Measure neural network device specific metrics (latency, flops, etc.)
Project description
torchprof
Measure neural network device specific metrics.
Each nested module is run individually.
Quickstart
pip install torchprof
import torch
import torchvision
import torchprof
model = torchvision.models.alexnet(pretrained=False).cuda()
x = torch.rand([1, 3, 224, 224]).cuda()
observer = torchprof.LatencyObserver(model, use_cuda=True)
raw_measurements = observer.measure_latency(x)
print(raw_measurements[:3])
# [(['AlexNet'], (3836.7989999999986, 13197.66349029541)), (['AlexNet', 'features'], (3527.07, 14528.191928863525)), (['AlexNet', 'features', '0'], (223.438, 1080.1919765472412))]
print(observer)
Module | CPU Time | CUDA Time
---------------|-----------|----------
AlexNet | 3.837ms | 13.198ms
├── features | 3.527ms | 14.528ms
│ ├── 0 | 223.438us | 1.080ms
│ ├── 1 | 18.270us | 20.448us
│ ├── 2 | 29.030us | 52.224us
│ ├── 3 | 76.570us | 1.108ms
│ ├── 4 | 17.480us | 17.600us
│ ├── 5 | 28.150us | 51.008us
│ ├── 6 | 83.519us | 475.840us
│ ├── 7 | 17.820us | 18.432us
│ ├── 8 | 83.370us | 541.664us
│ ├── 9 | 17.590us | 18.432us
│ ├── 10 | 82.769us | 425.920us
│ ├── 11 | 17.260us | 18.272us
│ └── 12 | 28.160us | 49.280us
├── avgpool | 28.130us | 54.272us
└── classifier | 187.109us | 716.000us
├── 0 | 29.179us | 52.992us
├── 1 | 37.800us | 419.904us
├── 2 | 17.319us | 17.536us
├── 3 | 28.860us | 52.096us
├── 4 | 37.629us | 202.752us
├── 5 | 17.270us | 17.408us
└── 6 | 37.520us | 75.648us
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