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| # 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. | |
| import torch | |
| import torch.nn.functional as F | |
| from ...models import builder | |
| from ...models.builder import DEPTHER | |
| from ...ops import resize | |
| from .base import BaseDepther | |
| 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(BaseDepther): | |
| """Encoder Decoder depther. | |
| EncoderDecoder typically consists of backbone, (neck) and decode_head. | |
| """ | |
| def __init__(self, backbone, decode_head, neck=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None): | |
| super(DepthEncoderDecoder, self).__init__(init_cfg) | |
| if pretrained is not None: | |
| assert backbone.get("pretrained") is None, "both backbone and depther set pretrained weight" | |
| backbone.pretrained = pretrained | |
| self.backbone = builder.build_backbone(backbone) | |
| self._init_decode_head(decode_head) | |
| if neck is not None: | |
| self.neck = builder.build_neck(neck) | |
| self.train_cfg = train_cfg | |
| self.test_cfg = test_cfg | |
| assert self.with_decode_head | |
| def _init_decode_head(self, decode_head): | |
| """Initialize ``decode_head``""" | |
| self.decode_head = builder.build_head(decode_head) | |
| self.align_corners = self.decode_head.align_corners | |
| def extract_feat(self, img): | |
| """Extract features from images.""" | |
| x = self.backbone(img) | |
| if self.with_neck: | |
| x = self.neck(x) | |
| return x | |
| 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, self.train_cfg, **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, self.test_cfg) | |
| 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.""" | |
| depth_pred = self.encode_decode(img, img_meta, rescale, size=size) | |
| return depth_pred | |
| def slide_inference(self, img, img_meta, rescale): | |
| """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 = self.test_cfg.stride | |
| h_crop, w_crop = self.test_cfg.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): | |
| """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 self.test_cfg.mode in ["slide", "whole"] | |
| ori_shape = img_meta[0]["ori_shape"] | |
| assert all(_["ori_shape"] == ori_shape for _ in img_meta) | |
| if self.test_cfg.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 | |