TensorRT Converting Model

[ifzhang]

TensorRT Converting Model

[Jumabek]

SOLUTION!

In my case I was using the mismatched architecture and weights:
Wrong: python3 tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar

Correct: python3 tools/trt.py -f exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar

[wojiaohumaocheng]

I met same problem how to resolve that

[Jumabek]

What if docker container is not an option?

I have met the similar problem

022-12-18 17:15:14.665 | ERROR    | __main__:<module>:74 - An error has been caught in function '<module>', process 'MainProcess' (637932), thread 'MainThread' (140239028696896):
Traceback (most recent call last):

> File "/home/juma/ByteTrack/tools/trt.py", line 74, in <module>
    main()
    └ <function main at 0x7f8a96822c20>

  File "/home/juma/ByteTrack/tools/trt.py", line 48, in main
    model.load_state_dict(ckpt["model"])
    │     │               └ {'start_epoch': 80, 'model': OrderedDict([('backbone.backbone.stem.conv.conv.weight', tensor([[[[ 1.1617e-02,  3.0379e-03, -2...
    │     └ <function Module.load_state_dict at 0x7f8b96d5df30>
    └ YOLOX(
        (backbone): YOLOPAFPN(
          (backbone): CSPDarknet(
            (stem): Focus(
              (conv): BaseConv(
                (conv): ...

  File "/home/juma/miniconda3/envs/byte2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(

RuntimeError: Error(s) in loading state_dict for YOLOX:
	Unexpected key(s) in state_dict: "backbone.backbone.dark2.1.m.1.conv1.conv.weight", "backbone.backbone.dark2.1.m.1.conv1.bn.weight", "backbone.backbone.dark2.1.m.1.conv1.bn.bias", "backbone.backbone.dark2.1.m.1.conv1.bn.running_mean", "backbone.backbone.dark2.1.m.1.conv1.bn.running_var", "backbone.backbone.dark2.1.m.1.conv1.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.1.conv2.conv.weight", "backbone.backbone.dark2.1.m.1.conv2.bn.weight", "backbone.backbone.dark2.1.m.1.conv2.bn.bias", "backbone.backbone.dark2.1.m.1.conv2.bn.running_mean", "backbone.backbone.dark2.1.m.1.conv2.bn.running_var", "backbone.backbone.dark2.1.m.1.conv2.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.2.conv1.conv.weight", "backbone.backbone.dark2.1.m.2.conv1.bn.weight", "backbone.backbone.dark2.1.m.2.conv1.bn.bias", "backbone.backbone.dark2.1.m.2.conv1.bn.running_mean", "backbone.backbone.dark2.1.m.2.conv1.bn.running_var", "backbone.backbone.dark2.1.m.2.conv1.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.2.conv2.conv.weight", "backbone.backbone.dark2.1.m.2.conv2.bn.weight", "backbone.backbone.dark2.1.m.2.conv2.bn.bias", "backbone.backbone.dark2.1.m.2.conv2.bn.running_mean", "backbone.backbone.dark2.1.m.2.conv2.bn.running_var", "backbone.backbone.dark2.1.m.2.conv2.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.3.conv1.conv.weight", "backbone.backbone.dark2.1.m.3.conv1.bn.weight", "backbone.backbone.dark2.1.m.3.conv1.bn.bias", "backbone.backbone.dark2.1.m.3.conv1.bn.running_mean", "backbone.backbone.dark2.1.m.3.conv1.bn.running_var", "backbone.backbone.dark2.1.m.3.conv1.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.3.conv2.conv.weight", "backbone.backbone.dark2.1.m.3.conv2.bn.weight", "backbone.backbone.dark2.1.m.3.conv2.bn.bias", "backbone.backbone.dark2.1.m.3.conv2.bn.running_mean", "backbone.backbone.dark2.1.m.3.conv2.bn.running_var", "backbone.backbone.dark2.1.m.3.conv2.bn.num_batches_tracked", 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"backbone.C3_n4.m.1.conv2.bn.bias", "backbone.C3_n4.m.1.conv2.bn.running_mean", "backbone.C3_n4.m.1.conv2.bn.running_var", "backbone.C3_n4.m.1.conv2.bn.num_batches_tracked", "backbone.C3_n4.m.2.conv1.conv.weight", "backbone.C3_n4.m.2.conv1.bn.weight", "backbone.C3_n4.m.2.conv1.bn.bias", "backbone.C3_n4.m.2.conv1.bn.running_mean", "backbone.C3_n4.m.2.conv1.bn.running_var", "backbone.C3_n4.m.2.conv1.bn.num_batches_tracked", "backbone.C3_n4.m.2.conv2.conv.weight", "backbone.C3_n4.m.2.conv2.bn.weight", "backbone.C3_n4.m.2.conv2.bn.bias", "backbone.C3_n4.m.2.conv2.bn.running_mean", "backbone.C3_n4.m.2.conv2.bn.running_var", "backbone.C3_n4.m.2.conv2.bn.num_batches_tracked", "backbone.C3_n4.m.3.conv1.conv.weight", "backbone.C3_n4.m.3.conv1.bn.weight", "backbone.C3_n4.m.3.conv1.bn.bias", "backbone.C3_n4.m.3.conv1.bn.running_mean", "backbone.C3_n4.m.3.conv1.bn.running_var", "backbone.C3_n4.m.3.conv1.bn.num_batches_tracked", "backbone.C3_n4.m.3.conv2.conv.weight", "backbone.C3_n4.m.3.conv2.bn.weight", "backbone.C3_n4.m.3.conv2.bn.bias", "backbone.C3_n4.m.3.conv2.bn.running_mean", "backbone.C3_n4.m.3.conv2.bn.running_var", "backbone.C3_n4.m.3.conv2.bn.num_batches_tracked". 
	size mismatch for backbone.backbone.stem.conv.conv.weight: copying a param with shape torch.Size([80, 12, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 12, 3, 3]).
	size mismatch for backbone.backbone.stem.conv.bn.weight: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.backbone.stem.conv.bn.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.backbone.stem.conv.bn.running_mean: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.backbone.stem.conv.bn.running_var: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.backbone.dark2.0.conv.weight: copying a param with shape torch.Size([160, 80, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 32, 3, 3]).
	size mismatch for backbone.backbone.dark2.0.bn.weight: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([64]).
	size mismatch for backbone.backbone.dark2.0.bn.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([64]).
	size mismatch for backbone.backbone.dark2.0.bn.running_mean: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([64]).
	size mismatch for backbone.backbone.dark2.0.bn.running_var: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([64]).
	size mismatch for backbone.backbone.dark2.1.conv1.conv.weight: copying a param with shape torch.Size([80, 160, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 64, 1, 1]).
	size mismatch for backbone.backbone.dark2.1.conv1.bn.weight: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.backbone.dark2.1.conv1.bn.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([32]).
	size mismatch for backbone.bac