Method crop_concat_back

src/trainer.py:122–134

(img, prediction, size=128)

                        crop = torch.cat((crop, one[:, :, i*size:(i+1)*size, j*size:(j+1)*size]), dim=0)
            return crop
        def crop_concat_back(img, prediction, size=128):
            shape = img.shape
            for i in range(shape[2]//size+1):
                for j in range(shape[3]//size+1):
                    if j == 0:
                        crop = prediction[(i*(shape[3]//size+1)+j)*shape[0]:(i*(shape[3]//size+1)+j+1)*shape[0], :, :, :]
                    else:
                        crop = torch.cat((crop, prediction[(i*(shape[3]//size+1)+j)*shape[0]:(i*(shape[3]//size+1)+j+1)*shape[0], :, :, :]), dim=3)
                if i == 0:
                    crop_concat = crop
                else:
                    crop_concat = torch.cat((crop_concat, crop), dim=2)
            return crop_concat[:, :, :shape[2], :shape[3]]

        def min_max(array):
            return (array - array.min()) / (array.max() - array.min())