# Copyright (c) Facebook, Inc. and its affiliates. from abc import ABC, abstractmethod from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple import torch from torch.nn import functional as F from detectron2.structures import BoxMode, Instances from densepose import DensePoseDataRelative LossDict = Dict[str, torch.Tensor] def _linear_interpolation_utilities(v_norm, v0_src, size_src, v0_dst, size_dst, size_z): """ Computes utility values for linear interpolation at points v. The points are given as normalized offsets in the source interval (v0_src, v0_src + size_src), more precisely: v = v0_src + v_norm * size_src / 256.0 The computed utilities include lower points v_lo, upper points v_hi, interpolation weights v_w and flags j_valid indicating whether the points falls into the destination interval (v0_dst, v0_dst + size_dst). Args: v_norm (:obj: `torch.Tensor`): tensor of size N containing normalized point offsets v0_src (:obj: `torch.Tensor`): tensor of size N containing left bounds of source intervals for normalized points size_src (:obj: `torch.Tensor`): tensor of size N containing source interval sizes for normalized points v0_dst (:obj: `torch.Tensor`): tensor of size N containing left bounds of destination intervals size_dst (:obj: `torch.Tensor`): tensor of size N containing destination interval sizes size_z (int): interval size for data to be interpolated Returns: v_lo (:obj: `torch.Tensor`): int tensor of size N containing indices of lower values used for interpolation, all values are integers from [0, size_z - 1] v_hi (:obj: `torch.Tensor`): int tensor of size N containing indices of upper values used for interpolation, all values are integers from [0, size_z - 1] v_w (:obj: `torch.Tensor`): float tensor of size N containing interpolation weights j_valid (:obj: `torch.Tensor`): uint8 tensor of size N containing 0 for points outside the estimation interval (v0_est, v0_est + size_est) and 1 otherwise """ v = v0_src + v_norm * size_src / 256.0 j_valid = (v - v0_dst >= 0) * (v - v0_dst < size_dst) v_grid = (v - v0_dst) * size_z / size_dst v_lo = v_grid.floor().long().clamp(min=0, max=size_z - 1) v_hi = (v_lo + 1).clamp(max=size_z - 1) v_grid = torch.min(v_hi.float(), v_grid) v_w = v_grid - v_lo.float() return v_lo, v_hi, v_w, j_valid class BilinearInterpolationHelper: """ Args: packed_annotations: object that contains packed annotations j_valid (:obj: `torch.Tensor`): uint8 tensor of size M containing 0 for points to be discarded and 1 for points to be selected y_lo (:obj: `torch.Tensor`): int tensor of indices of upper values in z_est for each point y_hi (:obj: `torch.Tensor`): int tensor of indices of lower values in z_est for each point x_lo (:obj: `torch.Tensor`): int tensor of indices of left values in z_est for each point x_hi (:obj: `torch.Tensor`): int tensor of indices of right values in z_est for each point w_ylo_xlo (:obj: `torch.Tensor`): float tensor of size M; contains upper-left value weight for each point w_ylo_xhi (:obj: `torch.Tensor`): float tensor of size M; contains upper-right value weight for each point w_yhi_xlo (:obj: `torch.Tensor`): float tensor of size M; contains lower-left value weight for each point w_yhi_xhi (:obj: `torch.Tensor`): float tensor of size M; contains lower-right value weight for each point """ def __init__( self, packed_annotations: Any, j_valid: torch.Tensor, y_lo: torch.Tensor, y_hi: torch.Tensor, x_lo: torch.Tensor, x_hi: torch.Tensor, w_ylo_xlo: torch.Tensor, w_ylo_xhi: torch.Tensor, w_yhi_xlo: torch.Tensor, w_yhi_xhi: torch.Tensor, ): for k, v in locals().items(): if k != "self": setattr(self, k, v) @staticmethod def from_matches( packed_annotations: Any, densepose_outputs_size_hw: