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update 10 NAS algs

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# Basic Classification Models
## Performance on CIFAR
| Model | FLOPs | Params (M) | Error on CIFAR-10 | Error on CIFAR-100 | Batch-GPU |
|:------------------:|:-----------:|:----------:|:-----------------:|:------------------:|:---------:|
| ResNet-08 | 12.50 M | 0.08 | 12.14 | 40.20 | 256-2 |
| ResNet-20 | 40.81 M | 0.27 | 7.26 | 31.38 | 256-2 |
| ResNet-32 | 69.12 M | 0.47 | 6.19 | 29.56 | 256-2 |
| ResNet-56 | 125.75 M | 0.86 | 5.74 | 26.82 | 256-2 |
| ResNet-110 | 253.15 M | 1.73 | 5.14 | 25.18 | 256-2 |
| ResNet-110 | 253.15 M | 1.73 | 5.06 | 25.49 | 256-1 |
| ResNet-164 | 247.65 M | 1.70 | 4.36 | 21.48 | 256-2 |
| ResNet-1001 | 1491.00 M | 10.33 | 5.34 | 22.50 | 256-2 |
| DenseNet-BC100-12 | 287.93 M | 0.77 | 4.68 | 22.76 | 256-2 |
| DenseNet-BC100-12 | 287.93 M | 0.77 | 4.25 | 21.54 | 128-2 |
| DenseNet-BC100-12 | 287.93 M | 0.77 | 5.51 | 24.67 | 64-1 |
| WRN-28-10 | 5243.33 M | 36.48 | 3.61 | 19.65 | 256-2 |
```
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 ResNet20 E300 L1 256 -1"
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 ResNet56 E300 L1 256 -1"
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 ResNet110 E300 L1 256 -1"
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 ResNet164 E300 L1 256 -1"
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 DenseBC100-12 E300 L1 256 -1"
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/base-train.sh cifar10 WRN28-10 E300 L1 256 -1"
CUDA_VISIBLE_DEVICES=0,1 python ./exps/basic-eval.py --data_path ${TORCH_HOME}/ILSVRC2012 --checkpoint
CUDA_VISIBLE_DEVICES=0,1 python ./exps/test-official-CNN.py --data_path ${TORCH_HOME}/ILSVRC2012
python ./scripts-cluster/submit.py yq01-v100-box-2-8 TEST-CIFAR10-1001 2 "bash ./scripts/base-train.sh cifar10 ResNet1001 E300 L1 256 1021"
```
Train some NAS models:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN1 256 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k DARTS 256 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
```
## Performance on ImageNet
| Model | FLOPs (GB) | Params (M) | Top-1 Error | Top-5 Error | Optimizer |
|:--------------:|:----------:|:----------:|:-----------:|:-----------:|:----------:|
| ResNet-18 | 1.814 | 11.69 | 30.24 | 10.92 | Official |
| ResNet-18 | 1.814 | 11.69 | 29.97 | 10.43 | Step-120 |
| ResNet-18 | 1.814 | 11.69 | 29.35 | 10.13 | Cosine-120 |
| ResNet-18 | 1.814 | 11.69 | 29.45 | 10.25 | Cosine-120 B1024 |
| ResNet-18 | 1.814 | 11.69 | 29.44 | 10.12 |Cosine-S-120|
| ResNet-18 (DS) | 2.053 | 11.71 | 28.53 | 9.69 |Cosine-S-120|
| ResNet-34 | 3.663 | 21.80 | 25.65 | 8.06 |Cosine-120 |
| ResNet-34 (DS) | 3.903 | 21.82 | 25.05 | 7.67 |Cosine-S-120|
| ResNet-50 | 4.089 | 25.56 | 23.85 | 7.13 | Official |
| ResNet-50 | 4.089 | 25.56 | 22.54 | 6.45 |Cosine-120 |
| ResNet-50 | 4.089 | 25.56 | 22.71 | 6.38 |Cosine-120 B1024 |
| ResNet-50 | 4.089 | 25.56 | 22.34 | 6.22 |Cosine-S-120|
| ResNet-50 (DS) | 4.328 | 25.58 | 22.67 | 6.39 | Step-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.94 | 6.23 | Cosine-120 |
| ResNet-50 (DS) | 4.328 | 25.58 | 21.71 | 5.99 |Cosine-S-120|
| ResNet-101 | 7.801 | 44.55 | 20.93 | 5.57 |Cosine-120 |
| ResNet-101 | 7.801 | 44.55 | 20.92 | 5.58 |Cosine-120 B1024 |
| ResNet-101 (DS)| 8.041 | 44.57 | 20.36 | 5.22 |Cosine-S-120|
| ResNet-152 | 11.514 | 60.19 | 20.10 | 5.17 |Cosine-120 B1024 |
| ResNet-152 (DS)| 11.753 | 60.21 | 19.83 | 5.02 |Cosine-S-120|
| ResNet-200 | 15.007 | 64.67 | 20.06 | 4.98 |Cosine-S-120|
| Next50-32x4d (DS)| 4.2 | 25.0 | 22.2 | - | Official |
| Next50-32x4d (DS)| 4.470 | 25.05 | 21.16 | 5.65 |Cosine-S-120|
| MobileNet-V2 | 0.300 | 3.40 | 28.0 | - | Official |
| MobileNet-V2 | 0.300 | 3.50 | 27.92 | 9.50 | MobileFast |
| MobileNet-V2 | 0.300 | 3.50 | 27.56 | 9.26 | MobileFast-Smooth |
| ShuffleNet-V2 1.0| 0.146 | 2.28 | 30.6 | 11.1 | Official |
| ShuffleNet-V2 1.0| 0.145 | 2.28 | | |Cosine-S-120|
| ShuffleNet-V2 1.5| 0.299 | | 27.4 | - | Official |
| ShuffleNet-V2 1.5| | | | |Cosine-S-120|
| ShuffleNet-V2 2.0| | | | |Cosine-S-120|
`DS` indicates deep-stem for the first convolutional layer.
```
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet18V1 Step-Soft 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet18V1 Cos-Soft 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet18V1 Cos-Soft 1024 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet18V1 Cos-Smooth 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet18V2 Cos-Smooth 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet34V2 Cos-Smooth 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet50V1 Cos-Soft 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet50V2 Step-Soft 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet50V2 Cos-Soft 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNet101V2 Cos-Smooth 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ResNext50-32x4dV2 Cos-Smooth 256 -1"
```
Train efficient models may require different hyper-parameters.
```
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh MobileNetV2-X MobileFast 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh MobileNetV2-X MobileFastS 256 -1"
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh MobileNetV2 Mobile 256 -1" (70.96 top-1, 90.05 top-5)
bash ./scripts-cluster/local.sh 0,1,2,3 "bash ./scripts/base-imagenet.sh ShuffleNetV2-X Shuffle 1024 -1"
```
# Train with Knowledge Distillation
ResNet110 -> ResNet20
```
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/KD-train.sh cifar10 ResNet20 ResNet110 0.9 4 -1"
```
ResNet110 -> ResNet110
```
bash ./scripts-cluster/local.sh 0,1 "bash ./scripts/KD-train.sh cifar10 ResNet110 ResNet110 0.9 4 -1"
```
Set alpha=0.9 and temperature=4 following `Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR 2017`.
# Linux
The following command will redirect the output of top command to `top.txt`.
```
top -b -n 1 > top.txt
```
## Download the ImageNet dataset
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset has 1000 categories and 1.2 million images. The images do not need to be preprocessed or packaged in any database, but the validation images need to be moved into appropriate subfolders.
1. Download the images from http://image-net.org/download-images
2. Extract the training data:
```bash
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..
```
3. Extract the validation data and move images to subfolders:
```bash
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
```

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# An Algorithm-Agnostic NAS Benchmark (AA-NAS-Bench)
We propose an Algorithm-Agnostic NAS Benchmark (AA-NAS-Bench) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
The design of our search space is inspired from that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in AA-NAS-Bench includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.
In this Markdown file, we provide:
- Detailed instruction to reproduce AA-NAS-Bench.
- 10 NAS algorithms evaluated in our paper.
Note: please use `PyTorch >= 1.1.0` and `Python >= 3.6.0`.
## How to Use AA-NAS-Bench
1. Creating AA-NAS-Bench API from a file:
```
from aa_nas_api import AANASBenchAPI
api = AANASBenchAPI('$path_to_meta_aa_nas_bench_file')
api = AANASBenchAPI('AA-NAS-Bench-v1_0.pth')
```
2. Show the number of architectures `len(api)` and each architecture `api[i]`:
```
num = len(api)
for i, arch_str in enumerate(api):
print ('{:5d}/{:5d} : {:}'.format(i, len(api), arch_str))
```
3. Show the results of all trials for a single architecture:
```
# show all information for a specific architecture
api.show(1)
api.show(2)
# show the mean loss and accuracy of an architecture
info = api.query_meta_info_by_index(1)
loss, accuracy = info.get_metrics('cifar10', 'train')
flops, params, latency = info.get_comput_costs('cifar100')
# get the detailed information
results = api.query_by_index(1, 'cifar100')
print ('There are {:} trials for this architecture [{:}] on cifar100'.format(len(results), api[1]))
print ('Latency : {:}'.format(results[0].get_latency()))
print ('Train Info : {:}'.format(results[0].get_train()))
print ('Valid Info : {:}'.format(results[0].get_eval('x-valid')))
print ('Test Info : {:}'.format(results[0].get_eval('x-test')))
# for the metric after a specific epoch
print ('Train Info [10-th epoch] : {:}'.format(results[0].get_train(10)))
```
4. Query the index of an architecture by string
```
index = api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|')
api.show(index)
```
5. For other usages, please see `lib/aa_nas_api/api.py`
## Instruction to Generate AA-NAS-Bench
1. generate the meta file for AA-NAS-Bench using the following script, where `AA-NAS-BENCH` indicates the name and `4` indicates the maximum number of nodes in a cell.
```
bash scripts-search/AA-NAS-meta-gen.sh AA-NAS-BENCH 4
```
2. train earch architecture on a single GPU (see commands in `output/AA-NAS-BENCH-4/meta-node-4.opt-script.txt` which is automatically generated by step-1).
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/AA-NAS-train-archs.sh 0 389 -1 '777 888 999'
```
This command will train 390 architectures (id from 0 to 389) using the following four kinds of splits with three random seeds (777, 888, 999).
| Dataset | Train | Eval |
|:---------------:|:-------------:|:-----:|
| CIFAR-10 | train | valid |
| CIFAR-10 | train + valid | test |
| CIFAR-100 | train | valid+test |
| ImageNet-16-120 | train | valid+test |
3. calculate the latency, merge the results of all architectures, and simplify the results.
(see commands in `output/AA-NAS-BENCH-4/meta-node-4.cal-script.txt` which is automatically generated by step-1).
```
OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0 python exps/AA-NAS-statistics.py --mode cal --target_dir 000000-000389-C16-N5
```
4. merge all results into a single file for AA-NAS-Bench-API.
```
OMP_NUM_THREADS=4 python exps/AA-NAS-statistics.py --mode merge
```
[option] train a single architecture on a single GPU.
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/AA-NAS-train-net.sh resnet 16 5
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/AA-NAS-train-net.sh '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|' 16 5
```
## To Reproduce 10 Baseline NAS Algorithms in AA-NAS-Bench
We have tried our best to implement each method. However, still, some algorithms might obtain non-optimal results since their hyper-parameters might not fit our AA-NAS-Bench.
If researchers can provide better results with different hyper-parameters, we are happy to update results according to the new experimental results. We also welcome more NAS algorithms to test on our dataset and would include them accordingly.
-[1] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1`
-[2] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1`
-[3] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 -1`
-[4] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 -1`
-[5] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/ENAS.sh cifar10 -1`
-[6] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/RANDOM-NAS.sh cifar10 -1`
-[7] `bash ./scripts-search/algos/R-EA.sh -1`
-[8] `bash ./scripts-search/algos/Random.sh -1`
-[9] `bash ./scripts-search/algos/REINFORCE.sh -1`
-[10] `bash ./scripts-search/algos/BOHB.sh -1`

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MIT License
Copyright (c) 2019 Xuanyi Dong [GitHub: https://github.com/D-X-Y]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# Nueral Architecture Search (NAS)
This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org). More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
- Network Pruning via Transformable Architecture Search, NeurIPS 2019
- One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
- Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
- several typical classification models, e.g., ResNet and DenseNet (see BASELINE.md)
## Requirements and Preparation
Please install `PyTorch>=1.1.0`, `Python>=3.6`, and `opencv`.
The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
### usefull tools
1. Compute the number of parameters and FLOPs of a model:
```
from utils import get_model_infos
flop, param = get_model_infos(net, (1,3,32,32))
```
## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717)
In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network.
You could see the highlight of our Transformable Architecture Search (TAS) at our [project page](https://xuanyidong.com/assets/projects/NeurIPS-2019-TAS.html).
<p float="left">
<img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="680px"/>
<img src="https://d-x-y.github.com/resources/videos/NeurIPS-2019-TAS/TAS-arch.gif?raw=true" width="180px"/>
</p>
### Usage
Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
If you do not have `ILSVRC2012` data, pleasee comment L12 in `./scripts/prepare.sh`.
Search the depth configuration of ResNet:
```
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-depth-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
```
Search the width configuration of ResNet:
```
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-width-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
```
Search for both depth and width configuration of ResNet:
```
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
```
args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel name, `CIFARX` indicates the searching hyper-parameters, `0.47/0.57` indicates the expected FLOP ratio, `-1` indicates the random seed.
## [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
<img align="right" src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
<strong>Highlight</strong>: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
### Usage
Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
```
The searching codes of SETN on a small search space:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 -1
```
## [Searching for A Robust Neural Architecture in Four GPU Hours](https://arxiv.org/abs/1910.04465)
<img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">
We proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS). GDAS is baseed on DARTS and improves it with Gumbel-softmax sampling.
Experiments on CIFAR-10, CIFAR-100, ImageNet, PTB, and WT2 are reported.
The old version is located at [`others/GDAS`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/GDAS) and a paddlepaddle implementation is locate at [`others/paddlepaddle`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/paddlepaddle).
### Usage
Please use the following scripts to train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 GDAS_V1 96 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_V1 256 -1
```
The GDAS searching codes on a small search space:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 -1
```
The baseline searching codes are DARTS:
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1
```
# Citation
If you find that this project helps your research, please consider citing some of the following papers:
```
@inproceedings{dong2019tas,
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2019}
}
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}
@inproceedings{dong2019search,
title = {Searching for A Robust Neural Architecture in Four GPU Hours},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {1761--1770},
year = {2019}
}
```

