how to keep pytorch model in redis cache to access model faster for video streaming?

Question

I have this code belonging to feature_extractor.py which is a part of this folder in here:

import torch
import torchvision.transforms as transforms
import numpy as np
import cv2
from .model import Net

class Extractor(object):
    def __init__(self, model_path, use_cuda=True):
        self.net = Net(reid=True)
        self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
        state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict']
        self.net.load_state_dict(state_dict)
        print("Loading weights from {}... Done!".format(model_path))
        self.net.to(self.device)
        self.size = (64, 128)
        self.norm = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ])

    def _preprocess(self, im_crops):
        def _resize(im, size):
            return cv2.resize(im.astype(np.float32) / 255., size)

        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
        return im_batch

    def __call__(self, im_crops):
        im_batch = self._preprocess(im_crops)
        with torch.no_grad():
            im_batch = im_batch.to(self.device)
            features = self.net(im_batch)
        return features.cpu().numpy()


if __name__ == '__main__':
    img = cv2.imread("demo.jpg")[:, :, (2, 1, 0)]
    extr = Extractor("checkpoint/ckpt.t7")
    feature = extr(img)
    print(feature.shape)

Now Imagine 200 requests are in row to proceed. The process of loading model for each request makes the code run slowly.

So I thought it might be a good idea to keep the pytorch model in cache. I modified it like this:

from redis import Redis
import msgpack as msg

r = Redis('111.222.333.444')

class Extractor(object):
    def __init__(self, model_path, use_cuda=True):
        try:
            self.net = msg.unpackb(r.get('REID_CKPT'))
        finally:
            self.net = Net(reid=True)
            self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
            state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict']
            self.net.load_state_dict(state_dict)
            print("Loading weights from {}... Done!".format(model_path))
            self.net.to(self.device)
            packed_net = msg.packb(self.net)
            r.set('REID_CKPT', packed_net)

        self.size = (64, 128)
        self.norm = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ])

Unfortunately this error comes up:

 File "msgpack/_packer.pyx", line 286, in msgpack._cmsgpack.Packer.pack
 File "msgpack/_packer.pyx", line 292, in msgpack._cmsgpack.Packer.pack
 File "msgpack/_packer.pyx", line 289, in msgpack._cmsgpack.Packer.pack
 File "msgpack/_packer.pyx", line 283, in msgpack._cmsgpack.Packer._pack
 TypeError: can not serialize 'Net' object

The reason obviously is because that it cannot convert Net object (pytorch nn.Module class) to bytes.

How can I efficiently save pytorch model in cache (or somehow keep it in RAM) and call for it for each request?

Thanks everyone.

Answer 1

If you only need to keep model state on RAM, Redis is not necessary. You could instead mount RAM as a virtual disk and store model state there. Check out tmpfs.