(img, size=128)
| 107 | |
| 108 | def test(self): |
| 109 | def crop_concat(img, size=128): |
| 110 | shape = img.shape |
| 111 | correct_shape = (size*(shape[2]//size+1), size*(shape[3]//size+1)) |
| 112 | one = torch.ones((shape[0], shape[1], correct_shape[0], correct_shape[1])) |
| 113 | one[:, :, :shape[2], :shape[3]] = img |
| 114 | # crop |
| 115 | for i in range(shape[2]//size+1): |
| 116 | for j in range(shape[3]//size+1): |
| 117 | if i == 0 and j == 0: |
| 118 | crop = one[:, :, i*size:(i+1)*size, j*size:(j+1)*size] |
| 119 | else: |
| 120 | crop = torch.cat((crop, one[:, :, i*size:(i+1)*size, j*size:(j+1)*size]), dim=0) |
| 121 | return crop |
| 122 | def crop_concat_back(img, prediction, size=128): |
| 123 | shape = img.shape |
| 124 | for i in range(shape[2]//size+1): |
nothing calls this directly
no outgoing calls
no test coverage detected