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lib/aa_nas_api/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .api import AANASBenchAPI
from .api import ArchResults, ResultsCount

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lib/aa_nas_api/api.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, copy, torch, numpy as np
from collections import OrderedDict

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lib/config_utils/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .configure_utils import load_config, dict2config, configure2str
from .basic_args import obtain_basic_args
from .attention_args import obtain_attention_args

3
lib/config_utils/basic_args.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, random, argparse
from .share_args import add_shared_args

3
lib/datasets/DownsampledImageNet.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, hashlib, torch
import numpy as np
from PIL import Image

3
lib/datasets/SearchDatasetWrap.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch, copy, random
import torch.utils.data as data

3
lib/datasets/__init__.py
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@ -1,2 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .get_dataset_with_transform import get_datasets
from .SearchDatasetWrap import SearchDataset

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lib/datasets/get_dataset_with_transform.py
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@ -1,3 +1,6 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, torch
import os.path as osp
import numpy as np

3
lib/datasets/test_utils.py
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@ -1,3 +1,6 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
def test_imagenet_data(imagenet):
total_length = len(imagenet)
assert total_length == 1281166 or total_length == 50000, 'The length of ImageNet is wrong : {}'.format(total_length)

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lib/log_utils/__init__.py
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@ -1,3 +1,6 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .logger import Logger
from .print_logger import PrintLogger
from .meter import AverageMeter

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lib/models/SharedUtils.py
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@ -1,3 +1,6 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
import torch.nn as nn

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lib/models/__init__.py
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@ -1,3 +1,6 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
from os import path as osp

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lib/models/cell_infers/__init__.py
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@ -1 +1,4 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .tiny_network import TinyNetwork

3
lib/models/cell_operations.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
import torch.nn as nn

3
lib/models/cell_searchs/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .search_model_darts_v1 import TinyNetworkDartsV1
from .search_model_darts_v2 import TinyNetworkDartsV2
from .search_model_gdas import TinyNetworkGDAS

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lib/models/cell_searchs/_test_module.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
from search_model_enas_utils import Controller

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lib/models/cell_searchs/cells.py
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import math, torch
import torch.nn as nn
import torch.nn.functional as F
from copy import deepcopy
from ..cell_operations import OPS
class SearchCell(nn.Module):
def __init__(self, C_in, C_out, stride, max_nodes, op_names):
super(SearchCell, self).__init__()
self.op_names = deepcopy(op_names)
self.edges = nn.ModuleDict()
self.max_nodes = max_nodes
self.in_dim = C_in
self.out_dim = C_out
for i in range(1, max_nodes):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
if j == 0:
xlists = [OPS[op_name](C_in , C_out, stride) for op_name in op_names]
else:
xlists = [OPS[op_name](C_in , C_out, 1) for op_name in op_names]
self.edges[ node_str ] = nn.ModuleList( xlists )
self.edge_keys = sorted(list(self.edges.keys()))
self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
self.num_edges = len(self.edges)
def extra_repr(self):
string = 'info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}'.format(**self.__dict__)
return string
def forward(self, inputs, weightss):
nodes = [inputs]
for i in range(1, self.max_nodes):
inter_nodes = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
weights = weightss[ self.edge2index[node_str] ]
inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
nodes.append( sum(inter_nodes) )
return nodes[-1]
# GDAS
def forward_acc(self, inputs, weightss, indexess):
nodes = [inputs]
for i in range(1, self.max_nodes):
inter_nodes = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
weights = weightss[ self.edge2index[node_str] ]
indexes = indexess[ self.edge2index[node_str] ].item()
import pdb; pdb.set_trace() # to-do
#inter_nodes.append( self.edges[node_str][indexes](nodes[j]) * weights[indexes] )
nodes.append( sum(inter_nodes) )
return nodes[-1]
# joint
def forward_joint(self, inputs, weightss):
nodes = [inputs]
for i in range(1, self.max_nodes):
inter_nodes = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
weights = weightss[ self.edge2index[node_str] ]
aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
inter_nodes.append( aggregation )
nodes.append( sum(inter_nodes) )
return nodes[-1]
# uniform random sampling per iteration
def forward_urs(self, inputs):
nodes = [inputs]
for i in range(1, self.max_nodes):
while True: # to avoid select zero for all ops
sops, has_non_zero = [], False
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
candidates = self.edges[node_str]
select_op = random.choice(candidates)
sops.append( select_op )
if not hasattr(select_op, 'is_zero') or select_op.is_zero == False: has_non_zero=True
if has_non_zero: break
inter_nodes = []
for j, select_op in enumerate(sops):
inter_nodes.append( select_op(nodes[j]) )
nodes.append( sum(inter_nodes) )
return nodes[-1]
# select the argmax
def forward_select(self, inputs, weightss):
nodes = [inputs]
for i in range(1, self.max_nodes):
inter_nodes = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
weights = weightss[ self.edge2index[node_str] ]
inter_nodes.append( self.edges[node_str][ weights.argmax().item() ]( nodes[j] ) )
#inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
nodes.append( sum(inter_nodes) )
return nodes[-1]
# select the argmax
def forward_dynamic(self, inputs, structure):
nodes = [inputs]
for i in range(1, self.max_nodes):
cur_op_node = structure.nodes[i-1]
inter_nodes = []
for op_name, j in cur_op_node:
node_str = '{:}<-{:}'.format(i, j)
op_index = self.op_names.index( op_name )
inter_nodes.append( self.edges[node_str][op_index]( nodes[j] ) )
nodes.append( sum(inter_nodes) )
return nodes[-1]

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lib/models/cell_searchs/genotypes.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from copy import deepcopy

3
lib/models/cell_searchs/search_cells.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, random, torch
import warnings
import torch.nn as nn

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lib/models/cell_searchs/search_model_darts_v1.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
########################################################
# DARTS: Differentiable Architecture Search, ICLR 2019 #
########################################################

2
lib/models/cell_searchs/search_model_darts_v2.py
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@ -1,3 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
########################################################
# DARTS: Differentiable Architecture Search, ICLR 2019 #
########################################################

2
lib/models/cell_searchs/search_model_enas.py
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@ -1,3 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##########################################################################
# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
##########################################################################

2
lib/models/cell_searchs/search_model_enas_utils.py
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@ -1,3 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##########################################################################
# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
##########################################################################

2
lib/models/cell_searchs/search_model_random.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##############################################################################
# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 #
##############################################################################

2
lib/models/cell_searchs/search_model_setn.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
######################################################################################
# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
######################################################################################

3
lib/models/shape_searchs/SearchCifarResNet.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, torch
from collections import OrderedDict
from bisect import bisect_right

3
lib/models/shape_searchs/SearchCifarResNet_depth.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, torch
from collections import OrderedDict
from bisect import bisect_right

3
lib/models/shape_searchs/SearchCifarResNet_width.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, torch
import torch.nn as nn
from ..initialization import initialize_resnet

3
lib/models/shape_searchs/SoftSelect.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, torch
import torch.nn as nn

3
lib/models/shape_searchs/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .SearchCifarResNet_width import SearchWidthCifarResNet
from .SearchCifarResNet_depth import SearchDepthCifarResNet
from .SearchCifarResNet import SearchShapeCifarResNet

3
lib/models/shape_searchs/test.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
import torch.nn as nn
from SoftSelect import ChannelWiseInter

3
lib/nas_infer_model/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
def obtain_nas_infer_model(config):

3
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
import torch.nn as nn

3
lib/procedures/__init__.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .starts import prepare_seed, prepare_logger, get_machine_info, save_checkpoint, copy_checkpoint
from .optimizers import get_optim_scheduler

3
lib/procedures/basic_main.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch
from log_utils import AverageMeter, time_string
from utils import obtain_accuracy

3
lib/procedures/optimizers.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import math, torch
import torch.nn as nn
from bisect import bisect_right

3
lib/procedures/search_main.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch
from log_utils import AverageMeter, time_string
from utils import obtain_accuracy

3
lib/procedures/search_main_v2.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch
from log_utils import AverageMeter, time_string
from utils import obtain_accuracy

3
lib/procedures/simple_KD_main.py
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch
import torch.nn.functional as F
# our modules

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lib/procedures/starts.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch, random, PIL, copy, numpy as np
from os import path as osp
from shutil import copyfile

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MIT License
Copyright (c) 2019 Xuanyi Dong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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## [Searching for A Robust Neural Architecture in Four GPU Hours](http://xuanyidong.com/publication/gradient-based-diff-sampler/)
We propose A Gradient-based neural architecture search approach using Differentiable Architecture Sampler (GDAS).
<img src="https://github.com/D-X-Y/NAS-Projects/blob/master/others/GDAS/data/GDAS.png" width="520">
Figure-1. We utilize a DAG to represent the search space of a neural cell. Different operations (colored arrows) transform one node (square) to its intermediate features (little circles). Meanwhile, each node is the sum of the intermediate features transformed from the previous nodes. As indicated by the solid connections, the neural cell in the proposed GDAS is a sampled sub-graph of this DAG. Specifically, among the intermediate features between every two nodes, GDAS samples one feature in a differentiable way.
### Requirements
- PyTorch 1.0.1
- Python 3.6
- opencv
```
conda install pytorch torchvision cuda100 -c pytorch
```
### Usages
Train the searched CNN on CIFAR
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_FG cifar10 cut
CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_F1 cifar10 cut
CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_V1 cifar100 cut
```
Train the searched CNN on ImageNet
```
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts-cnn/train-imagenet.sh GDAS_F1 52 14 B128 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts-cnn/train-imagenet.sh GDAS_V1 50 14 B256 -1
```
Evaluate a trained CNN model
```
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path}
CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint GDAS-V1-C50-N14-ImageNet.pth
```
Train the searched RNN
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V2
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh GDAS
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V2
CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
```
### Training Logs
You can find some training logs in [`./data/logs/`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/GDAS/data/logs).
You can also find some pre-trained models in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ).
### Experimental Results
<img src="https://github.com/D-X-Y/NAS-Projects/tree/master/others/GDAS/data/imagenet-results.png" width="700">
Figure-2. Top-1 and top-5 errors on ImageNet.
### Correction
The Gumbel-softmax tempurature during searching should decrease from 10 to 0.1.
### Citation
If you find that this project (GDAS) helps your research, please cite the paper:
```
@inproceedings{dong2019search,
title={Searching for A Robust Neural Architecture in Four GPU Hours},
author={Dong, Xuanyi and Yang, Yi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={1761--1770},
year={2019}
}
```

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{
"data_name" : ["str", "PTB"],
"data_path" : ["str", "./data/data/penn"],
"emsize" : ["int", 850],
"nhid" : ["int", 850],
"nhidlast" : ["int", 850],
"LR" : ["float", 20],
"clip" : ["float", 0.25],
"epochs" : ["int", 3000],
"train_batch": ["int", 64],
"eval_batch": ["int", 10],
"test_batch": ["int", 1],
"bptt" : ["int", 35],
"dropout" : ["float", 0.75],
"dropouth" : ["float", 0.25],
"dropoutx" : ["float", 0.75],
"dropouti" : ["float", 0.2],
"dropoute" : ["float", 0.1],
"nonmono" : ["int", 5],
"alpha" : ["float", 0],
"beta" : ["float", 1e-3],
"wdecay" : ["float", 8e-7],
"max_seq_len_delta" : ["int", 20]
}

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{
"data_name" : ["str", "WT2"],
"data_path" : ["str", "./data/data/wikitext-2"],
"emsize" : ["int", 700],
"nhid" : ["int", 700],
"nhidlast" : ["int", 700],
"LR" : ["float", 20],
"clip" : ["float", 0.25],
"epochs" : ["int", 3000],
"train_batch": ["int", 64],
"eval_batch": ["int", 10],
"test_batch": ["int", 1],
"bptt" : ["int", 35],
"dropout" : ["float", 0.75],
"dropouth" : ["float", 0.15],
"dropoutx" : ["float", 0.75],
"dropouti" : ["float", 0.2],
"dropoute" : ["float", 0.1],
"nonmono" : ["int", 5],
"alpha" : ["float", 0],
"beta" : ["float", 1e-3],
"wdecay" : ["float", 5e-7],
"max_seq_len_delta" : ["int", 20]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 128],
"epochs" : ["int", 1800],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0001],
"LR" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 128],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0005],
"LR" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 128],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 64],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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others/GDAS/configs/nas-cifar-cos-cutW1.config Normal file
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@ -0,0 +1,14 @@
{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0001],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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others/GDAS/configs/nas-cifar-cos-cutW5.config Normal file
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{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0005],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 16],
"drop_path_prob" : ["float", 0.2]
}

