问题描述
我发现这两个 API 之间明显不同（tf.image.crop_and_resize 与 torchvision.ops.roi_align）。我在这里提供示例：
import numpy as np
b = np.arange(100*100).reshape(1,100,1)
Boxes = [12,30,42,53]
x1 = Boxes[0] / (100-1)
x2 = Boxes[2] / (100-1)
y1 = Boxes[1] / (100-1)
y2 = Boxes[3] / (100-1)
import tensorflow as tf
res = sess.run(tf.image.crop_and_resize(b,[[y1,x1,y2,x2]],[0],[7,7])).transpose([0,3,1,2])
# transpose is for comparison with the result of pytorch
print("tensorflow:",res)
# pytorch 1.7
from torchvision.ops import roi_pool,roi_align
res1 = roi_align(torch.from_numpy(b.transpose([0,2]).astype(np.float32)),torch.from_numpy(np.asarray([[0,Boxes[0],Boxes[1],Boxes[2],Boxes[3]]]).astype(np.float32)),output_size=(7,7))
print(res1)
两个结果是：
tf：
[[[[3012. 3017. 3022. 3027. 3032. 3037.
3042. ]
[3395.3333 3400.3333 3405.3333 3410.3333 3415.3333 3420.3333
3425.3333]
[3778.6667 3783.6667 3788.6667 3793.6667 3798.6667 3803.6667
3808.6667]
[4162. 4167. 4172. 4177. 4182. 4187.
4192. ]
[4545.333 4550.333 4555.333 4560.333 4565.333 4570.333
4575.333 ]
[4928.6665 4933.6665 4938.6665 4943.6665 4948.6665 4953.6665
4958.6665]
[5312. 5317. 5322. 5327. 5332. 5337.
5342. ]]]]
pytorch：
tensor([[[[3178.4287,3182.7141,3187.0000,3191.2856,3195.5713,3199.8569,3204.1428],[3507.0000,3511.2856,3515.5710,3519.8569,3524.1426,3528.4285,3532.7141],[3835.5715,3839.8579,3844.1426,3848.4285,3852.7144,3857.0000,3861.2859],[4164.1426,4168.4287,4172.7139,4177.0000,4181.2856,4185.5713,4189.8574],[4492.7139,4497.0000,4501.2856,4505.5713,4509.8569,4514.1426,4518.4287],[4821.2861,4825.5718,4829.8574,4834.1431,4838.4287,4842.7144,4847.0000],[5149.8574,5154.1421,5158.4292,5162.7144,5167.0005,5171.2856,5175.5713]]]])
结果真的不一样！
有人知道原因吗？提前致谢。
解决方法
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