lsy19103118
commented
6 months ago Thank you very much for your letter!
I ran the following four commands:
1: python ./exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema.py --amp_backend native - b 8 --gpus 2 (nuscenes_infos_trainval.pkl)
2: python ./exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key.py --ckpt_path weights/P2D_res50.pth - b 8 --gpus 2 (nuscenes_infos_trainval.pkl)
3: python ./exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key.py --ckpt_path weights/P2D_res50.pth - b 8 --gpus 2 python ./exps/nuscenes/p2d/(p2d_deform_lss_r50_256x704_128x128_24e_3key.py --ckpt_path weights/P2D_res50.pth -e - b 8 --gpus 2)
(nuscenes_infos_trainval.pkl)
4: python ./exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key.py --ckpt_path weights/P2D_res50.pth -p - b 8 --gpus 2 (nuscenes_infos_trainval.pkl)
Only 4 of them can work properly, and 1, 2, and 3 all report the following error: The File "/ home/m840-02 / anaconda3 / envs/p2d/lib/python3.8 / site - packages/nuscenes/eval/detection/evaluate py", line 85, in init assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \ AssertionError: Samples in split doesn't match samples in predictions.
I found that 1, print(len(self.pred_boxes.sample_tokens)) = 3008
print(len(self.gt_boxes.sample_tokens)) = 6019
At the same time I modified:
info_path in scripts/gen_depth_gt.py = 'data/nuScenes/nuscenes_infos_trainval.pkl' #'data/nuScenes/nuscenes_infos_val.pkl' FileNotFoundError: [Errno 2] No such file or directory: 'data/nuScenes/depth_gt/n015-2018-09-27-15-33-17+0800CAM_FRONT_LEFT1538033983154844.jpg.bin' (Issue #4)
How can I modify the normal training and verification? Did you modify the nuscenes or pytorch_lightning source code? Or is the parameter wrong?) I suspect it might be because self.gt_boxes = load_gt(self.nusc, self.eval_set, DetectionBox, verbose=verbose) self.eval_set is "val" And we need "train" and "test."
I wish you a smooth scientific research life!
The complete error message is as follows：
/home/m840-02/anaconda3/envs/p2d/bin/python /home/m840-02/lsy/P2D-master/p2d/exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema.py --amp_backend native -b 8 --gpus 2
Global seed set to 0
2024-04-07 20:39:02,698 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:02,709 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([128, 256, 4, 4]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,709 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([128, 512, 2, 2]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([1024, 128, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([2048, 128, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,710 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:02,718 - mmcv - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2024-04-07 20:39:02,718 - mmcv - INFO - load model from: torchvision://resnet50
2024-04-07 20:39:02,719 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50
2024-04-07 20:39:02,822 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
2024-04-07 20:39:03,008 - mmcv - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2024-04-07 20:39:03,135 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,136 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,137 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,138 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,140 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,142 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,146 - mmcv - INFO -
conv1.weight - torch.Size([160, 160, 7, 7]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.0.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer1.1.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.conv1.weight - torch.Size([320, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.downsample.0.weight - torch.Size([320, 160, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.downsample.1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.0.downsample.1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,147 - mmcv - INFO -
layer2.1.conv1.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer2.1.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer2.1.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer2.1.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer2.1.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer2.1.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.conv1.weight - torch.Size([640, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.downsample.0.weight - torch.Size([640, 320, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.downsample.1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.0.downsample.1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.conv1.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,148 - mmcv - INFO -
layer3.1.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,173 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([160, 64, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([160, 64, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([320, 64, 4, 4]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,195 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([640, 64, 8, 8]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,196 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,196 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,377 - mmcv - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2024-04-07 20:39:03,506 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,507 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,508 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,509 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,511 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,513 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:03,518 - mmcv - INFO -
conv1.weight - torch.Size([160, 160, 7, 7]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,518 - mmcv - INFO -
bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.0.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.1.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.1.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,518 - mmcv - INFO -
layer1.1.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer1.1.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer1.1.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer1.1.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.conv1.weight - torch.Size([320, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.downsample.0.weight - torch.Size([320, 160, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.downsample.1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.0.downsample.1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.conv1.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer2.1.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.conv1.weight - torch.Size([640, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.downsample.0.weight - torch.Size([640, 320, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.downsample.1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.0.downsample.1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,519 - mmcv - INFO -
layer3.1.conv1.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,520 - mmcv - INFO -
layer3.1.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,520 - mmcv - INFO -
layer3.1.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,520 - mmcv - INFO -
layer3.1.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,520 - mmcv - INFO -
layer3.1.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:03,520 - mmcv - INFO -
layer3.1.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:03,545 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([160, 64, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([160, 64, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([320, 64, 4, 4]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([640, 64, 8, 8]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:03,567 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
/home/m840-02/lsy/P2D-master/p2d/layers/modules/temporal_self_attention.py:111: UserWarning: You'd better set embed_dims in MultiScaleDeformAttention to make the dimension of each attention head a power of 2 which is more efficient in our CUDA implementation.
