vision VS onnx-simplifier

Let's start by defining the ResNet module according to the Residual Network architecture, as replicated[1] by the torchvision implementation of the model we will import. Detailed architecture variants with a depth of 18, 34, 50, 101 and 152 layers can be found in the table below.

The goal is to do keypoint-detection of fish (eg nose, tail etc) in a fishtank. By using a stereocamera for this, I'm also getting depth information which lets me measure the fish-length underwater. Im only training on RGB-Images though. I'm transfer-learning pytorch's keypoint-rcnn-resnet50, because thats the only available one in https://github.com/pytorch/vision/blob/main/torchvision/models/detection/keypoint_rcnn.py.

Found relevant code at https://github.com/pytorch/vision + all code implementations here

* Why doesnt Pytorch have YOLO! https://github.com/pytorch/vision/issues/6341

Sorry to be annoying but I thought it was nice to give you some news as well. I was confused as to why there isnt yolo in pytorch, here it is why https://github.com/pytorch/vision/issues/6341

The problem is the industry; People are still using .ckpt/.pth files to share weights, and unfortunately in their research work, they would need to reproduce the works of others. even pytorch include pretrained weights using pickles. https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py

My question is more related to the variance of the batchs. If one batch contains samples that skip a connection and samples that do not ('row' mode in the Torchvision implementation), even if the values are ajusted to preserve the expected value, the variance will be much higher because we have in practice two distributions (for x_n and x_n + f(x_n)/p), which will mess up with the update of the batch normalization. Also, at inference time, all forward passes will be done as x_{n+1} = x_n + f(x_n), which has a different variance. The torchvision implementation also offers a 'batch' mode that kinda reduce this issue (because the global variance computed this way will be the mean of both distribution variances, instead of the variance of the joint distribution) but it does not seem to be the default mode (it does not even exist in the timm implementation).

RuntimeError: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.version and your torchvision version with torchvision.version and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.

$ python vgg16-cifar10.py --device "cuda" torch 1.11.0+cu102 device cuda Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz 170499072it [00:46, 3628424.66it/s] Extracting data/cifar-10-python.tar.gz to data Downloading: "https://github.com/pytorch/vision/archive/v0.11.0.zip" to /home/md/.cache/torch/hub/v0.11.0.zip Epoch: 001/001 | Batch 0000/1406 | Loss: 2.6563 Epoch: 001/001 | Batch 0100/1406 | Loss: 2.4686 Epoch: 001/001 | Batch 0200/1406 | Loss: 2.1224 Epoch: 001/001 | Batch 0300/1406 | Loss: 2.1879 Epoch: 001/001 | Batch 0400/1406 | Loss: 2.1733 Epoch: 001/001 | Batch 0500/1406 | Loss: 2.2413 Epoch: 001/001 | Batch 0600/1406 | Loss: 2.0518 Epoch: 001/001 | Batch 0700/1406 | Loss: 2.1621 Epoch: 001/001 | Batch 0800/1406 | Loss: 1.9033 Epoch: 001/001 | Batch 0900/1406 | Loss: 1.8379 Epoch: 001/001 | Batch 1000/1406 | Loss: 1.9572 Epoch: 001/001 | Batch 1100/1406 | Loss: 1.8823 Epoch: 001/001 | Batch 1200/1406 | Loss: 1.7978 Epoch: 001/001 | Batch 1300/1406 | Loss: 2.0239 Epoch: 001/001 | Batch 1400/1406 | Loss: 1.8389 Time / epoch without evaluation: 6.75 min <------------------ Epoch: 001/001 | Train: 25.52% | Validation: 26.40% | Best Validation (Ep. 001): 26.40% Time elapsed: 9.03 min Total Training Time: 9.03 min Test accuracy 26.54% Total Time: 9.48 min

It is here in the source repository https://github.com/pytorch/vision/blob/main/torchvision/datasets/utils.py

onnx-simplifier can shed some of incompatibilities in widespread use, but is itself bug ridden and lagging behind the standard. For any serious model, or when you don't get lucky simplifying the model upstream, you'd generally want good support of opset 11.

ONNX Simplifier：https://github.com/daquexian/onnx-simplifier

As far as I know, the ONNX format won't give you a performance boost on its own. However, there are ONNX optimizers for the ONNX runtime which will speed up your inference.

But if you are using Nvidia Hardware, then TensorRT should give you the best performance possible, especially if you change the precision level. Don't forget to simplify your ONNX model before you converting it to TensorRT though: https://github.com/daquexian/onnx-simplifier