90 lines
3.6 KiB
Python
90 lines
3.6 KiB
Python
import numpy as np
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import torch
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from torch.autograd import Function
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from modules.functional.backend import _backend
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__all__ = ["gather", "furthest_point_sample", "logits_mask"]
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class Gather(Function):
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@staticmethod
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def forward(ctx, features, indices):
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"""
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Gather
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:param ctx:
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:param features: features of points, FloatTensor[B, C, N]
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:param indices: centers' indices in points, IntTensor[b, m]
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:return:
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centers_coords: coordinates of sampled centers, FloatTensor[B, C, M]
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"""
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features = features.contiguous()
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indices = indices.int().contiguous()
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ctx.save_for_backward(indices)
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ctx.num_points = features.size(-1)
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return _backend.gather_features_forward(features, indices)
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@staticmethod
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def backward(ctx, grad_output):
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(indices,) = ctx.saved_tensors
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grad_features = _backend.gather_features_backward(grad_output.contiguous(), indices, ctx.num_points)
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return grad_features, None
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gather = Gather.apply
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def furthest_point_sample(coords, num_samples):
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"""
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Uses iterative furthest point sampling to select a set of npoint features that have the largest
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minimum distance to the sampled point set
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:param coords: coordinates of points, FloatTensor[B, 3, N]
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:param num_samples: int, M
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:return:
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centers_coords: coordinates of sampled centers, FloatTensor[B, 3, M]
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"""
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coords = coords.contiguous()
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indices = _backend.furthest_point_sampling(coords, num_samples)
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return gather(coords, indices)
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def logits_mask(coords, logits, num_points_per_object):
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"""
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Use logits to sample points
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:param coords: coords of points, FloatTensor[B, 3, N]
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:param logits: binary classification logits, FloatTensor[B, 2, N]
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:param num_points_per_object: M, #points per object after masking, int
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:return:
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selected_coords: FloatTensor[B, 3, M]
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masked_coords_mean: mean coords of selected points, FloatTensor[B, 3]
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mask: mask to select points, BoolTensor[B, N]
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"""
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batch_size, _, num_points = coords.shape
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mask = torch.lt(logits[:, 0, :], logits[:, 1, :]) # [B, N]
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num_candidates = torch.sum(mask, dim=-1, keepdim=True) # [B, 1]
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masked_coords = coords * mask.view(batch_size, 1, num_points) # [B, C, N]
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masked_coords_mean = (
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torch.sum(masked_coords, dim=-1) / torch.max(num_candidates, torch.ones_like(num_candidates)).float()
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) # [B, C]
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selected_indices = torch.zeros((batch_size, num_points_per_object), device=coords.device, dtype=torch.int32)
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for i in range(batch_size):
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current_mask = mask[i] # [N]
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current_candidates = current_mask.nonzero().view(-1)
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current_num_candidates = current_candidates.numel()
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if current_num_candidates >= num_points_per_object:
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choices = np.random.choice(current_num_candidates, num_points_per_object, replace=False)
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selected_indices[i] = current_candidates[choices]
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elif current_num_candidates > 0:
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choices = np.concatenate(
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[
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np.arange(current_num_candidates).repeat(num_points_per_object // current_num_candidates),
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np.random.choice(
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current_num_candidates, num_points_per_object % current_num_candidates, replace=False
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),
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]
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)
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np.random.shuffle(choices)
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selected_indices[i] = current_candidates[choices]
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selected_coords = gather(masked_coords - masked_coords_mean.view(batch_size, -1, 1), selected_indices)
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return selected_coords, masked_coords_mean, mask
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