test_load_pickle_pre_ntk_mnist.py

import pickle

# Load
with open('artifacts/cnn_mnist/ntk_1000_rn_1500_cnn_mnist_struct_pru_ntk_rn_block_10_layer_3_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_mnist"

input_shape = (28, 28, 1)
num_classes = 10


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
# 開啟
with open(f"{output_dir}/ntk_1000_rn_1500_cnn_mnist_struct_pru_ntk_rn_block_10_layer_3_agingevosearch_state.csv", "w", newline="") as csvfile:
  wr = csv.writer(csvfile)
  wr.writerow(["id", "val_acc", "test_acc", "peak_memory_usage", "model_size", "inference_latency","ntk","rn"])
  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
    '''
    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")
    '''
    EvaluatedPoint_list = [i, 1-val_error, 1-test_error]
    EvaluatedPoint_list.extend(resource_features)
    wr.writerow(EvaluatedPoint_list)