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How to use test_input_files method in gabbi
Best Python code snippet using gabbi_python
datasets.py
Source:datasets.py 
1import glob2import random3import os4import numpy as np5import torch6import cv27from torch.utils.data import Dataset8from PIL import Image9import torchvision.transforms as transforms10import torchvision.transforms.functional as TF11import torchvision_x_functional as TF_x12class ImageDataset_sRGB(Dataset):13 def __init__(self, root, mode="train", unpaird_data="fiveK", combined=True):14 self.mode = mode15 self.unpaird_data = unpaird_data16 file = open(os.path.join(root,'train_input.txt'),'r')17 set1_input_files = sorted(file.readlines())18 self.set1_input_files = list()19 self.set1_expert_files = list()20 for i in range(len(set1_input_files)):21 self.set1_input_files.append(os.path.join(root,"raw",set1_input_files[i][:-1]))22 self.set1_expert_files.append(os.path.join(root,"retouched",set1_input_files[i][:-1]))23 #file = open(os.path.join(root,'train_label.txt'),'r')24 #set2_input_files = sorted(file.readlines())25 #self.set2_input_files = list()26 #self.set2_expert_files = list()27 #for i in range(len(set2_input_files)):28 # self.set2_input_files.append(os.path.join(root,"raw", set2_input_files[i][:-1]))29 # self.set2_expert_files.append(os.path.join(root,"retouched", set2_input_files[i][:-1]))30 file = open(os.path.join(root,'test.txt'),'r')31 test_input_files = sorted(file.readlines())32 self.test_input_files = list()33 self.test_expert_files = list()34 for i in range(len(test_input_files)):35 self.test_input_files.append(os.path.join(root, "raw", test_input_files[i][:-1]))36 self.test_expert_files.append(os.path.join(root,"retouched", test_input_files[i][:-1]))37 if combined:38 self.set1_input_files = self.set1_input_files #+ self.set2_input_files39 self.set1_expert_files = self.set1_expert_files #+ self.set2_expert_files40 def __getitem__(self, index):41 if self.mode == "train":42 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]43 img_input = Image.open(self.set1_input_files[index % len(self.set1_input_files)])44 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])45 elif self.mode == "test":46 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]47 img_input = Image.open(self.test_input_files[index % len(self.test_input_files)])48 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])49 if self.mode == "train":50 ratio_H = np.random.uniform(0.6,1.0)51 ratio_W = np.random.uniform(0.6,1.0)52 W,H = img_input._size53 crop_h = round(H*ratio_H)54 crop_w = round(W*ratio_W)55 i, j, h, w = transforms.RandomCrop.get_params(img_input, output_size=(crop_h, crop_w))56 img_input = TF.crop(img_input, i, j, h, w)57 img_exptC = TF.crop(img_exptC, i, j, h, w)58 #img_input = TF.resized_crop(img_input, i, j, h, w, (320,320))59 #img_exptC = TF.resized_crop(img_exptC, i, j, h, w, (320,320))60 if np.random.random() > 0.5:61 img_input = TF.hflip(img_input)62 img_exptC = TF.hflip(img_exptC)63 a = np.random.uniform(0.8,1.2)64 img_input = TF.adjust_brightness(img_input,a)65 a = np.random.uniform(0.8,1.2)66 img_input = TF.adjust_saturation(img_input,a)67 img_input = TF.to_tensor(img_input)68 img_exptC = TF.to_tensor(img_exptC)69 return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}70 def __len__(self):71 if self.mode == "train":72 return len(self.set1_input_files)73 elif self.mode == "test":74 return len(self.test_input_files)75class ImageDataset_XYZ(Dataset):76 def __init__(self, root, mode="train", unpaird_data="fiveK", combined=True):77 self.mode = mode78 file = open(os.path.join(root,'train_input.txt'),'r')79 set1_input_files = sorted(file.readlines())80 self.set1_input_files = list()81 self.set1_expert_files = list()82 for i in range(len(set1_input_files)):83 self.set1_input_files.append(os.path.join(root,"raw", set1_input_files[i][:-1]))84 self.set1_expert_files.append(os.path.join(root,"retouched",set1_input_files[i][:-1]))85 #file = open(os.path.join(root,'train_input.txt'),'r')86 #set2_input_files = sorted(file.readlines())87 #self.set2_input_files = list()88 #self.set2_expert_files = list()89 #for i in range(len(set2_input_files)):90 # self.set2_input_files.append(os.path.join(root,"raw", set2_input_files[i][:-1]))91 # self.set2_expert_files.append(os.path.join(root,"retouched", set2_input_files[i][:-1]))92 file = open(os.path.join(root,'train_input.txt'),'r')93 test_input_files = sorted(file.readlines())94 self.test_input_files = list()95 self.test_expert_files = list()96 for i in range(len(test_input_files)):97 self.test_input_files.append(os.path.join(root,"raw", test_input_files[i][:-1]))98 self.test_expert_files.append(os.path.join(root,"retouched", test_input_files[i][:-1]))99 if combined:100 self.set1_input_files = self.set1_input_files #+ self.set2_input_files101 self.set1_expert_files = self.set1_expert_files #+ self.set2_expert_files102 def __getitem__(self, index):103 if self.mode == "train":104 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]105 img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)106 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])107 elif self.mode == "test":108 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]109 img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)110 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])111 #img_input = np.array(img_input)112 img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))113 if self.mode == "train":114 ratio_H = np.random.uniform(0.6,1.0)115 ratio_W = np.random.uniform(0.6,1.0)116 W,H = img_exptC._size117 crop_h = round(H*ratio_H)118 crop_w = round(W*ratio_W)119 i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))120 img_input = TF_x.crop(img_input, i, j, h, w)121 img_exptC = TF.crop(img_exptC, i, j, h, w)122 if np.random.random() > 0.5:123 img_input = TF_x.hflip(img_input)124 img_exptC = TF.hflip(img_exptC)125 a = np.random.uniform(0.6,1.4)126 img_input = TF_x.adjust_brightness(img_input,a)127 img_input = TF_x.to_tensor(img_input)128 img_exptC = TF.to_tensor(img_exptC)129 return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}130 def __len__(self):131 if self.mode == "train":132 return len(self.set1_input_files)133 elif self.mode == "test":134 return len(self.test_input_files)135class ImageDataset_sRGB_unpaired(Dataset):136 def __init__(self, root, mode="train", unpaird_data="fiveK"):137 self.mode = mode138 self.unpaird_data = unpaird_data139 file = open(os.path.join(root,'train_input.txt'),'r')140 set1_input_files = sorted(file.readlines())141 self.set1_input_files = list()142 self.set1_expert_files = list()143 for i in range(len(set1_input_files)):144 self.set1_input_files.append(os.path.join(root,"raw", set1_input_files[i][:-1]))145 self.set1_expert_files.append(os.path.join(root,"retouched", set1_input_files[i][:-1]))146 file = open(os.path.join(root,'train_label.txt'),'r')147 set2_input_files = sorted(file.readlines())148 self.set2_input_files = list()149 self.set2_expert_files = list()150 for i in range(len(set2_input_files)):151 self.set2_input_files.append(os.path.join(root,"raw",set2_input_files[i][:-1]))152 self.set2_expert_files.append(os.path.join(root,"retouched",set2_input_files[i][:-1]))153 file = open(os.path.join(root,'test.txt'),'r')154 test_input_files = sorted(file.readlines())155 self.test_input_files = list()156 self.test_expert_files = list()157 for i in range(len(test_input_files)):158 self.test_input_files.append(os.path.join(root,"raw", test_input_files[i][:-1]))159 self.test_expert_files.append(os.path.join(root,"retouched", test_input_files[i][:-1]))160 def __getitem__(self, index):161 if self.mode == "train":162 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]163 img_input = Image.open(self.set1_input_files[index % len(self.set1_input_files)])164 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])165 seed = random.randint(1,len(self.set2_expert_files))166 img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])167 elif self.mode == "test":168 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]169 img_input = Image.open(self.test_input_files[index % len(self.test_input_files)])170 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])171 img2 = img_exptC172 if self.mode == "train":173 ratio_H = np.random.uniform(0.6,1.0)174 ratio_W = np.random.uniform(0.6,1.0)175 W,H = img_input._size176 crop_h = round(H*ratio_H)177 crop_w = round(W*ratio_W)178 W2,H2 = img2._size179 crop_h = min(crop_h,H2)180 crop_w = min(crop_w,W2)181 i, j, h, w = transforms.RandomCrop.get_params(img_input, output_size=(crop_h, crop_w))182 img_input = TF.crop(img_input, i, j, h, w)183 img_exptC = TF.crop(img_exptC, i, j, h, w)184 i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))185 img2 = TF.crop(img2, i, j, h, w)186 if np.random.random() > 0.5:187 img_input = TF.hflip(img_input)188 img_exptC = TF.hflip(img_exptC)189 if np.random.random() > 0.5:190 img2 = TF.hflip(img2)191 #if np.random.random() > 0.5:192 # img_input = TF.vflip(img_input)193 # img_exptC = TF.vflip(img_exptC)194 # img2 = TF.vflip(img2)195 a = np.random.uniform(0.6,1.4)196 img_input = TF.adjust_brightness(img_input,a)197 a = np.random.uniform(0.8,1.2)198 img_input = TF.adjust_saturation(img_input,a)199 img_input = TF.to_tensor(img_input)200 img_exptC = TF.to_tensor(img_exptC)201 img2 = TF.to_tensor(img2)202 return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}203 def __len__(self):204 if self.mode == "train":205 return len(self.set1_input_files)206 elif self.mode == "test":207 return len(self.test_input_files)208class ImageDataset_XYZ_unpaired(Dataset):209 def __init__(self, root, mode="train", unpaird_data="fiveK"):210 self.mode = mode211 self.unpaird_data = unpaird_data212 file = open(os.path.join(root,'train_input.txt'),'r')213 set1_input_files = sorted(file.readlines())214 self.set1_input_files = list()215 self.set1_expert_files = list()216 for i in range(len(set1_input_files)):217 self.set1_input_files.append(os.path.join(root,"raw",set1_input_files[i][:-1]))218 self.set1_expert_files.append(os.path.join(root,"retouched",set1_input_files[i][:-1]))219 file = open(os.path.join(root,'train_label.txt'),'r')220 set2_input_files = sorted(file.readlines())221 self.set2_input_files = list()222 self.set2_expert_files = list()223 for i in range(len(set2_input_files)):224 self.set2_input_files.append(os.path.join(root,"raw",set2_input_files[i][:-1]))225 self.set2_expert_files.append(os.path.join(root,"retouched",set2_input_files[i][:-1]))226 file = open(os.path.join(root,'test.txt'),'r')227 test_input_files = sorted(file.readlines())228 self.test_input_files = list()229 self.test_expert_files = list()230 for i in range(len(test_input_files)):231 self.test_input_files.append(os.path.join(root,"raw",test_input_files[i][:-1]))232 self.test_expert_files.append(os.path.join(root,"retouched",test_input_files[i][:-1]))233 def __getitem__(self, index):234 if self.mode == "train":235 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]236 img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)237 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])238 seed = random.randint(1,len(self.set2_expert_files))239 img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])240 elif self.mode == "test":241 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]242 img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)243 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])244 img2 = img_exptC245 img_input = np.array(img_input)246 #img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))247 if self.mode == "train":248 ratio_H = np.random.uniform(0.6,1.0)249 ratio_W = np.random.uniform(0.6,1.0)250 W,H = img_exptC._size251 crop_h = round(H*ratio_H)252 crop_w = round(W*ratio_W)253 W2,H2 = img2._size254 crop_h = min(crop_h,H2)255 crop_w = min(crop_w,W2)256 i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))257 img_input = TF_x.crop(img_input, i, j, h, w)258 img_exptC = TF.crop(img_exptC, i, j, h, w)259 i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))260 img2 = TF.crop(img2, i, j, h, w)261 if np.random.random() > 0.5:262 img_input = TF_x.hflip(img_input)263 img_exptC = TF.hflip(img_exptC)264 if np.random.random() > 0.5:265 img2 = TF.hflip(img2)266 a = np.random.uniform(0.6,1.4)267 img_input = TF_x.adjust_brightness(img_input,a)268 img_input = TF_x.to_tensor(img_input)269 img_exptC = TF.to_tensor(img_exptC)270 img2 = TF.to_tensor(img2)271 return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}272 def __len__(self):273 if self.mode == "train":274 return len(self.set1_input_files)275 elif self.mode == "test":276 