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 0.17000079154968262 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 0.14800000190734863 0.15600085258483887 0.1529989242553711 0.14699888229370117 0.14899921417236328 0.1512279510498047 0.1525120735168457 0.1549992561340332 0.16200017929077148 0.1529998779296875 0.1510009765625 0.14804387092590332 0.14800000190734863 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 0.019999980926513672 0.022031784057617188 0.019968032836914062 0.023028850555419922 0.020004987716674805 0.01996612548828125 0.022998332977294922 0.020999431610107422 0.02000117301940918 0.019997119903564453 0.02300119400024414 0.02200031280517578 0.01899886131286621 0.01999974250793457 0.021999835968017578 0.02000284194946289 0.02000141143798828 0.02000117301940918 0.02099919319152832 0.020000457763671875 0.021001338958740234 0.020998477935791016 0.020000219345092773 0.020998477935791016 0.022002458572387695 0.02502727508544922 0.02000284194946289 0.021997690200805664 0.021001100540161133 0.024999141693115234 0.0299990177154541 0.02599787712097168 0.029999256134033203 0.029999256134033203 0.02700185775756836 0.02520275115966797 0.02800154685974121 0.032999277114868164 0.02400040626525879 0.02900218963623047 TOTAL TIME: 2.065340518951416 ```
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