invalid device ordinal

[Malledive]

Hi, when starting to train it produces the following error:

Error] Traceback (most recent call last): File "train.py", line 226, in train cls_loss, reg_loss = model(imgs, annot, obj_list=params.obj_list) File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in call result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 148, in forward inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) File "/content/drive/My Drive/Colab Notebooks/Yet-Another-EfficientDet-Pytorch/utils/utils.py", line 205, in scatter for device_idx in range(len(devices))], \ File "/content/drive/My Drive/Colab Notebooks/Yet-Another-EfficientDet-Pytorch/utils/utils.py", line 205, in for device_idx in range(len(devices))], \ RuntimeError: CUDA error: invalid device ordinal

[Malledive]

loading annotations into memory... Done (t=0.00s) creating index... index created! loading annotations into memory... Done (t=0.00s) creating index... index created! [Info] initializing weights... Step: 36. Epoch: 0/500. Iteration: 37/37. Cls loss: 152.74318. Reg loss: 0.00000. Total loss: 152.74318: 100% 37/37 [00:32<00:00, 1.15it/s] Val. Epoch: 0/500. Classification loss: 0.05725. Regression loss: 0.00000. Total loss: 0.05725 Step: 73. Epoch: 1/500. Iteration: 37/37. Cls loss: 58.51061. Reg loss: 0.00000. Total loss: 58.51061: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 1/500. Classification loss: 0.05707. Regression loss: 0.00000. Total loss: 0.05707 Step: 110. Epoch: 2/500. Iteration: 37/37. Cls loss: 44.43995. Reg loss: 0.00000. Total loss: 44.43995: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 2/500. Classification loss: 0.05713. Regression loss: 0.00000. Total loss: 0.05713 Step: 147. Epoch: 3/500. Iteration: 37/37. Cls loss: 46.96254. Reg loss: 0.00000. Total loss: 46.96254: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 3/500. Classification loss: 0.05734. Regression loss: 0.00000. Total loss: 0.05734 Step: 184. Epoch: 4/500. Iteration: 37/37. Cls loss: 34.39653. Reg loss: 0.00000. Total loss: 34.39653: 100% 37/37 [00:32<00:00, 1.13it/s] Val. Epoch: 4/500. Classification loss: 0.05767. Regression loss: 0.00000. Total loss: 0.05767 Step: 221. Epoch: 5/500. Iteration: 37/37. Cls loss: 18.23889. Reg loss: 0.00000. Total loss: 18.23889: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 5/500. Classification loss: 0.05805. Regression loss: 0.00000. Total loss: 0.05805 Step: 258. Epoch: 6/500. Iteration: 37/37. Cls loss: 23.31059. Reg loss: 0.00000. Total loss: 23.31059: 100% 37/37 [00:32<00:00, 1.13it/s] Val. Epoch: 6/500. Classification loss: 0.05858. Regression loss: 0.00000. Total loss: 0.05858 Step: 295. Epoch: 7/500. Iteration: 37/37. Cls loss: 17.64239. Reg loss: 0.00000. Total loss: 17.64239: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 7/500. Classification loss: 0.05914. Regression loss: 0.00000. Total loss: 0.05914 Step: 332. Epoch: 8/500. Iteration: 37/37. Cls loss: 13.08778. Reg loss: 0.00000. Total loss: 13.08778: 100% 37/37 [00:32<00:00, 1.14it/s] Val. Epoch: 8/500. Classification loss: 0.05977. Regression loss: 0.00000. Total loss: 0.05977

Building a model from scratch, but the Regression loss stays at zero

[Malledive]

It now works with pre-trained models, but:

