chinakook commented on issue #17907: Depthwise in windows is 10 times slower
than linux on gpu
URL:
https://github.com/apache/incubator-mxnet/issues/17907#issuecomment-603938898
There is another version in windows, pytorch takes 2.0s while mxnet takes
10.2s. I think this is a bug for a long time.
MXNET version:
```python
import os
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
import time
import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from gluoncv.model_zoo import get_model
ctx = mx.gpu()
net = get_model('mobilenetv2_1.0', norm_layer=gluon.nn.BatchNorm)
net.initialize()
net.collect_params().reset_ctx(ctx)
s = time.time()
for i in range(50):
x = mx.nd.random.uniform(shape=(1,3,512,512), ctx=ctx)
t = time.time()
y = net(x)
mx.nd.waitall()
print(time.time() - t)
print('TOTAL TIME: ', time.time() - s)
```
```
0.2889983654022217
0.15599989891052246
0.14120268821716309
0.1549980640411377
0.14800024032592773
0.1549973487854004
0.1419973373413086
0.16100192070007324
0.15399909019470215
0.14299798011779785
0.1490001678466797
0.1779154968262
0.1530005931854248
0.14499974250793457
0.1569969654083252
0.15002942085266113
0.14699625968933105
0.14600133895874023
0.143998384475708
0.15400242805480957
0.1439976692199707
0.14451003074645996
0.16103625297546387
0.15851068496704102
0.15300440788269043
0.15399932861328125
0.15399956703186035
0.14400243759155273
0.15401935577392578
0.14500117301940918
0.14951753616333008
0.14799976348876953
0.1480190734863
0.15600085258483887
0.1529989242553711
0.14699888229370117
0.14899921417236328
0.1512279510498047
0.1525120735168457
0.1549992561340332
0.16200017929077148
0.1529998779296875
0.1510009765625
0.14804387092590332
0.1480190734863
0.15600061416625977
0.15230464935302734
0.15199899673461914
0.14699792861938477
0.1289997100830078
TOTAL TIME: 10.248228788375854
```
PYTORCH version:
```python
import time
import torch
import torchvision
torch.backends.cudnn.benchmark=False
net = torchvision.models.mobilenet_v2()
net.cuda()
net.eval()
s = time.time()
for i in range(50):
t = time.time()
x = torch.rand([1,3,512,512]).cuda()
y = net(x)
print(time.time() - t)
print('TOTAL TIME: ', time.time() - s)
```
```
0.9051487445831299
0.04097485542297363
0.019997835159301758
0.018999099731445312
0.023026704788208008
0.021998167037963867
0.020003795623779297
0.0209958553314209
0.020031213760375977
0.020966291427612305
0.01980926513672
0.022031784057617188
0.019968032836914062
0.023028850555419922
0.020004987716674805
0.01996612548828125
0.022998332977294922
0.020999431610107422
0.02000117301940918
0.019997119903564453
0.02300119400024414
0.02200031280517578
0.01899886131286621
0.0174250793457
0.021999835968017578
0.02000284194946289
0.02000141143798828
0.02000117301940918
0.02099919319152832
0.02457763671875
0.021001338958740234
0.020998477935791016
0.02219345092773
0.020998477935791016
0.022002458572387695
0.02502727508544922
0.02000284194946289
0.021997690200805664
0.021001100540161133
0.024999141693115234
0.020177154541
0.02599787712097168
0.02256134033203
0.02256134033203
0.02700185775756836
0.02520275115966797
0.02800154685974121
0.032999277114868164
0.02400040626525879
0.02900218963623047
TOTAL TIME: 2.065340518951416
```
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