Issue description
Trying to compare and transfer models between Caffe, TF and Pytorch found difference in output of bilinear interpolations between all. Caffe is using depthwise transposed convolutions instead of straightforward resize, so it's easy to reimplement both in TF and Pytorch.
However, there is difference between output for TF and Pytorch with align_corners=False, which is default for both.
Code example
img = cv2.resize(cv2.imread('./lenna.png')[:, :, ::-1], (256, 256))
img = img.reshape(1, 256, 256, 3).astype('float32') / 255.
img = tf.convert_to_tensor(img)
output_size = [512, 512]
output = tf.image.resize_bilinear(img, output_size, align_corners=True)
with tf.Session() as sess:
values = sess.run([output])
out_tf = values[0].astype('float32')[0]
img = img.reshape(1, 256, 256, 3).transpose(0, 3, 1, 2).astype('float32') / 255.
out_pt = nn.functional.interpolate(torch.from_numpy(nimg),
scale_factor=2,
mode='bilinear',
align_corners=True)
out_pt = out_pt.data.numpy().transpose(0, 2, 3, 1)[0]
print(np.max(np.abs(out_pt - out_tf)))
# output 5.6624413e-06But
img = cv2.resize(cv2.imread('./lenna.png')[:, :, ::-1], (256, 256))
img = img.reshape(1, 256, 256, 3).astype('float32') / 255.
img = tf.convert_to_tensor(img)
output_size = [512, 512]
output = tf.image.resize_bilinear(img, output_size, align_corners=False)
with tf.Session() as sess:
values = sess.run([output])
out_tf = values[0].astype('float32')[0]
img = img.reshape(1, 256, 256, 3).transpose(0, 3, 1, 2).astype('float32') / 255.
out_pt = nn.functional.interpolate(torch.from_numpy(nimg),
scale_factor=2,
mode='bilinear',
align_corners=False)
out_pt = out_pt.data.numpy().transpose(0, 2, 3, 1)[0]
print(np.max(np.abs(out_pt - out_tf)))
# output 0.22745097Output diff * 10:
Output of CoreML is consistent with TF, so it seems that there is a bug with implementation of bilinear interpolation with align_corners=False in Pytorch.
Diff is reproducible both on cpu and cuda with cudnn 7.1, cuda 9.1.
Issue description
Trying to compare and transfer models between Caffe, TF and Pytorch found difference in output of bilinear interpolations between all. Caffe is using depthwise transposed convolutions instead of straightforward resize, so it's easy to reimplement both in TF and Pytorch.
However, there is difference between output for TF and Pytorch with
align_corners=False, which is default for both.Code example
But
Output diff * 10:
Output of CoreML is consistent with TF, so it seems that there is a bug with implementation of bilinear interpolation with
align_corners=Falsein Pytorch.Diff is reproducible both on cpu and cuda with cudnn 7.1, cuda 9.1.