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others/GDAS/configs/nas-cifar-cos-nocut.config Normal file
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{
"type" : ["str", "cosine"],
"batch_size": ["int", 96],
"epochs" : ["int", 600],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0003],
"LR" : ["float", 0.025],
"LR_MIN" : ["float", 0.0001],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"cutout" : ["int", 0],
"drop_path_prob" : ["float", 0.3]
}

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others/GDAS/configs/nas-imagenet-B128.config Normal file
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{
"type" : ["str", "steplr"],
"batch_size": ["int", 128],
"epochs" : ["int", 250],
"decay_period": ["int", 1],
"gamma" : ["float", 0.97],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.00003],
"LR" : ["float", 0.1],
"label_smooth": ["float", 0.1],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"drop_path_prob" : ["float", 0]
}

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others/GDAS/configs/nas-imagenet-B256.config Normal file
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{
"type" : ["str", "steplr"],
"batch_size": ["int", 256],
"epochs" : ["int", 250],
"decay_period": ["int", 1],
"gamma" : ["float", 0.97],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.00003],
"LR" : ["float", 0.1],
"label_smooth": ["float", 0.1],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"drop_path_prob" : ["float", 0]
}

15
others/GDAS/configs/nas-imagenet.config Normal file
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{
"type" : ["str", "steplr"],
"batch_size": ["int", 128],
"epochs" : ["int", 250],
"decay_period": ["int", 1],
"gamma" : ["float", 0.97],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.00003],
"LR" : ["float", 0.1],
"label_smooth": ["float", 0.1],
"auxiliary" : ["bool", 1],
"auxiliary_weight" : ["float", 0.4],
"grad_clip" : ["float", 5],
"drop_path_prob" : ["float", 0]
}

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others/GDAS/configs/pyramidC10.config Normal file
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{
"type" : ["str", "multistep"],
"batch_size": ["int", 128],
"epochs" : ["int", 300],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0001],
"LR" : ["float", 0.1],
"milestones": ["int", [150, 225]],
"gammas" : ["float", [0.1, 0.1]]
}

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others/GDAS/configs/pyramidC100.config Normal file
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{
"type" : ["str", "multistep"],
"batch_size": ["int", 128],
"epochs" : ["int", 300],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0001],
"LR" : ["float", 0.5],
"milestones": ["int", [150, 225]],
"gammas" : ["float", [0.1, 0.1]]
}

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others/GDAS/configs/resnet165.config Normal file
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{
"type" : ["str", "multistep"],
"batch_size": ["int", 128],
"epochs" : ["int", 165],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0001],
"LR" : ["float", 0.01],
"milestones": ["int", [1, 83, 124]],
"gammas" : ["float", [10, 0.1, 0.1]]
}

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others/GDAS/configs/resnet200.config Normal file
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{
"type" : ["str", "multistep"],
"batch_size": ["int", 128],
"epochs" : ["int", 200],
"momentum" : ["float", 0.9],
"decay" : ["float", 0.0005],
"LR" : ["float", 0.01],
"milestones": ["int", [1 , 60, 120, 160]],
"gammas" : ["float", [10, 0.2, 0.2, 0.2]]
}

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others/GDAS/exps-cnn/cvpr-vis.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
# python ./exps-nas/cvpr-vis.py --save_dir ./snapshots/NAS-VIS/
import os, sys, time, glob, random, argparse
import numpy as np
from copy import deepcopy
import torch
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from nas import DMS_V1, DMS_F1
from nas_rnn import DARTS_V2, GDAS
from graphviz import Digraph
parser = argparse.ArgumentParser("Visualize the Networks")
parser.add_argument('--save_dir', type=str, help='The directory to save the network plot.')
args = parser.parse_args()
def plot_cnn(genotype, filename):
g = Digraph(
format='pdf',
edge_attr=dict(fontsize='20', fontname="times"),
node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
engine='dot')
g.body.extend(['rankdir=LR'])
g.node("c_{k-2}", fillcolor='darkseagreen2')
g.node("c_{k-1}", fillcolor='darkseagreen2')
assert len(genotype) % 2 == 0, '{:}'.format(genotype)
steps = len(genotype) // 2
for i in range(steps):
g.node(str(i), fillcolor='lightblue')
for i in range(steps):
for k in [2*i, 2*i + 1]:
op, j, weight = genotype[k]
if j == 0:
u = "c_{k-2}"
elif j == 1:
u = "c_{k-1}"
else:
u = str(j-2)
v = str(i)
g.edge(u, v, label=op, fillcolor="gray")
g.node("c_{k}", fillcolor='palegoldenrod')
for i in range(steps):
g.edge(str(i), "c_{k}", fillcolor="gray")
g.render(filename, view=False)
def plot_rnn(genotype, filename):
g = Digraph(
format='pdf',
edge_attr=dict(fontsize='20', fontname="times"),
node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
engine='dot')
g.body.extend(['rankdir=LR'])
g.node("x_{t}", fillcolor='darkseagreen2')
g.node("h_{t-1}", fillcolor='darkseagreen2')
g.node("0", fillcolor='lightblue')
g.edge("x_{t}", "0", fillcolor="gray")
g.edge("h_{t-1}", "0", fillcolor="gray")
steps = len(genotype)
for i in range(1, steps + 1):
g.node(str(i), fillcolor='lightblue')
for i, (op, j) in enumerate(genotype):
g.edge(str(j), str(i + 1), label=op, fillcolor="gray")
g.node("h_{t}", fillcolor='palegoldenrod')
for i in range(1, steps + 1):
g.edge(str(i), "h_{t}", fillcolor="gray")
g.render(filename, view=False)
if __name__ == '__main__':
save_dir = Path(args.save_dir)
save_path = str(save_dir / 'DMS_V1-normal')
plot_cnn(DMS_V1.normal, save_path)
save_path = str(save_dir / 'DMS_V1-reduce')
plot_cnn(DMS_V1.reduce, save_path)
save_path = str(save_dir / 'DMS_F1-normal')
plot_cnn(DMS_F1.normal, save_path)
save_path = str(save_dir / 'DARTS-V2-RNN')
plot_rnn(DARTS_V2.recurrent, save_path)
save_path = str(save_dir / 'GDAS-V1-RNN')
plot_rnn(GDAS.recurrent, save_path)

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others/GDAS/exps-cnn/evaluate.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
# For evaluating the learned model
import os, sys, time, glob, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from utils import AverageMeter, time_string, convert_secs2time
from utils import print_log, obtain_accuracy
from utils import Cutout, count_parameters_in_MB
from nas import model_types as models
from train_utils import main_procedure
from train_utils_imagenet import main_procedure_imagenet
from scheduler import load_config
parser = argparse.ArgumentParser("Evaluate-CNN")
parser.add_argument('--data_path', type=str, help='Path to dataset.')
parser.add_argument('--checkpoint', type=str, help='Choose between Cifar10/100 and ImageNet.')
args = parser.parse_args()
assert torch.cuda.is_available(), 'torch.cuda is not available'
def main():
assert os.path.isdir( args.data_path ), 'invalid data-path : {:}'.format(args.data_path)
assert os.path.isfile( args.checkpoint ), 'invalid checkpoint : {:}'.format(args.checkpoint)
checkpoint = torch.load( args.checkpoint )
xargs = checkpoint['args']
config = load_config(xargs.model_config)
genotype = models[xargs.arch]
# clear GPU cache
torch.cuda.empty_cache()
if xargs.dataset == 'imagenet':
main_procedure_imagenet(config, args.data_path, xargs, genotype, xargs.init_channels, xargs.layers, checkpoint['state_dict'], None)
else:
main_procedure(config, xargs.dataset, args.data_path, xargs, genotype, xargs.init_channels, xargs.layers, checkpoint['state_dict'], None)
if __name__ == '__main__':
main()

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others/GDAS/exps-cnn/train_base.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, glob, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from utils import AverageMeter, time_string, convert_secs2time
from utils import print_log, obtain_accuracy
from utils import Cutout, count_parameters_in_MB
from nas import model_types as models
from train_utils import main_procedure
from train_utils_imagenet import main_procedure_imagenet
from scheduler import load_config
parser = argparse.ArgumentParser("Train-CNN")
parser.add_argument('--data_path', type=str, help='Path to dataset')
parser.add_argument('--dataset', type=str, choices=['imagenet', 'cifar10', 'cifar100'], help='Choose between Cifar10/100 and ImageNet.')
parser.add_argument('--arch', type=str, choices=models.keys(), help='the searched model.')
#
parser.add_argument('--grad_clip', type=float, help='gradient clipping')
parser.add_argument('--model_config', type=str , help='the model configuration')
parser.add_argument('--init_channels', type=int , help='the initial number of channels')
parser.add_argument('--layers', type=int , help='the number of layers.')
# log
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
parser.add_argument('--save_path', type=str, help='Folder to save checkpoints and log.')
parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
parser.add_argument('--manualSeed', type=int, help='manual seed')
args = parser.parse_args()
if 'CUDA_VISIBLE_DEVICES' not in os.environ: print('Can not find CUDA_VISIBLE_DEVICES in os.environ')
else : print('Find CUDA_VISIBLE_DEVICES={:}'.format(os.environ['CUDA_VISIBLE_DEVICES']))
assert torch.cuda.is_available(), 'torch.cuda is not available'
if args.manualSeed is None or args.manualSeed < 0:
args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
cudnn.benchmark = True
cudnn.enabled = True
torch.manual_seed(args.manualSeed)
torch.cuda.manual_seed_all(args.manualSeed)
def main():
# Init logger
#args.save_path = os.path.join(args.save_path, 'seed-{:}'.format(args.manualSeed))
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = open(os.path.join(args.save_path, 'seed-{:}-log.txt'.format(args.manualSeed)), 'w')
print_log('Save Path : {:}'.format(args.save_path), log)
state = {k: v for k, v in args._get_kwargs()}
print_log(state, log)
print_log("Random Seed : {:}".format(args.manualSeed), log)
print_log("Python version : {:}".format(sys.version.replace('\n', ' ')), log)
print_log("Torch version : {:}".format(torch.__version__), log)
print_log("CUDA version : {:}".format(torch.version.cuda), log)
print_log("cuDNN version : {:}".format(cudnn.version()), log)
print_log("Num of GPUs : {:}".format(torch.cuda.device_count()), log)
args.dataset = args.dataset.lower()
config = load_config(args.model_config)
genotype = models[args.arch]
print_log('configuration : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
# clear GPU cache
torch.cuda.empty_cache()
if args.dataset == 'imagenet':
main_procedure_imagenet(config, args.data_path, args, genotype, args.init_channels, args.layers, None, log)
else:
main_procedure(config, args.dataset, args.data_path, args, genotype, args.init_channels, args.layers, None, log)
log.close()
if __name__ == '__main__':
main()

169
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time
from copy import deepcopy
import torch
import torchvision.transforms as transforms
from shutil import copyfile
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
from utils import count_parameters_in_MB
from utils import Cutout
from nas import NetworkCIFAR as Network
from datasets import get_datasets
def obtain_best(accuracies):
if len(accuracies) == 0: return (0, 0)
tops = [value for key, value in accuracies.items()]
s2b = sorted( tops )
return s2b[-1]
def main_procedure(config, dataset, data_path, args, genotype, init_channels, layers, pure_evaluate, log):
train_data, test_data, class_num = get_datasets(dataset, data_path, config.cutout)
print_log('-------------------------------------- main-procedure', log)
print_log('config : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('init_channels : {:}'.format(init_channels), log)
print_log('layers : {:}'.format(layers), log)
print_log('class_num : {:}'.format(class_num), log)
basemodel = Network(init_channels, class_num, layers, config.auxiliary, genotype)
model = torch.nn.DataParallel(basemodel).cuda()
total_param, aux_param = count_parameters_in_MB(basemodel), count_parameters_in_MB(basemodel.auxiliary_param())
print_log('Network =>\n{:}'.format(basemodel), log)
print_log('Parameters : {:} - {:} = {:.3f} MB'.format(total_param, aux_param, total_param - aux_param), log)
print_log('config : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('args : {:}'.format(args), log)
print_log('Train-Dataset : {:}'.format(train_data), log)
print_log('Train-Trans : {:}'.format(train_data.transform), log)
print_log('Test--Dataset : {:}'.format(test_data ), log)
print_log('Test--Trans : {:}'.format(test_data.transform ), log)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data , batch_size=config.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay)
#optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nestero=True)
if config.type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs), eta_min=float(config.LR_MIN))
else:
raise ValueError('Can not find the schedular type : {:}'.format(config.type))
checkpoint_path = os.path.join(args.save_path, 'seed-{:}-checkpoint-{:}-model.pth'.format(args.manualSeed, dataset))
checkpoint_best = os.path.join(args.save_path, 'seed-{:}-checkpoint-{:}-best.pth'.format(args.manualSeed, dataset))
if pure_evaluate:
print_log('-'*20 + 'Pure Evaluation' + '-'*20, log)
basemodel.load_state_dict( pure_evaluate )
with torch.no_grad():
valid_acc1, valid_acc5, valid_los = _train(test_loader, model, criterion, optimizer, 'test', -1, config, args.print_freq, log)
return (valid_acc1, valid_acc5)
elif os.path.isfile(checkpoint_path):
checkpoint = torch.load( checkpoint_path )
start_epoch = checkpoint['epoch']
basemodel.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
accuracies = checkpoint['accuracies']
print_log('Load checkpoint from {:} with start-epoch = {:}'.format(checkpoint_path, start_epoch), log)
else:
start_epoch, accuracies = 0, {}
print_log('Train model from scratch without pre-trained model or snapshot', log)
# Main loop
start_time, epoch_time = time.time(), AverageMeter()
for epoch in range(start_epoch, config.epochs):
scheduler.step()
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size), log)
basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
train_acc1, train_acc5, train_los = _train(train_loader, model, criterion, optimizer, 'train', epoch, config, args.print_freq, log)
with torch.no_grad():
valid_acc1, valid_acc5, valid_los = _train(test_loader, model, criterion, optimizer, 'test', epoch, config, args.print_freq, log)
accuracies[epoch] = (valid_acc1, valid_acc5)
torch.save({'epoch' : epoch + 1,
'args' : deepcopy(args),
'state_dict': basemodel.state_dict(),
'optimizer' : optimizer.state_dict(),
'scheduler' : scheduler.state_dict(),
'accuracies': accuracies},
checkpoint_path)
best_acc = obtain_best( accuracies )
if accuracies[epoch] == best_acc: copyfile(checkpoint_path, checkpoint_best)
print_log('----> Best Accuracy : Acc@1={:.2f}, Acc@5={:.2f}, Error@1={:.2f}, Error@5={:.2f}'.format(best_acc[0], best_acc[1], 100-best_acc[0], 100-best_acc[1]), log)
print_log('----> Save into {:}'.format(checkpoint_path), log)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
return obtain_best( accuracies )
def _train(xloader, model, criterion, optimizer, mode, epoch, config, print_freq, log):
data_time, batch_time, losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
if mode == 'train':
model.train()
elif mode == 'test':
model.eval()
else: raise ValueError("The mode is not right : {:}".format(mode))
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
# measure data loading time
data_time.update(time.time() - end)
# calculate prediction and loss
targets = targets.cuda(non_blocking=True)
if mode == 'train': optimizer.zero_grad()
if config.auxiliary and model.training:
logits, logits_aux = model(inputs)
else:
logits = model(inputs)
loss = criterion(logits, targets)
if config.auxiliary and model.training:
loss_aux = criterion(logits_aux, targets)
loss += config.auxiliary_weight * loss_aux
if mode == 'train':
loss.backward()
if config.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update (prec1.item(), inputs.size(0))
top5.update (prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0 or (i+1) == len(xloader):
Sstr = ' {:5s}'.format(mode) + time_string() + ' Epoch: [{:03d}][{:03d}/{:03d}]'.format(epoch, i, len(xloader))
Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
Lstr = 'Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})'.format(loss=losses, top1=top1, top5=top5)
print_log(Sstr + ' ' + Tstr + ' ' + Lstr, log)
print_log ('{TIME:} **{mode:}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'.format(TIME=time_string(), mode=mode, top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, loss=losses.avg), log)
return top1.avg, top5.avg, losses.avg

