Comments (1)
Hi @MRinel,
We used a version of TVM that was between v0.4 and v0.5. To get the specific version of TVM used in our pipeline, please do:
git clone --recursive https://github.com/dmlc/tvm
cd tvm
git reset --hard ab4946c8b80da510a5a518dca066d8159473345f
git submodule update --init
You could then proceed with building TVM as specified here.
Operators are defined in tvm/nnvm/python/nnvm/frontend/onnx.py
. You can search that file for class Upsample(OnnxOpConverter)
. In older versions of TVM, the Upsample operator had not yet been implemented, which may explain why you are seeing a NotImplementedError. In the version specified above, this operator had been added.
For our work, we modified this operator slightly to:
class Upsample(OnnxOpConverter):
""" Operator converter for Upsample (nearest mode).
"""
@classmethod
def _impl_v7(cls, inputs, attr, params):
scales = attr.get('scales')
#assert len(scales) == 4 and scales[0] == 1.0 and scales[1] == 1.0 and scales[2] == scales[3]
mode = attr.get('mode')
if mode == b'nearest':
method = "NEAREST_NEIGHBOR"
elif mode == b'bilinear':
method = "BILINEAR"
else:
raise ValueError("Invalid ONNX upsample mode: {}".format(mode))
return _sym.upsampling(inputs[0], scale=2, method=method, layout='NCHW')
After modifying this file, you would need to rerun the Python package installation.
Yes, the pipeline we used was mobilenet-nnconv5dw-skipadd-pruned.pth.tar --> onnx model --> compile/deploy with TVM
. We performed auto-tuning for CPU and GPU backends using AutoTVM.
from fast-depth.
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