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WANJI
points: torch.Size([115195, 5])
frame_id: (1,)
gt_boxes: torch.Size([1, 25, 8])
use_lead_xyz: torch.Size([1])
voxels: torch.Size([34529, 100, 4])
voxel_coords: torch.Size([34529, 4])
voxel_num_points: torch.Size([34529])
batch_size: 1
voxel_features: torch.Size([34529, 4])
encoded_spconv_tensor: <spconv.SparseConvTensor object at 0x7f525852a250>
encoded_spconv_tensor_stride: 8
multi_scale_3d_features: {'x_conv1': <spconv.SparseConvTensor object at 0x7f5258577eb0>,
'x_conv2': <spconv.SparseConvTensor object at 0x7f5258577fa0>,
'x_conv3': <spconv.SparseConvTensor object at 0x7f525852a040>,
'x_conv4': <spconv.SparseConvTensor object at 0x7f525852a160>}
multi_scale_3d_strides: {'x_conv1': 1, 'x_conv2': 2, 'x_conv3': 4, 'x_conv4': 8}
spatial_features: torch.Size([1, 512, 70, 72])
spatial_features_stride: 8
RuntimeError: Given groups=1, weight of size [128, 256, 3, 3],
expected input[1, 512, 72, 74] to have 256 channels, but got 512 channels instead
"==========================================================================================================="
KITTI
frame_id : (2,)
calib : (2,)
gt_boxes : torch.Size([2, 12, 8])
road_plane : torch.Size([2, 4])
points : torch.Size([35973, 5])
use_lead_xyz : torch.Size([2])
voxels : torch.Size([28847, 5, 4])
voxel_coords : torch.Size([28847, 4])
voxel_num_points : torch.Size([28847])
image_shape : (2, 2)
batch_size : 2
voxel_features : torch.Size([28847, 4])
encoded_spconv_tensor : <spconv.SparseConvTensor object at 0x7f5f1d6492b0>
encoded_spconv_tensor_stride : 8
multi_scale_3d_features : {'x_conv1': <spconv.SparseConvTensor object at 0x7f5f1d737760>,
'x_conv2': <spconv.SparseConvTensor object at 0x7f5f1d649250>,
'x_conv3': <spconv.SparseConvTensor object at 0x7f5f1d649340>,
'x_conv4': <spconv.SparseConvTensor object at 0x7f5f1d6491c0>}
multi_scale_3d_strides : {'x_conv1': 1, 'x_conv2': 2, 'x_conv3': 4, 'x_conv4': 8}
spatial_features : torch.Size([2, 256, 200, 176])
spatial_features_stride : 8
"==========================================================================================================="
KITTI
frame_id: (2,)
- calib: (2,)
gt_boxes: (2, 4, 8)
- road_plane: (2, 4)
points: (36139, 5)
use_lead_xyz: (2,)
voxels: (28793, 5, 4)
voxel_coords: (28793, 4)
voxel_num_points: (28793,)
- image_shape: (2, 2)
batch_size: 2
"==========================================================================================================="
WANJI
points: (230388, 5)
frame_id: (2,)
gt_boxes: (2, 38, 8)
use_lead_xyz: (2,)
voxels: (68683, 100, 4)
voxel_coords: (68683, 4)
voxel_num_points: (68683,)
batch_size: 2
"==========================================================================================================="
KITTI_PDV.PY
frame_id: (2,)
- calib: (2,)
gt_boxes: torch.Size([2, 4, 8])
- road_plane: torch.Size([2, 4])
points: torch.Size([38174, 5])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([31121, 5, 4])
voxel_coords: torch.Size([31121, 4])
voxel_num_points: torch.Size([31121])
- image_shape: (2, 2)
batch_size: 2
"==========================================================================================================="
WANJI_PDV.PY
points: torch.Size([230388, 5])
frame_id: (2,)
gt_boxes: torch.Size([2, 35, 8])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([68916, 100, 4])
voxel_coords: torch.Size([68916, 4])
voxel_num_points: torch.Size([68916])
batch_size: 2
====================================================>
points: torch.Size([230394, 5])
frame_id: (2,)
gt_boxes: torch.Size([2, 42, 8])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([68428, 100, 4])
voxel_coords: torch.Size([68428, 4])
voxel_num_points: torch.Size([68428])
batch_size: 2
=====================================================>
points: torch.Size([230394, 5])
frame_id: (2,)
gt_boxes: torch.Size([2, 42, 8])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([68428, 100, 4])
voxel_coords: torch.Size([68428, 4])
voxel_num_points: torch.Size([68428])
batch_size: 2
voxel_features: torch.Size([68428, 4])
=====================================================>
points: torch.Size([230394, 5])
frame_id: (2,)
