EfficientNetV2 implementation using PyTorch
imagenetpath by changingdata_dirinmain.pybash ./main.sh $ --trainfor training model,$is number of GPUsEfficientNetclass innets/nn.pyfor different versions
- the default training configuration is for
EfficientNetV2-S
python main.py --benchmark
Number of parameters: 21458488
Time per operator type:
1504.95 ms. 80.5982%. Conv
225.509 ms. 12.0772%. Sigmoid
115.112 ms. 6.1649%. Mul
12.7341 ms. 0.681982%. Add
7.50523 ms. 0.401946%. AveragePool
1.40185 ms. 0.0750768%. FC
0.0112697 ms. 0.000603555%. Flatten
1867.22 ms in Total
FLOP per operator type:
16.7287 GFLOP. 99.708%. Conv
0.0412707 GFLOP. 0.245986%. Mul
0.00516096 GFLOP. 0.0307609%. Add
0.002561 GFLOP. 0.0152643%. FC
16.7777 GFLOP in Total
Feature Memory Read per operator type:
291.409 MB. 51.8224%. Mul
224.497 MB. 39.9231%. Conv
41.2877 MB. 7.34234%. Add
5.12912 MB. 0.912131%. FC
562.323 MB in Total
Feature Memory Written per operator type:
165.083 MB. 50.2087%. Mul
143.062 MB. 43.5114%. Conv
20.6438 MB. 6.27867%. Add
0.004 MB. 0.00121657%. FC
328.793 MB in Total
Parameter Memory per operator type:
79.9537 MB. 93.9773%. Conv
5.124 MB. 6.02273%. FC
0 MB. 0%. Add
0 MB. 0%. Mul
85.0777 MB in Total
python main.py --testfor trained model testing
| name | resolution | acc@1 | acc@5 | #params | FLOPS | resample | training loss |
|---|---|---|---|---|---|---|---|
| EfficientNetV2-S | 384x384 | 83.9 | 96.7 | 21.46 | 16.7777 | BILINEAR | CrossEntropy |
| EfficientNetV2-S | 384x384 | - | - | 21.46 | 16.7777 | BILINEAR | PolyLoss |
| EfficientNetV2-M | - | - | - | - | - | - | - |
| EfficientNetV2-L | - | - | - | - | - | - | - |