:, :, y1:y2, x1:x2] += 1
          assert (count_mat == 0).sum() == 0
          if torch.onnx.is_in_onnx_export():
              # cast count_mat to constant while exporting to ONNX
              count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
          preds = preds / count_mat
          return preds
  
      def inference(self, img, img_meta, rescale, size=None, mode="whole"):
          """Inference with slide/whole style.
  
          Args:
              img (Tensor): The input image of shape (N, 3, H, W).
              img_meta (dict): Image info dict where each dict has: 'img_shape',
                  'scale_factor', 'flip', and may also contain
                  'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                  For details on the values of these keys see
                  `depth/datasets/pipelines/formatting.py:Collect`.
              rescale (bool): Whether rescale back to original shape.
  
          Returns:
              Tensor: The output depth map.
          """
  
          assert mode in ["slide", "whole"]
          ori_shape = img_meta[0]["ori_shape"]
          assert all(_["ori_shape"] == ori_shape for _ in img_meta)
          if mode == "slide":
              depth_pred = self.slide_inference(img, img_meta, rescale)
          else:
              depth_pred = self.whole_inference(img, img_meta, rescale, size=size)
          output = depth_pred
          flip = img_meta[0]["flip"]
          if flip:
              flip_direction = img_meta[0]["flip_direction"]
              assert flip_direction in ["horizontal", "vertical"]
              if flip_direction == "horizontal":
                  output = output.flip(dims=(3,))
              elif flip_direction == "vertical":
                  output = output.flip(dims=(2,))
  
          return output
  
      def simple_test(self, img, img_meta, rescale=True):
          """Simple test with single image."""
          depth_pred = self.inference(img, img_meta, rescale)
          if torch.onnx.is_in_onnx_export():
              # our inference backend only support 4D output
              depth_pred = depth_pred.unsqueeze(0)
              return depth_pred
          depth_pred = depth_pred.cpu().numpy()
          # unravel batch dim
          depth_pred = list(depth_pred)
          return depth_pred
  
      def aug_test(self, imgs, img_metas, rescale=True):
          """Test with augmentations.
  
          Only rescale=True is supported.
          """
          # aug_test rescale all imgs back to ori_shape for now
          assert rescale
          # to save memory, we get augmented depth logit inplace
          depth_pred = self.inference(imgs[0], img_metas[0], rescale)
          for i in range(1, len(imgs)):
              cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:])
              depth_pred += cur_depth_pred
          depth_pred /= len(imgs)
          depth_pred = depth_pred.cpu().numpy()
          # unravel batch dim
          depth_pred = list(depth_pred)
          return depth_pred
  
      def forward_test(self, imgs, img_metas, **kwargs):
          """
          Args:
              imgs (List[Tensor]): the outer list indicates test-time
                  augmentations and inner Tensor should have a shape NxCxHxW,
                  which contains all images in the batch.
              img_metas (List[List[dict]]): the outer list indicates test-time
                  augs (multiscale, flip, etc.) and the inner list indicates
                  images in a batch.
          """
          for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
              if not isinstance(var, list):
                  raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
          num_augs = len(imgs)
          if num_augs != len(img_metas):
              raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})")
          # all images in the same aug batch all of the same ori_shape and pad
          # shape
          for img_meta in img_metas:
              ori_shapes = [_["ori_shape"] for _ in img_meta]
              assert all(shape == ori_shapes[0] for shape in ori_shapes)
              img_shapes = [_["img_shape"] for _ in img_meta]
              assert all(shape == img_shapes[0] for shape in img_shapes)
              pad_shapes = [_["pad_shape"] for _ in img_meta]
              assert all(shape == pad_shapes[0] for shape in pad_shapes)
  
          if num_augs == 1:
              return self.simple_test(imgs[0], img_metas[0], **kwargs)
          else:
              return self.aug_test(imgs, img_metas, **kwargs)
  
      def forward(self, img, img_metas, return_loss=True, **kwargs):
          """Calls either :func:`forward_train` or :func:`forward_test` depending
          on whether ``return_loss`` is ``True``.
  
          Note this setting will change the expected inputs. When
          ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
          and List[dict]), and when ``resturn_loss=False``, img and img_meta
          should be double nested (i.e.  List[Tensor], List[List[dict]]), with
          the outer list indicating test time augmentations.
          """
          if return_loss:
              return self.forward_train(img, img_metas, **kwargs)
          else:
              return self.forward_test(img, img_metas, **kwargs)
  
      def train_step(self, data_batch, optimizer, **kwargs):
          """The iteration step during training.
  
          This method defines an iteration step during training, except for the
          back propagation and optimizer updating, which are done in an optimizer
          hook. Note that in some complicated cases or models, the whole process
          including back propagation and optimizer updating is also defined in
          this method, such as GAN.
  
          Args:
              data (dict): The output of dataloader.
              optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
                  runner is passed to ``train_step()``. This argument is unused
                  and reserved.
  
          Returns:
              dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
                  ``num_samples``.
                  ``loss`` is a tensor for back propagation, which can be a
                  weighted sum of multiple losses.
                  ``log_vars`` contains all the variables to be sent to the
                  logger.
                  ``num_samples`` indicates the batch size (when the model is
                  DDP, it means the batch size on each GPU), which is used for
                  averaging the logs.
          """
          losses = self(**data_batch)
  
          # split losses and images
          real_losses = {}
          log_imgs = {}
          for k, v in losses.items():
              if "img" in k:
                  log_imgs[k] = v
              else:
                  real_losses[k] = v
  
          loss, log_vars = self._parse_losses(real_losses)
  
          outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs)
  
          return outputs
  
      def val_step(self, data_batch, **kwargs):
          """The iteration step during validation.
  
          This method shares the same signature as :func:`train_step`, but used
          during val epochs. Note that the evaluation after training epochs is
          not implemented with this method, but an evaluation hook.
          """
          output = self(**data_batch, **kwargs)
          return output
  
      @staticmethod
      def _parse_losses(losses):
          import torch.distributed as dist
  
          """Parse the raw outputs (losses) of the network.
  
          Args:
              losses (dict): Raw output of the network, which usually contain
                  losses and other necessary information.
  
          Returns:
              tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
                  which may be a weighted sum of all losses, log_vars contains
                  all the variables to be sent to the logger.
          """
          log_vars = OrderedDict()
          for loss_name, loss_value in losses.items():
              if isinstance(loss_value, torch.Tensor):
                  log_vars[loss_name] = loss_value.mean()
              elif isinstance(loss_value, list):
                  log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
              else:
                  raise TypeError(f"{loss_name} is not a tensor or list of tensors")
  
          loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
  
          log_vars["loss"] = loss
          for loss_name, loss_value in log_vars.items():
              # reduce loss when distributed training
              if dist.is_available() and dist.is_initialized():
                  loss_value = loss_value.data.clone()
                  dist.all_reduce(loss_value.div_(dist.get_world_size()))
              log_vars[loss_name] = loss_value.item()
  
          return loss, log_vars
  