ziplab / qtool Goto Github PK
View Code? Open in Web Editor NEWCollections of model quantization algorithms. Any issues, please contact Peng Chen ([email protected])
License: Other
Collections of model quantization algorithms. Any issues, please contact Peng Chen ([email protected])
License: Other
I want to use the part of super resolution. When download the quantization version of EDSR-PyTorch project. i get:
fatal: repository 'https://github.com/blueardour/EDSR-PyTorch/' not found
I think i find a bug in model-quantization/task_cls.py: you shold add import utils
or it will caused an NameError
when i try to import my own pretrained model.
detectron2 Quantization 404. 链接有更新了吗?
hi, when i try to train a quant model using configdetectron2/configs/COCO-Detection/retinanet_R_18_FPN_1x-Full-SyncBN-lsq-2bit.yaml
, and the loss became nan
at iterations 390
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/zhangjinhe/anaconda3/envs/torch/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/launch.py", line 125, in _distributed_worker main_func(*args)
File "/home/zhangjinhe/QTools/git/detectron2/tools/train_net.py", line 154, in main
return trainer.train()
File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/defaults.py", line 489, in train super().train(self.start_iter, self.max_iter)
File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/train_loop.py", line 149, in train self.run_step() File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/defaults.py", line 499, in run_step self._trainer.run_step() File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/train_loop.py", line 289, in run_step self._write_metrics(loss_dict, data_time) File "/home/zhangjinhe/QTools/git/detectron2/detectron2/engine/train_loop.py", line 332, in _write_metrics
f"Loss became infinite or NaN at iteration={self.iter}!\n"
FloatingPointError: Loss became infinite or NaN at iteration=390!
The commang i use is python tools/train_net.py --config-file configs/COCO-Detection/retinanet_R_18_FPN_1x-Full-SyncBN-lsq-2bit.yaml --num-gpus 4 MODEL.WEIGHTS output/coco-detection/retinanet_R_18_FPN_1x-Full_BN/model_final.pth
I change the input_size from (640, 672, 704, 736, 768, 800)
to (800,)
and the checkpoint file is the result of another experiment using config retinanet_R_18_FPN_1x-Full-BN.yaml
Any ideas why?
Thanks for your great paper on SR quantization. I have one problem about the method:
DAIA, Is there any other difference from LSQ expcept your first warm-up to initilize the step size?
or did you make specification of LSQ for SR task, thus you get your Distribution-Aware Interval Adaptation?
I haven’t found the ADQ method related code in the project. Haven’t I uploaded it yet?
Rebasing the repo:
Import issuses from old url:
ShechemKS:
After reading the paper "AQD: Towards Accurate Quantized Object Detection", I have been using this repo to quantize an object detector. After reading the code, I realized that the biases of the convolutions (if it has biases) and batch normalization is not quantized. However, the paper "AQD: Towards Accurate Quantized Object Detection" states
We propose an Accurate Quantized object Detection (AQD) method to fully get rid of floating-point computation in each layer of the network, including convolutional layers, normalization layers and skip connections.
Specifically, I cannot find the code that corresponds to the equations given in section 3.2.2 of the paper. Am I missing something? How does that work in the code? Am I not using the correct keywords? (I have used the default ones provided: keyword: ["debug", "dorefa", "lsq"]). The biases don't seem to be quantized either.
Additionally, in the default configurations, the weights are quantized using the adaptive mode var-mean (i.e. the weights are normalized before being quantized, to my understanding). Is this also part of the method adopted in the paper, or should I disable this if I am to replicate those results?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.