File size: 10,576 Bytes
b4959be |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 |
import os
import sys
import importlib
import pickle
import lzma
import PIL.Image
import numpy as np
import torch
# %%
class Attributes:
pass
class UnitTest:
def __init__(self,
easyocr_module,
test_data = "./data/EasyOcrUnitTestPackage.pickle",
image_data_dir = "../examples",
verbose = 0,
numeric_acceptance_error = 0.1):
self.verbose = verbose
easy_ocr_init = os.path.join(easyocr_module, "__init__.py")
if not os.path.isfile(easy_ocr_init):
raise FileNotFoundError("Invalid easyocr_module. The directory should contain __init__.py.")
spec = importlib.util.spec_from_file_location("easyocr", easy_ocr_init)
easyocr = importlib.util.module_from_spec(spec)
sys.modules["easyocr"] = easyocr
spec.loader.exec_module(easyocr)
self.easyocr = easyocr
if not hasattr(self.easyocr, 'utils'):
setattr(self.easyocr, 'utils', importlib.import_module('easyocr.utils'))
if not hasattr(self.easyocr, 'detection'):
setattr(self.easyocr, 'detection', importlib.import_module('easyocr.detection'))
if not hasattr(self.easyocr, 'recognition'):
setattr(self.easyocr, 'recognition', importlib.import_module('easyocr.recognition'))
self.easyocr_dir = os.path.dirname(easyocr.__file__)
print("Unit test is set for EasyOCR at {}".format(os.path.abspath(self.easyocr_dir)))
self.image_data_dir = image_data_dir
self.set_data(test_data)
self.set_easyocr()
self.numeric_acceptance_error = numeric_acceptance_error
def set_data(self, test_data):
self.inputs = Attributes()
with lzma.open(test_data, 'rb') as fid:
solution_book = pickle.load(fid)
self.test_book = solution_book['tests']
if any([file not in os.listdir(self.image_data_dir) for file in solution_book['inputs']['images'].keys()]):
raise FileNotFoundError("Cannot find {} in {}.").format(', '.join([file for file in solution_book['inputs']['images'].keys()
if file not in os.listdir(self.image_data_dir)], self.image_data_dir))
images = {os.path.splitext(file)[0]: {
key: np.asarray(PIL.Image.open(os.path.join(self.image_data_dir, file)).crop(crop_box))[:,:,::-1] for (key,crop_box) in page.items()
} for (file,page) in solution_book['inputs']['images'].items()}
english_mini_bgr, english_mini_gray = self.easyocr.utils.reformat_input(images['english']['mini'])
english_small_bgr, english_small_gray = self.easyocr.utils.reformat_input(images['english']['small'])
images['english'].update({'mini_bgr': english_mini_bgr,
'mini_gray': english_mini_gray,
'small_bgr': english_small_bgr,
'small_gray': english_small_gray,
})
setattr(self.inputs, 'images', self.dict2attr(images))
setattr(self.inputs, 'easyocr_config', self.dict2attr(solution_book['inputs']['easyocr_config']))
def dict2attr(self, dict_):
attr = Attributes()
[setattr(attr, key, self.dict2attr(value)) if isinstance(value, dict) else setattr(attr, key, value) for (key,value) in dict_.items()]
return attr
def count_parameters(self, model):
return sum([param.numel() for param in model.parameters()])
def get_weight_norm(self, model):
with torch.no_grad():
return sum([param.norm() for param in model.parameters()]).cpu().item()
def get_nested_attr(self, parent, attr):
if len(attr.split(".")) == 1:
return getattr(parent, attr)
else:
attrs = attr.split(".")
