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|
| import random |
| from typing import Optional, Tuple |
| import torch |
| from torch.nn import functional as F |
|
|
| from detectron2.config import CfgNode |
| from detectron2.structures import Instances |
|
|
| from densepose.converters.base import IntTupleBox |
|
|
| from .densepose_cse_base import DensePoseCSEBaseSampler |
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|
|
| class DensePoseCSEConfidenceBasedSampler(DensePoseCSEBaseSampler): |
| """ |
| Samples DensePose data from DensePose predictions. |
| Samples for each class are drawn using confidence value estimates. |
| """ |
|
|
| def __init__( |
| self, |
| cfg: CfgNode, |
| use_gt_categories: bool, |
| embedder: torch.nn.Module, |
| confidence_channel: str, |
| count_per_class: int = 8, |
| search_count_multiplier: Optional[float] = None, |
| search_proportion: Optional[float] = None, |
| ): |
| """ |
| Constructor |
| |
| Args: |
| cfg (CfgNode): the config of the model |
| embedder (torch.nn.Module): necessary to compute mesh vertex embeddings |
| confidence_channel (str): confidence channel to use for sampling; |
| possible values: |
| "coarse_segm_confidence": confidences for coarse segmentation |
| (default: "coarse_segm_confidence") |
| 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__(cfg, use_gt_categories, embedder, 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): a tensor of length k that contains 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[0]) |
| 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_mask_and_results( |
| self, instance: Instances, bbox_xywh: IntTupleBox |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
| """ |
| Method to get labels and DensePose results from an instance |
| |
| Args: |
| instance (Instances): an instance of |
| `DensePoseEmbeddingPredictorOutputWithConfidences` |
| bbox_xywh (IntTupleBox): the corresponding bounding box |
| |
| Return: |
| mask (torch.Tensor): shape [H, W], DensePose segmentation mask |
| embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W] |
| DensePose CSE Embeddings |
| other_values: a tensor of shape [1, H, W], DensePose CSE confidence |
| """ |
| _, _, w, h = bbox_xywh |
| densepose_output = instance.pred_densepose |
| mask, embeddings, _ = super()._produce_mask_and_results(instance, bbox_xywh) |
| other_values = F.interpolate( |
| getattr(densepose_output, self.confidence_channel), |
| size=(h, w), |
| mode="bilinear", |
| )[0].cpu() |
| return mask, embeddings, other_values |
|
|