gt_boxes: torch.Size([2, 42, 8])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([68428, 100, 4])
voxel_coords: torch.Size([68428, 4])
voxel_num_points: torch.Size([68428])
batch_size: 2
voxel_features: torch.Size([68428, 4])
encoded_spconv_tensor: <spconv.SparseConvTensor object at 0x7f4fe055de20>
encoded_spconv_tensor_stride: 8
multi_scale_3d_features: {'x_conv1': <spconv.SparseConvTensor object at 0x7f4fe055db20>, 'x_conv2': <spconv.SparseConvTensor object at 0x7f4fe055dac0>, 'x_conv3': <spconv.SparseConvTensor object at 0x7f4fe055dc70>, 'x_conv4': <spconv.SparseConvTensor object at 0x7f4fe055dd30>}
multi_scale_3d_strides: {'x_conv1': 1, 'x_conv2': 2, 'x_conv3': 4, 'x_conv4': 8}
=====================================================>
points: torch.Size([230394, 5])
frame_id: (2,)
gt_boxes: torch.Size([2, 42, 8])
use_lead_xyz: torch.Size([2])
voxels: torch.Size([68428, 100, 4])
voxel_coords: torch.Size([68428, 4])
voxel_num_points: torch.Size([68428])
batch_size: 2
voxel_features: torch.Size([68428, 4])
encoded_spconv_tensor: <spconv.SparseConvTensor object at 0x7f4fe055de20>
encoded_spconv_tensor_stride: 8
multi_scale_3d_features: {'x_conv1': <spconv.SparseConvTensor object at 0x7f4fe055db20>, 'x_conv2': <spconv.SparseConvTensor object at 0x7f4fe055dac0>, 'x_conv3': <spconv.SparseConvTensor object at 0x7f4fe055dc70>, 'x_conv4': <spconv.SparseConvTensor object at 0x7f4fe055dd30>}
multi_scale_3d_strides: {'x_conv1': 1, 'x_conv2': 2, 'x_conv3': 4, 'x_conv4': 8}
spatial_features: torch.Size([2, 512, 70, 72])
spatial_features_stride: 8
Traceback (most recent call last): | 63/1700 [01:49<39:23, 1.44s/it, total_it=63]
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 233, in <module>
main()
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 177, in main
train_model(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 91, in train_model
accumulated_iter = train_one_epoch(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 43, in train_one_epoch
loss, tb_dict, disp_dict = model_func(model, batch)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/__init__.py", line 42, in model_func
ret_dict, tb_dict, disp_dict = model(batch_dict)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/detectors/pdv.py", line 27, in forward
batch_dict = cur_module(batch_dict)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/roi_heads/pdv_head.py", line 254, in forward
attention_output = self.attention_head(pooled_features, positional_input, src_key_padding_mask) # (BxN, 6x6x6, C)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/model_utils/attention_utils.py", line 44, in forward
attended_features[~empty_rois_mask] = self.transformer_encoder(attended_features_filtered,
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/transformer.py", line 181, in forward
output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/transformer.py", line 293, in forward
src2 = self.self_attn(src, src, src, attn_mask=src_mask,
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/activation.py", line 978, in forward
return F.multi_head_attention_forward(
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/functional.py", line 4265, in multi_head_attention_forward
k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 0, 128] because the unspecified dimension size -1 can be any value and is ambiguous
Traceback (most recent call last):
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 233, in <module>
main()
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 177, in main
train_model(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 91, in train_model
accumulated_iter = train_one_epoch(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 43, in train_one_epoch
loss, tb_dict, disp_dict = model_func(model, batch)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/__init__.py", line 42, in model_func
ret_dict, tb_dict, disp_dict = model(batch_dict)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/detectors/pdv.py", line 36, in forward
loss, tb_dict, disp_dict = self.get_training_loss()
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/detectors/pdv.py", line 49, in get_training_loss
loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/roi_heads/roi_head_template.py", line 226, in get_loss
