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from functools import partial |
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import torch.nn as nn |
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from ...utils.spconv_utils import replace_feature, spconv |
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def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0, |
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conv_type='subm', norm_fn=None): |
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if conv_type == 'subm': |
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conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key) |
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elif conv_type == 'spconv': |
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conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, |
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bias=False, indice_key=indice_key) |
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elif conv_type == 'inverseconv': |
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conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False) |
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else: |
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raise NotImplementedError |
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m = spconv.SparseSequential( |
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conv, |
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norm_fn(out_channels), |
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nn.ReLU(), |
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) |
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return m |
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class SparseBasicBlock(spconv.SparseModule): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, bias=None, norm_fn=None, downsample=None, indice_key=None): |
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super(SparseBasicBlock, self).__init__() |
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assert norm_fn is not None |
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if bias is None: |
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bias = norm_fn is not None |
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self.conv1 = spconv.SubMConv3d( |
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inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key |
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) |
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self.bn1 = norm_fn(planes) |
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self.relu = nn.ReLU() |
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self.conv2 = spconv.SubMConv3d( |
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key |
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) |
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self.bn2 = norm_fn(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = replace_feature(out, self.bn1(out.features)) |
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out = replace_feature(out, self.relu(out.features)) |
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out = self.conv2(out) |
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out = replace_feature(out, self.bn2(out.features)) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out = replace_feature(out, out.features + identity.features) |
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out = replace_feature(out, self.relu(out.features)) |
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return out |
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class VoxelResBackBone8x(nn.Module): |
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def __init__(self, model_cfg, input_channels, grid_size, **kwargs): |
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super().__init__() |
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self.model_cfg = model_cfg |
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use_bias = self.model_cfg.get('USE_BIAS', None) |
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norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01) |
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self.sparse_shape = grid_size[::-1] + [1, 0, 0] |
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self.conv_input = spconv.SparseSequential( |
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spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'), |
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norm_fn(16), |
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nn.ReLU(), |
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) |
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block = post_act_block |
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self.conv1 = spconv.SparseSequential( |
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SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'), |
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SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'), |
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) |
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self.conv2 = spconv.SparseSequential( |
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block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'), |
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SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'), |
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SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'), |
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) |
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self.conv3 = spconv.SparseSequential( |
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block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'), |
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SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'), |
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SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'), |
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) |
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self.conv4 = spconv.SparseSequential( |
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block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'), |
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SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'), |
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SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'), |
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) |
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last_pad = 0 |
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last_pad = self.model_cfg.get('last_pad', last_pad) |
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self.conv_out = spconv.SparseSequential( |
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spconv.SparseConv3d(128, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad, |
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bias=False, indice_key='spconv_down2'), |
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norm_fn(128), |
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nn.ReLU(), |
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) |
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self.num_point_features = 128 |
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self.backbone_channels = { |
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'x_conv1': 16, |
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'x_conv2': 32, |
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'x_conv3': 64, |
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'x_conv4': 128 |
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} |
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def forward(self, batch_dict): |
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""" |
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Args: |
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batch_dict: |
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batch_size: int |
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vfe_features: (num_voxels, C) |
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voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx] |
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Returns: |
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batch_dict: |
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encoded_spconv_tensor: sparse tensor |
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""" |
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voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords'] |
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batch_size = batch_dict['batch_size'] |
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input_sp_tensor = spconv.SparseConvTensor( |
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features=voxel_features, |
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indices=voxel_coords.int(), |
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spatial_shape=self.sparse_shape, |
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batch_size=batch_size |
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) |
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x = self.conv_input(input_sp_tensor) |
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x_conv1 = self.conv1(x) |
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x_conv2 = self.conv2(x_conv1) |
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x_conv3 = self.conv3(x_conv2) |
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x_conv4 = self.conv4(x_conv3) |
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out = self.conv_out(x_conv4) |
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batch_dict.update({ |
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'encoded_spconv_tensor': out, |
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'encoded_spconv_tensor_stride': 8 |
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}) |
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batch_dict.update({ |
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'multi_scale_3d_features': { |
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'x_conv1': x_conv1, |
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'x_conv2': x_conv2, |
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'x_conv3': x_conv3, |
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'x_conv4': x_conv4, |
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} |
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}) |
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batch_dict.update({ |
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'multi_scale_3d_strides': { |
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'x_conv1': 1, |
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'x_conv2': 2, |
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'x_conv3': 4, |
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'x_conv4': 8, |
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} |
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}) |
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return batch_dict |
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