RuntimeError: Error(s) in loading state_dict for EfficientDetBackbone: Missing key(s) in state_dict: "bifpn.5.p6_w1", "bifpn.5.p5_w1", "bifpn.5.p4_w1", "bifpn.5.p3_w1", "bifpn.5.p4_w2", "bifpn.5.p5_w2", "bifpn.5.p6_w2", "bifpn.5.p7_w2", "bifpn.5.conv6_up.depthwise_conv.conv.weight", "bifpn.5.conv6_up.pointwise_conv.conv.weight", "bifpn.5.conv6_up.pointwise_conv.conv.bias", "bifpn.5.conv6_up.bn.weight", "bifpn.5.conv6_up.bn.bias", "bifpn.5.conv6_up.bn.running_mean", "bifpn.5.conv6_up.bn.running_var", "bifpn.5.conv5_up.depthwise_conv.conv.weight", "bifpn.5.conv5_up.pointwise_conv.conv.weight", "bifpn.5.conv5_up.pointwise_conv.conv.bias", "bifpn.5.conv5_up.bn.weight", "bifpn.5.conv5_up.bn.bias", "bifpn.5.conv5_up.bn.running_mean", "bifpn.5.conv5_up.bn.running_var", "bifpn.5.conv4_up.depthwise_conv.conv.weight", "bifpn.5.conv4_up.pointwise_conv.conv.weight", "bifpn.5.conv4_up.pointwise_conv.conv.bias", "bifpn.5.conv4_up.bn.weight", "bifpn.5.conv4_up.bn.bias", 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"backbone_net.model._blocks.38._bn1.running_mean", "backbone_net.model._blocks.38._bn1.running_var", "backbone_net.model._blocks.38._se_reduce.conv.weight", "backbone_net.model._blocks.38._se_reduce.conv.bias", "backbone_net.model._blocks.38._se_expand.conv.weight", "backbone_net.model._blocks.38._se_expand.conv.bias", "backbone_net.model._blocks.38._project_conv.conv.weight", "backbone_net.model._blocks.38._bn2.weight", "backbone_net.model._blocks.38._bn2.bias", "backbone_net.model._blocks.38._bn2.running_mean", "backbone_net.model._blocks.38._bn2.running_var". Unexpected key(s) in state_dict: "backbone_net.model._blocks.2._expand_conv.conv.weight", "backbone_net.model._blocks.2._bn0.weight", "backbone_net.model._blocks.2._bn0.bias", "backbone_net.model._blocks.2._bn0.running_mean", "backbone_net.model._blocks.2._bn0.running_var", "backbone_net.model._blocks.2._bn0.num_batches_tracked". size mismatch for bifpn.0.conv6_up.depthwise_conv.conv.weight: copying a param with shape torch.Size([112, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([288, 1, 3, 3]). size mismatch for bifpn.0.conv6_up.pointwise_conv.conv.weight: copying a param with shape torch.Size([112, 112, 1, 1]) from checkpoint, the shape in current model is torch.Size([288, 288, 1, 1]). size mismatch for bifpn.0.conv6_up.pointwise_conv.conv.bias: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv6_up.bn.weight: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv6_up.bn.bias: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv6_up.bn.running_mean: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv6_up.bn.running_var: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv5_up.depthwise_conv.conv.weight: copying a param with shape torch.Size([112, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([288, 1, 3, 3]). size mismatch for bifpn.0.conv5_up.pointwise_conv.conv.weight: copying a param with shape torch.Size([112, 112, 1, 1]) from checkpoint, the shape in current model is torch.Size([288, 288, 1, 1]). size mismatch for bifpn.0.conv5_up.pointwise_conv.conv.bias: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv5_up.bn.weight: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv5_up.bn.bias: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv5_up.bn.running_mean: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv5_up.bn.running_var: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv4_up.depthwise_conv.conv.weight: copying a param with shape torch.Size([112, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([288, 1, 3, 3]). size mismatch for bifpn.0.conv4_up.pointwise_conv.conv.weight: copying a param with shape torch.Size([112, 112, 1, 1]) from checkpoint, the shape in current model is torch.Size([288, 288, 1, 1]). size mismatch for bifpn.0.conv4_up.pointwise_conv.conv.bias: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]). size mismatch for bifpn.0.conv4_up.bn.weight: copying a param with shape torch.Size([112]) from checkpoint, the shape in current model is torch.Size([288]).

[Malledive]

..so, I think I have to train my own weights. But is it normal, that my Regression Loss stays literally zero?

[zylo117]

can you share your training command and your project file

[Malledive]

!pip install pycocotools numpy opencv-python tqdm tensorboard tensorboardX pyyaml webcolors
!pip install torch==1.4.0
!pip install torchvision==0.5.0

from google.colab import drive
drive.mount('/content/drive')

!python train.py -c 0 -p project_malte --head_only True --lr 1e-3 --batch_size 32 --load_weights weights/efficientdet-d0.pth  --num_epochs 50 --save_interval 100

#get latest weight file
%cd logs/project_malte
weight_file = !ls -Art | grep efficientdet
%cd ../..

#uncomment the next line to specify a weight file
weight_file[-1] = 'efficientdet-d1_499_18500.pth'

%cd /content/drive/MyDrive/Colab Notebooks/Yet-Another-EfficientDet-Pytorch
!python coco_eval.py -c 1 -p project_malte -w "logs/project_malte/{weight_file[-1]}" 

[Malledive]

It did work - but still no Regression loss and somehow size-format errors

[zylo117]

Why your weights is efficientdet d1 while you trained d0. I think you loaded the wrong weights

[Malledive]

I trained with self-trained, d0, d1, d2 - the screenshots are not subsequently.

[Malledive]

I tried out different weights, but neither of them solved the Regression loss error

[zylo117]

I see, coco_eval.py also need to specify the co-efficient, like -c 1, 0 by default. So you never really loaded your weights

[zylo117]

About that 0 reg loss, can you visualize your annotations by drawing boxes on your images

[Malledive]

Image and labels look like this::

[zylo117]

ok, I'll take a look first

[Malledive]

Thank you so much!

[zylo117]

Found it, category id should start from 1, instead of 0.

[Malledive]

oh, wow! I will try it out immediately! this is awesome, thank you so much