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# Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
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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