test_luci_eval.py

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import numpy as np
import tensorflow as tf
import subprocess
import os
 
 
def luci_eval_verify(test_name,
                     tflite_dir,
                     circle_dir,
                     eval_driver,
                     rtolf32=1e-5,
                     atolf32=1e-5):
    tflite_model = os.path.join(tflite_dir, test_name + ".tflite")
    circle_model = os.path.join(circle_dir, test_name + ".after.circle")
 
    # NOTE reuse f32 value as int value too
    rtolint = int(rtolf32)
    atolint = int(atolf32)
 
    # Build TFLite interpreter.
    interpreter = tf.lite.Interpreter(tflite_model)
    interpreter.allocate_tensors()
 
    # Read SignatureDef and get output tensor id orders for remapping
    full_signatures = interpreter._get_full_signature_list()
    full_signatures_outputs_remap = None
    if full_signatures != None:
        signature_serving_default = full_signatures.get('serving_default', None)
        if signature_serving_default != None:
            signature_outputs = signature_serving_default['outputs']
 
            full_signatures_outputs_remap = []
            for index, (key, value) in enumerate(signature_outputs.items()):
                full_signatures_outputs_remap.append(value)
 
    # Generate random input data.
    num_inputs = len(interpreter.get_input_details())
    for i in range(num_inputs):
        input_details = interpreter.get_input_details()[i]
        if input_details["dtype"] == np.float32:
            input_data = np.array(np.random.random_sample(input_details["shape"]),
                                  input_details["dtype"])
        elif input_details["dtype"] == np.uint8:
            input_data = np.array(np.random.randint(0, 256, size=input_details["shape"]),
                                  input_details["dtype"])
        elif input_details["dtype"] == np.int16:
            input_data = np.array(np.random.randint(0, 100, size=input_details["shape"]),
                                  input_details["dtype"])
        elif input_details["dtype"] == np.int32:
            input_data = np.array(np.random.randint(0, 100, size=input_details["shape"]),
                                  input_details["dtype"])
        elif input_details["dtype"] == np.int64:
            input_data = np.array(np.random.randint(0, 100, size=input_details["shape"]),
                                  input_details["dtype"])
        elif input_details["dtype"] == np.bool_:
            input_data = np.array(
                np.random.choice(a=[True, False], size=input_details["shape"]),
                input_details["dtype"])
        else:
            assert False, "Unsupported input dtype"
 
        interpreter.set_tensor(input_details["index"], input_data)
        input_data.tofile(circle_model + ".input" + str(i))
 
    # Do inference
    interpreter.invoke()
 
    # Execute luci interpreter.
    subprocess.run([
        eval_driver, circle_model,
        str(num_inputs), circle_model + ".input", circle_model + ".output"
    ],
                   check=True)
 
    # Compare the results.
    inpt_output_details = interpreter.get_output_details()
    for idx in range(len(inpt_output_details)):
        output_details = inpt_output_details[idx]
        output_data = np.fromfile(circle_model + ".output" + str(idx),
                                  output_details["dtype"])
        shape_file = open(circle_model + ".output" + str(idx) + ".shape", 'r')
        output_shape = [int(i) for i in shape_file.read().split(',')]
        luci_output_data = np.reshape(output_data, output_shape)
        output_tensor = output_details["index"]
        if full_signatures_outputs_remap != None:
            output_tensor = full_signatures_outputs_remap[idx]
        intp_output_data = interpreter.get_tensor(output_tensor)
        err_msg = "Execution result of " + tflite_model + " does not match with " + circle_model
        if output_details["dtype"] == np.uint8:
            assert np.allclose(luci_output_data,
                               intp_output_data,
                               rtol=rtolint,
                               atol=atolint), err_msg
        elif output_details["dtype"] == np.float32:
            assert np.allclose(luci_output_data,
                               intp_output_data,
                               rtol=rtolf32,
                               atol=atolf32), err_msg
        elif output_details["dtype"] == np.int64:
            assert np.allclose(luci_output_data,
                               intp_output_data,
                               rtol=rtolint,
                               atol=atolint), err_msg
        elif output_details["dtype"] == np.int32:
            assert np.allclose(luci_output_data,
                               intp_output_data,
                               rtol=rtolint,
                               atol=atolint), err_msg
        elif output_details["dtype"] == np.int16:
            assert np.allclose(luci_output_data,
                               intp_output_data,
                               rtol=rtolint,
                               atol=atolint), err_msg
        elif output_details["dtype"] == np.bool_:
            assert np.allclose(luci_output_data, intp_output_data, rtol=0,
                               atol=0), err_msg
        else:
            assert False, "Unsupported data type: " + output_details["dtype"]
 
 
# arguments must be in sync with `conftest.py`
def test_luci_eval(test_name: str, tflite_dir: str, circle_dir: str,
                   eval_driver_path: str):
    luci_eval_verify(test_name, tflite_dir, circle_dir, eval_driver_path)