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])