warnings.warn(
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Global seed set to 0
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2
Global seed set to 0
2024-04-07 20:39:36,826 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([128, 256, 4, 4]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([128, 512, 2, 2]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([1024, 128, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,837 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([2048, 128, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:36,838 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,838 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([128]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:36,851 - mmcv - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2024-04-07 20:39:36,851 - mmcv - INFO - load model from: torchvision://resnet50
2024-04-07 20:39:36,852 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50
2024-04-07 20:39:36,948 - mmcv - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
2024-04-07 20:39:37,131 - mmcv - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2024-04-07 20:39:37,254 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,255 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,257 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,258 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,259 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,262 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,266 - mmcv - INFO -
conv1.weight - torch.Size([160, 160, 7, 7]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,266 - mmcv - INFO -
bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.0.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.1.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.1.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,266 - mmcv - INFO -
layer1.1.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer1.1.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer1.1.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer1.1.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.conv1.weight - torch.Size([320, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.downsample.0.weight - torch.Size([320, 160, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.downsample.1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.0.downsample.1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.conv1.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer2.1.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.conv1.weight - torch.Size([640, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.downsample.0.weight - torch.Size([640, 320, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.downsample.1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.0.downsample.1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,267 - mmcv - INFO -
layer3.1.conv1.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,268 - mmcv - INFO -
layer3.1.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,268 - mmcv - INFO -
layer3.1.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,268 - mmcv - INFO -
layer3.1.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,268 - mmcv - INFO -
layer3.1.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,268 - mmcv - INFO -
layer3.1.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,292 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([160, 64, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([160, 64, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([320, 64, 4, 4]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([640, 64, 8, 8]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,314 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,496 - mmcv - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': 'Conv2d'}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2024-04-07 20:39:37,627 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,628 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,629 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,630 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,632 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,634 - mmcv - INFO - initialize BasicBlock with init_cfg {'type': 'Constant', 'val': 0, 'override': {'name': 'norm2'}}
2024-04-07 20:39:37,639 - mmcv - INFO -
conv1.weight - torch.Size([160, 160, 7, 7]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.0.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.conv1.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.bn1.weight - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.bn1.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.conv2.weight - torch.Size([160, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.bn2.weight - torch.Size([160]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,639 - mmcv - INFO -
layer1.1.bn2.bias - torch.Size([160]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,639 - mmcv - INFO -
layer2.0.conv1.weight - torch.Size([320, 160, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.downsample.0.weight - torch.Size([320, 160, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.downsample.1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.0.downsample.1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.conv1.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.bn1.weight - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.bn1.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.conv2.weight - torch.Size([320, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.bn2.weight - torch.Size([320]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer2.1.bn2.bias - torch.Size([320]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.conv1.weight - torch.Size([640, 320, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.downsample.0.weight - torch.Size([640, 320, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.downsample.1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.0.downsample.1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.conv1.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.bn1.weight - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.bn1.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.conv2.weight - torch.Size([640, 640, 3, 3]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.bn2.weight - torch.Size([640]):
ConstantInit: val=0, bias=0
2024-04-07 20:39:37,640 - mmcv - INFO -
layer3.1.bn2.bias - torch.Size([640]):
The value is the same before and after calling init_weights of ResNet
2024-04-07 20:39:37,666 - mmcv - INFO - initialize SECONDFPN with init_cfg [{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.0.0.weight - torch.Size([160, 64, 1, 1]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.0.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.0.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.1.0.weight - torch.Size([160, 64, 2, 2]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.1.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.1.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.2.0.weight - torch.Size([320, 64, 4, 4]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.2.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.2.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.3.0.weight - torch.Size([640, 64, 8, 8]):
KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.3.1.weight - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
2024-04-07 20:39:37,688 - mmcv - INFO -
deblocks.3.1.bias - torch.Size([64]):
The value is the same before and after calling init_weights of SECONDFPN
/home/m840-02/lsy/P2D-master/p2d/layers/modules/temporal_self_attention.py:111: UserWarning: You'd better set embed_dims in MultiScaleDeformAttention to make the dimension of each attention head a power of 2 which is more efficient in our CUDA implementation.
