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https://github.com/chenxi116/PNASNet.pytorch
PyTorch implementation of PNASNet-5 on ImageNet
https://github.com/chenxi116/PNASNet.pytorch
automl deep-learning imagenet neural-architecture-search pytorch
Last synced: about 1 month ago
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PyTorch implementation of PNASNet-5 on ImageNet
- Host: GitHub
- URL: https://github.com/chenxi116/PNASNet.pytorch
- Owner: chenxi116
- License: apache-2.0
- Created: 2018-07-08T20:26:13.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-08-04T20:12:17.000Z (over 2 years ago)
- Last Synced: 2024-08-01T22:50:07.321Z (4 months ago)
- Topics: automl, deep-learning, imagenet, neural-architecture-search, pytorch
- Language: Python
- Homepage:
- Size: 147 KB
- Stars: 315
- Watchers: 14
- Forks: 44
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-pytorch : https://github.com/chenxi116/PNASNet.pytorch
- awesome-image-classification - unofficial-pytorch : https://github.com/chenxi116/PNASNet.pytorch
- awesome-AutoML-and-Lightweight-Models - chenxi116/PNASNet.pytorch
README
# PNASNet.pytorch
PyTorch implementation of [PNASNet-5](https://arxiv.org/1712.00559). Specifically, PyTorch code from [this repository](https://github.com/quark0/darts) is adapted to completely match both [my implemetation](https://github.com/chenxi116/PNASNet.TF) and the [official implementation](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py) of PNASNet-5, both written in TensorFlow. This complete match allows the pretrained TF model to be exactly converted to PyTorch: see `convert.py`.
If you use the code, please cite:
```bash
@inproceedings{liu2018progressive,
author = {Chenxi Liu and
Barret Zoph and
Maxim Neumann and
Jonathon Shlens and
Wei Hua and
Li{-}Jia Li and
Li Fei{-}Fei and
Alan L. Yuille and
Jonathan Huang and
Kevin Murphy},
title = {Progressive Neural Architecture Search},
booktitle = {European Conference on Computer Vision},
year = {2018}
}
```
## Requirements
- TensorFlow 1.8.0 (for image preprocessing)
- PyTorch 0.4.0
- torchvision 0.2.1
## Data and Model Preparation
- Download the ImageNet validation set and move images to labeled subfolders. To do the latter, you can use [this script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh). Make sure the folder `val` is under `data/`.
- Download [PNASNet.TF](https://github.com/chenxi116/PNASNet.TF) and follow its README to download the `PNASNet-5_Large_331` pretrained model.
- Convert TensorFlow model to PyTorch model:
```bash
python convert.py
```
## Notes on Model Conversion
- In both TensorFlow implementations, `net[0]` means `prev` and `net[1]` means `prev_prev`. However, in the [PyTorch implementation](https://github.com/quark0/darts), `states[0]` means `prev_prev` and `states[1]` means `prev`. I followed the PyTorch implemetation in this repository. This is why the 0 and 1 in PNASCell specification are reversed.
- The default value of `eps` in BatchNorm layers is `1e-3` in TensorFlow and `1e-5` in PyTorch. I changed all BatchNorm `eps` values to `1e-3` (see `operations.py`) to exactly match the TensorFlow pretrained model.
- The TensorFlow pretrained model uses `tf.image.resize_bilinear` to resize the image (see `utils.py`). I cannot find a python function that exactly matches this function's behavior (also see [this thread](https://github.com/tensorflow/tensorflow/issues/6720) and [this post](https://hackernoon.com/how-tensorflows-tf-image-resize-stole-60-days-of-my-life-aba5eb093f35) on this topic), so currently in `main.py` I call TensorFlow to do the image preprocessing, in order to guarantee both models have the identical input.
- When converting the model from TensorFlow to PyTorch (i.e. `convert.py`), I use input image size of 323 instead of 331. This is because the 'SAME' padding in TensorFlow may differ from padding in PyTorch in some layers (see [this link](https://stackoverflow.com/questions/37674306/what-is-the-difference-between-same-and-valid-padding-in-tf-nn-max-pool-of-t); basically TF may only pad 1 right and bottom, whereas PyTorch always pads 1 for all four margins). However, they behave exactly the same when image size is 323: `conv0` does not have padding, so feature size becomes 161, then 81, 41, etc.
- The exact conversion when image size is 323 is also corroborated by the following table:
Image Size | Official TensorFlow Model | Converted PyTorch Model
--- | --- | ---
(331, 331) | (0.829, 0.962) | (0.828, 0.961)
(323, 323) | (0.827, 0.961) | (0.827, 0.961)
## Usage
```bash
python main.py
```
The last printed line should read:
```bash
Test: [50000/50000] Prec@1 0.828 Prec@5 0.961
```