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# 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
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