Tuple[int, int] ) -> "BilinearInterpolationHelper": """ Args: packed_annotations: annotations packed into tensors, the following attributes are required: - bbox_xywh_gt - bbox_xywh_est - x_gt - y_gt - point_bbox_with_dp_indices - point_bbox_indices densepose_outputs_size_hw (tuple [int, int]): resolution of DensePose predictor outputs (H, W) Return: An instance of `BilinearInterpolationHelper` used to perform interpolation for the given annotation points and output resolution """ zh, zw = densepose_outputs_size_hw x0_gt, y0_gt, w_gt, h_gt = packed_annotations.bbox_xywh_gt[ packed_annotations.point_bbox_with_dp_indices ].unbind(dim=1) x0_est, y0_est, w_est, h_est = packed_annotations.bbox_xywh_est[ packed_annotations.point_bbox_with_dp_indices ].unbind(dim=1) x_lo, x_hi, x_w, jx_valid = _linear_interpolation_utilities( packed_annotations.x_gt, x0_gt, w_gt, x0_est, w_est, zw ) y_lo, y_hi, y_w, jy_valid = _linear_interpolation_utilities( packed_annotations.y_gt, y0_gt, h_gt, y0_est, h_est, zh ) j_valid = jx_valid * jy_valid w_ylo_xlo = (1.0 - x_w) * (1.0 - y_w) w_ylo_xhi = x_w * (1.0 - y_w) w_yhi_xlo = (1.0 - x_w) * y_w w_yhi_xhi = x_w * y_w return BilinearInterpolationHelper( packed_annotations, j_valid, y_lo, y_hi, x_lo, x_hi, w_ylo_xlo, # pyre-ignore[6] w_ylo_xhi, # pyre-fixme[6]: Expected `Tensor` for 9th param but got `float`. w_yhi_xlo, w_yhi_xhi, ) def extract_at_points( self, z_est, slice_fine_segm=None, w_ylo_xlo=None, w_ylo_xhi=None, w_yhi_xlo=None, w_yhi_xhi=None, ): """ Extract ground truth values z_gt for valid point indices and estimated values z_est using bilinear interpolation over top-left (y_lo, x_lo), top-right (y_lo, x_hi), bottom-left (y_hi, x_lo) and bottom-right (y_hi, x_hi) values in z_est with corresponding weights: w_ylo_xlo, w_ylo_xhi, w_yhi_xlo and w_yhi_xhi. Use slice_fine_segm to slice dim=1 in z_est """ slice_fine_segm = ( self.packed_annotations.fine_segm_labels_gt if slice_fine_segm is None else slice_fine_segm ) w_ylo_xlo = self.w_ylo_xlo if w_ylo_xlo is None else w_ylo_xlo w_ylo_xhi = self.w_ylo_xhi if w_ylo_xhi is None else w_ylo_xhi w_yhi_xlo = self.w_yhi_xlo if w_yhi_xlo is None else w_yhi_xlo w_yhi_xhi = self.w_yhi_xhi if w_yhi_xhi is None else w_yhi_xhi index_bbox = self.packed_annotations.point_bbox_indices z_est_sampled = ( z_est[index_bbox, slice_fine_segm, self.y_lo, self.x_lo] * w_ylo_xlo + z_est[index_bbox, slice_fine_segm, self.y_lo, self.x_hi] * w_ylo_xhi + z_est[index_bbox, slice_fine_segm, self.y_hi, self.x_lo] * w_yhi_xlo + z_est[index_bbox, slice_fine_segm, self.y_hi, self.x_hi] * w_yhi_xhi ) return z_est_sampled def resample_data( z, bbox_xywh_src, bbox_xywh_dst, wout, hout, mode: str = "nearest", padding_mode: str = "zeros" ): """ Args: z (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with data to be resampled bbox_xywh_src (:obj: `torch.Tensor`): tensor of size (N,4) containing source bounding boxes in format XYWH bbox_xywh_dst (:obj: `torch.Tensor`): tensor of size (N,4) containing destination bounding boxes in format XYWH Return: zresampled (:obj: `torch.Tensor`): tensor of size (N, C, Hout, Wout) with resampled values of z, where D is the discretization size """ n = bbox_xywh_src.size(0) assert n == bbox_xywh_dst.size(0), ( "The number of " "source ROIs for resampling ({}) should be equal to the number " "of destination ROIs ({})".format(bbox_xywh_src.size(0), bbox_xywh_dst.size(0)) ) x0src, y0src, wsrc, hsrc = bbox_xywh_src.unbind(dim=1) x0dst, y0dst, wdst, hdst = bbox_xywh_dst.unbind(dim=1) x0dst_norm = 2 * (x0dst - x0src) / wsrc - 1 y0dst_norm = 2 * (y0dst - y0src) / hsrc - 1 x1dst_norm = 2 * (x0dst + wdst - x0src) / wsrc - 1 y1dst_norm = 2 * (y0dst + hdst - y0src) / hsrc - 1 grid_w = torch.arange(wout, device=z.device, dtype=torch.float) / wout