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OpenPCDet/pcdet/models/backbones_3d/spconv_backbone_focal_cuda.py

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+from functools import partial
+
+import torch
+import spconv.pytorch as spconv
+import torch.nn as nn
+
+#from .focal_sparse_conv.focal_sparse_conv import FocalSparseConv
+from .focal_sparse_conv.focal_sparse_conv_cuda import FocalSparseConvCUDA as FocalSparseConv
+from .focal_sparse_conv.SemanticSeg.pyramid_ffn import PyramidFeat2D
+from spconv.core import ConvAlgo
+
+
+class objDict:
+    @staticmethod
+    def to_object(obj: object, **data):
+        obj.__dict__.update(data)
+
+class ConfigDict:
+    def __init__(self, name):
+        self.name = name
+    def __getitem__(self, item):
+        return getattr(self, item)
+
+
+class SparseSequentialBatchdict(spconv.SparseSequential):
+    def __init__(self, *args, **kwargs):
+        super(SparseSequentialBatchdict, self).__init__(*args, **kwargs)
+
+    def forward(self, input, batch_dict=None):
+        loss = 0
+        for k, module in self._modules.items():
+            if module is None:
+                continue
+            if isinstance(module, (FocalSparseConv,)):
+                input, batch_dict, _loss = module(input, batch_dict)
+                loss += _loss
+            else:
+                input = module(input)
+        return input, batch_dict, loss
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(True),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv3d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU(True)
+        self.conv2 = spconv.SubMConv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = out.replace_feature(self.bn1(out.features))
+        out = out.replace_feature(self.relu(out.features))
+
+        out = self.conv2(out)
+        out = out.replace_feature(self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = out.replace_feature(out.features + identity.features)
+        out = out.replace_feature(self.relu(out.features))
+
+        return out
+
+
+class VoxelBackBone8xFocal(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
+
+        self.conv_input = spconv.SparseSequential(
+            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm_input'),
+            norm_fn(16),
+            nn.ReLU(True),
+        )
+
+        block = post_act_block
+
+        use_img = model_cfg.get('USE_IMG', False)
+        topk = model_cfg.get('TOPK', True)
+        threshold = model_cfg.get('THRESHOLD', 0.5)
+        kernel_size = model_cfg.get('KERNEL_SIZE', 3)
+        mask_multi = model_cfg.get('MASK_MULTI', False)
+        skip_mask_kernel = model_cfg.get('SKIP_MASK_KERNEL', False)
+        skip_mask_kernel_image =  model_cfg.get('SKIP_MASK_KERNEL_IMG', False)
+        enlarge_voxel_channels = model_cfg.get('ENLARGE_VOXEL_CHANNELS', -1)
+        img_pretrain = model_cfg.get('IMG_PRETRAIN', "../checkpoints/deeplabv3_resnet50_coco-cd0a2569.pth")
+        use_stages = model_cfg.get('USE_STAGES', [1, 2, 3])
+
+        if use_img:
+            model_cfg_seg=dict(
+                name='SemDeepLabV3',
+                backbone='ResNet50',
+                num_class=21, # pretrained on COCO
+                args={"feat_extract_layer": ["layer1"],
+                    "pretrained_path": img_pretrain},
+                channel_reduce={
+                    "in_channels": [256],
+                    "out_channels": [16],
+                    "kernel_size": [1],
+                    "stride": [1],
+                    "bias": [False]
+                }
+            )
+            cfg_dict = ConfigDict('SemDeepLabV3')
+            objDict.to_object(cfg_dict, **model_cfg_seg)
+            self.semseg = PyramidFeat2D(optimize=True, model_cfg=cfg_dict)
+
+            self.conv_focal_multimodal = FocalSparseConv(16, 16, image_channel=model_cfg_seg['channel_reduce']['out_channels'][0],
+                                        topk=topk, threshold=threshold, use_img=True, skip_mask_kernel=skip_mask_kernel_image,
