I’m not sure I would do it. Here’s the main part of the code using instance net:
from rcia_tools.helpers.for_torch import UNet, predict_bscans, torch
net = UNet(n_channels=1, n_classes=1)
for group_id in base.groups:
for idx in range(base.size[group_id]):
mask_probabilities = predict_bscans(net, np.array(bscans), device, batch_num=n_size)
def __init__(self, n_channels, n_classes, bilinear=True):
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.up1 = Up(1024, 512, bilinear)
self.up2 = Up(512, 256, bilinear)
self.up3 = Up(256, 128, bilinear)
self.up4 = Up(128, 64 * factor, bilinear)
self.outc = OutConv(64, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
"""(convolution => [BN] => ReLU) * 2"""
"""Downscaling with maxpool then double conv"""
"""Upscaling then double conv"""
def predict_bscans(unet, bscans, device, batch_num=2):
"""Tiles and Segments Bscan"""
This is essentially all the PyTorch part in my code. I'm just wondering how I could apply `torch.compile()` to check if I can benefit from it. Or any other particular optimisations.
And that worked. However, I can’t really say if it got faster. I don’t have a proper benchmark and I’m using a busy platform (shared with other colleagues). That said, I did a few runs with and without compile and It seems to save like 10 to 30s: ~120s (compiled), ~150s (not compiled). But it may saving more on the long run.