LiangHao151941 commented on issue #4828: [QNN][TFLite] TFLite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828#issuecomment-583939949 > Let us break the model into layer by layer and compare with tflite. I want to describe my development way, maybe it could help you. For example, we have mobilenetv2 quantized model, you could get the quantized tensorflow and tflite model. Then you could call tflite_convert (feed it quantized tensorflow model) and set the output layer (for example, just the first convolution layer), then you get one quantized tflite model only contain the first convolution layer of mobilenet v2. After you verify it correctly, you could go on until you finish the whole model e2e correctly. Command example: tflite_convert --input_format=TENSORFLOW_GRAPHDEF --graph_def_file="xx.pb" --output_file=xx.tflite --output_format=TFLITE --input_arrays=input --input_shapes=1,224,224,3 --std_dev_values=127 --mean_values=127 --inference_type=QUANTIZED_UINT8 --inference_input_type=QUANTIZED_UINT8 --default_ranges_min=0 --default_ranges_max=6 --output_arrays=xx Thanks for the detailed instruction here. Will follow up. > I think when you verify, you could run on cpu firstly locally to find issue, then consider gpu ci issue. No problem.
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