I have a strange problem in trying to use OpenVino.
I have exported my pytorch model to onnx and then imported it to OpenVino using the following command:
python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model ~/Downloads/unet2d.onnx --disable_resnet_optimization --disable_fusing --disable_gfusing --data_type=FP32
So for the test case, I have disabled the optimizations.
Now, using the sample python applications, I run inference using the model as follows:
from openvino.inference_engine import IENetwork, IECore
import numpy as np
model_xml = path.expanduser('model.xml')
model_bin = path.expanduser('model.bin')
ie = IECore()
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = 1
exec_net = ie.load_network(network=net, device_name='CPU')
np.random.seed(0)
x = np.random.randn(1, 2, 256, 256) # expected input shape
res = exec_net.infer(inputs={input_blob: x})
res = res[out_blob]
The problem is that this seems to output something completely different from my onnx or the pytorch model.
Additionally, I realized that I do not even have to pass an input, so if I do something like:
x = None
res = exec_net.infer(inputs={input_blob: x})
This still returns me the same output! So it seems to suggest that somehow my input is getting ignored or something like that?
