def score(
self,
bundle,
bundle_size=32,
verbose=True,
):
loader = Loader(bundle)
data = loader.data_test(bundle_size)
averages_mse = 0.0
averages_psnr = 0.0
for i in range(0, len(data[0])):
image = numpy.array(data[0][i])
image *= 255
image = Image.fromarray(numpy.uint8(image))
image = image.convert('RGB')
image = image.resize((
int(image.size[0] * self._scale_factor),
int(image.size[1] * self._scale_factor),
), self._scale_type)
image = numpy.array(image)
image = image.astype('float32')
image /= 255
averages_mse += mse_np(data[1][i], image)
averages_psnr += psnr_np(data[1][i], image)
return [
averages_mse / len(data[0]),
averages_psnr / len(data[0]),
]
def test(
self,
bundle,
bundle_size=32,
verbose=True,
):
loader = Loader(bundle)
data = loader.data_test(bundle_size)
result = []
for image in data[0]:
image = numpy.array(image)
image *= 255
image = Image.fromarray(numpy.uint8(image))
image = image.convert('RGB')
image = image.resize((
int(image.size[0] * self._scale_factor),
int(image.size[1] * self._scale_factor),
), self._scale_type)
image = numpy.array(image)
image = image.astype('float32')
image /= 255
result.append(image)
keeper = Keeper(bundle, self.name())
for i in range(0, len(data[0])):
keeper.save(f'{bundle}-{i+1}', data[1][i], data[0][i], result[i])
def test(
self,
bundle,
bundle_size=32,
verbose=True,
):
loader = Loader(bundle)
data = loader.data_test(bundle_size)
result = self.model().predict(
data[0],
batch_size=bundle_size,
verbose=verbose,
)
keeper = Keeper(bundle, self.name())
for i in range(0, len(data[0])):
keeper.save(f'{bundle}-{i+1}', data[1][i], data[0][i], result[i])
