Create example.py

example.py

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+# coding: utf-8
+#================================================================#
+#   Copyright (C) 2021 Freecss All rights reserved.
+#   
+#   File Name     ：share_example.py
+#   Author        ：freecss
+#   Email         ：karlfreecss@gmail.com
+#   Created Date  ：2021/06/07
+#   Description   ：
+#
+#================================================================#
+
+from utils.plog import logger
+from models.wabl_models import DecisionTree, KNN
+import pickle as pk
+import numpy as np
+import time
+import framework
+import utils.plog as plog
+import torch.nn as nn
+import torch
+
+from models.lenet5 import LeNet5
+from models.basic_model import BasicModel
+from models.wabl_models import MyModel
+
+from multiprocessing import Pool
+import os
+from datasets.data_generator import generate_data_via_codes, code_generator
+from collections import defaultdict
+from abducer.abducer_base import AbducerBase
+from abducer.kb import add_KB, hwf_KB
+from datasets.mnist_add.get_mnist_add import get_mnist_add
+from datasets.hwf.get_hwf import get_hwf
+
+class Params:
+    imgH = 45
+    imgW = 45
+    keep_ratio = True
+    saveInterval = 10
+    batchSize = 16
+    workers = 16
+    n_epoch = 10
+    stop_loss = None
+
+def run_test():
+    
+    result_dir = 'results'
+
+    recorder_file_path = f"{result_dir}/1116.pk"#
+
+    # words = code_generator(code_len, code_num, letter_num)
+    kb = add_KB()
+    abducer = AbducerBase(kb)
+
+    recorder = logger()
+    recorder.set_savefile("test.log")
+
+
+    train_X, train_Y, test_X, test_Y = get_mnist_add()
+    # train_X, train_Y, test_X, test_Y = get_hwf()
+
+
+    recorder = plog.ResultRecorder()
+    cls = LeNet5()
+    
+    criterion = nn.CrossEntropyLoss(size_average=True)
+    optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    sign_list = list(range(10))
+    base_model = BasicModel(cls, criterion, optimizer, device, Params(), sign_list, recorder=recorder)
+    model = MyModel(base_model)
+
+    res = framework.train(model, abducer, train_X, train_Y, logic_forward = kb.logic_forward, sample_num = 10000, verbose = 1)
+    print(res)
+    
+
+    recorder.dump(open(recorder_file_path, "wb"))
+    return True
+
+if __name__ == "__main__":
+    os.system("mkdir results")
+    
+    run_test()
+