parent = getattr(parent, attrs[0])
attr = ".".join(attrs[1:])
attr = self.get_nested_attr(parent, attr)
return attr
def easyocr_read_as(self, image, language):
if not isinstance(language, list):
language = [language]
reader = self.easyocr.Reader(language)
_, pred, confidence = reader.readtext(image)[0]
reader = None
torch.cuda.empty_cache()
return pred, confidence
def set_easyocr(self):
ocr = self.easyocr.Reader([self.inputs.easyocr_config.main_language])
setattr(self.easyocr, 'ocr', ocr)
def validate(self, test, solution, dtype):
if dtype == str:
return test == solution
elif np.issubdtype(dtype, np.integer):
return abs(1-test/solution) < self.numeric_acceptance_error
elif np.issubdtype(dtype, np.inexact):
return abs(1-test/solution) < self.numeric_acceptance_error
elif dtype == dict:
return self.are_dicts_equal(test, solution)
elif dtype == list or dtype == tuple:
return self.are_lists_equal(test, solution)
elif dtype == np.ndarray:
return (abs(1-test/solution) < self.numeric_acceptance_error).all()
elif dtype == torch.Tensor:
return (abs(1-test/solution) < self.numeric_acceptance_error).all()
else:
raise TypeError("Unsupport data type ({}) to validate. Supporting types are str, int, float, dict, list, np.ndarray, or torch.Tensor".format(dtype))
def are_dicts_equal(self, test, solution):
if test.keys() == solution.keys():
return all([self.validate(test[key], solution[key], type(solution[key])) for key in solution.keys()])
else:
return False
def are_lists_equal(self, test, solution):
if len(test) == len(solution):
return all([self.validate(tt, ss, type(ss)) for (tt,ss) in zip(test, solution)])
else:
return False
def is_list_or_tuple(self, test):
return isinstance(test, list) or isinstance(test, tuple)
#Should check length of results/solutions/dtypes
def validate_all(self, results, solutions, dtypes):
if not isinstance(results, list):
results = [results]
if not isinstance(solutions, list):
solutions = [solutions]
if not isinstance(dtypes, list):
dtypes = [dtypes]
validation = []
for (result, solution, dtype) in zip(results, solutions, dtypes):
if (not self.is_list_or_tuple(result)
and not self.is_list_or_tuple(result)
and not self.is_list_or_tuple(result)
):
validation.append(self.validate(result, solution, type(solution)))
elif(self.is_list_or_tuple(result)
and self.is_list_or_tuple(result)
and self.is_list_or_tuple(result)
):
validation.append(self.validate_all(results, solutions, type(solution)))
else:
raise
return all(validation)
def do_test(self, verbose = None):
if verbose is not None:
self.verbose = verbose
num_module_to_test = len(self.test_book)
num_module_pass = 0
print("Testing EasyOCR: {:d} modules will be tested.\n".format(num_module_to_test))
for name,tests in self.test_book.items():
num_test = len(tests)
num_passed = 0
min_pass = sum([test['severity'] == 'Error' for test in tests.values()])
if self.verbose > 0:
print("##Testing module {}: {:d} tests will be performed.".format(name, num_test))
for test_id, test in tests.items():
if self.verbose > 1:
print("#### {}: {}".format(test_id, test['description']))
if test['method'].startswith('unit_test.'):
test['method'] = '.'.join(test['method'].split('.')[1:])
test_method = self.get_nested_attr(self, test['method'])
test['input'] = [(self.get_nested_attr(self, '.'.join(input_.split('.')[1:]))
if input_.startswith('unit_test.') else input_) if isinstance(input_, str) else input_ for input_ in test['input']]
if verbose > 3:
print("###### Input: {}".format(test['input']))
results = test_method(*test['input'])
if verbose > 2:
print("###### Expected output: {}".format(test['output']))
print("###### Received output: {}".format(results))
test_result = self.validate(results, test['output'], type(test['output']))
if test_result:
num_passed += 1
if self.verbose > 1:
print("#### Passed. [{:d}/{:d}]".format(num_passed, num_test))
else:
if test['severity'] == "Warning":
num_passed += 1
if self.verbose > 1:
print("#### Passed. [{:d}/{:d}]".format(num_passed, num_test))
if self.verbose > 2:
print("##### Warning: While the result is considered as passed, the test yields results ({}) \
that are different from the expected values ({}). It is strongly recommended to make sure \
that this is expected.".format(results, test['output']))
else:
if self.verbose > 1:
print("#### Failed")
if self.verbose > 2:
print("##### The test yields results ({}) which are different from the expected values ({}).")
if num_passed >= min_pass:
num_module_pass += 1
if self.verbose > 0:
print("##Module {}: Passed.\n".format(name))
else:
print("##Module {}: Failed.\n".format(name))
print("#"*50)
if num_module_pass >= num_module_to_test:
print("Testing completed:\n Final result: Passed.")
else:
print("Testing completed:\n Final result: Failed.")
|