|
|
|
|
|
|
|
|
|
import random
|
|
from typing import Optional, Tuple
|
|
import torch
|
|
|
|
from densepose.converters import ToChartResultConverterWithConfidences
|
|
|
|
from .densepose_base import DensePoseBaseSampler
|
|
|
|
|
|
class DensePoseConfidenceBasedSampler(DensePoseBaseSampler):
|
|
"""
|
|
Samples DensePose data from DensePose predictions.
|
|
Samples for each class are drawn using confidence value estimates.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
confidence_channel: str,
|
|
count_per_class: int = 8,
|
|
search_count_multiplier: Optional[float] = None,
|
|
search_proportion: Optional[float] = None,
|
|
):
|
|
"""
|
|
Constructor
|
|
|
|
Args:
|
|
confidence_channel (str): confidence channel to use for sampling;
|
|
possible values:
|
|
"sigma_2": confidences for UV values
|
|
"fine_segm_confidence": confidences for fine segmentation
|
|
"coarse_segm_confidence": confidences for coarse segmentation
|
|
(default: "sigma_2")
|
|
count_per_class (int): the sampler produces at most `count_per_class`
|
|
samples for each category (default: 8)
|
|
search_count_multiplier (float or None): if not None, the total number
|
|
of the most confident estimates of a given class to consider is
|
|
defined as `min(search_count_multiplier * count_per_class, N)`,
|
|
where `N` is the total number of estimates of the class; cannot be
|
|
specified together with `search_proportion` (default: None)
|
|
search_proportion (float or None): if not None, the total number of the
|
|
of the most confident estimates of a given class to consider is
|
|
defined as `min(max(search_proportion * N, count_per_class), N)`,
|
|
where `N` is the total number of estimates of the class; cannot be
|
|
specified together with `search_count_multiplier` (default: None)
|
|
"""
|
|
super().__init__(count_per_class)
|
|
self.confidence_channel = confidence_channel
|
|
self.search_count_multiplier = search_count_multiplier
|
|
self.search_proportion = search_proportion
|
|
assert (search_count_multiplier is None) or (search_proportion is None), (
|
|
f"Cannot specify both search_count_multiplier (={search_count_multiplier})"
|
|
f"and search_proportion (={search_proportion})"
|
|
)
|
|
|
|
def _produce_index_sample(self, values: torch.Tensor, count: int):
|
|
"""
|
|
Produce a sample of indices to select data based on confidences
|
|
|
|
Args:
|
|
values (torch.Tensor): an array of size [n, k] that contains
|
|
estimated values (U, V, confidences);
|
|
n: number of channels (U, V, confidences)
|
|
k: number of points labeled with part_id
|
|
count (int): number of samples to produce, should be positive and <= k
|
|
|
|
Return:
|
|
list(int): indices of values (along axis 1) selected as a sample
|
|
"""
|
|
k = values.shape[1]
|
|
if k == count:
|
|
index_sample = list(range(k))
|
|
else:
|
|
|
|
|
|
|
|
_, sorted_confidence_indices = torch.sort(values[2])
|
|
if self.search_count_multiplier is not None:
|
|
search_count = min(int(count * self.search_count_multiplier), k)
|
|
elif self.search_proportion is not None:
|
|
search_count = min(max(int(k * self.search_proportion), count), k)
|
|
else:
|
|
search_count = min(count, k)
|
|
sample_from_top = random.sample(range(search_count), count)
|
|
index_sample = sorted_confidence_indices[:search_count][sample_from_top]
|
|
return index_sample
|
|
|
|
def _produce_labels_and_results(self, instance) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Method to get labels and DensePose results from an instance, with confidences
|
|
|
|
Args:
|
|
instance (Instances): an instance of `DensePoseChartPredictorOutputWithConfidences`
|
|
|
|
Return:
|
|
labels (torch.Tensor): shape [H, W], DensePose segmentation labels
|
|
dp_result (torch.Tensor): shape [3, H, W], DensePose results u and v
|
|
stacked with the confidence channel
|
|
"""
|
|
converter = ToChartResultConverterWithConfidences
|
|
chart_result = converter.convert(instance.pred_densepose, instance.pred_boxes)
|
|
labels, dp_result = chart_result.labels.cpu(), chart_result.uv.cpu()
|
|
dp_result = torch.cat(
|
|
(dp_result, getattr(chart_result, self.confidence_channel)[None].cpu())
|
|
)
|
|
|
|
return labels, dp_result
|
|
|
