2a5cadaf934b4ca056b97c2bced8046171ceea8e,DCSCN.py,SuperResolution,load_datasets,#SuperResolution#Any#Any#Any#Any#Any#,147

Before Change

		if stride_size == 0:
			stride_size = batch_image_size // 2

		if self.bicubic_init:
			resampling_method = "bicubic"
		else:
			resampling_method = "nearest"

		datasets = loader.DataSets(self.scale, batch_image_size, stride_size, channels=self.channels,
		                           jpeg_mode=self.jpeg_mode, max_value=self.max_value, resampling_method=resampling_method)

		if not datasets.is_batch_exist(batch_dir):

After Change

			self.test = datasets

			for i in range(self.test.input.count):
				self.test.input.images[i] = util.scale(self.test.input.images[i], 0, 255, 0, self.input_max)
				self.test.input.quad_images[i] = util.scale(self.test.input.quad_images[i], 0, 255, self.output_min, self.output_max)
				self.test.true.quad_images[i] = util.scale(self.test.true.quad_images[i], 0, 255, self.output_min, self.output_max)

	def open_datasets(self, target, data_dir, batch_image_size, stride_size=0):

Instances