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import random |
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from typing import Optional, Tuple |
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import torch |
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from torch.nn import functional as F |
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from detectron2.config import CfgNode |
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from detectron2.structures import Instances |
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from densepose.converters.base import IntTupleBox |
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from .densepose_cse_base import DensePoseCSEBaseSampler |
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class DensePoseCSEConfidenceBasedSampler(DensePoseCSEBaseSampler): |
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""" |
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Samples DensePose data from DensePose predictions. |
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Samples for each class are drawn using confidence value estimates. |
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""" |
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def __init__( |
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self, |
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cfg: CfgNode, |
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use_gt_categories: bool, |
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embedder: torch.nn.Module, |
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confidence_channel: str, |
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count_per_class: int = 8, |
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search_count_multiplier: Optional[float] = None, |
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search_proportion: Optional[float] = None, |
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): |
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""" |
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Constructor |
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Args: |
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cfg (CfgNode): the config of the model |
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embedder (torch.nn.Module): necessary to compute mesh vertex embeddings |
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confidence_channel (str): confidence channel to use for sampling; |
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possible values: |
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"coarse_segm_confidence": confidences for coarse segmentation |
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(default: "coarse_segm_confidence") |
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count_per_class (int): the sampler produces at most `count_per_class` |
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samples for each category (default: 8) |
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search_count_multiplier (float or None): if not None, the total number |
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of the most confident estimates of a given class to consider is |
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defined as `min(search_count_multiplier * count_per_class, N)`, |
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where `N` is the total number of estimates of the class; cannot be |
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specified together with `search_proportion` (default: None) |
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search_proportion (float or None): if not None, the total number of the |
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of the most confident estimates of a given class to consider is |
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defined as `min(max(search_proportion * N, count_per_class), N)`, |
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where `N` is the total number of estimates of the class; cannot be |
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specified together with `search_count_multiplier` (default: None) |
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""" |
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super().__init__(cfg, use_gt_categories, embedder, count_per_class) |
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self.confidence_channel = confidence_channel |
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self.search_count_multiplier = search_count_multiplier |
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self.search_proportion = search_proportion |
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assert (search_count_multiplier is None) or (search_proportion is None), ( |
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f"Cannot specify both search_count_multiplier (={search_count_multiplier})" |
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f"and search_proportion (={search_proportion})" |
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) |
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def _produce_index_sample(self, values: torch.Tensor, count: int): |
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""" |
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Produce a sample of indices to select data based on confidences |
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Args: |
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values (torch.Tensor): a tensor of length k that contains confidences |
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k: number of points labeled with part_id |
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count (int): number of samples to produce, should be positive and <= k |
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Return: |
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list(int): indices of values (along axis 1) selected as a sample |
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""" |
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k = values.shape[1] |
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if k == count: |
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index_sample = list(range(k)) |
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else: |
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_, sorted_confidence_indices = torch.sort(values[0]) |
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if self.search_count_multiplier is not None: |
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search_count = min(int(count * self.search_count_multiplier), k) |
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elif self.search_proportion is not None: |
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search_count = min(max(int(k * self.search_proportion), count), k) |
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else: |
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search_count = min(count, k) |
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sample_from_top = random.sample(range(search_count), count) |
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index_sample = sorted_confidence_indices[-search_count:][sample_from_top] |
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return index_sample |
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def _produce_mask_and_results( |
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self, instance: Instances, bbox_xywh: IntTupleBox |
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
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""" |
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Method to get labels and DensePose results from an instance |
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Args: |
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instance (Instances): an instance of |
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`DensePoseEmbeddingPredictorOutputWithConfidences` |
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bbox_xywh (IntTupleBox): the corresponding bounding box |
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Return: |
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mask (torch.Tensor): shape [H, W], DensePose segmentation mask |
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embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W] |
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DensePose CSE Embeddings |
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other_values: a tensor of shape [1, H, W], DensePose CSE confidence |
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""" |
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_, _, w, h = bbox_xywh |
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densepose_output = instance.pred_densepose |
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mask, embeddings, _ = super()._produce_mask_and_results(instance, bbox_xywh) |
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other_values = F.interpolate( |
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getattr(densepose_output, self.confidence_channel), |
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size=(h, w), |
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mode="bilinear", |
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)[0].cpu() |
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return mask, embeddings, other_values |
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