thank you for the response
Verify the numerical correctness upto 3 decimal places
np.testing.assert_almost_equal(torch_out.data.cpu().numpy(), c2_out, decimal=3)
in this we are comparing two multidimensional arrays
for this i ran example of super resolution and here is the output
import io
import numpy as np
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.onnx
torch_model = SuperResolutionNet(upscale_factor=3)
Input to the model
x = torch.randn(batch_size, 1, 224, 224, requires_grad=True)
Export the model
torch_out = torch.onnx._export(torch_model,             # model being run
x,                       # model input (or a tuple for multiple inputs)
“super_resolution.onnx”, # where to save the model (can be a file or file-like object)
export_params=True,verbose=True)      # store the trained parameter weights inside the model file
print(“torch_out”,torch_out)
this is the output i.e. graph and to see what is the output of torch_out i.e. tensor value
torch_out tensor([[[[ 6.8571e-01,  9.3982e-02,  1.8855e-01,  …,  1.1247e-01,
3.9000e-01, -3.4897e-01],
[ 8.2722e-02, -6.9161e-01,  1.0651e-01,  …,  1.6725e-01,
8.2437e-02, -1.2075e-02],
[ 5.9089e-02, -1.4693e-01, -2.1743e-01,  …, -1.1221e-01,
9.0878e-02,  2.8896e-01],
…,
[-9.9215e-02,  6.8443e-02,  3.1096e-01,  …,  6.0085e-05,
6.3814e-02,  5.8048e-02],
[ 8.1212e-02, -8.7285e-02,  3.3845e-02,  …, -4.4617e-03,
-7.5614e-02,  2.7088e-01],
[-1.1385e-01,  3.1267e-01, -3.3085e-01,  …, -2.5969e-01,
5.3383e-01, -2.2986e-01]]]], grad_fn=)
and
import onnx
import onnx_caffe2.backend
Load the ONNX ModelProto object. model is a standard Python protobuf object
model = onnx.load(“super_resolution.onnx”)
prepare the caffe2 backend for executing the model this converts the ONNX model into a
Caffe2 NetDef that can execute it. Other ONNX backends, like one for CNTK will be
availiable soon.
prepared_backend = onnx_caffe2.backend.prepare(model)
run the model in Caffe2
Construct a map from input names to Tensor data.
The graph of the model itself contains inputs for all weight parameters, after the input image.
Since the weights are already embedded, we just need to pass the input image.
Set the first input.
W = {model.graph.input[0].name: x.data.numpy()}
Run the Caffe2 net:
c2_out = prepared_backend.run(W)[0]
print(“c2_out”,c2_out)
Verify the numerical correctness upto 3 decimal places
np.testing.assert_almost_equal(torch_out.data.cpu().numpy(), c2_out, decimal=3)
print(“Exported model has been executed on Caffe2 backend, and the result looks good!”)
here output of c2_out is tensor which is
c2_out [[[[ 0.19614297 -0.14641185  0.63868225 … -0.12567244 -0.09246139
0.1514894 ]
[ 0.2977414   0.30597484  0.07216635 …  0.3880918   0.31021857
-0.00942096]
[ 0.29691827  0.26158085  0.67230344 …  0.5879603  -0.22218955
-0.04444573]
…
[-0.40999678 -0.31105435 -0.9089918  …  0.965673   -0.32325625
0.47099867]
[-0.34081718 -0.12496098 -0.7071778  … -0.48262012 -0.28351355
0.305478  ]
[-0.10641275 -0.42513472 -0.41691086 …  0.01121937  0.36214358
0.11300252]]]]