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others/GDAS/exps-cnn/train_utils_imagenet.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time
from copy import deepcopy
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from shutil import copyfile
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
from utils import count_parameters_in_MB
from utils import print_FLOPs
from utils import Cutout
from nas import NetworkImageNet as Network
from datasets import get_datasets
def obtain_best(accuracies):
if len(accuracies) == 0: return (0, 0)
tops = [value for key, value in accuracies.items()]
s2b = sorted( tops )
return s2b[-1]
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, inputs, targets):
log_probs = self.logsoftmax(inputs)
targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
loss = (-targets * log_probs).mean(0).sum()
return loss
def main_procedure_imagenet(config, data_path, args, genotype, init_channels, layers, pure_evaluate, log):
# training data and testing data
train_data, valid_data, class_num = get_datasets('imagenet-1k', data_path, -1)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=config.batch_size, shuffle= True, pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=config.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
print_log('-------------------------------------- main-procedure', log)
print_log('config : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('init_channels : {:}'.format(init_channels), log)
print_log('layers : {:}'.format(layers), log)
print_log('class_num : {:}'.format(class_num), log)
basemodel = Network(init_channels, class_num, layers, config.auxiliary, genotype)
model = torch.nn.DataParallel(basemodel).cuda()
total_param, aux_param = count_parameters_in_MB(basemodel), count_parameters_in_MB(basemodel.auxiliary_param())
print_log('Network =>\n{:}'.format(basemodel), log)
print_FLOPs(basemodel, (1,3,224,224), [print_log, log])
print_log('Parameters : {:} - {:} = {:.3f} MB'.format(total_param, aux_param, total_param - aux_param), log)
print_log('config : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('Train-Dataset : {:}'.format(train_data), log)
print_log('Valid--Dataset : {:}'.format(valid_data), log)
print_log('Args : {:}'.format(args), log)
criterion = torch.nn.CrossEntropyLoss().cuda()
criterion_smooth = CrossEntropyLabelSmooth(class_num, config.label_smooth).cuda()
optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nesterov=True)
if config.type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs))
elif config.type == 'steplr':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, config.decay_period, gamma=config.gamma)
else:
raise ValueError('Can not find the schedular type : {:}'.format(config.type))
checkpoint_path = os.path.join(args.save_path, 'seed-{:}-checkpoint-imagenet-model.pth'.format(args.manualSeed))
checkpoint_best = os.path.join(args.save_path, 'seed-{:}-checkpoint-imagenet-best.pth'.format(args.manualSeed))
if pure_evaluate:
print_log('-'*20 + 'Pure Evaluation' + '-'*20, log)
basemodel.load_state_dict( pure_evaluate )
with torch.no_grad():
valid_acc1, valid_acc5, valid_los = _train(valid_queue, model, criterion, None, 'test' , -1, config, args.print_freq, log)
return (valid_acc1, valid_acc5)
elif os.path.isfile(checkpoint_path):
checkpoint = torch.load( checkpoint_path )
start_epoch = checkpoint['epoch']
basemodel.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
accuracies = checkpoint['accuracies']
print_log('Load checkpoint from {:} with start-epoch = {:}'.format(checkpoint_path, start_epoch), log)
else:
start_epoch, accuracies = 0, {}
print_log('Train model from scratch without pre-trained model or snapshot', log)
# Main loop
start_time, epoch_time = time.time(), AverageMeter()
for epoch in range(start_epoch, config.epochs):
scheduler.step()
basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}, Drop-Path-Prob={:}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size, basemodel.get_drop_path()), log)
train_acc1, train_acc5, train_los = _train(train_queue, model, criterion_smooth, optimizer, 'train', epoch, config, args.print_freq, log)
with torch.no_grad():
valid_acc1, valid_acc5, valid_los = _train(valid_queue, model, criterion, None, 'test' , epoch, config, args.print_freq, log)
accuracies[epoch] = (valid_acc1, valid_acc5)
torch.save({'epoch' : epoch + 1,
'args' : deepcopy(args),
'state_dict': basemodel.state_dict(),
'optimizer' : optimizer.state_dict(),
'scheduler' : scheduler.state_dict(),
'accuracies': accuracies},
checkpoint_path)
best_acc = obtain_best( accuracies )
if accuracies[epoch] == best_acc: copyfile(checkpoint_path, checkpoint_best)
print_log('----> Best Accuracy : Acc@1={:.2f}, Acc@5={:.2f}, Error@1={:.2f}, Error@5={:.2f}'.format(best_acc[0], best_acc[1], 100-best_acc[0], 100-best_acc[1]), log)
print_log('----> Save into {:}'.format(checkpoint_path), log)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
return obtain_best( accuracies )
def _train(xloader, model, criterion, optimizer, mode, epoch, config, print_freq, log):
data_time, batch_time, losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
if mode == 'train':
model.train()
elif mode == 'test':
model.eval()
else: raise ValueError("The mode is not right : {:}".format(mode))
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
# measure data loading time
data_time.update(time.time() - end)
# calculate prediction and loss
targets = targets.cuda(non_blocking=True)
if mode == 'train': optimizer.zero_grad()
if config.auxiliary and model.training:
logits, logits_aux = model(inputs)
else:
logits = model(inputs)
loss = criterion(logits, targets)
if config.auxiliary and model.training:
loss_aux = criterion(logits_aux, targets)
loss += config.auxiliary_weight * loss_aux
if mode == 'train':
loss.backward()
if config.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update (prec1.item(), inputs.size(0))
top5.update (prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0 or (i+1) == len(xloader):
Sstr = ' {:5s}'.format(mode) + time_string() + ' Epoch: [{:03d}][{:03d}/{:03d}]'.format(epoch, i, len(xloader))
Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
Lstr = 'Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})'.format(loss=losses, top1=top1, top5=top5)
print_log(Sstr + ' ' + Tstr + ' ' + Lstr, log)
print_log ('{TIME:} **{mode:}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'.format(TIME=time_string(), mode=mode, top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, loss=losses.avg), log)
return top1.avg, top5.avg, losses.avg

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others/GDAS/exps-cnn/vis-arch.py Normal file
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import os, sys, time, glob, random, argparse
import numpy as np
from copy import deepcopy
import torch
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from graphviz import Digraph
parser = argparse.ArgumentParser("Visualize the Networks")
parser.add_argument('--checkpoint', type=str, help='The path to the checkpoint.')
parser.add_argument('--save_dir', type=str, help='The directory to save the network plot.')
args = parser.parse_args()
def plot(genotype, filename):
g = Digraph(
format='pdf',
edge_attr=dict(fontsize='20', fontname="times"),
node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
engine='dot')
g.body.extend(['rankdir=LR'])
g.node("c_{k-2}", fillcolor='darkseagreen2')
g.node("c_{k-1}", fillcolor='darkseagreen2')
assert len(genotype) % 2 == 0
steps = len(genotype) // 2
for i in range(steps):
g.node(str(i), fillcolor='lightblue')
for i in range(steps):
for k in [2*i, 2*i + 1]:
op, j, weight = genotype[k]
if j == 0:
u = "c_{k-2}"
elif j == 1:
u = "c_{k-1}"
else:
u = str(j-2)
v = str(i)
g.edge(u, v, label=op, fillcolor="gray")
g.node("c_{k}", fillcolor='palegoldenrod')
for i in range(steps):
g.edge(str(i), "c_{k}", fillcolor="gray")
g.render(filename, view=False)
if __name__ == '__main__':
checkpoint = args.checkpoint
assert os.path.isfile(checkpoint), 'Invalid path for checkpoint : {:}'.format(checkpoint)
checkpoint = torch.load( checkpoint, map_location='cpu' )
genotypes = checkpoint['genotypes']
save_dir = Path(args.save_dir)
subs = ['normal', 'reduce']
for sub in subs:
if not (save_dir / sub).exists():
(save_dir / sub).mkdir(parents=True, exist_ok=True)
for key, network in genotypes.items():
save_path = str(save_dir / 'normal' / 'epoch-{:03d}'.format( int(key) ))
print('save into {:}'.format(save_path))
plot(network.normal, save_path)
save_path = str(save_dir / 'reduce' / 'epoch-{:03d}'.format( int(key) ))
print('save into {:}'.format(save_path))
plot(network.reduce, save_path)

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others/GDAS/exps-rnn/train_rnn_base.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, gc, sys, math, time, glob, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import multiprocessing
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
print ('lib-dir : {:}'.format(lib_dir))
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from utils import AverageMeter, time_string, time_file_str, convert_secs2time
from utils import print_log, obtain_accuracy
from utils import count_parameters_in_MB
from nas_rnn import DARTS_V1, DARTS_V2, GDAS
from train_rnn_utils import main_procedure
from scheduler import load_config
Networks = {'DARTS_V1': DARTS_V1,
'DARTS_V2': DARTS_V2,
'GDAS' : GDAS}
parser = argparse.ArgumentParser("RNN")
parser.add_argument('--arch', type=str, choices=Networks.keys(), help='the network architecture')
parser.add_argument('--config_path', type=str, help='the training configure for the discovered model')
# log
parser.add_argument('--save_path', type=str, help='Folder to save checkpoints and log.')
parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--threads', type=int, default=4, help='the number of threads')
args = parser.parse_args()
assert torch.cuda.is_available(), 'torch.cuda is not available'
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
cudnn.benchmark = True
cudnn.enabled = True
torch.manual_seed(args.manualSeed)
torch.cuda.manual_seed_all(args.manualSeed)
torch.set_num_threads(args.threads)
def main():
# Init logger
args.save_path = os.path.join(args.save_path, 'seed-{:}'.format(args.manualSeed))
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = open(os.path.join(args.save_path, 'log-seed-{:}-{:}.txt'.format(args.manualSeed, time_file_str())), 'w')
print_log('save path : {:}'.format(args.save_path), log)
state = {k: v for k, v in args._get_kwargs()}
print_log(state, log)
print_log("Random Seed: {}".format(args.manualSeed), log)
print_log("Python version : {}".format(sys.version.replace('\n', ' ')), log)
print_log("Torch version : {}".format(torch.__version__), log)
print_log("CUDA version : {}".format(torch.version.cuda), log)
print_log("cuDNN version : {}".format(cudnn.version()), log)
print_log("Num of GPUs : {}".format(torch.cuda.device_count()), log)
print_log("Num of CPUs : {}".format(multiprocessing.cpu_count()), log)
config = load_config( args.config_path )
genotype = Networks[ args.arch ]
main_procedure(config, genotype, args.save_path, args.print_freq, log)
log.close()
if __name__ == '__main__':
main()

