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| import numpy as np
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| from typing import Any, List, Tuple, Union
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| import torch
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| from torch.nn import functional as F
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| class Keypoints:
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| """
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| Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
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| containing the x,y location and visibility flag of each keypoint. This tensor has shape
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| (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.
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|
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| The visibility flag follows the COCO format and must be one of three integers:
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| * v=0: not labeled (in which case x=y=0)
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| * v=1: labeled but not visible
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| * v=2: labeled and visible
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| """
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|
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| def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):
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| """
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| Arguments:
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| keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.
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| The shape should be (N, K, 3) where N is the number of
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| instances, and K is the number of keypoints per instance.
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| """
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| device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu")
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| keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)
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| assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape
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| self.tensor = keypoints
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|
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| def __len__(self) -> int:
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| return self.tensor.size(0)
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|
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| def to(self, *args: Any, **kwargs: Any) -> "Keypoints":
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| return type(self)(self.tensor.to(*args, **kwargs))
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|
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| @property
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| def device(self) -> torch.device:
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| return self.tensor.device
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| def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:
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| """
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| Convert keypoint annotations to a heatmap of one-hot labels for training,
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| as described in :paper:`Mask R-CNN`.
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|
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| Arguments:
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| boxes: Nx4 tensor, the boxes to draw the keypoints to
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|
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| Returns:
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| heatmaps:
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| A tensor of shape (N, K), each element is integer spatial label
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| in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
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| valid:
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| A tensor of shape (N, K) containing whether each keypoint is in the roi or not.
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| """
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| return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)
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| def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints":
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| """
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| Create a new `Keypoints` by indexing on this `Keypoints`.
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| The following usage are allowed:
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| 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.
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| 2. `new_kpts = kpts[2:10]`: return a slice of key points.
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| 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor
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| with `length = len(kpts)`. Nonzero elements in the vector will be selected.
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| Note that the returned Keypoints might share storage with this Keypoints,
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| subject to Pytorch's indexing semantics.
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| """
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| if isinstance(item, int):
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| return Keypoints([self.tensor[item]])
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| return Keypoints(self.tensor[item])
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|
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| def __repr__(self) -> str:
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| s = self.__class__.__name__ + "("
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| s += "num_instances={})".format(len(self.tensor))
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| return s
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| @staticmethod
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| def cat(keypoints_list: List["Keypoints"]) -> "Keypoints":
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| """
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| Concatenates a list of Keypoints into a single Keypoints
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| Arguments:
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| keypoints_list (list[Keypoints])
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| Returns:
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| Keypoints: the concatenated Keypoints
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| """
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| assert isinstance(keypoints_list, (list, tuple))
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| assert len(keypoints_list) > 0
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| assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list)
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| cat_kpts = type(keypoints_list[0])(
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| torch.cat([kpts.tensor for kpts in keypoints_list], dim=0)
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| )
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| return cat_kpts
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| def _keypoints_to_heatmap(
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| keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int
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| ) -> Tuple[torch.Tensor, torch.Tensor]:
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| """
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| Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space.
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| Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the
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| closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the
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| continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"):
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| d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
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| Arguments:
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| keypoints: tensor of keypoint locations in of shape (N, K, 3).
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| rois: Nx4 tensor of rois in xyxy format
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| heatmap_size: integer side length of square heatmap.
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| Returns:
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| heatmaps: A tensor of shape (N, K) containing an integer spatial label
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| in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
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| valid: A tensor of shape (N, K) containing whether each keypoint is in
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| the roi or not.
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| """
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| if rois.numel() == 0:
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| return rois.new().long(), rois.new().long()
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| offset_x = rois[:, 0]
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| offset_y = rois[:, 1]
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| scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
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| scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
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| offset_x = offset_x[:, None]
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| offset_y = offset_y[:, None]
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| scale_x = scale_x[:, None]
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| scale_y = scale_y[:, None]
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| x = keypoints[..., 0]
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| y = keypoints[..., 1]
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| x_boundary_inds = x == rois[:, 2][:, None]
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| y_boundary_inds = y == rois[:, 3][:, None]
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| x = (x - offset_x) * scale_x
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| x = x.floor().long()
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| y = (y - offset_y) * scale_y
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| y = y.floor().long()
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| x[x_boundary_inds] = heatmap_size - 1
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| y[y_boundary_inds] = heatmap_size - 1
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| valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
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| vis = keypoints[..., 2] > 0
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| valid = (valid_loc & vis).long()
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| lin_ind = y * heatmap_size + x
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| heatmaps = lin_ind * valid
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| return heatmaps, valid
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| @torch.jit.script_if_tracing
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| def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor:
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| """
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| Extract predicted keypoint locations from heatmaps.
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| Args:
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| maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for
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| each ROI and each keypoint.
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| rois (Tensor): (#ROIs, 4). The box of each ROI.
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| Returns:
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| Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to
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| (x, y, logit, score) for each keypoint.
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| When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate,
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| we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from
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| Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
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| """
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| offset_x = rois[:, 0]
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| offset_y = rois[:, 1]
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| widths = (rois[:, 2] - rois[:, 0]).clamp(min=1)
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| heights = (rois[:, 3] - rois[:, 1]).clamp(min=1)
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| widths_ceil = widths.ceil()
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| heights_ceil = heights.ceil()
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| num_rois, num_keypoints = maps.shape[:2]
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| xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4)
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| width_corrections = widths / widths_ceil
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| height_corrections = heights / heights_ceil
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| keypoints_idx = torch.arange(num_keypoints, device=maps.device)
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| for i in range(num_rois):
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| outsize = (int(heights_ceil[i]), int(widths_ceil[i]))
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| roi_map = F.interpolate(maps[[i]], size=outsize, mode="bicubic", align_corners=False)
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| roi_map = roi_map.reshape(roi_map.shape[1:])
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| max_score, _ = roi_map.view(num_keypoints, -1).max(1)
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| max_score = max_score.view(num_keypoints, 1, 1)
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| tmp_full_resolution = (roi_map - max_score).exp_()
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| tmp_pool_resolution = (maps[i] - max_score).exp_()
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| roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True)
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| w = roi_map.shape[2]
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| pos = roi_map.view(num_keypoints, -1).argmax(1)
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| x_int = pos % w
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| y_int = (pos - x_int) // w
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| assert (
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| roi_map_scores[keypoints_idx, y_int, x_int]
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| == roi_map_scores.view(num_keypoints, -1).max(1)[0]
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| ).all()
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| x = (x_int.float() + 0.5) * width_corrections[i]
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| y = (y_int.float() + 0.5) * height_corrections[i]
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| xy_preds[i, :, 0] = x + offset_x[i]
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| xy_preds[i, :, 1] = y + offset_y[i]
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| xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int]
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| xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int]
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| return xy_preds
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|
