@@ -86,12 +86,10 @@ class BasicConvolutionBlock(nn.Module):
8686 def __init__ (self , inc , outc , ks = 3 , stride = 1 , dilation = 1 ):
8787 super ().__init__ ()
8888 self .net = nn.Sequential(
89- spnn.Conv3d(inc,
90- outc,
91- kernel_size = ks,
92- dilation = dilation,
93- stride = stride), spnn.BatchNorm(outc),
94- spnn.ReLU(True ))
89+ spnn.Conv3d(inc, outc, kernel_size = ks, dilation = dilation, stride = stride),
90+ spnn.BatchNorm(outc),
91+ spnn.ReLU(True )
92+ )
9593
9694 def forward (self , x ):
9795 out = self .net(x)
@@ -138,18 +136,14 @@ You are also welcomed to check out our [SPVNAS](https://github.com/mit-han-lab/e
138136
139137## Speed Comparison Between torchsparse and MinkowskiEngine
140138
141- We benchmark the performance of our torchsparse and latest [ MinkowskiEngine V0.4.3] ( https://github.com/NVIDIA/MinkowskiEngine ) here:
142-
143- | Library | Network | Latency (GTX1080Ti) |
144- | :--------------------: | :----------------------: | :-----------------: |
145- | MinkowskiEngine V0.4.3 | MinkUNet18C (MACs / 10) | 224.7 |
146- | MinkowskiEngine V0.4.3 | MinkUNet18C (MACs / 4) | 244.3 |
147- | MinkowskiEngine V0.4.3 | MinkUNet18C (MACs / 2.5) | 269.6 |
148- | MinkowskiEngine V0.4.3 | MinkUNet18C | 323.5 |
149- | torchsparse V1.0.0 | MinkUNet18C (MACs / 10) | 124.3 |
150- | torchsparse V1.0.0 | MinkUNet18C (MACs / 4) | 160.9 |
151- | torchsparse V1.0.0 | MinkUNet18C (MACs / 2.5) | 214.3 |
152- | torchsparse V1.0.0 | MinkUNet18C | 294.0 |
139+ We benchmark the performance of our torchsparse and latest [ MinkowskiEngine V0.4.3] ( https://github.com/NVIDIA/MinkowskiEngine ) here, latency is measured on NVIDIA GTX 1080Ti GPU:
140+
141+ | Network | Latency (ME V0.4.3) | Latency (torchsparse V1.0.0) |
142+ | :----------------------: | :-----------------: | :--------------------------: |
143+ | MinkUNet18C (MACs / 10) | 224.7 | 124.3 |
144+ | MinkUNet18C (MACs / 4) | 244.3 | 160.9 |
145+ | MinkUNet18C (MACs / 2.5) | 269.6 | 214.3 |
146+ | MinkUNet18C | 323.5 | 294.0 |
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