wuxun-zhang commented on issue #17884:
URL: https://github.com/apache/incubator-mxnet/pull/17884#issuecomment-616912500
**Performance numbers for Conv3d op:**
shape | w/o mkldnn | w/mkldnn |
-- | -- | -- |
(3, 3, 16, 224, 224) | 3.696679 sec | 0.046571 sec|
(3, 3, 128, 128, 128) | 11.716535 sec | 0.165749 sec|
**Test script:**
```
import mxnet as mx
from mxnet import nd, gluon
import time
data_shape = [(3, 3, 16, 224, 224), (3, 3, 128, 128, 128)]
for shape in data_shape:
data = mx.random.uniform(shape=shape)
weight_shape = (32, shape[1], 3, 3, 3)
weight = mx.nd.ones(shape=weight_shape)
num_iter = 10
dry_run = 5
for i in range(num_iter):
if i == dry_run:
tic = time.time()
out = mx.nd.Convolution(data, weight, kernel=(3,3,3),
stride=(1,1,1), num_filter=weight_shape[0], pad=(0,0,0), dilate=(2,2,2),
no_bias=True, cudnn_off=True, name='conv3d')
out.asnumpy()
print("For shape : {}".format(shape))
print("Average time cost is %f sec" % ((time.time() -
tic)/(num_iter-dry_run))
```
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