return len(self.test_input_files)277class ImageDataset_HDRplus(Dataset):278 def __init__(self, root, mode="train", combined=True):279 self.mode = mode280 file = open(os.path.join(root,'train.txt'),'r')281 set1_input_files = sorted(file.readlines())282 self.set1_input_files = list()283 self.set1_expert_files = list()284 for i in range(len(set1_input_files)):285 self.set1_input_files.append(os.path.join(root,"middle_480p",set1_input_files[i][:-1] + ".png"))286 self.set1_expert_files.append(os.path.join(root,"output_480p",set1_input_files[i][:-1] + ".jpg"))287 file = open(os.path.join(root,'test.txt'),'r')288 test_input_files = sorted(file.readlines())289 self.test_input_files = list()290 self.test_expert_files = list()291 for i in range(len(test_input_files)):292 self.test_input_files.append(os.path.join(root,"middle_480p",test_input_files[i][:-1] + ".png"))293 self.test_expert_files.append(os.path.join(root,"output_480p",test_input_files[i][:-1] + ".jpg"))294 def __getitem__(self, index):295 if self.mode == "train":296 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]297 img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)298 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])299 elif self.mode == "test":300 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]301 img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)302 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])303 img_input = np.array(img_input)304 #img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))305 if self.mode == "train":306 ratio = np.random.uniform(0.6,1.0)307 W,H = img_exptC._size308 crop_h = round(H*ratio)309 crop_w = round(W*ratio)310 i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))311 try:312 img_input = TF_x.crop(img_input, i, j, h, w)313 except:314 print(crop_h,crop_w,img_input.shape())315 img_exptC = TF.crop(img_exptC, i, j, h, w)316 if np.random.random() > 0.5:317 img_input = TF_x.hflip(img_input)318 img_exptC = TF.hflip(img_exptC)319 a = np.random.uniform(0.6,1.4)320 img_input = TF_x.adjust_brightness(img_input,a)321 #a = np.random.uniform(0.8,1.2)322 #img_input = TF_x.adjust_saturation(img_input,a)323 img_input = TF_x.to_tensor(img_input)324 img_exptC = TF.to_tensor(img_exptC)325 return {"A_input": img_input, "A_exptC": img_exptC, "input_name": img_name}326 def __len__(self):327 if self.mode == "train":328 return len(self.set1_input_files)329 elif self.mode == "test":330 return len(self.test_input_files)331class ImageDataset_HDRplus_unpaired(Dataset):332 def __init__(self, root, mode="train"):333 self.mode = mode334 file = open(os.path.join(root,'train.txt'),'r')335 set1_input_files = sorted(file.readlines())336 self.set1_input_files = list()337 self.set1_expert_files = list()338 for i in range(len(set1_input_files)):339 self.set1_input_files.append(os.path.join(root,"middle_480p",set1_input_files[i][:-1] + ".png"))340 self.set1_expert_files.append(os.path.join(root,"output_480p",set1_input_files[i][:-1] + ".jpg"))341 file = open(os.path.join(root,'train.txt'),'r')342 set2_input_files = sorted(file.readlines())343 self.set2_input_files = list()344 self.set2_expert_files = list()345 for i in range(len(set2_input_files)):346 self.set2_input_files.append(os.path.join(root,"middle_480p",set2_input_files[i][:-1] + ".png"))347 self.set2_expert_files.append(os.path.join(root,"output_480p",set2_input_files[i][:-1] + ".jpg"))348 file = open(os.path.join(root,'test.txt'),'r')349 test_input_files = sorted(file.readlines())350 self.test_input_files = list()351 self.test_expert_files = list()352 for i in range(len(test_input_files)):353 self.test_input_files.append(os.path.join(root,"middle_480p",test_input_files[i][:-1] + ".png"))354 self.test_expert_files.append(os.path.join(root,"output_480p",test_input_files[i][:-1] + ".jpg"))355 def __getitem__(self, index):356 if self.mode == "train":357 img_name = os.path.split(self.set1_input_files[index % len(self.set1_input_files)])[-1]358 img_input = cv2.imread(self.set1_input_files[index % len(self.set1_input_files)],-1)359 img_exptC = Image.open(self.set1_expert_files[index % len(self.set1_expert_files)])360 seed = random.randint(1,len(self.set2_expert_files))361 img2 = Image.open(self.set2_expert_files[(index + seed) % len(self.set2_expert_files)])362 elif self.mode == "test":363 img_name = os.path.split(self.test_input_files[index % len(self.test_input_files)])[-1]364 img_input = cv2.imread(self.test_input_files[index % len(self.test_input_files)],-1)365 img_exptC = Image.open(self.test_expert_files[index % len(self.test_expert_files)])366 img2 = img_exptC367 img_input = np.array(img_input)368 #img_input = np.array(cv2.cvtColor(img_input,cv2.COLOR_BGR2RGB))369 if self.mode == "train":370 ratio = np.random.uniform(0.6,1.0)371 W,H = img_exptC._size372 crop_h = round(H*ratio)373 crop_w = round(W*ratio)374 W2,H2 = img2._size375 crop_h = min(crop_h,H2)376 crop_w = min(crop_w,W2)377 i, j, h, w = transforms.RandomCrop.get_params(img_exptC, output_size=(crop_h, crop_w))378 img_input = TF_x.crop(img_input, i, j, h, w)379 img_exptC = TF.crop(img_exptC, i, j, h, w)380 i, j, h, w = transforms.RandomCrop.get_params(img2, output_size=(crop_h, crop_w))381 img2 = TF.crop(img2, i, j, h, w)382 if np.random.random() > 0.5:383 img_input = TF_x.hflip(img_input)384 img_exptC = TF.hflip(img_exptC)385 if np.random.random() > 0.5:386 img2 = TF.hflip(img2)387 a = np.random.uniform(0.8,1.2)388 img_input = TF_x.adjust_brightness(img_input,a)389 img_input = TF_x.to_tensor(img_input)390 img_exptC = TF.to_tensor(img_exptC)391 img2 = TF.to_tensor(img2)392 return {"A_input": img_input, "A_exptC": img_exptC, "B_exptC": img2, "input_name": img_name}393 def __len__(self):394 if self.mode == "train":395 return len(self.set1_input_files)396 elif self.mode == "test":...
test_input_reader.py
Source:test_input_reader.py
1"""Test cases to read inputs from text files."""2import input_reader as ir # pylint: disable=import-error3import os4import unittest5from .test_config import test_config_data6CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))7class TestInputReader(unittest.TestCase):8 """Tests of input reader functions."""9 def test_if_correct_number_of_rows_are_returned_from_input_file(self):10 input_file = os.path.join(11 CURRENT_DIR, "test_input_files/test_input.txt")12 expected = 313 actual = len(ir.read_input(input_file))14 self.assertEqual(expected, actual)15 def test_if_correct_strings_are_returned_with_secret_message_striped_for_white_spaces_at_ends(16 self):17 input_file = os.path.join(18 CURRENT_DIR, "test_input_files/test_input.txt")19 expected = ["KINGDOMONE SECRETMESSAGEONE",20 "KINGDOMTWO SECRET MESSAGE TWO",21 "KINGDOMTHREE SECRET MESSAGETHREE"22 ]23 actual = ir.read_input(input_file)24 self.assertEqual(expected, actual)25 def test_if_True_is_returned_if_input_format_is_correct(self):26 input_file = os.path.join(27 CURRENT_DIR, "test_input_files/test_input.txt")28 lines = ir.read_input(input_file)29 regex = test_config_data.get("std_input_format")30 result = ir.validate_format(regex, lines)31 self.assertTrue(result)32 def test_if_False_is_returned_if_input_format_is_not_correct(self):33 input_file = os.path.join(34 CURRENT_DIR, "test_input_files/bad_test_input.txt")35 lines = ir.read_input(input_file)36 regex = test_config_data.get("std_input_format")37 with self.assertRaises(ir.InputFormatError):...
test_add_js_files.py
Source:test_add_js_files.py
1from govuk_tech_docs_sphinx_theme import add_js_files2from pathlib import Path3from typing import List4from unittest.mock import MagicMock, call5import pytest6# Define the input file path list for the test case7args_test_add_js_files = [8 [Path("hello/world.js")],9 [Path("foo/bar.js")],10 [Path("hello/world.js"), Path("foo/bar.js")],11]12@pytest.mark.parametrize("test_input_files", args_test_add_js_files)13class TestAddJsFiles:14 def test_calls_add_js_file_correctly(self, test_input_files: List[Path]) -> None:15 """Test that the `Sphinx.add_js_file` method is called correctly."""16 # Mock the `app`17 test_input_app = MagicMock()18 # Execute the function, and assert the `add_js_file` method is called correctly19 add_js_files(test_input_app, test_input_files)20 assert test_input_app.add_js_file.call_count == len(test_input_files)21 test_input_app.add_js_file.assert_has_calls(22 [call(a) for a in test_input_files], any_order=False...
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