warnings.warn(
Global seed set to 0
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2
distributed_backend=nccl All distributed processes registered. Starting with 2 processes
Missing logger folder: ./outputs/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema/lightning_logs Missing logger folder: ./outputs/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema/lightning_logs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1] LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]
| Name | Type | Params
0 | model | P2D | 100.0 M
100.0 M Trainable params 9.5 K Non-trainable params 100.0 M Total params 199.943 Total estimated model params size (MB) Epoch 0: 0%| | 0/1946 [00:00<?, ?it/s]/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.) return torch.floor_divide(self, other) /home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.) return torch.floor_divide(self, other) /home/m840-02/lsy/P2D-master/p2d/layers/heads/bev_depth_head.py:367: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than tensor.new_tensor(sourceTensor). num = torch.clamp(reduce_mean(target_box.new_tensor(num)), /home/m840-02/lsy/P2D-master/p2d/layers/heads/bev_depth_head.py:367: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than tensor.new_tensor(sourceTensor). num = torch.clamp(reduce_mean(target_box.new_tensor(num)), /home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:212: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_gradnorm; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior. torch.nn.utils.clip_gradnorm(parameters, clip_val) /home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:212: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_gradnorm; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior. torch.nn.utils.clip_gradnorm(parameters, clip_val) Epoch 0: 90%|█████████ | 1758/1946 [54:48<05:51, 1.87s/it, loss=638, v_num=0] Validation: 0it [00:00, ?it/s] Validation DataLoader 0: 0%| | 0/188 [00:06<?, ?it/s]/home/m840-02/lsy/P2D-master/mmdetection3d/mmdet3d/core/bbox/coders/centerpoint_bbox_coders.py:201: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor). self.post_center_range = torch.tensor( /home/m840-02/lsy/P2D-master/mmdetection3d/mmdet3d/core/bbox/coders/centerpoint_bbox_coders.py:201: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor). self.post_center_range = torch.tensor( /home/m840-02/lsy/P2D-master/mmdetection3d/mmdet3d/core/bbox/coders/centerpoint_bbox_coders.py:201: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor). self.post_center_range = torch.tensor( /home/m840-02/lsy/P2D-master/mmdetection3d/mmdet3d/core/bbox/coders/centerpoint_bbox_coders.py:201: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor). self.post_center_range = torch.tensor(
Epoch 0: 90%|█████████ | 1759/1946 [55:11<05:52, 1.88s/it, loss=638, v_num=0] Epoch 0: 90%|█████████ | 1760/1946 [55:12<05:50, 1.88s/it, loss=638, v_num=0]
Epoch 0: 100%|██████████| 1946/1946 [59:06<00:00, 1.82s/it, loss=638, v_num=0] Formating bboxes of img_bbox Start to convert detection format... [>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3008/3008, 317.6 task/s, elapsed: 9s, ETA: 0s Results writes to ./outputs/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema/results_nusc.json Evaluating bboxes of img_bbox 0%| | 0/6019 [00:00<?, ?it/s] 97%|█████████▋| 5854/6019 [00:09<00:00, 1292.88it/s]
3008 6019 Traceback (most recent call last): File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 722, in _call_and_handle_interrupt return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, kwargs) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch return function(*args, *kwargs) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 812, in _fit_impl results = self._run(model, ckpt_path=self.ckpt_path) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run results = self._run_stage() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1327, in _run_stage return self._run_train() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1357, in _run_train self.fit_loop.run() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run self.advance(args, kwargs) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance self._outputs = self.epoch_loop.run(self._data_fetcher) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 205, in run self.on_advance_end() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 255, in on_advance_end self._run_validation() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 309, in _run_validation self.val_loop.run() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 211, in run output = self.on_run_end() File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 187, in on_run_end self._evaluation_epoch_end(self._outputs) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 309, in _evaluation_epoch_end self.trainer._call_lightning_module_hook("validation_epoch_end", output_or_outputs) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1599, in _call_lightning_module_hook output = fn(*args, **kwargs) File "/home/m840-02/lsy/P2D-master/p2d/exps/nuscenes/base_exp.py", line 372, in validation_epoch_end self.evaluator.evaluate(all_pred_results, all_img_metas) File "/home/m840-02/lsy/P2D-master/p2d/evaluators/det_evaluators.py", line 212, in evaluate self._evaluate_single(result_files[name]) File "/home/m840-02/lsy/P2D-master/p2d/evaluators/det_evaluators.py", line 90, in _evaluate_single nusc_eval = NuScenesEval(nusc, File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/nuscenes/eval/detection/evaluate.py", line 85, in init assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \ AssertionError: Samples in split doesn't match samples in predictions.
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "/home/m840-02/lsy/P2D-master/p2d/exps/nuscenes/p2d/p2d_deform_lss_r50_256x704_128x128_24e_3key_ema.py", line 10, in <module> run_cli(P2DLightningModel, File "/home/m840-02/lsy/P2D-master/p2d/exps/base_cli.py", line 89, in run_cli trainer.fit(model) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit self._call_and_handle_interrupt( File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 737, in _call_and_handle_interrupt self.strategy.reconciliate_processes(traceback.format_exc()) File "/home/m840-02/anaconda3/envs/p2d/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 446, in reconciliate_processes raise DeadlockDetectedExcep
sanmin0312
Could you provide additional context or details?