grid_h = torch.arange(hout, device=z.device, dtype=torch.float) / hout grid_w_expanded = grid_w[None, None, :].expand(n, hout, wout) grid_h_expanded = grid_h[None, :, None].expand(n, hout, wout) dx_expanded = (x1dst_norm - x0dst_norm)[:, None, None].expand(n, hout, wout) dy_expanded = (y1dst_norm - y0dst_norm)[:, None, None].expand(n, hout, wout) x0_expanded = x0dst_norm[:, None, None].expand(n, hout, wout) y0_expanded = y0dst_norm[:, None, None].expand(n, hout, wout) grid_x = grid_w_expanded * dx_expanded + x0_expanded grid_y = grid_h_expanded * dy_expanded + y0_expanded grid = torch.stack((grid_x, grid_y), dim=3) # resample Z from (N, C, H, W) into (N, C, Hout, Wout) zresampled = F.grid_sample(z, grid, mode=mode, padding_mode=padding_mode, align_corners=True) return zresampled class AnnotationsAccumulator(ABC): """ Abstract class for an accumulator for annotations that can produce dense annotations packed into tensors. """ @abstractmethod def accumulate(self, instances_one_image: Instances): """ Accumulate instances data for one image Args: instances_one_image (Instances): instances data to accumulate """ pass @abstractmethod def pack(self) -> Any: """ Pack data into tensors """ pass @dataclass class PackedChartBasedAnnotations: """ Packed annotations for chart-based model training. The following attributes are defined: - fine_segm_labels_gt (tensor [K] of `int64`): GT fine segmentation point labels - x_gt (tensor [K] of `float32`): GT normalized X point coordinates - y_gt (tensor [K] of `float32`): GT normalized Y point coordinates - u_gt (tensor [K] of `float32`): GT point U values - v_gt (tensor [K] of `float32`): GT point V values - coarse_segm_gt (tensor [N, S, S] of `float32`): GT segmentation for bounding boxes - bbox_xywh_gt (tensor [N, 4] of `float32`): selected GT bounding boxes in XYWH format - bbox_xywh_est (tensor [N, 4] of `float32`): selected matching estimated bounding boxes in XYWH format - point_bbox_with_dp_indices (tensor [K] of `int64`): indices of bounding boxes with DensePose annotations that correspond to the point data - point_bbox_indices (tensor [K] of `int64`): indices of bounding boxes (not necessarily the selected ones with DensePose data) that correspond to the point data - bbox_indices (tensor [N] of `int64`): global indices of selected bounding boxes with DensePose annotations; these indices could be used to access features that are computed for all bounding boxes, not only the ones with DensePose annotations. Here K is the total number of points and N is the total number of instances with DensePose annotations. """ fine_segm_labels_gt: torch.Tensor x_gt: torch.Tensor y_gt: torch.Tensor u_gt: torch.Tensor v_gt: torch.Tensor coarse_segm_gt: Optional[torch.Tensor] bbox_xywh_gt: torch.Tensor bbox_xywh_est: torch.Tensor point_bbox_with_dp_indices: torch.Tensor point_bbox_indices: torch.Tensor bbox_indices: torch.Tensor class ChartBasedAnnotationsAccumulator(AnnotationsAccumulator): """ Accumulates annotations by batches that correspond to objects detected on individual images. Can pack them together into single tensors. """ def __init__(self): self.i_gt = [] self.x_gt = [] self.y_gt = [] self.u_gt = [] self.v_gt = [] self.s_gt = [] self.bbox_xywh_gt = [] self.bbox_xywh_est = [] self.point_bbox_with_dp_indices = [] self.point_bbox_indices = [] self.bbox_indices = [] self.nxt_bbox_with_dp_index = 0 self.nxt_bbox_index = 0 def accumulate(self, instances_one_image: Instances): """ Accumulate instances data for one image Args: instances_one_image (Instances): instances data to accumulate """ boxes_xywh_est = BoxMode.convert( instances_one_image.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS ) boxes_xywh_gt = BoxMode.convert( instances_one_image.