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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| 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: | |
| # take the best count * search_count_multiplier pixels, | |
| # sample from them uniformly | |
| # (here best = smallest variance) | |
| _, 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 | |