Description
I was trying to convert MDNet model to TRT engine. I can successfully output onnx model. However, whatever I used onnx2trt or onnx parser to convert the trt engine. It got this error.
[2020-04-16 09:56:51 WARNING] /home/chieh/github/onnx-tensorrt/onnx2trt_utils.cpp:235: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
While parsing node number 25 [Pad -> "68"]:
ERROR: /home/chieh/github/onnx-tensorrt/builtin_op_importers.cpp:2100 In function importPad:
[8] Assertion failed: onnx_padding.size() == 8 && onnx_padding[0] == 0 && onnx_padding[1] == 0 && onnx_padding[4] == 0 && onnx_padding[5] == 0 && "This version of TensorRT only supports padding on the outer two dimensions on 4D tensors!"
I tested the opset version 10 and 11, but I got the same error.
Here is the partial code of MDNet model.
class MDNet(nn.Module):
def __init__(self, model_path=None, K=1):
super(MDNet, self).__init__()
self.K = K
self.layers = nn.Sequential(OrderedDict([
('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
nn.ReLU(inplace=True),
nn.LocalResponseNorm(2),
nn.MaxPool2d(kernel_size=3, stride=2))),
('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
nn.ReLU(inplace=True),
nn.LocalResponseNorm(2),
nn.MaxPool2d(kernel_size=3, stride=2))),
('conv3', nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1),
nn.ReLU(inplace=True))),
('fc4', nn.Sequential(nn.Linear(512 * 3 * 3, 512),
nn.ReLU(inplace=True))),
('fc5', nn.Sequential(nn.Dropout(0.5),
nn.Linear(512, 512),
nn.ReLU(inplace=True)))]))
self.branches = nn.ModuleList([nn.Sequential(nn.Dropout(0.5),
nn.Linear(512, 2)) for _ in range(K)])
for m in self.layers.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0.1)
for m in self.branches.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
if model_path is not None:
if os.path.splitext(model_path)[1] == '.pth':
self.load_model(model_path)
elif os.path.splitext(model_path)[1] == '.mat':
self.load_mat_model(model_path)
else:
raise RuntimeError('Unkown model format: {:s}'.format(model_path))
self.build_param_dict()
Environment
TensorRT version: 7.0.0.11
Cuda version: 10.0
TensorFlow-gpu: 1.14.0
Cudnn version: 7.6.5
GPU: GTX1060
Ubuntu: 18.04
Python 3.6.9
Nvidia driver version: 440.33.01
PyTorch version: 1.3.1
Steps To Reproduce
- git clone https://github.com/hyeonseobnam/py-MDNet
- Add there lines in the
py-MDNet/pretrain/train_mdnet.pybeforeif __name__ == "__main__"
x = torch.randn(1, 3, 107, 107, requires_grad=True).cuda() # Input
torch.onnx.export(model, # model being run
x, # model input (or a tuple for multiple inputs)
"MDNet.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input'], # the model's input names
output_names = ['output']) # the model's output names
- Downlaod the vot dataset from here.
- Use this command:
python3 pretrain/train_mdnet.py -d vot
After we get the onnx model, we can convert to trt engine.
onnx2trt MDNet.onnx -o model.trt
Thank you!