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How to use test_set_data method in tox

Best Python code snippet using tox_python

CatDogBuildDataset.py

Source:CatDogBuildDataset.py

1# -*- coding: utf-8 -*-2'''3æ°æ®éå è½½4'''5# @Time : 2021/4/8 22:58 6# @Author : LINYANZHEN7# @File : CatDogBuildDataset.py8import torch9from torch.utils.data import Dataset10import cv211import random12import os13import numpy as np14def cat_dog_build_dataset(dataset_path):15    l = [i for i in range(100)]16    train_set_data = np.zeros((140, 3, 128, 128))17    test_set_data = np.zeros((60, 3, 128, 128))18    train_set_label = np.zeros((140))19    test_set_label = np.zeros((60))20    random.shuffle(l)21    # å70å¼ ç«ä½ä¸ºè®ç»é22    for i in range(70):23        image = cv2.imread(os.path.join(dataset_path, "cat.{}.jpg".format(l[i])))24        image = cv2.resize(image, (128, 128))25        r = image[:, :, 2]26        g = image[:, :, 1]27        b = image[:, :, 0]28        train_set_label[i] = 029        train_set_data[i, 0, :, :] = r30        train_set_data[i, 1, :, :] = g31        train_set_data[i, 2, :, :] = b32    # å©ä¸30å¼ ç«ä½ä¸ºæµè¯é33    for i in range(70, 100):34        image = cv2.imread(os.path.join(dataset_path, "cat.{}.jpg".format(l[i])))35        image = cv2.resize(image, (128, 128))36        r = image[:, :, 2]37        g = image[:, :, 1]38        b = image[:, :, 0]39        test_set_label[i - 70] = 040        test_set_data[i - 70, 0, :, :] = r41        test_set_data[i - 70, 1, :, :] = g42        test_set_data[i - 70, 2, :, :] = b43    random.shuffle(l)44    # åéæºå70å¼ ç45    for i in range(70):46        image = cv2.imread(os.path.join(dataset_path, "dog.{}.jpg".format(l[i])))47        image = cv2.resize(image, (128, 128))48        r = image[:, :, 2]49        g = image[:, :, 1]50        b = image[:, :, 0]51        train_set_label[i + 70] = 152        train_set_data[i + 70, 0, :, :] = r53        train_set_data[i + 70, 1, :, :] = g54        train_set_data[i + 70, 2, :, :] = b55    # å©ä¸30å¼ çä½ä¸ºæµè¯é56    for i in range(70, 100):57        image = cv2.imread(os.path.join(dataset_path, "dog.{}.jpg".format(l[i])))58        image = cv2.resize(image, (128, 128))59        r = image[:, :, 2]60        g = image[:, :, 1]61        b = image[:, :, 0]62        test_set_label[i - 70 + 30] = 163        test_set_data[i - 70 + 30, 0, :, :] = r64        test_set_data[i - 70 + 30, 1, :, :] = g65        test_set_data[i - 70 + 30, 2, :, :] = b66    print("train_set_data", train_set_data)67    print("test_set_data", test_set_data)68    print("train_set_label", train_set_label)69    print("test_set_label", test_set_label)70    np.save("train_set_data", train_set_data)71    np.save("test_set_data", test_set_data)72    np.save("train_set_label", train_set_label)73    np.save("test_set_label", test_set_label)74class CDDataset(Dataset):75    def __init__(self, mode="train"):76        super(CDDataset, self).__init__()77        self.labels = []78        self.images = []79        self.len = 080        if mode == "train":81            # æ¯è®ç»é82            self.labels = torch.tensor(np.load("train_set_label.npy"), dtype=torch.long)83            self.images = torch.tensor(np.load("train_set_data.npy") / 256.0, dtype=torch.float32)84            self.len = len(self.labels)85        elif mode == "test":86            self.labels = torch.tensor(np.load("test_set_label.npy"), dtype=torch.long)87            self.images = torch.tensor(np.load("test_set_data.npy") / 256.0, dtype=torch.float32)88            self.len = len(self.labels)89    def __getitem__(self, index):90        return self.images[index, :, :, :], self.labels[index]91    def __len__(self):92        return self.len93if __name__ == '__main__':...

main.py

Source:main.py

1import sys2import theano3import numpy as np4from cnn import *5from libparser import *6# theano.config.exception_verbosity='high'7if __name__=='__main__':8	if len(sys.argv)!=2:9		print 'Usage: python main.py <dataset>(cifar-10 or svhm)'10		exit(0)11	else:12		path=sys.argv[1]+'/'13	train=unzip(path+'train_data')14	validate=unzip(path+'validate_data')15	test=unzip(path+'test_data')16	train_data=np.asarray(train['data'],dtype=theano.config.floatX)17	train_label=np.asarray(train['labels'],dtype=theano.config.floatX)18	validate_data=np.asarray(validate['data'],dtype=theano.config.floatX)19	validate_label=np.asarray(validate['labels'],dtype=theano.config.floatX)20	test_data=np.asarray(test['data'],dtype=theano.config.floatX)21	test_label=np.asarray(test['labels'],dtype=theano.config.floatX)22	machine=model(23		learn_rate=0.005,24		n_epochs=100,25		filters=[128,128,128,256,256],26		batch_size=2527		)28	train_set_data=np.asarray(train_data,dtype=theano.config.floatX)29	train_set_label=np.asarray(train_label,dtype=int)30	validate_set_data=np.asarray(validate_data,dtype=theano.config.floatX)31	validate_set_label=np.asarray(validate_label,dtype=int)32	test_set_data=np.asarray(test_data,dtype=theano.config.floatX)33	test_set_label=np.asarray(test_label,dtype=int)34	for i in xrange(len(train_set_data)):35		train_set_data[i]-=train_set_data[i].mean()36	for i in xrange(len(validate_set_data)):37		validate_set_data[i]-=validate_set_data[i].mean()38	for i in xrange(len(test_set_data)):39		test_set_data[i]-=test_set_data[i].mean()40	machine.train_validate_test(41		train_set_data,train_set_label,42		validate_set_data,validate_set_label,43		test_set_data,test_set_label...

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