Hi,can you tell how to test?

[louxiantuo]

When I do img = open_image("older.jpg") test = learn.predict(img) test ....... I find test is following....

(ImageSegment (1, 458, 690), tensor([[[2, 2, 2, ..., 2, 2, 2], [2, 2, 2, ..., 2, 2, 2], [2, 2, 2, ..., 2, 2, 2], ..., [2, 2, 2, ..., 2, 2, 2], [2, 2, 2, ..., 2, 2, 2], [2, 2, 2, ..., 2, 2, 2]]]), tensor([[[3.6979e-06, 7.3119e-08, 2.9660e-09, ..., 4.1485e-08, 1.9896e-06, 1.1032e-05], [6.5727e-07, 6.1509e-09, 1.5598e-10, ..., 3.4065e-09, 1.8370e-07, 1.5965e-06], [1.7654e-08, 9.9346e-11, 3.1559e-11, ..., 1.6351e-08, 1.3358e-07, 5.9525e-07], ..., [3.6530e-07, 3.0111e-09, 4.1133e-11, ..., 1.8417e-08, 7.4627e-07, 2.5805e-05], [2.8213e-05, 7.2198e-07, 1.0170e-08, ..., 2.7274e-07, 6.1814e-06, 1.0532e-04], [3.7413e-04, 7.2257e-05, 1.4296e-06, ..., 1.4689e-05, 2.6787e-04, 1.0736e-03]],

     [[1.3330e-02, 2.3582e-02, 4.0389e-02,  ..., 2.3371e-02,
       2.7196e-02, 2.3291e-02],
      [1.0827e-02, 1.5447e-02, 8.1077e-02,  ..., 3.5533e-02,
       3.7849e-02, 3.3390e-02],
      [1.3397e-03, 4.4763e-03, 2.5936e-02,  ..., 1.4551e-02,
       9.3334e-03, 3.6815e-03],
      ...,
      [1.2707e-03, 4.3078e-03, 1.4861e-02,  ..., 1.7563e-02,
       3.0178e-02, 3.0226e-02],
      [6.0737e-03, 1.4402e-02, 9.5362e-03,  ..., 2.7181e-02,
       1.8623e-02, 3.7493e-02],
      [8.2562e-03, 1.3454e-02, 4.6989e-03,  ..., 2.0105e-02,
       2.3377e-02, 4.2189e-02]],

     [[9.8470e-01, 9.7550e-01, 9.5956e-01,  ..., 9.7643e-01,
       9.7117e-01, 9.7075e-01],
      [9.8890e-01, 9.8449e-01, 9.1892e-01,  ..., 9.6443e-01,
       9.6194e-01, 9.6518e-01],
      [9.9864e-01, 9.9552e-01, 9.7406e-01,  ..., 9.8543e-01,
       9.9060e-01, 9.9610e-01],
      ...,
      [9.9871e-01, 9.9569e-01, 9.8514e-01,  ..., 9.8242e-01,
       9.6963e-01, 9.6897e-01],
      [9.9356e-01, 9.8551e-01, 9.9045e-01,  ..., 9.7274e-01,
       9.8039e-01, 9.5627e-01],
      [9.9020e-01, 9.8621e-01, 9.9527e-01,  ..., 9.7975e-01,
       9.7424e-01, 9.4556e-01]],

     [[1.8751e-03, 9.1332e-04, 4.7539e-05,  ..., 1.9073e-04,
       1.4394e-03, 5.3749e-03],
      [2.3538e-04, 5.7913e-05, 5.1389e-06,  ..., 3.6389e-05,
       1.8035e-04, 1.3502e-03],
      [1.7660e-05, 6.6649e-06, 2.9371e-07,  ..., 1.6607e-05,
       6.2467e-05, 2.0719e-04],
      ...,
      [1.5543e-05, 4.8209e-06, 5.8299e-07,  ..., 2.0024e-05,
       1.8108e-04, 7.3783e-04],
      [2.4462e-04, 8.5649e-05, 1.0901e-05,  ..., 6.9705e-05,
       9.5447e-04, 5.8892e-03],
      [8.6777e-04, 2.2145e-04, 2.7193e-05,  ..., 1.0620e-04,
       1.8858e-03, 9.8557e-03]],

     [[9.2091e-05, 8.9558e-06, 1.8392e-06,  ..., 1.1357e-05,
       1.8704e-04, 5.5892e-04],
      [3.8761e-05, 1.4357e-06, 4.5802e-07,  ..., 1.6239e-06,
       2.5324e-05, 7.5669e-05],
      [5.3265e-07, 1.2561e-08, 1.0463e-08,  ..., 6.6597e-07,
       2.7711e-06, 6.5525e-06],
      ...,
      [2.6031e-06, 1.4770e-07, 1.8590e-08,  ..., 9.2297e-07,
       6.5323e-06, 3.7649e-05],
      [9.2035e-05, 6.4348e-06, 3.9746e-07,  ..., 4.3601e-06,
       2.7216e-05, 2.2773e-04],
      [2.9929e-04, 4.3456e-05, 1.8736e-06,  ..., 1.9836e-05,
       2.1583e-04, 1.2487e-03]],

     [[1.3169e-07, 7.0419e-09, 3.4694e-10,  ..., 1.4720e-08,
       1.6918e-06, 1.8847e-05],
      [9.6758e-09, 1.5801e-10, 1.2171e-11,  ..., 1.8872e-09,
       1.6208e-07, 3.4754e-06],
      [9.9405e-12, 1.7170e-13, 3.2716e-14,  ..., 3.7634e-10,
       7.7533e-09, 6.0567e-08],
      ...,
      [2.2491e-09, 2.5714e-11, 3.8911e-13,  ..., 4.8573e-10,
       1.7509e-08, 4.1398e-07],
      [3.0080e-07, 9.7822e-09, 2.5110e-10,  ..., 1.6623e-08,
       5.9903e-07, 1.4452e-05],
      [2.6021e-06, 3.0973e-07, 8.1212e-09,  ..., 2.3559e-07,
       1.3945e-05, 7.0989e-05]]]))