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| import random
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| from typing import Tuple
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| import torch
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| from torch import nn
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| from torch.nn import functional as F
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|
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| from detectron2.config import CfgNode
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|
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| from densepose.structures.mesh import create_mesh
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| from .utils import sample_random_indices
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| class ShapeToShapeCycleLoss(nn.Module):
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| """
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| Cycle Loss for Shapes.
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| Inspired by:
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| "Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes".
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| """
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| def __init__(self, cfg: CfgNode):
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| super().__init__()
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| self.shape_names = list(cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS.keys())
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| self.all_shape_pairs = [
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| (x, y) for i, x in enumerate(self.shape_names) for y in self.shape_names[i + 1 :]
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| ]
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| random.shuffle(self.all_shape_pairs)
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| self.cur_pos = 0
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| self.norm_p = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P
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| self.temperature = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE
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| self.max_num_vertices = (
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| cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES
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| )
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| def _sample_random_pair(self) -> Tuple[str, str]:
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| """
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| Produce a random pair of different mesh names
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| Return:
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| tuple(str, str): a pair of different mesh names
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| """
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| if self.cur_pos >= len(self.all_shape_pairs):
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| random.shuffle(self.all_shape_pairs)
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| self.cur_pos = 0
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| shape_pair = self.all_shape_pairs[self.cur_pos]
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| self.cur_pos += 1
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| return shape_pair
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|
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| def forward(self, embedder: nn.Module):
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| """
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| Do a forward pass with a random pair (src, dst) pair of shapes
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| Args:
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| embedder (nn.Module): module that computes vertex embeddings for different meshes
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| """
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| src_mesh_name, dst_mesh_name = self._sample_random_pair()
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| return self._forward_one_pair(embedder, src_mesh_name, dst_mesh_name)
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|
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| def fake_value(self, embedder: nn.Module):
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| losses = []
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| for mesh_name in embedder.mesh_names:
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| losses.append(embedder(mesh_name).sum() * 0)
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| return torch.mean(torch.stack(losses))
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| def _get_embeddings_and_geodists_for_mesh(
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| self, embedder: nn.Module, mesh_name: str
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| ) -> Tuple[torch.Tensor, torch.Tensor]:
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| """
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| Produces embeddings and geodesic distance tensors for a given mesh. May subsample
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| the mesh, if it contains too many vertices (controlled by
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| SHAPE_CYCLE_LOSS_MAX_NUM_VERTICES parameter).
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| Args:
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| embedder (nn.Module): module that computes embeddings for mesh vertices
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| mesh_name (str): mesh name
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| Return:
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| embeddings (torch.Tensor of size [N, D]): embeddings for selected mesh
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| vertices (N = number of selected vertices, D = embedding space dim)
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| geodists (torch.Tensor of size [N, N]): geodesic distances for the selected
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| mesh vertices (N = number of selected vertices)
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| """
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| embeddings = embedder(mesh_name)
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| indices = sample_random_indices(
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| embeddings.shape[0], self.max_num_vertices, embeddings.device
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| )
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| mesh = create_mesh(mesh_name, embeddings.device)
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| geodists = mesh.geodists
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| if indices is not None:
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| embeddings = embeddings[indices]
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| geodists = geodists[torch.meshgrid(indices, indices)]
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| return embeddings, geodists
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|
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| def _forward_one_pair(
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| self, embedder: nn.Module, mesh_name_1: str, mesh_name_2: str
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| ) -> torch.Tensor:
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| """
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| Do a forward pass with a selected pair of meshes
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| Args:
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| embedder (nn.Module): module that computes vertex embeddings for different meshes
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| mesh_name_1 (str): first mesh name
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| mesh_name_2 (str): second mesh name
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| Return:
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| Tensor containing the loss value
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| """
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| embeddings_1, geodists_1 = self._get_embeddings_and_geodists_for_mesh(embedder, mesh_name_1)
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| embeddings_2, geodists_2 = self._get_embeddings_and_geodists_for_mesh(embedder, mesh_name_2)
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| sim_matrix_12 = embeddings_1.mm(embeddings_2.T)
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| c_12 = F.softmax(sim_matrix_12 / self.temperature, dim=1)
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| c_21 = F.softmax(sim_matrix_12.T / self.temperature, dim=1)
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| c_11 = c_12.mm(c_21)
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| c_22 = c_21.mm(c_12)
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| loss_cycle_11 = torch.norm(geodists_1 * c_11, p=self.norm_p)
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| loss_cycle_22 = torch.norm(geodists_2 * c_22, p=self.norm_p)
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|
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| return loss_cycle_11 + loss_cycle_22
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|