221
others/GDAS/exps-rnn/train_rnn_utils.py Normal file
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# Modified from https://github.com/quark0/darts
import os, gc, sys, time, math
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
from utils import count_parameters_in_MB
from datasets import Corpus
from nas_rnn import batchify, get_batch, repackage_hidden
from nas_rnn import DARTSCell, RNNModel
def obtain_best(accuracies):
if len(accuracies) == 0: return (0, 0)
tops = [value for key, value in accuracies.items()]
s2b = sorted( tops )
return s2b[-1]
def main_procedure(config, genotype, save_dir, print_freq, log):
print_log('-'*90, log)
print_log('save-dir : {:}'.format(save_dir), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('config : {:}'.format(config), log)
corpus = Corpus(config.data_path)
train_data = batchify(corpus.train, config.train_batch, True)
valid_data = batchify(corpus.valid, config.eval_batch , True)
test_data = batchify(corpus.test, config.test_batch , True)
ntokens = len(corpus.dictionary)
print_log("Train--Data Size : {:}".format(train_data.size()), log)
print_log("Valid--Data Size : {:}".format(valid_data.size()), log)
print_log("Test---Data Size : {:}".format( test_data.size()), log)
print_log("ntokens = {:}".format(ntokens), log)
model = RNNModel(ntokens, config.emsize, config.nhid, config.nhidlast,
config.dropout, config.dropouth, config.dropoutx, config.dropouti, config.dropoute,
cell_cls=DARTSCell, genotype=genotype)
model = model.cuda()
print_log('Network =>\n{:}'.format(model), log)
print_log('Genotype : {:}'.format(genotype), log)
print_log('Parameters : {:.3f} MB'.format(count_parameters_in_MB(model)), log)
checkpoint_path = os.path.join(save_dir, 'checkpoint-{:}.pth'.format(config.data_name))
Soptimizer = torch.optim.SGD (model.parameters(), lr=config.LR, weight_decay=config.wdecay)
Aoptimizer = torch.optim.ASGD(model.parameters(), lr=config.LR, t0=0, lambd=0., weight_decay=config.wdecay)
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
Soptimizer.load_state_dict( checkpoint['SGD_optimizer'] )
Aoptimizer.load_state_dict( checkpoint['ASGD_optimizer'] )
epoch = checkpoint['epoch']
use_asgd = checkpoint['use_asgd']
print_log('load checkpoint from {:} and start train from {:}'.format(checkpoint_path, epoch), log)
else:
epoch, use_asgd = 0, False
start_time, epoch_time = time.time(), AverageMeter()
valid_loss_from_sgd, losses = [], {-1 : 1e9}
while epoch < config.epochs:
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:04d}/{:04d}] {:}".format(time_string(), epoch, config.epochs, need_time), log)
if use_asgd : optimizer = Aoptimizer
else : optimizer = Soptimizer
try:
Dtime, Btime = train(model, optimizer, corpus, train_data, config, epoch, print_freq, log)
except:
torch.cuda.empty_cache()
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
Soptimizer.load_state_dict( checkpoint['SGD_optimizer'] )
Aoptimizer.load_state_dict( checkpoint['ASGD_optimizer'] )
epoch = checkpoint['epoch']
use_asgd = checkpoint['use_asgd']
valid_loss_from_sgd = checkpoint['valid_loss_from_sgd']
continue
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
val_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
for prm in model.parameters():
prm.data = tmp[prm].clone()
else:
val_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
if len(valid_loss_from_sgd) > config.nonmono and val_loss > min(valid_loss_from_sgd):
use_asgd = True
valid_loss_from_sgd.append( val_loss )
print_log('{:} end of epoch {:3d} with {:} | valid loss {:5.2f} | valid ppl {:8.2f}'.format(time_string(), epoch, 'ASGD' if use_asgd else 'SGD', val_loss, math.exp(val_loss)), log)
if val_loss < min(losses.values()):
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
torch.save({'epoch' : epoch,
'use_asgd' : use_asgd,
'valid_loss_from_sgd': valid_loss_from_sgd,
'state_dict': model.state_dict(),
'SGD_optimizer' : Soptimizer.state_dict(),
'ASGD_optimizer': Aoptimizer.state_dict()},
checkpoint_path)
if use_asgd:
for prm in model.parameters():
prm.data = tmp[prm].clone()
print_log('save into {:}'.format(checkpoint_path), log)
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
test_loss = evaluate(model, corpus, test_data, config.test_batch, config.bptt)
if use_asgd:
for prm in model.parameters():
prm.data = tmp[prm].clone()
print_log('| epoch={:03d} | test loss {:5.2f} | test ppl {:8.2f}'.format(epoch, test_loss, math.exp(test_loss)), log)
losses[epoch] = val_loss
epoch = epoch + 1
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
print_log('--------------------- Finish Training ----------------', log)
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
test_loss = evaluate(model, corpus, test_data , config.test_batch, config.bptt)
print_log('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(test_loss, math.exp(test_loss)), log)
vali_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
print_log('| End of training | valid loss {:5.2f} | valid ppl {:8.2f}'.format(vali_loss, math.exp(vali_loss)), log)
def evaluate(model, corpus, data_source, batch_size, bptt):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss, total_length = 0.0, 0.0
with torch.no_grad():
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
for i in range(0, data_source.size(0) - 1, bptt):
data, targets = get_batch(data_source, i, bptt)
targets = targets.view(-1)
log_prob, hidden = model(data, hidden)
loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets)
total_loss += loss.item() * len(data)
total_length += len(data)
hidden = repackage_hidden(hidden)
return total_loss / total_length
def train(model, optimizer, corpus, train_data, config, epoch, print_freq, log):
# Turn on training mode which enables dropout.
total_loss, data_time, batch_time = 0, AverageMeter(), AverageMeter()
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden_train = model.init_hidden(config.train_batch)
batch, i = 0, 0
while i < train_data.size(0) - 1 - 1:
bptt = config.bptt if np.random.random() < 0.95 else config.bptt / 2.
# Prevent excessively small or negative sequence lengths
seq_len = max(5, int(np.random.normal(bptt, 5)))
# There's a very small chance that it could select a very long sequence length resulting in OOM
seq_len = min(seq_len, config.bptt + config.max_seq_len_delta)
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 * seq_len / config.bptt
model.train()
data, targets = get_batch(train_data, i, seq_len)
targets = targets.contiguous().view(-1)
# count data preparation time
data_time.update(time.time() - start_time)
optimizer.zero_grad()
hidden_train = repackage_hidden(hidden_train)
log_prob, hidden_train, rnn_hs, dropped_rnn_hs = model(data, hidden_train, return_h=True)
raw_loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets)
loss = raw_loss
# Activiation Regularization
if config.alpha > 0:
loss = loss + sum(config.alpha * dropped_rnn_h.pow(2).mean() for dropped_rnn_h in dropped_rnn_hs[-1:])
# Temporal Activation Regularization (slowness)
loss = loss + sum(config.beta * (rnn_h[1:] - rnn_h[:-1]).pow(2).mean() for rnn_h in rnn_hs[-1:])
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip)
optimizer.step()
gc.collect()
optimizer.param_groups[0]['lr'] = lr2
total_loss += raw_loss.item()
assert torch.isnan(loss) == False, '--- Epoch={:04d} :: {:03d}/{:03d} Get Loss = Nan'.format(epoch, batch, len(train_data)//config.bptt)
batch_time.update(time.time() - start_time)
start_time = time.time()
batch, i = batch + 1, i + seq_len
if batch % print_freq == 0:
cur_loss = total_loss / print_freq
print_log(' >> Epoch: {:04d} :: {:03d}/{:03d} || loss = {:5.2f}, ppl = {:8.2f}'.format(epoch, batch, len(train_data) // config.bptt, cur_loss, math.exp(cur_loss)), log)
total_loss = 0
return data_time.sum, batch_time.sum

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others/GDAS/lib/datasets/LanguageDataset.py Normal file
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import os
import torch
from collections import Counter
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
self.counter = Counter()
self.total = 0
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
token_id = self.word2idx[word]
self.counter[token_id] += 1
self.total += 1
return self.word2idx[word]
def __len__(self):
return len(self.idx2word)
class Corpus(object):
def __init__(self, path):
self.dictionary = Dictionary()
self.train = self.tokenize(os.path.join(path, 'train.txt'))
self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
self.test = self.tokenize(os.path.join(path, 'test.txt'))
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r', encoding='utf-8') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r', encoding='utf-8') as f:
ids = torch.LongTensor(tokens)
token = 0
for line in f:
words = line.split() + ['<eos>']
for word in words:
ids[token] = self.dictionary.word2idx[word]
token += 1
return ids
class SentCorpus(object):
def __init__(self, path):
self.dictionary = Dictionary()
self.train = self.tokenize(os.path.join(path, 'train.txt'))
self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
self.test = self.tokenize(os.path.join(path, 'test.txt'))
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r', encoding='utf-8') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
sents = []
with open(path, 'r', encoding='utf-8') as f:
for line in f:
if not line:
continue
words = line.split() + ['<eos>']
sent = torch.LongTensor(len(words))
for i, word in enumerate(words):
sent[i] = self.dictionary.word2idx[word]
sents.append(sent)
return sents
class BatchSentLoader(object):
def __init__(self, sents, batch_size, pad_id=0, cuda=False, volatile=False):
self.sents = sents
self.batch_size = batch_size
self.sort_sents = sorted(sents, key=lambda x: x.size(0))
self.cuda = cuda
self.volatile = volatile
self.pad_id = pad_id
def __next__(self):
if self.idx >= len(self.sort_sents):
raise StopIteration
batch_size = min(self.batch_size, len(self.sort_sents)-self.idx)
batch = self.sort_sents[self.idx:self.idx+batch_size]
max_len = max([s.size(0) for s in batch])
tensor = torch.LongTensor(max_len, batch_size).fill_(self.pad_id)
for i in range(len(batch)):
s = batch[i]
tensor[:s.size(0),i].copy_(s)
if self.cuda:
tensor = tensor.cuda()
self.idx += batch_size
return tensor
next = __next__
def __iter__(self):
self.idx = 0
return self

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others/GDAS/lib/datasets/MetaBatchSampler.py Normal file
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# coding=utf-8
import numpy as np
import torch
class MetaBatchSampler(object):
def __init__(self, labels, classes_per_it, num_samples, iterations):
'''
Initialize MetaBatchSampler
Args:
- labels: an iterable containing all the labels for the current dataset
samples indexes will be infered from this iterable.
- classes_per_it: number of random classes for each iteration
- num_samples: number of samples for each iteration for each class (support + query)
- iterations: number of iterations (episodes) per epoch
'''
super(MetaBatchSampler, self).__init__()
self.labels = labels.copy()
self.classes_per_it = classes_per_it
self.sample_per_class = num_samples
self.iterations = iterations
self.classes, self.counts = np.unique(self.labels, return_counts=True)
assert len(self.classes) == np.max(self.classes) + 1 and np.min(self.classes) == 0
assert classes_per_it < len(self.classes), '{:} vs. {:}'.format(classes_per_it, len(self.classes))
self.classes = torch.LongTensor(self.classes)
# create a matrix, indexes, of dim: classes X max(elements per class)
# fill it with nans
# for every class c, fill the relative row with the indices samples belonging to c
# in numel_per_class we store the number of samples for each class/row
self.indexes = { x.item() : [] for x in self.classes }
indexes = { x.item() : [] for x in self.classes }
for idx, label in enumerate(self.labels):
indexes[ label.item() ].append( idx )
for key, value in indexes.items():
self.indexes[ key ] = torch.LongTensor( value )
def __iter__(self):
# yield a batch of indexes
spc = self.sample_per_class
cpi = self.classes_per_it
for it in range(self.iterations):
batch_size = spc * cpi
batch = torch.LongTensor(batch_size)
assert cpi < len(self.classes), '{:} vs. {:}'.format(cpi, len(self.classes))
c_idxs = torch.randperm(len(self.classes))[:cpi]
for i, cls in enumerate(self.classes[c_idxs]):
s = slice(i * spc, (i + 1) * spc)
num = self.indexes[ cls.item() ].nelement()
assert spc < num, '{:} vs. {:}'.format(spc, num)
sample_idxs = torch.randperm( num )[:spc]
batch[s] = self.indexes[ cls.item() ][sample_idxs]
batch = batch[torch.randperm(len(batch))]
yield batch
def __len__(self):
# returns the number of iterations (episodes) per epoch
return self.iterations

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others/GDAS/lib/datasets/TieredImageNet.py Normal file
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from __future__ import print_function
import numpy as np
from PIL import Image
import pickle as pkl
import os, cv2, csv, glob
import torch
import torch.utils.data as data
class TieredImageNet(data.Dataset):
def __init__(self, root_dir, split, transform=None):
self.split = split
self.root_dir = root_dir
self.transform = transform
splits = split.split('-')
images, labels, last = [], [], 0
for split in splits:
labels_name = '{:}/{:}_labels.pkl'.format(self.root_dir, split)
images_name = '{:}/{:}_images.npz'.format(self.root_dir, split)
# decompress images if npz not exits
if not os.path.exists(images_name):
png_pkl = images_name[:-4] + '_png.pkl'
if os.path.exists(png_pkl):
decompress(images_name, png_pkl)
else:
raise ValueError('png_pkl {:} not exits'.format( png_pkl ))
assert os.path.exists(images_name) and os.path.exists(labels_name), '{:} & {:}'.format(images_name, labels_name)
print ("Prepare {:} done".format(images_name))
try:
with open(labels_name) as f:
data = pkl.load(f)
label_specific = data["label_specific"]
except:
with open(labels_name, 'rb') as f:
data = pkl.load(f, encoding='bytes')
label_specific = data[b'label_specific']
with np.load(images_name, mmap_mode="r", encoding='latin1') as data:
image_data = data["images"]
images.append( image_data )
label_specific = label_specific + last
labels.append( label_specific )
last = np.max(label_specific) + 1
print ("Load {:} done, with image shape = {:}, label shape = {:}, [{:} ~ {:}]".format(images_name, image_data.shape, label_specific.shape, np.min(label_specific), np.max(label_specific)))
images, labels = np.concatenate(images), np.concatenate(labels)
self.images = images
self.labels = labels
self.n_classes = int( np.max(labels) + 1 )
self.dict_index_label = {}
for cls in range(self.n_classes):
idxs = np.where(labels==cls)[0]
self.dict_index_label[cls] = idxs
self.length = len(labels)
print ("There are {:} images, {:} labels [{:} ~ {:}]".format(images.shape, labels.shape, np.min(labels), np.max(labels)))
def __repr__(self):
return ('{name}(length={length}, classes={n_classes})'.format(name=self.__class__.__name__, **self.__dict__))
def __len__(self):
return self.length
def __getitem__(self, index):
assert index >= 0 and index < self.length, 'invalid index = {:}'.format(index)
image = self.images[index].copy()
label = int(self.labels[index])
image = Image.fromarray(image[:,:,::-1].astype('uint8'), 'RGB')
if self.transform is not None:
image = self.transform( image )
return image, label
def decompress(path, output):
with open(output, 'rb') as f:
array = pkl.load(f, encoding='bytes')
images = np.zeros([len(array), 84, 84, 3], dtype=np.uint8)
for ii, item in enumerate(array):
im = cv2.imdecode(item, 1)
images[ii] = im
np.savez(path, images=images)

