# Set verbose=True for more output
torch.onnx.export(model(), dummy_input, file_name, export_params=True, verbose=False)
onnx_model = onnx.load(file_name)
- shapes = { '0' : input_size }
- expr, params = relay.frontend.from_onnx(onnx_model, shape=shapes)
+ for target, ctx in ctx_list():
+ input_data = np.random.uniform(size=input_size).astype('int32')
+ c2_out = get_caffe2_output(onnx_model, input_data)
+ tvm_out = get_tvm_output(onnx_model, input_data, target, ctx)
+ tvm.testing.assert_allclose(c2_out, tvm_out)
def test_resnet():
check_torch_conversion(torchvision.models.resnet18, (1,3,224,224))