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pcdet/models/backbones_3d/spconv_backbone_focal.py
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269
pcdet/models/backbones_3d/spconv_backbone_focal.py
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from functools import partial
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import torch
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from pcdet.utils.spconv_utils import spconv
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import torch.nn as nn
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from .focal_sparse_conv.focal_sparse_conv import FocalSparseConv
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from .focal_sparse_conv.SemanticSeg.pyramid_ffn import PyramidFeat2D
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class objDict:
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@staticmethod
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def to_object(obj: object, **data):
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obj.__dict__.update(data)
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class ConfigDict:
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def __init__(self, name):
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self.name = name
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def __getitem__(self, item):
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return getattr(self, item)
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class SparseSequentialBatchdict(spconv.SparseSequential):
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def __init__(self, *args, **kwargs):
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super(SparseSequentialBatchdict, self).__init__(*args, **kwargs)
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def forward(self, input, batch_dict=None):
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loss = 0
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for k, module in self._modules.items():
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if module is None:
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continue
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if isinstance(module, (FocalSparseConv,)):
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input, batch_dict, _loss = module(input, batch_dict)
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loss += _loss
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else:
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input = module(input)
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return input, batch_dict, loss
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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(True),
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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, 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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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(True)
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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 = out.replace_feature(self.bn1(out.features))
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out = out.replace_feature(self.relu(out.features))
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out = self.conv2(out)
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out = out.replace_feature(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 = out.replace_feature(out.features + identity.features)
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out = out.replace_feature(self.relu(out.features))
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return out
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class VoxelBackBone8xFocal(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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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(True),
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)
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block = post_act_block
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use_img = model_cfg.get('USE_IMG', False)
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topk = model_cfg.get('TOPK', True)
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threshold = model_cfg.get('THRESHOLD', 0.5)
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kernel_size = model_cfg.get('KERNEL_SIZE', 3)
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mask_multi = model_cfg.get('MASK_MULTI', False)
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skip_mask_kernel = model_cfg.get('SKIP_MASK_KERNEL', False)
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skip_mask_kernel_image = model_cfg.get('SKIP_MASK_KERNEL_IMG', False)
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enlarge_voxel_channels = model_cfg.get('ENLARGE_VOXEL_CHANNELS', -1)
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img_pretrain = model_cfg.get('IMG_PRETRAIN', "../checkpoints/deeplabv3_resnet50_coco-cd0a2569.pth")
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if use_img:
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model_cfg_seg=dict(
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name='SemDeepLabV3',
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backbone='ResNet50',
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num_class=21, # pretrained on COCO
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args={"feat_extract_layer": ["layer1"],
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"pretrained_path": img_pretrain},
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channel_reduce={
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"in_channels": [256],
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"out_channels": [16],
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"kernel_size": [1],
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"stride": [1],
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"bias": [False]
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}
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)
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cfg_dict = ConfigDict('SemDeepLabV3')
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objDict.to_object(cfg_dict, **model_cfg_seg)
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self.semseg = PyramidFeat2D(optimize=True, model_cfg=cfg_dict)
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self.conv_focal_multimodal = FocalSparseConv(16, 16, image_channel=model_cfg_seg['channel_reduce']['out_channels'][0],
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topk=topk, threshold=threshold, use_img=True, skip_mask_kernel=skip_mask_kernel_image,
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voxel_stride=1, norm_fn=norm_fn, indice_key='spconv_focal_multimodal')
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special_spconv_fn = partial(FocalSparseConv, mask_multi=mask_multi, enlarge_voxel_channels=enlarge_voxel_channels,
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topk=topk, threshold=threshold, kernel_size=kernel_size, padding=kernel_size//2,
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skip_mask_kernel=skip_mask_kernel)
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self.use_img = use_img
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self.conv1 = SparseSequentialBatchdict(
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block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
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special_spconv_fn(16, 16, voxel_stride=1, norm_fn=norm_fn, indice_key='focal1'),
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)
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self.conv2 =SparseSequentialBatchdict(
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# [1600, 1408, 41] <- [800, 704, 21]
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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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block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
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block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
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special_spconv_fn(32, 32, voxel_stride=2, norm_fn=norm_fn, indice_key='focal2'),
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)
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self.conv3 = SparseSequentialBatchdict(
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# [800, 704, 21] <- [400, 352, 11]
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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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block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
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block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
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special_spconv_fn(64, 64, voxel_stride=4, norm_fn=norm_fn, indice_key='focal3'),
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)
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self.conv4 = SparseSequentialBatchdict(
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# [400, 352, 11] <- [200, 176, 5]
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block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
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block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
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block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
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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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# [200, 150, 5] -> [200, 150, 2]
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spconv.SparseConv3d(64, 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(True),
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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': 64
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}
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self.forward_ret_dict = {}
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def get_loss(self, tb_dict=None):
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loss = self.forward_ret_dict['loss_box_of_pts']
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if tb_dict is None:
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tb_dict = {}
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tb_dict['loss_box_of_pts'] = loss.item()
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return loss, tb_dict
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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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loss_img = 0
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x = self.conv_input(input_sp_tensor)
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x_conv1, batch_dict, loss1 = self.conv1(x, batch_dict)
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if self.use_img:
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x_image = self.semseg(batch_dict['images'])['layer1_feat2d']
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x_conv1, batch_dict, loss_img = self.conv_focal_multimodal(x_conv1, batch_dict, x_image)
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x_conv2, batch_dict, loss2 = self.conv2(x_conv1, batch_dict)
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x_conv3, batch_dict, loss3 = self.conv3(x_conv2, batch_dict)
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x_conv4, batch_dict, loss4 = self.conv4(x_conv3, batch_dict)
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self.forward_ret_dict['loss_box_of_pts'] = loss1 + loss2 + loss3 + loss4 + loss_img
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# for detection head
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# [200, 176, 5] -> [200, 176, 2]
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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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