| |
|
|
| 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 |
|
|
