Training becomes very slow with these transforms. about augraphy

[shivanference]

Thanks. The image size is around 900x1100.

[kwcckw]

Hi, could you include the snippet of code on how you using Augraphy in your training?

[shivanference]

Sure, following is the flow.

Importing transforms

from augraphy.augmentations import ShadowCast, ReflectedLight, Folding....

Creating a wrapper around it

class GenericTransforms:
    def __init__(self, transform, prob):
        self.transform = transform
        self.prob = prob

    def __call__(self, img):
        if torch.rand(1) < self.prob:
            return self.transform(img)
        return img
  
Folding_ = GenericTransforms(Folding(), prob=0.05)
ReflectedLight_ = GenericTransforms(ReflectedLight(), prob=0.1)
ShadowCast_ = GenericTransforms(ShadowCast(), prob=0.2)
# ....

Then I compose the transforms using torchvision.transforms

def _compose_transforms_from_config(transform_config):

    preprocessing_transforms = []
    transform_map = {'ReflectedLight': ReflectedLight_, 'Folding': Folding_, 'ShadowCast': ShadowCast_,...}
    for transform in transform_config:
        trans_type = transform_map[transform['Type']]
        transform_instance = trans_type(**transform['Kwargs'])
        preprocessing_transforms.append(transform_instance)
    preprocess_transforms = transforms.Compose(preprocessing_transforms)
    return preprocess_transforms

Then these transforms are used in dataset class.

def __getitem__(self, idx):
    img = self.read_image(idx)
    x = self.transforms(ecg)
    return x, self.targets[idx]

Note: I am using other 5-6 custom transforms in same manner. But as I include augraphy transforms, training becomes too slow with load average shooting up. But ram is under control. I am training on a 20 core machine.

Please let me know of any other information is required.

[kwcckw]

By looking at the benchmark results:
https://github.com/sparkfish/augraphy/tree/dev/benchmark

ReflectedLight is one of the slowest augmentation. I think you can try to remove that and see if the speed have increased? If speed is a concern, you may consider using only those augmentations with higher value of Img/sec.

[shivanference]

I did that, I only considered the augmentation, for which Img/sec was more than or around 1. But did not help much in speeding.

[kwcckw]

I did that, I only considered the augmentation, for which Img/sec was more than or around 1. But did not help much in speeding.

So probably you can let me know roughly your image size? Then i can try to reproduce this with the code above from my end too.

[shivanference]

I have narrowed down the issue.

As I mentioned, load average shoots up with these transforms. When I limit the threads used by Numpy and Opencv to 1, then the transforms run 5-6 times faster.

os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
cv2.setNumThreads(1)

But the thread limiting is somehow not working for the subprocesses. Hence fetching data from dataloader is still slow as it spawns multiple workers.

[kwcckw]

When I limit the threads used by Numpy and Opencv to 1, then the transforms run 5-6 times faster.

Okay, and looks like your provided code above is not complete, what would be this transform_config?

Then in x = self.transforms(ecg), preprocess_transforms is used?

In your 20 cores machine, you uses multi GPUs too?

[shivanference]

A minor correction in the code:

This is how the transforms are defined:

class Moire_:

    def __init__(self, prob=0.15):
        self.moire = Moire()
        self.prob = prob

    def __call__(self, img):
        if torch.rand(1) < self.prob:
            return self.moire(img)
        return img

This is how transform config look like:

- Type: ReflectedLight
  Kwargs: {}
- Type: DirtyDrum
  Kwargs: {}
- Type: Folding
  Kwargs: {}

We use 2 GPUs to train.

[kwcckw]

I tried with colab with image size of (1100,1100,3) but i only see an increase of 30% processing time with Augraphy, and that is with probability of 1 with all 3 augmentations.

Here's the notebooks:
https://drive.google.com/drive/folders/1kaUWqVY5xKhKzDJP2zyiDoyOWtgtpVQU?usp=sharing

Probably there's some overhead in the custom augmentation functions in multi gpu or multi cores. Have you try with other created augmentation function instead of Augraphy?