55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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import torch.nn as nn
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from loguru import logger
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from .pvcnn2 import create_pointnet2_sa_components
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# implement the global encoder for VAE model
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class PointNetPlusEncoder(nn.Module):
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sa_blocks = [
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[[32, 2, 32], [1024, 0.1, 32, [32, 32]]],
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[[32, 1, 16], [256, 0.2, 32, [32, 64]]]
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]
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force_att = 0 # add attention to all layers
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def __init__(self, zdim, input_dim, extra_feature_channels=0, args={}):
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super().__init__()
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sa_blocks = self.sa_blocks
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layers, sa_in_channels, channels_sa_features, _ = \
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create_pointnet2_sa_components(sa_blocks,
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extra_feature_channels, input_dim=input_dim,
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embed_dim=0, force_att=self.force_att,
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use_att=True, with_se=True)
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self.mlp = nn.Linear(channels_sa_features, zdim*2)
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self.zdim = zdim
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logger.info('[Encoder] zdim={}, out_sigma={}; force_att: {}', zdim, True, self.force_att)
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self.layers = nn.ModuleList(layers)
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self.voxel_dim = [n[1][-1][-1] for n in self.sa_blocks]
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def forward(self, x):
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"""
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Args:
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x: B,N,3
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Returns:
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mu, sigma: B,D
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"""
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output = {}
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x = x.transpose(1, 2) # B,3,N
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xyz = x ## x[:,:3,:]
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features = x
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for layer_id, layer in enumerate(self.layers):
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features, xyz, _ = layer( (features, xyz, None) )
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# features: B,D,N; xyz: B,3,N
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features = features.max(-1)[0]
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features = self.mlp(features)
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mu_1d, sigma_1d = features[:, :self.zdim], features[:, self.zdim:]
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output.update({'mu_1d': mu_1d, 'sigma_1d': sigma_1d})
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return output
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