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others/GDAS/lib/datasets/__init__.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .MetaBatchSampler import MetaBatchSampler
from .TieredImageNet import TieredImageNet
from .LanguageDataset import Corpus
from .get_dataset_with_transform import get_datasets

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others/GDAS/lib/datasets/get_dataset_with_transform.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, torch
import os.path as osp
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from utils import Cutout
from .TieredImageNet import TieredImageNet
Dataset2Class = {'cifar10' : 10,
'cifar100': 100,
'tiered' : -1,
'imagenet-1k' : 1000,
'imagenet-100': 100}
def get_datasets(name, root, cutout):
# Mean + Std
if name == 'cifar10':
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
elif name == 'cifar100':
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
std = [x / 255 for x in [68.2, 65.4, 70.4]]
elif name == 'tiered':
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
elif name == 'imagenet-1k' or name == 'imagenet-100':
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
else: raise TypeError("Unknow dataset : {:}".format(name))
# Data Argumentation
if name == 'cifar10' or name == 'cifar100':
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
transforms.Normalize(mean, std)]
if cutout > 0 : lists += [Cutout(cutout)]
train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
elif name == 'tiered':
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
if cutout > 0 : lists += [Cutout(cutout)]
train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
elif name == 'imagenet-1k' or name == 'imagenet-100':
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2),
transforms.ToTensor(),
normalize,
])
test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
else: raise TypeError("Unknow dataset : {:}".format(name))
if name == 'cifar10':
train_data = dset.CIFAR10 (root, train=True , transform=train_transform, download=True)
test_data = dset.CIFAR10 (root, train=False, transform=test_transform , download=True)
elif name == 'cifar100':
train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
elif name == 'imagenet-1k' or name == 'imagenet-100':
train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
test_data = dset.ImageFolder(osp.join(root, 'val'), test_transform)
else: raise TypeError("Unknow dataset : {:}".format(name))
class_num = Dataset2Class[name]
return train_data, test_data, class_num

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others/GDAS/lib/datasets/test_NLP.py Normal file
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import os, sys, torch
from LanguageDataset import SentCorpus, BatchSentLoader
if __name__ == '__main__':
path = '../../data/data/penn'
corpus = SentCorpus( path )
loader = BatchSentLoader(corpus.test, 10)
for i, d in enumerate(loader):
print('{:} :: {:}'.format(i, d.size()))

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others/GDAS/lib/datasets/test_dataset.py Normal file
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import os, sys, torch
import torchvision.transforms as transforms
from TieredImageNet import TieredImageNet
from MetaBatchSampler import MetaBatchSampler
root_dir = os.environ['TORCH_HOME'] + '/tiered-imagenet'
print ('root : {:}'.format(root_dir))
means, stds = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(84, padding=8), transforms.ToTensor(), transforms.Normalize(means, stds)]
transform = transforms.Compose(lists)
dataset = TieredImageNet(root_dir, 'val-test', transform)
image, label = dataset[111]
print ('image shape = {:}, label = {:}'.format(image.size(), label))
print ('image : min = {:}, max = {:} ||| label : {:}'.format(image.min(), image.max(), label))
sampler = MetaBatchSampler(dataset.labels, 250, 100, 10)
dataloader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler)
print ('the length of dataset : {:}'.format( len(dataset) ))
print ('the length of loader : {:}'.format( len(dataloader) ))
for images, labels in dataloader:
print ('images : {:}'.format( images.size() ))
print ('labels : {:}'.format( labels.size() ))
for i in range(3):
print ('image-value-[{:}] : {:} ~ {:}, mean={:}, std={:}'.format(i, images[:,i].min(), images[:,i].max(), images[:,i].mean(), images[:,i].std()))
print('-----')

89
others/GDAS/lib/nas/CifarNet.py Normal file
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import torch
import torch.nn as nn
from .construct_utils import Cell, Transition
class AuxiliaryHeadCIFAR(nn.Module):
def __init__(self, C, num_classes):
"""assuming input size 8x8"""
super(AuxiliaryHeadCIFAR, self).__init__()
self.features = nn.Sequential(
nn.ReLU(inplace=True),
nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
nn.Conv2d(C, 128, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 768, 2, bias=False),
nn.BatchNorm2d(768),
nn.ReLU(inplace=True)
)
self.classifier = nn.Linear(768, num_classes)
def forward(self, x):
x = self.features(x)
x = self.classifier(x.view(x.size(0),-1))
return x
class NetworkCIFAR(nn.Module):
def __init__(self, C, num_classes, layers, auxiliary, genotype):
super(NetworkCIFAR, self).__init__()
self._layers = layers
stem_multiplier = 3
C_curr = stem_multiplier*C
self.stem = nn.Sequential(
nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
nn.BatchNorm2d(C_curr)
)
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
self.cells = nn.ModuleList()
reduction_prev = False
for i in range(layers):
if i in [layers//3, 2*layers//3]:
C_curr *= 2
reduction = True
else:
reduction = False
if reduction and genotype.reduce is None:
cell = Transition(C_prev_prev, C_prev, C_curr, reduction_prev)
else:
cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
reduction_prev = reduction
self.cells.append( cell )
C_prev_prev, C_prev = C_prev, cell.multiplier*C_curr
if i == 2*layers//3:
C_to_auxiliary = C_prev
if auxiliary:
self.auxiliary_head = AuxiliaryHeadCIFAR(C_to_auxiliary, num_classes)
else:
self.auxiliary_head = None
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(C_prev, num_classes)
self.drop_path_prob = -1
def update_drop_path(self, drop_path_prob):
self.drop_path_prob = drop_path_prob
def auxiliary_param(self):
if self.auxiliary_head is None: return []
else: return list( self.auxiliary_head.parameters() )
def forward(self, inputs):
s0 = s1 = self.stem(inputs)
for i, cell in enumerate(self.cells):
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
if i == 2*self._layers//3:
if self.auxiliary_head and self.training:
logits_aux = self.auxiliary_head(s1)
out = self.global_pooling(s1)
out = out.view(out.size(0), -1)
logits = self.classifier(out)
if self.auxiliary_head and self.training:
return logits, logits_aux
else:
return logits

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others/GDAS/lib/nas/ImageNet.py Normal file
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import torch
import torch.nn as nn
from .construct_utils import Cell, Transition
class AuxiliaryHeadImageNet(nn.Module):
def __init__(self, C, num_classes):
"""assuming input size 14x14"""
super(AuxiliaryHeadImageNet, self).__init__()
self.features = nn.Sequential(
nn.ReLU(inplace=True),
nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
nn.Conv2d(C, 128, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 768, 2, bias=False),
# NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
# Commenting it out for consistency with the experiments in the paper.
# nn.BatchNorm2d(768),
nn.ReLU(inplace=True)
)
self.classifier = nn.Linear(768, num_classes)
def forward(self, x):
x = self.features(x)
x = self.classifier(x.view(x.size(0),-1))
return x
class NetworkImageNet(nn.Module):
def __init__(self, C, num_classes, layers, auxiliary, genotype):
super(NetworkImageNet, self).__init__()
self._layers = layers
self.stem0 = nn.Sequential(
nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(C // 2),
nn.ReLU(inplace=True),
nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(C),
)
self.stem1 = nn.Sequential(
nn.ReLU(inplace=True),
nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(C),
)
C_prev_prev, C_prev, C_curr = C, C, C
self.cells = nn.ModuleList()
reduction_prev = True
for i in range(layers):
if i in [layers // 3, 2 * layers // 3]:
C_curr *= 2
reduction = True
else:
reduction = False
if reduction and genotype.reduce is None:
cell = Transition(C_prev_prev, C_prev, C_curr, reduction_prev)
else:
cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
reduction_prev = reduction
self.cells += [cell]
C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
if i == 2 * layers // 3:
C_to_auxiliary = C_prev
if auxiliary:
self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
else:
self.auxiliary_head = None
self.global_pooling = nn.AvgPool2d(7)
self.classifier = nn.Linear(C_prev, num_classes)
self.drop_path_prob = -1
def update_drop_path(self, drop_path_prob):
self.drop_path_prob = drop_path_prob
def get_drop_path(self):
return self.drop_path_prob
def auxiliary_param(self):
if self.auxiliary_head is None: return []
else: return list( self.auxiliary_head.parameters() )
def forward(self, input):
s0 = self.stem0(input)
s1 = self.stem1(s0)
for i, cell in enumerate(self.cells):
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
#print ('{:} : {:} - {:}'.format(i, s0.size(), s1.size()))
if i == 2 * self._layers // 3:
if self.auxiliary_head and self.training:
logits_aux = self.auxiliary_head(s1)
out = self.global_pooling(s1)
logits = self.classifier(out.view(out.size(0), -1))
if self.auxiliary_head and self.training:
return logits, logits_aux
else:
return logits

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others/GDAS/lib/nas/SE_Module.py Normal file
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import torch
import torch.nn as nn
# Squeeze and Excitation module
class SqEx(nn.Module):
def __init__(self, n_features, reduction=16):
super(SqEx, self).__init__()
if n_features % reduction != 0:
raise ValueError('n_features must be divisible by reduction (default = 16)')
self.linear1 = nn.Linear(n_features, n_features // reduction, bias=True)
self.nonlin1 = nn.ReLU(inplace=True)
self.linear2 = nn.Linear(n_features // reduction, n_features, bias=True)
self.nonlin2 = nn.Sigmoid()
def forward(self, x):
y = F.avg_pool2d(x, kernel_size=x.size()[2:4])
y = y.permute(0, 2, 3, 1)
y = self.nonlin1(self.linear1(y))
y = self.nonlin2(self.linear2(y))
y = y.permute(0, 3, 1, 2)
y = x * y
return y

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others/GDAS/lib/nas/__init__.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .CifarNet import NetworkCIFAR
from .ImageNet import NetworkImageNet
# genotypes
from .genotypes import model_types
from .construct_utils import return_alphas_str

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others/GDAS/lib/nas/construct_utils.py Normal file
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import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from .operations import OPS, FactorizedReduce, ReLUConvBN, Identity
def random_select(length, ratio):
clist = []
index = random.randint(0, length-1)
for i in range(length):
if i == index or random.random() < ratio:
clist.append( 1 )
else:
clist.append( 0 )
return clist
def all_select(length):
return [1 for i in range(length)]
def drop_path(x, drop_prob):
if drop_prob > 0.:
keep_prob = 1. - drop_prob
mask = x.new_zeros(x.size(0), 1, 1, 1)
mask = mask.bernoulli_(keep_prob)
x.div_(keep_prob)
x.mul_(mask)
return x
def return_alphas_str(basemodel):
string = 'normal : {:}'.format( F.softmax(basemodel.alphas_normal, dim=-1) )
if hasattr(basemodel, 'alphas_reduce'):
string = string + '\nreduce : {:}'.format( F.softmax(basemodel.alphas_reduce, dim=-1) )
return string
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
print(C_prev_prev, C_prev, C)
if reduction_prev:
self.preprocess0 = FactorizedReduce(C_prev_prev, C)
else:
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
if reduction:
op_names, indices, values = zip(*genotype.reduce)
concat = genotype.reduce_concat
else:
op_names, indices, values = zip(*genotype.normal)
concat = genotype.normal_concat
self._compile(C, op_names, indices, values, concat, reduction)
def _compile(self, C, op_names, indices, values, concat, reduction):
assert len(op_names) == len(indices)
self._steps = len(op_names) // 2
self._concat = concat
self.multiplier = len(concat)
self._ops = nn.ModuleList()
for name, index in zip(op_names, indices):
stride = 2 if reduction and index < 2 else 1
op = OPS[name](C, stride, True)
self._ops.append( op )
self._indices = indices
self._values = values
def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2*i]]
h2 = states[self._indices[2*i+1]]
op1 = self._ops[2*i]
op2 = self._ops[2*i+1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
class Transition(nn.Module):
def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier=4):
super(Transition, self).__init__()
if reduction_prev:
self.preprocess0 = FactorizedReduce(C_prev_prev, C)
else:
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
self.multiplier = multiplier
self.reduction = True
self.ops1 = nn.ModuleList(
[nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True)),
nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True))])
self.ops2 = nn.ModuleList(
[nn.Sequential(
nn.MaxPool2d(3, stride=2, padding=1),
nn.BatchNorm2d(C, affine=True)),
nn.Sequential(
nn.MaxPool2d(3, stride=2, padding=1),
nn.BatchNorm2d(C, affine=True))])
def forward(self, s0, s1, drop_prob = -1):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
X0 = self.ops1[0] (s0)
X1 = self.ops1[1] (s1)
if self.training and drop_prob > 0.:
X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
#X2 = self.ops2[0] (X0+X1)
X2 = self.ops2[0] (s0)
X3 = self.ops2[1] (s1)
if self.training and drop_prob > 0.:
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
return torch.cat([X0, X1, X2, X3], dim=1)

