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https://github.com/mfigurnov/sact
Spatially Adaptive Computation Time for Residual Networks
https://github.com/mfigurnov/sact
tensorflow
Last synced: about 1 month ago
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Spatially Adaptive Computation Time for Residual Networks
- Host: GitHub
- URL: https://github.com/mfigurnov/sact
- Owner: mfigurnov
- License: apache-2.0
- Created: 2017-03-23T15:06:00.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T22:20:45.000Z (over 1 year ago)
- Last Synced: 2024-05-13T09:32:56.723Z (4 months ago)
- Topics: tensorflow
- Language: Python
- Size: 923 KB
- Stars: 245
- Watchers: 13
- Forks: 55
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Spatially Adaptive Computation Time for Residual Networks
This code implements a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
The architecture is end-to-end trainable, deterministic and problem-agnostic.
The included code applies this to the CIFAR-10 an ImageNet image classification problems.
It is implemented using TensorFlow and TF-Slim.
Paper describing the project:
Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov. Spatially Adaptive Computation Time for Residual Networks. *CVPR 2017* [[arxiv]](https://arxiv.org/abs/1612.02297).
Image (with detections) | Ponder cost map
:-------------------------------:|:--------------------------------------:
|
## Setup
Install prerequisites:
``` bash
pip install -r requirements.txt # CPU
pip install -r requirements-gpu.txt # GPU
```
Prerequisite packages:
- Python 2.x/3.x (mostly tested with Python 2.7)
- Tensorflow 1.0
- NumPy
- (Optional) nose
- (Optional) h5py
- (Optional) matplotlib
Run tests. It takes a couple of minutes:
``` bash
nosetests --logging-level=WARNING
```
## CIFAR-10
Download and convert CIFAR-10 dataset:
``` bash
PYTHONPATH=external python external/download_and_convert_cifar10.py --dataset_dir="${HOME}/tensorflow/data/cifar10"
```
Let's train and continuously evaluate a CIFAR-10 Adaptive Computation Time model with five residual units per block (ResNet-32):
``` bash
export ACT_LOGDIR='/tmp/cifar10_resnet_5_act_1e-2'
python cifar_main.py --model_type=act --model=5 --tau=0.01 --train_log_dir="${ACT_LOGDIR}/train" --save_summaries_secs=300 &
python cifar_main.py --model_type=act --model=5 --tau=0.01 --checkpoint_dir="${ACT_LOGDIR}/train" --eval_dir="${ACT_LOGDIR}/eval" --mode=eval
```
Or, for _spatially_ adaptive computation time (SACT):
``` bash
export SACT_LOGDIR='/tmp/cifar10_resnet_5_sact_1e-2'
python cifar_main.py --model_type=sact --model=5 --tau=0.01 --train_log_dir="${SACT_LOGDIR}/train" --save_summaries_secs=300 &
python cifar_main.py --model_type=sact --model=5 --tau=0.01 --checkpoint_dir="${SACT_LOGDIR}/train" --eval_dir="${SACT_LOGDIR}/eval" --mode=eval
```
To download and evaluate a [pretrained ResNet-32 SACT model](https://s3.us-east-2.amazonaws.com/sact-models/cifar10_resnet_5_sact_1e-2.tar.gz) (1.8 MB file):
``` bash
mkdir -p models && curl https://s3.us-east-2.amazonaws.com/sact-models/cifar10_resnet_5_sact_1e-2.tar.gz | tar xv -C models
python cifar_main.py --model_type=sact --model=5 --tau=0.01 --checkpoint_dir='models/cifar10_resnet_5_sact_1e-2' --mode=eval --eval_dir='/tmp' --evaluate_once
```
This model is expected to achieve an accuracy of 91.82%, with the output looking like so:
```
eval/Accuracy[0.9182]
eval/Mean Loss[0.59591407]
Total Flops/mean[82393168]
Total Flops/std[7588926]
...
```
## ImageNet
Follow the [instructions](https://github.com/tensorflow/models/tree/master/research/inception#getting-started) to prepare the ImageNet dataset in TF-Slim format.
The default directory for the dataset is `~/tensorflow/imagenet`.
You can change it with the `--dataset_dir` flag.
We initialized all ACT/SACT models with a [pretrained ResNet-101 model](https://s3.us-east-2.amazonaws.com/sact-models/imagenet_101.tar.gz) (159MB file).
Download [pretrained ResNet-101 SACT model](https://s3.us-east-2.amazonaws.com/sact-models/imagenet_101_sact_5e-3.tar.gz), trained with tau=0.005 (160 MB file):
``` bash
mkdir -p models && curl https://s3.us-east-2.amazonaws.com/sact-models/imagenet_101_sact_5e-3.tar.gz | tar xv -C models
```
Evaluate the pretrained model
``` bash
python imagenet_eval.py --model_type=sact --model=101 --tau=0.005 --checkpoint_dir=models/imagenet_101_sact_5e-3 --eval_dir=/tmp --evaluate_once
```
Expected output:
```
eval/Accuracy[0.75609803]
eval/Recall@5[0.9274632117722329]
Total Flops/mean[1.1100941e+10]
Total Flops/std[4.5691142e+08]
...
```
Note that evaluation on the full validation dataset will take some time using only CPU.
Add the arguments `--num_examples=10 --batch_size=10` for a quicker test.
Draw some images from ImageNet validation set and the corresponding ponder cost maps:
``` bash
python imagenet_export.py --model_type=sact --model=101 --tau=0.005 --checkpoint_dir=models/imagenet_101_sact_5e-3 --export_path=/tmp/maps.h5 --batch_size=1 --num_examples=200
mkdir /tmp/maps
python draw_ponder_maps.py --input_file=/tmp/maps.h5 --output_dir=/tmp/maps
```
Example visualizations. See Figure 9 of the paper for more
Image | Ponder cost map
:-------------------------:|:----------------------------:
 | 
 | 
 | 
Apply the pretrained model to your own jpeg images.
For best results, first resize them to somewhere between 320x240 and 640x480.
``` bash
python2 imagenet_ponder_map.py --model=101 --checkpoint_dir=models/imagenet_101_sact_5e-3 --images_pattern=pics/gasworks.jpg --output_dir output/
```
Image | Ponder cost map | Colorbar
:--------------------:|:------------------------------:|---------
|  | 
 |  | 
Note that an ImageNet-pretrained model tends to ignore people - there is no "person" class in ImageNet!
## Disclaimer
This is not an official Google product.