+                                        voxel_stride=1, norm_fn=norm_fn, indice_key='spconv_focal_multimodal')
+
+        special_spconv_fn = partial(FocalSparseConv, mask_multi=mask_multi, enlarge_voxel_channels=enlarge_voxel_channels, 
+                                    topk=topk, threshold=threshold, kernel_size=kernel_size, padding=kernel_size//2, 
+                                    skip_mask_kernel=skip_mask_kernel)
+        self.use_img = use_img
+
+        self.conv1 = SparseSequentialBatchdict(
+            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+            special_spconv_fn(16, 16, voxel_stride=1, norm_fn=norm_fn, indice_key='focal1') if 1 in use_stages else None, 
+        )
+
+        self.conv2 =SparseSequentialBatchdict(
+            # [1600, 1408, 41] <- [800, 704, 21]
+            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            special_spconv_fn(32, 32, voxel_stride=2, norm_fn=norm_fn, indice_key='focal2') if 2 in use_stages else None,
+        )
+
+        self.conv3 = SparseSequentialBatchdict(
+            # [800, 704, 21] <- [400, 352, 11]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            special_spconv_fn(64, 64, voxel_stride=4, norm_fn=norm_fn, indice_key='focal3') if 3 in use_stages else None,
+        )
+
+        self.conv4 = SparseSequentialBatchdict(
+            # [400, 352, 11] <- [200, 176, 5]
+            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+
+        last_pad = 0
+        last_pad = self.model_cfg.get('last_pad', last_pad)
+        self.conv_out = spconv.SparseSequential(
+            # [200, 150, 5] -> [200, 150, 2]
+            spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad,
+                                bias=False, indice_key='spconv_down2'),
+            norm_fn(128),
+            nn.ReLU(True),
+        )
+        self.num_point_features = 128
+        self.backbone_channels = {
+            'x_conv1': 16,
+            'x_conv2': 32,
+            'x_conv3': 64,
+            'x_conv4': 64
+        }
+
+        self.forward_ret_dict = {}
+
+    def get_loss(self, tb_dict=None):
+        loss = self.forward_ret_dict['loss_box_of_pts']
+        if tb_dict is None:
+            tb_dict = {}
+        tb_dict['loss_box_of_pts'] = loss.item()
+        return loss, tb_dict
+
+    def forward(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+                batch_size: int
+                vfe_features: (num_voxels, C)
+                voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]
+        Returns:
+            batch_dict:
+                encoded_spconv_tensor: sparse tensor
+        """
+        voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=voxel_features,
+            indices=voxel_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+
+        batch_dict['loss_box_of_pts'] = 0
+
+        x = self.conv_input(input_sp_tensor)
+        #from IPython import embed; embed()
+        #raise ValueError('Stop.')
+
+        x_conv1, batch_dict, loss1 = self.conv1(x, batch_dict)
+
+        if self.use_img:
+            x_image = self.semseg(batch_dict['images'])['layer1_feat2d']
+            x_conv1, batch_dict, loss_img = self.conv_focal_multimodal(x_conv1, batch_dict, x_image)
+        else:
+            loss_img = 0
+
+        x_conv2, batch_dict, loss2 = self.conv2(x_conv1, batch_dict)
+        x_conv3, batch_dict, loss3 = self.conv3(x_conv2, batch_dict)
+        x_conv4, batch_dict, loss4 = self.conv4(x_conv3, batch_dict)
+
+        self.forward_ret_dict['loss_box_of_pts'] = loss1 + loss2 + loss3 + loss4 + loss_img
+        # for detection head
+        # [200, 176, 5] -> [200, 176, 2]
+        out = self.conv_out(x_conv4)
+
+        batch_dict.update({
+            'encoded_spconv_tensor': out,
+            'encoded_spconv_tensor_stride': 8
+        })
+        batch_dict.update({
+            'multi_scale_3d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_3d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+            }
+        })
+
+        return batch_dict
+