Description
We are experimenting with DLA in our Jetson Xavier Embedded Device.
We first ran YOLOV5 model in DLA using following steps.
- We trained a
YOLOV5model in Host Machine - We then converted the model to
ONNX - We transferred this
ONNXfile to Jetson Xavier machine and used
trtexec --onnx=model_gn.onnx --shapes=input:1x3x640x640 --saveEngine=model_gn.engine --exportProfile=model_gn.json --int8 --useDLACore=0 --allowGPUFallback --useSpinWait --separateProfileRun > model_gn.log
This perfectly worked as expected.
Now we wanted to do one more experiment, where we wanted to run model only in DLA (basically we wanted to avoid using GPU). However, we knew that, DLA supports only few types of layers. So, we build a very small network, which is below,
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
We followed the same steps described above for YOLOV5 but without --allowGPUFallback. We are getting error saying fc1.weight not supported.
Can you please tell, where I am going wrong?
