test_load_pickle_example_ntk_cifar10.py

import os
import argparse
from pathlib import Path


def main():
    # 取得參數
    parser = argparse.ArgumentParser("uNAS Search")
    #configs/cnn_cifar10_struct_pru.py
    parser.add_argument("config_file", type=str, help="A config file describing the search parameters")
    args = parser.parse_args()
    # 執行config_file(.py)內之code,configs 則是全域變數(以字典型態儲存)
    configs = {}
    exec(Path(args.config_file).read_text(), configs)
    training_config = configs["training_config"]
    #load dataset
    dataset = training_config.dataset

    import pickle

    # Load
    with open('artifacts/cnn_cifar10/example_cnn_cifar10_struct_pru_agingevosearch_state.pickle', 'rb') as f:
        EvaluatedPoint = pickle.load(f)
    print("------------------------------")
    print("len of EvaluatedPoint:",len(EvaluatedPoint))
    print("-----------------------------")
    '''
    @dataclass
    class EvaluatedPoint:
        point: ArchitecturePoint
        val_error: float
        test_error: float
        resource_features: List[Union[int, float]]
    ''' 
    
    import numpy as np
    import tensorflow as tf

    from architecture import Architecture
    from cnn import CnnSearchSpace
    from resource_models.models import model_size, peak_memory_usage

    output_dir = "artifacts/cnn_cifar10"

    input_shape = (32, 32, 3)
    num_classes = 10
    #del metrics
    save_path = './tmp/metrics'
    train_loader_save_path = f'{save_path}/train_loader.pickle'
    val_loader_save_path = f'{save_path}/val_loader.pickle'
    if os.path.isfile(train_loader_save_path) and os.path.isfile(val_loader_save_path):
        print (f"train_loader_save_path is already exist:{train_loader_save_path}")
        print (f"val_loader_save_path is already exist:{val_loader_save_path}")
        os.remove(train_loader_save_path)
        os.remove(val_loader_save_path)

    def get_resource_requirements(arch: Architecture):
        rg = arch.to_resource_graph(input_shape, num_classes)
        return model_size(rg), peak_memory_usage(rg, exclude_inputs=False)

    def convert_to_tflite(arch, output_file):
        model = arch.to_keras_model(input_shape, num_classes)
        model.summary()
        converter = tf.lite.TFLiteConverter.from_keras_model(model)

        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.representative_dataset = \
            lambda: [[np.random.random((1,) + input_shape).astype("float32")] for _ in range(5)]
        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
        converter.inference_input_type = tf.int8
        converter.inference_output_type = tf.int8
        model_bytes = converter.convert()
    
        with open(output_file, "wb") as f:
            f.write(model_bytes)

    import csv
    #ntk
    from metrics_file import ModelMetricsFile

    # 開啟
    with open(f"{output_dir}/example_cnn_cifar10_struct_pru_agingevosearch_state_ntk.csv", "w", newline="") as csvfile:
        wr = csv.writer(csvfile)
        wr.writerow(["id", "val_acc", "test_acc", "peak_memory_usage", "model_size", "inference_latency","ntk"])
        EvaluatedPoint_num = len(EvaluatedPoint)
        for i in range(0, EvaluatedPoint_num):
            val_error = EvaluatedPoint[i].val_error
            test_error = EvaluatedPoint[i].test_error
            resource_features = EvaluatedPoint[i].resource_features

            # pre ntk
            arch = EvaluatedPoint[i].point.arch
            model = arch.to_keras_model(input_shape, num_classes)
            ntks = ModelMetricsFile(training_config).get_metrics(model, num_batch=1)
            ntk = np.mean(ntks).astype('int64')
            resource_features.append(ntk)
            '''
            cnn_arch = EvaluatedPoint[i*100-1].point.arch
            print("------------------------------")
            print(f"val_error of speech_command_EvaluatedPoint[{i*100-1}]_point_arch:", val_error)
            print(f"test_error of speech_command_EvaluatedPoint[{i*100-1}]_point_arch:", test_error)
            print("resource_features: [peak_memory_usage, model_size, inference_latency]")
            print(f"resource_features of speech_command_EvaluatedPoint[{i*100-1}]_point_arch:", resource_features)
            print("------------------------------")
            convert_to_tflite(cnn_arch, output_file=f"{output_dir}/speech_command_EvaluatedPoint[{i*100-1}]_point_arch.tflite")
            '''

            #################### TEGNAS testntk #################### 



            EvaluatedPoint_list = [i, 1-val_error, 1-test_error]
            EvaluatedPoint_list.extend(resource_features)
            wr.writerow(EvaluatedPoint_list)

if __name__ == "__main__":
    main()