def create_emnist_lines_support_files() -> None:
"""Create EMNIST Lines test images."""
shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
SUPPORT_DIRNAME.mkdir()
# TODO: maybe have to add args to dataset.
dataset = EmnistLinesDataset(
init_token="<sos>",
pad_token="_",
eos_token="<eos>",
transform=[{"type": "ToTensor", "args": {}}],
target_transform=[
{
"type": "AddTokens",
"args": {"init_token": "<sos>", "pad_token": "_", "eos_token": "<eos>"},
}
],
) # nosec: S106
dataset.load_or_generate_data()
for index in [5, 7, 9]:
image, target = dataset[index]
if len(image.shape) == 3:
image = image.squeeze(0)
print(image.sum(), image.dtype)
label = "".join(dataset.mapper(label) for label in target[1:]).strip(
dataset.mapper.pad_token
)
print(label)
image = image.numpy()
util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png"))
def test_evaluate(self):
predictor = LinePredictor(EmnistLinesDataset)
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
t = time()
metric = predictor.evaluate(dataset)
time_taken = time() - t
print(f'acc: {metric}, time_taken: {time_taken}')
self.assertGreater(metric, 0.6)
self.assertLess(time_taken, 60)
def create_emnist_lines_support_files():
shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
SUPPORT_DIRNAME.mkdir()
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
for ind in [0, 1, 3]:
image = dataset.x_test[ind]
print(image.sum(), image.dtype)
label = ''.join(dataset.mapping[label] for label in np.argmax(
dataset.y_test[ind], axis=-1).flatten()).strip(' _')
print(label)
util.write_image(image, str(SUPPORT_DIRNAME / f'{label}.png'))
def create_emnist_lines_support_files():
shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
SUPPORT_DIRNAME.mkdir()
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
for ind in [0, 1, 3]:
image = dataset.x_test[ind]
print(image.sum(), image.dtype)
label = "".join(dataset.mapping[label] for label in np.argmax(
dataset.y_test[ind], axis=-1).flatten()).strip(" _")
print(label)
util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png"))
# Hide lines below until Lab 8
# Inverted version
image = -(image - 255)
util.write_image(image, str(SUPPORT_DIRNAME / f"{label}_i.png"))
def test_evaluate(self):
# predictor = LinePredictor(IamLinesDataset)
# dataset = IamLinesDataset()
#instantiates the line predictor
predictor = LinePredictor(EmnistLinesDataset)
#load dataset
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
t = time()
#evaluate on the dataset
metric = predictor.evaluate(dataset)
time_taken = time() - t
print(f'acc: {metric}, time_taken: {time_taken}')
#threshold values for expected performance
self.assertGreater(metric, 0.8)
self.assertLess(time_taken, 60)
def test_evaluate_accuracy(self, set_leaderboard_value=None):
predictor = LinePredictor()
dataset = EmnistLinesDataset()
dataset.load_or_generate_data()
metric = predictor.evaluate(dataset)
set_leaderboard_value(metric)