How to use step_test method in autotest

Best Python code snippet using autotest_python Github


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1import random2import numpy as np3import matplotlib.pyplot as plt4import timeit5678from string import ascii_lowercase9101112def plotter(r_k_time, b_f_time, kmp_time, time, filename):13 '''14 plots using information based on time15 '''16 plt.title(f'Complexity Analysis when m = 500')17 plt.plot(time, b_f_time, label="Brute Force")18 plt.plot(time, r_k_time,19 'tab:orange', label="Rabin Karp")20 plt.plot(time, kmp_time21 , 'tab:green', label="KMP")22 plt.xlabel("n")23 plt.ylabel('time in seconds')24 plt.legend()2526 plt.plot()27 plt.savefig(f"{filename}.png")28 print("plotted")293031def get_random_string(length, letters):32 '''33 Generates random string of length length using letters34 '''35 result_str = ''.join(random.choice(letters) for i in range(length))36 return result_str373839def insert(source_str, insert_str, pos):40 '''41 Fits a string into another string and results in a new string42 '''43 return source_str[:pos]+insert_str+source_str[pos:]4445start_test,end_test,step_test = 5000,1000001,500046times = list(range(start_test,end_test,step_test))4748b_f_temp = list(range(start_test,end_test,step_test))49r_k_temp = list(range(start_test,end_test,step_test))50kmp_temp = list(range(start_test,end_test,step_test))5152for i,n in enumerate(range(start_test,end_test,step_test)):53 pattern = get_random_string(500,ascii_lowercase)54 string_to_search = get_random_string(n-len(pattern), ascii_lowercase)55 string_to_search = insert(56 string_to_search, pattern, random.randint(0, len(string_to_search)-1))57 a = timeit.timeit(stmt="NaiveAlgorithm(pattern,text)",58 setup=f"from BruteForce import NaiveAlgorithm;text='{string_to_search}';pattern='{pattern}';", number=3)59 60 b = timeit.timeit(stmt=f"RabinKarp(pattern,text,524287)",61 setup=f"from RabinKarp import RabinKarp;text='{string_to_search}';pattern='{pattern}';", number=3)62 63 c = timeit.timeit(stmt="KMP_search(pattern,text)",64 setup=f"from Knuth import KMP_search;text='{string_to_search}';pattern='{pattern}';", number=3)65 b_f_temp[i] = a/366 r_k_temp[i] = b/367 kmp_temp[i] = c/368 import time69 if n % 100 == 0:70 print(n)71print("done") ...

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1# file: test.py2#3# Restores a learned CNN model and tests it.4#5# Store your test images in a sub-folder for each6# class in <dataset_root>/validation/7# e.g.8# <dataset_root>/validation/bikes9# <dataset_root>/validation/cars10# 11# ---12# Prof. Dr. Juergen Brauer, www.juergenbrauer.org13from dataset_reader import dataset14dataset_root = "V:/01_job/12_datasets/imagenet/cars_vs_bikes_prepared/"15testing = dataset(dataset_root + "validation", ".jpeg")16import tensorflow as tf17import numpy as np18# Parameters19batch_size = 120ckpt = tf.train.get_checkpoint_state("saved_model_exp03")21saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta')22pred = tf.get_collection("pred")[0]23x = tf.get_collection("x")[0]24keep_prob = tf.get_collection("keep_prob")[0]25sess = tf.Session()26saver.restore(sess, ckpt.model_checkpoint_path)27# test28step_test = 029correct=030while step_test * batch_size < len(testing):31 # testing_ys and testing_xs are tuples32 testing_ys, testing_xs = testing.nextBatch(batch_size)33 # get first image and first ground truth vector34 # from the tuples35 first_img = testing_xs[0]36 first_groundtruth_vec = testing_ys[0]37 # at first iteration:38 # show shape of image and ground truth vector39 if step_test == 0:40 print("Shape of testing_xs is :", first_img.shape)41 print("Shape of testing_ys is :", first_groundtruth_vec.shape)42 # given the input image,43 # let the CNN predict the category!44 predict =, feed_dict={x: testing_xs, keep_prob: 1.})45 # get ground truth label and46 # predicted label from output vector47 groundtruth_label = np.argmax(first_groundtruth_vec)48 predicted_label = np.argmax(predict, 1)[0]49 print("\nImage test: ", step_test)50 print("Ground truth label :", testing.label2category[groundtruth_label])51 print("Label predicted by CNN:", testing.label2category[predicted_label])52 if predicted_label == groundtruth_label:53 correct+=154 step_test += 155print("\n---")56print("Classified", correct, "images correct of", step_test,"in total.")...

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