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from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
PRIMITIVES = [
'none',
'max_pool_3x3',
'avg_pool_3x3',
'skip_connect',
'sep_conv_3x3',
'sep_conv_5x5',
'dil_conv_3x3',
'dil_conv_5x5'
]
NASNet = Genotype(
normal = [
('sep_conv_5x5', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('sep_conv_5x5', 0, 1.0),
('sep_conv_3x3', 0, 1.0),
('avg_pool_3x3', 1, 1.0),
('skip_connect', 0, 1.0),
('avg_pool_3x3', 0, 1.0),
('avg_pool_3x3', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('skip_connect', 1, 1.0),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
('sep_conv_5x5', 1, 1.0),
('sep_conv_7x7', 0, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_7x7', 0, 1.0),
('avg_pool_3x3', 1, 1.0),
('sep_conv_5x5', 0, 1.0),
('skip_connect', 3, 1.0),
('avg_pool_3x3', 2, 1.0),
('sep_conv_3x3', 2, 1.0),
('max_pool_3x3', 1, 1.0),
],
reduce_concat = [4, 5, 6],
)
AmoebaNet = Genotype(
normal = [
('avg_pool_3x3', 0, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('sep_conv_5x5', 2, 1.0),
('sep_conv_3x3', 0, 1.0),
('avg_pool_3x3', 3, 1.0),
('sep_conv_3x3', 1, 1.0),
('skip_connect', 1, 1.0),
('skip_connect', 0, 1.0),
('avg_pool_3x3', 1, 1.0),
],
normal_concat = [4, 5, 6],
reduce = [
('avg_pool_3x3', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('max_pool_3x3', 0, 1.0),
('sep_conv_7x7', 2, 1.0),
('sep_conv_7x7', 0, 1.0),
('avg_pool_3x3', 1, 1.0),
('max_pool_3x3', 0, 1.0),
('max_pool_3x3', 1, 1.0),
('conv_7x1_1x7', 0, 1.0),
('sep_conv_3x3', 5, 1.0),
],
reduce_concat = [3, 4, 6]
)
DARTS_V1 = Genotype(
normal=[
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('skip_connect', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('skip_connect', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('skip_connect', 2, 1.0)],
normal_concat=[2, 3, 4, 5],
reduce=[
('max_pool_3x3', 0, 1.0),
('max_pool_3x3', 1, 1.0),
('skip_connect', 2, 1.0),
('max_pool_3x3', 0, 1.0),
('max_pool_3x3', 0, 1.0),
('skip_connect', 2, 1.0),
('skip_connect', 2, 1.0),
('avg_pool_3x3', 0, 1.0)],
reduce_concat=[2, 3, 4, 5]
)
DARTS_V2 = Genotype(
normal=[
('sep_conv_3x3', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 1, 1.0),
('skip_connect', 0, 1.0),
('skip_connect', 0, 1.0),
('dil_conv_3x3', 2, 1.0)],
normal_concat=[2, 3, 4, 5],
reduce=[
('max_pool_3x3', 0, 1.0),
('max_pool_3x3', 1, 1.0),
('skip_connect', 2, 1.0),
('max_pool_3x3', 1, 1.0),
('max_pool_3x3', 0, 1.0),
('skip_connect', 2, 1.0),
('skip_connect', 2, 1.0),
('max_pool_3x3', 1, 1.0)],
reduce_concat=[2, 3, 4, 5]
)
PNASNet = Genotype(
normal = [
('sep_conv_5x5', 0, 1.0),
('max_pool_3x3', 0, 1.0),
('sep_conv_7x7', 1, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_5x5', 1, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 4, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('skip_connect', 1, 1.0),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
('sep_conv_5x5', 0, 1.0),
('max_pool_3x3', 0, 1.0),
('sep_conv_7x7', 1, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_5x5', 1, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 4, 1.0),
('max_pool_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('skip_connect', 1, 1.0),
],
reduce_concat = [2, 3, 4, 5, 6],
)
# https://arxiv.org/pdf/1802.03268.pdf
ENASNet = Genotype(
normal = [
('sep_conv_3x3', 1, 1.0),
('skip_connect', 1, 1.0),
('sep_conv_5x5', 1, 1.0),
('skip_connect', 0, 1.0),
('avg_pool_3x3', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('avg_pool_3x3', 1, 1.0),
('sep_conv_5x5', 1, 1.0),
('avg_pool_3x3', 0, 1.0),
],
normal_concat = [2, 3, 4, 5, 6],
reduce = [
('sep_conv_5x5', 0, 1.0),
('sep_conv_3x3', 1, 1.0), # 2
('sep_conv_3x3', 1, 1.0),
('avg_pool_3x3', 1, 1.0), # 3
('sep_conv_3x3', 1, 1.0),
('avg_pool_3x3', 1, 1.0), # 4
('avg_pool_3x3', 1, 1.0),
('sep_conv_5x5', 4, 1.0), # 5
('sep_conv_3x3', 5, 1.0),
('sep_conv_5x5', 0, 1.0),
],
reduce_concat = [2, 3, 4, 5, 6],
)
DARTS = DARTS_V2
# Search by normal and reduce
GDAS_V1 = Genotype(
normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
normal_concat=range(2, 6),
reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
reduce_concat=range(2, 6)
)
# Search by normal and fixing reduction
GDAS_F1 = Genotype(
normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
normal_concat=[2, 3, 4, 5],
reduce=None,
reduce_concat=[2, 3, 4, 5],
)
# Combine DMS_V1 and DMS_F1
GDAS_GF = Genotype(
normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
normal_concat=range(2, 6),
reduce=None,
reduce_concat=range(2, 6)
)
GDAS_FG = Genotype(
normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
normal_concat=range(2, 6),
reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
reduce_concat=range(2, 6)
)
PDARTS = Genotype(
normal=[
('skip_connect', 0, 1.0),
('dil_conv_3x3', 1, 1.0),
('skip_connect', 0, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 1, 1.0),
('sep_conv_3x3', 3, 1.0),
('sep_conv_3x3', 0, 1.0),
('dil_conv_5x5', 4, 1.0)],
normal_concat=range(2, 6),
reduce=[
('avg_pool_3x3', 0, 1.0),
('sep_conv_5x5', 1, 1.0),
('sep_conv_3x3', 0, 1.0),
('dil_conv_5x5', 2, 1.0),
('max_pool_3x3', 0, 1.0),
('dil_conv_3x3', 1, 1.0),
('dil_conv_3x3', 1, 1.0),
('dil_conv_5x5', 3, 1.0)],
reduce_concat=range(2, 6)
)
model_types = {'DARTS_V1': DARTS_V1,
'DARTS_V2': DARTS_V2,
'NASNet' : NASNet,
'PNASNet' : PNASNet,
'AmoebaNet': AmoebaNet,
'ENASNet' : ENASNet,
'PDARTS' : PDARTS,
'GDAS_V1' : GDAS_V1,
'GDAS_F1' : GDAS_F1,
'GDAS_GF' : GDAS_GF,
'GDAS_FG' : GDAS_FG}

19
others/GDAS/lib/nas/head_utils.py Normal file
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import torch
import torch.nn as nn
class ImageNetHEAD(nn.Sequential):
def __init__(self, C, stride=2):
super(ImageNetHEAD, self).__init__()
self.add_module('conv1', nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False))
self.add_module('bn1' , nn.BatchNorm2d(C // 2))
self.add_module('relu1', nn.ReLU(inplace=True))
self.add_module('conv2', nn.Conv2d(C // 2, C, kernel_size=3, stride=stride, padding=1, bias=False))
self.add_module('bn2' , nn.BatchNorm2d(C))
class CifarHEAD(nn.Sequential):
def __init__(self, C):
super(CifarHEAD, self).__init__()
self.add_module('conv', nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False))
self.add_module('bn', nn.BatchNorm2d(C))

122
others/GDAS/lib/nas/operations.py Normal file
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import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.Sequential(
nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
nn.BatchNorm2d(C, affine=False) ),
'max_pool_3x3' : lambda C, stride, affine: nn.Sequential(
nn.MaxPool2d(3, stride=stride, padding=1),
nn.BatchNorm2d(C, affine=False) ),
'skip_connect' : lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
'sep_conv_3x3' : lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
'sep_conv_5x5' : lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
'dil_conv_3x3' : lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
'dil_conv_5x5' : lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
'conv_7x1_1x7' : lambda C, stride, affine: Conv717(C, C, stride, affine),
}
class Conv717(nn.Module):
def __init__(self, C_in, C_out, stride, affine):
super(Conv717, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in , C_out, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
nn.Conv2d(C_out, C_out, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class ReLUConvBN(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(ReLUConvBN, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class DilConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
super(DilConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class SepConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(SepConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_in, affine=affine),
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False),
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class Zero(nn.Module):
def __init__(self, stride):
super(Zero, self).__init__()
self.stride = stride
def forward(self, x):
if self.stride == 1:
return x.mul(0.)
return x[:,:,::self.stride,::self.stride].mul(0.)
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, affine=True):
super(FactorizedReduce, self).__init__()
assert C_out % 2 == 0
self.relu = nn.ReLU(inplace=False)
self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.bn = nn.BatchNorm2d(C_out, affine=affine)
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
def forward(self, x):
x = self.relu(x)
y = self.pad(x)
out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1)
out = self.bn(out)
return out

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others/GDAS/lib/nas_rnn/__init__.py Normal file
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# utils
from .utils import batchify, get_batch, repackage_hidden
# models
from .model_search import RNNModelSearch
from .model_search import DARTSCellSearch
from .basemodel import DARTSCell, RNNModel
# architecture
from .genotypes import DARTS_V1, DARTS_V2
from .genotypes import GDAS

181
others/GDAS/lib/nas_rnn/basemodel.py Normal file
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .genotypes import STEPS
from .utils import mask2d, LockedDropout, embedded_dropout
INITRANGE = 0.04
def none_func(x):
return x * 0
class DARTSCell(nn.Module):
def __init__(self, ninp, nhid, dropouth, dropoutx, genotype):
super(DARTSCell, self).__init__()
self.nhid = nhid
self.dropouth = dropouth
self.dropoutx = dropoutx
self.genotype = genotype
# genotype is None when doing arch search
steps = len(self.genotype.recurrent) if self.genotype is not None else STEPS
self._W0 = nn.Parameter(torch.Tensor(ninp+nhid, 2*nhid).uniform_(-INITRANGE, INITRANGE))
self._Ws = nn.ParameterList([
nn.Parameter(torch.Tensor(nhid, 2*nhid).uniform_(-INITRANGE, INITRANGE)) for i in range(steps)
])
def forward(self, inputs, hidden, arch_probs):
T, B = inputs.size(0), inputs.size(1)
if self.training:
x_mask = mask2d(B, inputs.size(2), keep_prob=1.-self.dropoutx)
h_mask = mask2d(B, hidden.size(2), keep_prob=1.-self.dropouth)
else:
x_mask = h_mask = None
hidden = hidden[0]
hiddens = []
for t in range(T):
hidden = self.cell(inputs[t], hidden, x_mask, h_mask, arch_probs)
hiddens.append(hidden)
hiddens = torch.stack(hiddens)
return hiddens, hiddens[-1].unsqueeze(0)
def _compute_init_state(self, x, h_prev, x_mask, h_mask):
if self.training:
xh_prev = torch.cat([x * x_mask, h_prev * h_mask], dim=-1)
else:
xh_prev = torch.cat([x, h_prev], dim=-1)
c0, h0 = torch.split(xh_prev.mm(self._W0), self.nhid, dim=-1)
c0 = c0.sigmoid()
h0 = h0.tanh()
s0 = h_prev + c0 * (h0-h_prev)
return s0
def _get_activation(self, name):
if name == 'tanh':
f = torch.tanh
elif name == 'relu':
f = torch.relu
elif name == 'sigmoid':
f = torch.sigmoid
elif name == 'identity':
f = lambda x: x
elif name == 'none':
f = none_func
else:
raise NotImplementedError
return f
def cell(self, x, h_prev, x_mask, h_mask, _):
s0 = self._compute_init_state(x, h_prev, x_mask, h_mask)
states = [s0]
for i, (name, pred) in enumerate(self.genotype.recurrent):
s_prev = states[pred]
if self.training:
ch = (s_prev * h_mask).mm(self._Ws[i])
else:
ch = s_prev.mm(self._Ws[i])
c, h = torch.split(ch, self.nhid, dim=-1)
c = c.sigmoid()
fn = self._get_activation(name)
h = fn(h)
s = s_prev + c * (h-s_prev)
states += [s]
output = torch.mean(torch.stack([states[i] for i in self.genotype.concat], -1), -1)
return output
class RNNModel(nn.Module):
"""Container module with an encoder, a recurrent module, and a decoder."""
def __init__(self, ntoken, ninp, nhid, nhidlast,
dropout=0.5, dropouth=0.5, dropoutx=0.5, dropouti=0.5, dropoute=0.1,
cell_cls=None, genotype=None):
super(RNNModel, self).__init__()
self.lockdrop = LockedDropout()
self.encoder = nn.Embedding(ntoken, ninp)
assert ninp == nhid == nhidlast
if cell_cls == DARTSCell:
assert genotype is not None
rnns = [cell_cls(ninp, nhid, dropouth, dropoutx, genotype)]
else:
assert genotype is None
rnns = [cell_cls(ninp, nhid, dropouth, dropoutx)]
self.rnns = torch.nn.ModuleList(rnns)
self.decoder = nn.Linear(ninp, ntoken)
self.decoder.weight = self.encoder.weight
self.init_weights()
self.arch_weights = None
self.ninp = ninp
self.nhid = nhid
self.nhidlast = nhidlast
self.dropout = dropout
self.dropouti = dropouti
self.dropoute = dropoute
self.ntoken = ntoken
self.cell_cls = cell_cls
# acceleration
self.tau = None
self.use_gumbel = False
def set_gumbel(self, use_gumbel, set_check):
self.use_gumbel = use_gumbel
for i, rnn in enumerate(self.rnns):
rnn.set_check(set_check)
def set_tau(self, tau):
self.tau = tau
def get_tau(self):
return self.tau
def init_weights(self):
self.encoder.weight.data.uniform_(-INITRANGE, INITRANGE)
self.decoder.bias.data.fill_(0)
self.decoder.weight.data.uniform_(-INITRANGE, INITRANGE)
def forward(self, input, hidden, return_h=False):
batch_size = input.size(1)
emb = embedded_dropout(self.encoder, input, dropout=self.dropoute if self.training else 0)
emb = self.lockdrop(emb, self.dropouti)
raw_output = emb
new_hidden = []
raw_outputs = []
outputs = []
if self.arch_weights is None:
arch_probs = None
else:
if self.use_gumbel: arch_probs = F.gumbel_softmax(self.arch_weights, self.tau, False)
else : arch_probs = F.softmax(self.arch_weights, dim=-1)
for l, rnn in enumerate(self.rnns):
current_input = raw_output
raw_output, new_h = rnn(raw_output, hidden[l], arch_probs)
new_hidden.append(new_h)
raw_outputs.append(raw_output)
hidden = new_hidden
output = self.lockdrop(raw_output, self.dropout)
outputs.append(output)
logit = self.decoder(output.view(-1, self.ninp))
log_prob = nn.functional.log_softmax(logit, dim=-1)
model_output = log_prob
model_output = model_output.view(-1, batch_size, self.ntoken)
if return_h: return model_output, hidden, raw_outputs, outputs
else : return model_output, hidden
def init_hidden(self, bsz):
weight = next(self.parameters()).clone()
return [weight.new(1, bsz, self.nhid).zero_()]

