Add clarification for bias quantization in QlinearConv Op spec #2464
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@linkerzhang , @fdwr : Add the clarification to the spec as discussed |
linkerzhang
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Nov 26, 2019
fdokic
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Dec 18, 2019
fdokic
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* Update Argmin/Argmax (onnx#2461) * update argmin/argmax * gen doc and tests * fix typo * update doc * Updated with correct URL to LICENSE (onnx#2468) Updated license to https://github.com/onnx/onnx/blob/master/LICENSE * remove workshop update since it is done (onnx#2460) * doc: fix some typos at ONNXIFI (onnx#2473) * Add remove operator and function requirements to the add new op doc. (onnx#2486) * Add clarification for bias quantization in QlinearConv Op spec (onnx#2464) * fix the optimize pass of fuse_consecutive_transposes (onnx#2471) * fix the optimize pass of fuse_consecutive_transposes * update the bound checker * make sure the graph and input/output tensors are consistent before and after optimization * Minor correction type (onnx#2411) * correct typeerror * value data type correction * little value * Edited PythonAPIOverview.md (onnx#2491) * Edited PythonAPIOverview.md The example given in the "Creating an ONNX Model Using Helper Functions" example. * Edited PythonAPIOverview.md The example given in the "Creating an ONNX Model Using Helper Functions" example was not working as expected. Running the given code would throw a ValidationError() regarding the node specification (i.e.: "Context: Bad node spec"). This change uses the current node specification, solving the issue. * python_out does not recognize dllexport_decl. (onnx#2482) * add partial_update_inputs_outputs_dims() * Update update_model_dims.py
jcwchen
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Add clarification for bias quantization.
This change should not need version update because for QlinearConv op to produce correct result there is only one way to quantize bias. Current PR only add clear documentation on how bias should be quantized.