rcnn_loss_cls, cls_tb_dict = self.get_box_cls_layer_loss(self.forward_ret_dict)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/roi_heads/roi_head_template.py", line 220, in get_box_cls_layer_loss
tb_dict = {'rcnn_loss_cls': rcnn_loss_cls.item()}
RuntimeError: CUDA error: device-side assert triggered
Traceback (most recent call last):
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 233, in <module>
main()
File "/media/disk/02drive/05yueye/code/PDV/tools/train_wanji.py", line 177, in main
train_model(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 91, in train_model
accumulated_iter = train_one_epoch(
File "/media/disk/02drive/05yueye/code/PDV/tools/train_utils/train_utils.py", line 43, in train_one_epoch
loss, tb_dict, disp_dict = model_func(model, batch)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/__init__.py", line 42, in model_func
ret_dict, tb_dict, disp_dict = model(batch_dict)
File "/media/disk/02drive/05yueye/anaconda3/envs/pdv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/detectors/pdv.py", line 36, in forward
loss, tb_dict, disp_dict = self.get_training_loss()
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/detectors/pdv.py", line 49, in get_training_loss
loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/roi_heads/roi_head_template.py", line 226, in get_loss
rcnn_loss_cls, cls_tb_dict = self.get_box_cls_layer_loss(self.forward_ret_dict)
File "/media/disk/02drive/05yueye/code/PDV/pcdet/models/roi_heads/roi_head_template.py", line 220, in get_box_cls_layer_loss
tb_dict = {'rcnn_loss_cls': rcnn_loss_cls.item()}
RuntimeError: CUDA error: device-side assert triggered
{'pred_boxes': tensor([ [ 19.5410, -41.2759, -3.2735, 1.8341, 0.7182, 1.6929, 1.6878],
[-33.0337, -19.0032, -3.3915, 4.7380, 1.8684, 1.7428, 6.2125],
[ 8.5352, 29.9874, -3.8905, 4.5740, 1.8973, 1.6264, 4.5597],
[ 28.8583, -16.6247, -3.7126, 4.7659, 2.0245, 1.6362, 3.1292],
[ 30.0449, -22.6536, -3.6283, 4.7661, 1.9681, 1.6390, 3.0820],
[ 28.9487, -19.6083, -3.6683, 4.6409, 2.0074, 1.6135, 3.1318],
[ 41.4667, -14.1744, -3.8537, 4.6580, 1.8680, 1.6450, 3.1322],
[-18.4131, -20.2696, -3.3445, 4.7192, 1.8481, 1.6753, 6.2765],
[ 41.2604, -32.1378, -3.6526, 4.8660, 1.9800, 1.6380, 6.2130],
[-16.8493, -23.7719, -3.3494, 4.8728, 1.9437, 1.7365, 6.2582],
[-24.2226, -26.2218, -3.4263, 4.7124, 1.9047, 1.8106, 6.2692],
[-33.6617, -22.3387, -3.2877, 4.5527, 1.8648, 1.8188, 6.2647],
[ 12.6629, -6.8329, -3.7821, 2.0894, 0.9259, 1.5948, 2.8362],
[ 48.2315, -14.7985, -3.8840, 4.4418, 1.8007, 1.6394, 3.1219],
[ -0.7231, -2.9176, -3.7259, 1.9771, 0.9401, 1.6460, 3.6515],
[ 54.9148, -15.3457, -3.9670, 4.5432, 1.7952, 1.6411, 3.1212],
[-17.8738, -27.3538, -3.3212, 4.5926, 1.8456, 1.6224, 6.2672],
[ 37.2216, -20.1320, -3.7786, 4.5995, 1.9053, 1.6259, 3.1196],
[-14.3859, -30.8288, -3.3381, 4.8413, 1.9103, 1.7073, 6.2448],
[-26.0805, -19.9099, -3.3311, 4.7942, 1.9077, 1.7560, 6.2375],
[-49.0657, -20.9781, -2.8732, 4.5520, 1.8605, 1.7652, 6.2546],
[-52.0479, -28.0262, -3.2847, 4.4425, 1.7616, 1.7913, 6.2210],
[-25.2707, -37.0635, -3.2896, 1.8772, 0.9050, 1.7603, 6.1788],
[-30.9159, -29.2176, -2.5419, 12.3200, 2.9492, 3.6587, 6.2434],
[ 52.4876, -20.8795, -3.7685, 4.4442, 1.7944, 1.6415, 3.1304],
[ 60.4799, -22.5846, -3.8271, 4.5314, 1.8095, 1.6355, 3.1218],
[-56.7734, -20.5858, -2.7483, 4.6521, 1.8922, 1.9109, 6.2958],
[ 32.5599, -13.9954, -3.8226, 4.5092, 1.8978, 1.6401, 3.0895],
[ 7.1725, 45.8518, -3.8769, 1.8415, 0.9024, 1.6466, 4.6074],
[ -0.9181, -9.9909, -3.5945, 2.1588, 0.9147, 1.6141, 3.0099],
[ 36.2433, -23.2370, -3.6508, 4.5083, 1.8720, 1.6261, 3.0840],
[-25.5986, -23.0842, -3.3621, 4.4366, 1.8061, 1.6390, 6.2727],
[-45.2270, -28.0345, -3.3782, 4.4327, 1.8254, 1.7561, 6.0492]],device='cuda:0'),
'pred_scores': tensor( [1.0000, 1.0000, 0.9999, 0.9999, 0.9999, 0.9999, 0.9998, 0.9982, 0.9947,
0.9880, 0.9880, 0.9868, 0.9847, 0.9809, 0.9717, 0.9612, 0.9588, 0.9562,
0.9086, 0.9059, 0.8930, 0.8820, 0.8165, 0.8117, 0.8094, 0.7750, 0.7237,
0.7210, 0.6476, 0.6276, 0.4964, 0.3896, 0.3184], device='cuda:0'),
'pred_labels': tensor( [3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 3, 3, 2, 2, 2], device='cuda:0')}