Hi All,
I tried to quantize the inception model using tvm quantization. Non-Quantized model accuracy on imagenet validation dataset is 76.1% Quantized model accuracy on imagenet validation dataset is 15.7%
Below script modification we have done to enable the quantization.
shape_dict = {‘DecodeJpeg/contents’: (299, 299, 3)} dtype_dict = {‘DecodeJpeg/contents’: ‘uint8’} mod, params = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict) mod = relay.quantize.quantize(mod[‘main’], params) with relay.build_config(opt_level=3): graph, lib, params = relay.build(mod, target=target, target_host=target_host, params=params) m = graph_runtime.create(graph, lib, ctx) m.set_input(‘DecodeJpeg/contents’, tvm.nd.array(x.astype(dtype))) m.set_input(**params) m.run()
Someone please correct me if anything wrong in the script? Or the current TVM quantization is giving poor accuracy?