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+# pota-quantization-value-test
+
+`pota-quantization-value-test` checks whether a Circle model listed in `test.lst` is correctly quantized (`pota` denotes post-training affine). The circle models are generated from the recipes saved in `res/TensorFlowLiteRecipes`.
+
+Write `test.local.lst` for local test.
+
+### Test process
+
+#### Step 1. Fake quantization
+
+Run `circle-quantizer` with `--quantize_dequantize_weights` option.
+
+Dump the fake-quantized model with `circle-tensordump`.
+
+Compare the dumped model with the expected output in "expected_outputs/<model_name>/\<granularity\>/<quantized_type>/fake_quantization/<tensor_name>.json"
+
+The expected output should include
+ (1) values of weights (only for conv, transposed_conv, depthwise_conv, and fc layers)
+
+#### Step 2. Record moving avg of min and moving avg of max for activations
+
+Run `record-minmax` with the fake-quantized model (input data is saved in "test_inputs/<model_name>/\<granularity\>/<quantized_type>/<record_number>.txt")
+
+Dump the minmax-recorded model with `circle-tensordump`.
+Compare the dumped model with the expected output in "expected_outputs/<model_name>/\<granularity\>/<quantized_type>/record_minmax/<tensor_name>.json"
+
+The expected output should include
+ (1) min/max of activations
+
+#### Step 3. Quantization
+
+Run `circle-quantizer` with `--quantize_with_minmax` option.
+
+Dump the quantized model with `circle-tensordump`.
+
+Compare the dumped model with the expected output in "expected_outputs/<model_name>/\<granularity\>/<quantized_type>/quantization/<tensor_name>.json"
+
+The expected output should include
+ (1) scale, zero point of activations
+ (2) scale, zero point, values of weights
+ (3) scale, values (weights) of bias