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS ) n_matches = len(boxes_xywh_gt) assert n_matches == len( boxes_xywh_est ), f"Got {len(boxes_xywh_est)} proposal boxes and {len(boxes_xywh_gt)} GT boxes" if not n_matches: # no detection - GT matches return if ( not hasattr(instances_one_image, "gt_densepose") or instances_one_image.gt_densepose is None ): # no densepose GT for the detections, just increase the bbox index self.nxt_bbox_index += n_matches return for box_xywh_est, box_xywh_gt, dp_gt in zip( boxes_xywh_est, boxes_xywh_gt, instances_one_image.gt_densepose ): if (dp_gt is not None) and (len(dp_gt.x) > 0): # pyre-fixme[6]: For 1st argument expected `Tensor` but got `float`. # pyre-fixme[6]: For 2nd argument expected `Tensor` but got `float`. self._do_accumulate(box_xywh_gt, box_xywh_est, dp_gt) self.nxt_bbox_index += 1 def _do_accumulate( self, box_xywh_gt: torch.Tensor, box_xywh_est: torch.Tensor, dp_gt: DensePoseDataRelative ): """ Accumulate instances data for one image, given that the data is not empty Args: box_xywh_gt (tensor): GT bounding box box_xywh_est (tensor): estimated bounding box dp_gt (DensePoseDataRelative): GT densepose data """ self.i_gt.append(dp_gt.i) self.x_gt.append(dp_gt.x) self.y_gt.append(dp_gt.y) self.u_gt.append(dp_gt.u) self.v_gt.append(dp_gt.v) if hasattr(dp_gt, "segm"): self.s_gt.append(dp_gt.segm.unsqueeze(0)) self.bbox_xywh_gt.append(box_xywh_gt.view(-1, 4)) self.bbox_xywh_est.append(box_xywh_est.view(-1, 4)) self.point_bbox_with_dp_indices.append( torch.full_like(dp_gt.i, self.nxt_bbox_with_dp_index) ) self.point_bbox_indices.append(torch.full_like(dp_gt.i, self.nxt_bbox_index)) self.bbox_indices.append(self.nxt_bbox_index) self.nxt_bbox_with_dp_index += 1 def pack(self) -> Optional[PackedChartBasedAnnotations]: """ Pack data into tensors """ if not len(self.i_gt): # TODO: # returning proper empty annotations would require # creating empty tensors of appropriate shape and # type on an appropriate device; # we return None so far to indicate empty annotations return None return PackedChartBasedAnnotations( fine_segm_labels_gt=torch.cat(self.i_gt, 0).long(), x_gt=torch.cat(self.x_gt, 0), y_gt=torch.cat(self.y_gt, 0), u_gt=torch.cat(self.u_gt, 0), v_gt=torch.cat(self.v_gt, 0), # ignore segmentation annotations, if not all the instances contain those coarse_segm_gt=torch.cat(self.s_gt, 0) if len(self.s_gt) == len(self.bbox_xywh_gt) else None, bbox_xywh_gt=torch.cat(self.bbox_xywh_gt, 0), bbox_xywh_est=torch.cat(self.bbox_xywh_est, 0), point_bbox_with_dp_indices=torch.cat(self.point_bbox_with_dp_indices, 0).long(), point_bbox_indices=torch.cat(self.point_bbox_indices, 0).long(), bbox_indices=torch.as_tensor( self.bbox_indices, dtype=torch.long, device=self.x_gt[0].device ).long(), ) def extract_packed_annotations_from_matches( proposals_with_targets: List[Instances], accumulator: AnnotationsAccumulator ) -> Any: for proposals_targets_per_image in proposals_with_targets: accumulator.accumulate(proposals_targets_per_image) return accumulator.pack() def sample_random_indices( n_indices: int, n_samples: int, device: Optional[torch.device] = None ) -> Optional[torch.Tensor]: """ Samples `n_samples` random indices from range `[0..n_indices - 1]`. If `n_indices` is smaller than `n_samples`, returns `None` meaning that all indices are selected. Args: n_indices (int): total number of indices n_samples (int): number of indices to sample device (torch.device): the desired device of returned tensor Return: Tensor of selected vertex indices, or `None`, if all vertices are selected """ if (n_samples <= 0) or (n_indices <= n_samples): return None indices = torch.randperm(n_indices, device=device)[:n_samples] return indices