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from typing import Any, Dict, List, 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 ...converters.base import IntTupleBox |
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from ...data.utils import get_class_to_mesh_name_mapping |
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from ...modeling.cse.utils import squared_euclidean_distance_matrix |
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from ...structures import DensePoseDataRelative |
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from .densepose_base import DensePoseBaseSampler |
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class DensePoseCSEBaseSampler(DensePoseBaseSampler): |
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""" |
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Base DensePose sampler to produce DensePose data from parse_utils.densepose predictions. |
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Samples for each class are drawn according to some distribution over all pixels estimated |
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to belong to that class. |
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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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count_per_class: int = 8, |
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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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count_per_class (int): the sampler produces at most `count_per_class` |
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samples for each category |
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""" |
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super().__init__(count_per_class) |
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self.embedder = embedder |
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self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) |
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self.use_gt_categories = use_gt_categories |
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def _sample(self, instance: Instances, bbox_xywh: IntTupleBox) -> Dict[str, List[Any]]: |
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""" |
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Sample DensPoseDataRelative from estimation results |
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""" |
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if self.use_gt_categories: |
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instance_class = instance.dataset_classes.tolist()[0] |
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else: |
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instance_class = instance.pred_classes.tolist()[0] |
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mesh_name = self.class_to_mesh_name[instance_class] |
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annotation = { |
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DensePoseDataRelative.X_KEY: [], |
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DensePoseDataRelative.Y_KEY: [], |
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DensePoseDataRelative.VERTEX_IDS_KEY: [], |
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DensePoseDataRelative.MESH_NAME_KEY: mesh_name, |
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} |
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mask, embeddings, other_values = self._produce_mask_and_results(instance, bbox_xywh) |
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indices = torch.nonzero(mask, as_tuple=True) |
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selected_embeddings = embeddings.permute(1, 2, 0)[indices].cpu() |
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values = other_values[:, indices[0], indices[1]] |
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k = values.shape[1] |
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count = min(self.count_per_class, k) |
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if count <= 0: |
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return annotation |
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index_sample = self._produce_index_sample(values, count) |
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closest_vertices = squared_euclidean_distance_matrix( |
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selected_embeddings[index_sample], self.embedder(mesh_name) |
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) |
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closest_vertices = torch.argmin(closest_vertices, dim=1) |
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sampled_y = indices[0][index_sample] + 0.5 |
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sampled_x = indices[1][index_sample] + 0.5 |
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_, _, w, h = bbox_xywh |
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x = (sampled_x / w * 256.0).cpu().tolist() |
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y = (sampled_y / h * 256.0).cpu().tolist() |
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annotation[DensePoseDataRelative.X_KEY].extend(x) |
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annotation[DensePoseDataRelative.Y_KEY].extend(y) |
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annotation[DensePoseDataRelative.VERTEX_IDS_KEY].extend(closest_vertices.cpu().tolist()) |
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return annotation |
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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 `DensePoseEmbeddingPredictorOutput` |
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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 (Tuple[torch.Tensor]): a tensor of shape [0, H, W], |
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for potential other values |
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""" |
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densepose_output = instance.pred_densepose |
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S = densepose_output.coarse_segm |
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E = densepose_output.embedding |
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_, _, w, h = bbox_xywh |
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embeddings = F.interpolate(E, size=(h, w), mode="bilinear")[0] |
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coarse_segm_resized = F.interpolate(S, size=(h, w), mode="bilinear")[0] |
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mask = coarse_segm_resized.argmax(0) > 0 |
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other_values = torch.empty((0, h, w), device=E.device) |
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return mask, embeddings, other_values |
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def _resample_mask(self, output: Any) -> torch.Tensor: |
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""" |
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Convert DensePose predictor output to segmentation annotation - tensors of size |
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(256, 256) and type `int64`. |
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Args: |
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output: DensePose predictor output with the following attributes: |
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- coarse_segm: tensor of size [N, D, H, W] with unnormalized coarse |
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segmentation scores |
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Return: |
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Tensor of size (S, S) and type `int64` with coarse segmentation annotations, |
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where S = DensePoseDataRelative.MASK_SIZE |
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""" |
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sz = DensePoseDataRelative.MASK_SIZE |
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mask = ( |
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F.interpolate(output.coarse_segm, (sz, sz), mode="bilinear", align_corners=False) |
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.argmax(dim=1) |
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.long() |
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.squeeze() |
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.cpu() |
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) |
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return mask |
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