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others/GDAS/lib/nas_rnn/genotypes.py Normal file
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from collections import namedtuple
Genotype = namedtuple('Genotype', 'recurrent concat')
PRIMITIVES = [
'none',
'tanh',
'relu',
'sigmoid',
'identity'
]
STEPS = 8
CONCAT = 8
ENAS = Genotype(
recurrent = [
('tanh', 0),
('tanh', 1),
('relu', 1),
('tanh', 3),
('tanh', 3),
('relu', 3),
('relu', 4),
('relu', 7),
('relu', 8),
('relu', 8),
('relu', 8),
],
concat = [2, 5, 6, 9, 10, 11]
)
DARTS_V1 = Genotype(
recurrent = [
('relu', 0),
('relu', 1),
('tanh', 2),
('relu', 3), ('relu', 4), ('identity', 1), ('relu', 5), ('relu', 1)
],
concat=range(1, 9)
)
DARTS_V2 = Genotype(
recurrent = [
('sigmoid', 0), ('relu', 1), ('relu', 1),
('identity', 1), ('tanh', 2), ('sigmoid', 5),
('tanh', 3), ('relu', 5)
],
concat=range(1, 9)
)
GDAS = Genotype(
recurrent=[('relu', 0), ('relu', 0), ('identity', 1), ('relu', 1), ('tanh', 0), ('relu', 2), ('identity', 4), ('identity', 2)],
concat=range(1, 9)
)

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others/GDAS/lib/nas_rnn/model_search.py Normal file
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import copy, torch
import torch.nn as nn
import torch.nn.functional as F
from collections import namedtuple
from .genotypes import PRIMITIVES, STEPS, CONCAT, Genotype
from .basemodel import DARTSCell, RNNModel
class DARTSCellSearch(DARTSCell):
def __init__(self, ninp, nhid, dropouth, dropoutx):
super(DARTSCellSearch, self).__init__(ninp, nhid, dropouth, dropoutx, genotype=None)
self.bn = nn.BatchNorm1d(nhid, affine=False)
self.check_zero = False
def set_check(self, check_zero):
self.check_zero = check_zero
def cell(self, x, h_prev, x_mask, h_mask, arch_probs):
s0 = self._compute_init_state(x, h_prev, x_mask, h_mask)
s0 = self.bn(s0)
if self.check_zero:
arch_probs_cpu = arch_probs.cpu().tolist()
#arch_probs = F.softmax(self.weights, dim=-1)
offset = 0
states = s0.unsqueeze(0)
for i in range(STEPS):
if self.training:
masked_states = states * h_mask.unsqueeze(0)
else:
masked_states = states
ch = masked_states.view(-1, self.nhid).mm(self._Ws[i]).view(i+1, -1, 2*self.nhid)
c, h = torch.split(ch, self.nhid, dim=-1)
c = c.sigmoid()
s = torch.zeros_like(s0)
for k, name in enumerate(PRIMITIVES):
if name == 'none':
continue
fn = self._get_activation(name)
unweighted = states + c * (fn(h) - states)
if self.check_zero:
INDEX, INDDX = [], []
for jj in range(offset, offset+i+1):
if arch_probs_cpu[jj][k] > 0:
INDEX.append(jj)
INDDX.append(jj-offset)
if len(INDEX) == 0: continue
s += torch.sum(arch_probs[INDEX, k].unsqueeze(-1).unsqueeze(-1) * unweighted[INDDX, :, :], dim=0)
else:
s += torch.sum(arch_probs[offset:offset+i+1, k].unsqueeze(-1).unsqueeze(-1) * unweighted, dim=0)
s = self.bn(s)
states = torch.cat([states, s.unsqueeze(0)], 0)
offset += i+1
output = torch.mean(states[-CONCAT:], dim=0)
return output
class RNNModelSearch(RNNModel):
def __init__(self, *args):
super(RNNModelSearch, self).__init__(*args)
self._args = copy.deepcopy( args )
k = sum(i for i in range(1, STEPS+1))
self.arch_weights = nn.Parameter(torch.Tensor(k, len(PRIMITIVES)))
nn.init.normal_(self.arch_weights, 0, 0.001)
def base_parameters(self):
lists = list(self.lockdrop.parameters())
lists += list(self.encoder.parameters())
lists += list(self.rnns.parameters())
lists += list(self.decoder.parameters())
return lists
def arch_parameters(self):
return [self.arch_weights]
def genotype(self):
def _parse(probs):
gene = []
start = 0
for i in range(STEPS):
end = start + i + 1
W = probs[start:end].copy()
#j = sorted(range(i + 1), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[0]
j = sorted(range(i + 1), key=lambda x: -max(W[x][k] for k in range(len(W[x])) ))[0]
k_best = None
for k in range(len(W[j])):
#if k != PRIMITIVES.index('none'):
# if k_best is None or W[j][k] > W[j][k_best]:
# k_best = k
if k_best is None or W[j][k] > W[j][k_best]:
k_best = k
gene.append((PRIMITIVES[k_best], j))
start = end
return gene
with torch.no_grad():
gene = _parse(F.softmax(self.arch_weights, dim=-1).cpu().numpy())
genotype = Genotype(recurrent=gene, concat=list(range(STEPS+1)[-CONCAT:]))
return genotype

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others/GDAS/lib/nas_rnn/utils.py Normal file
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import torch
import torch.nn as nn
import os, shutil
import numpy as np
def repackage_hidden(h):
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def batchify(data, bsz, use_cuda):
nbatch = data.size(0) // bsz
data = data.narrow(0, 0, nbatch * bsz)
data = data.view(bsz, -1).t().contiguous()
if use_cuda: return data.cuda()
else : return data
def get_batch(source, i, seq_len):
seq_len = min(seq_len, len(source) - 1 - i)
data = source[i:i+seq_len].clone()
target = source[i+1:i+1+seq_len].clone()
return data, target
def embedded_dropout(embed, words, dropout=0.1, scale=None):
if dropout:
mask = embed.weight.data.new().resize_((embed.weight.size(0), 1)).bernoulli_(1 - dropout).expand_as(embed.weight) / (1 - dropout)
mask.requires_grad_(True)
masked_embed_weight = mask * embed.weight
else:
masked_embed_weight = embed.weight
if scale:
masked_embed_weight = scale.expand_as(masked_embed_weight) * masked_embed_weight
padding_idx = embed.padding_idx
if padding_idx is None:
padding_idx = -1
X = torch.nn.functional.embedding(
words, masked_embed_weight,
padding_idx, embed.max_norm, embed.norm_type,
embed.scale_grad_by_freq, embed.sparse)
return X
class LockedDropout(nn.Module):
def __init__(self):
super(LockedDropout, self).__init__()
def forward(self, x, dropout=0.5):
if not self.training or not dropout:
return x
m = x.data.new(1, x.size(1), x.size(2)).bernoulli_(1 - dropout)
mask = m.div_(1 - dropout).detach()
mask = mask.expand_as(x)
return mask * x
def mask2d(B, D, keep_prob, cuda=True):
m = torch.floor(torch.rand(B, D) + keep_prob) / keep_prob
if cuda: return m.cuda()
else : return m

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others/GDAS/lib/scheduler/__init__.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .utils import load_config
from .scheduler import MultiStepLR, obtain_scheduler

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others/GDAS/lib/scheduler/scheduler.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
from bisect import bisect_right
class MultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, milestones, gammas, last_epoch=-1):
if not list(milestones) == sorted(milestones):
raise ValueError('Milestones should be a list of'
' increasing integers. Got {:}', milestones)
assert len(milestones) == len(gammas), '{:} vs {:}'.format(milestones, gammas)
self.milestones = milestones
self.gammas = gammas
super(MultiStepLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
LR = 1
for x in self.gammas[:bisect_right(self.milestones, self.last_epoch)]: LR = LR * x
return [base_lr * LR for base_lr in self.base_lrs]
def obtain_scheduler(config, optimizer):
if config.type == 'multistep':
scheduler = MultiStepLR(optimizer, milestones=config.milestones, gammas=config.gammas)
elif config.type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, config.epochs)
else:
raise ValueError('Unknown learning rate scheduler type : {:}'.format(config.type))
return scheduler

42
others/GDAS/lib/scheduler/utils.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, json
from pathlib import Path
from collections import namedtuple
support_types = ('str', 'int', 'bool', 'float')
def convert_param(original_lists):
assert isinstance(original_lists, list), 'The type is not right : {:}'.format(original_lists)
ctype, value = original_lists[0], original_lists[1]
assert ctype in support_types, 'Ctype={:}, support={:}'.format(ctype, support_types)
is_list = isinstance(value, list)
if not is_list: value = [value]
outs = []
for x in value:
if ctype == 'int':
x = int(x)
elif ctype == 'str':
x = str(x)
elif ctype == 'bool':
x = bool(int(x))
elif ctype == 'float':
x = float(x)
else:
raise TypeError('Does not know this type : {:}'.format(ctype))
outs.append(x)
if not is_list: outs = outs[0]
return outs
def load_config(path):
path = str(path)
assert os.path.exists(path), 'Can not find {:}'.format(path)
# Reading data back
with open(path, 'r') as f:
data = json.load(f)
f.close()
content = { k: convert_param(v) for k,v in data.items()}
Arguments = namedtuple('Configure', ' '.join(content.keys()))
content = Arguments(**content)
return content

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others/GDAS/lib/utils/__init__.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from .utils import AverageMeter, RecorderMeter, convert_secs2time
from .utils import time_file_str, time_string
from .utils import test_imagenet_data
from .utils import print_log
from .evaluation_utils import obtain_accuracy
#from .draw_pts import draw_points
from .gpu_manager import GPUManager
from .save_meta import Save_Meta
from .model_utils import count_parameters_in_MB
from .model_utils import Cutout
from .flop_benchmark import print_FLOPs

41
others/GDAS/lib/utils/draw_pts.py Normal file
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import os, sys, time
import numpy as np
import matplotlib
import random
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
def draw_points(points, labels, save_path):
title = 'the visualized features'
dpi = 100
width, height = 1000, 1000
legend_fontsize = 10
figsize = width / float(dpi), height / float(dpi)
fig = plt.figure(figsize=figsize)
classes = np.unique(labels).tolist()
colors = cm.rainbow(np.linspace(0, 1, len(classes)))
legends = []
legendnames = []
for cls, c in zip(classes, colors):
indexes = labels == cls
ptss = points[indexes, :]
x = ptss[:,0]
y = ptss[:,1]
if cls % 2 == 0: marker = 'x'
else: marker = 'o'
legend = plt.scatter(x, y, color=c, s=1, marker=marker)
legendname = '{:02d}'.format(cls+1)
legends.append( legend )
legendnames.append( legendname )
plt.legend(legends, legendnames, scatterpoints=1, ncol=5, fontsize=8)
if save_path is not None:
fig.savefig(save_path, dpi=dpi, bbox_inches='tight')
print ('---- save figure {} into {}'.format(title, save_path))
plt.close(fig)

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others/GDAS/lib/utils/evaluation_utils.py Normal file
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import torch
def obtain_accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res

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others/GDAS/lib/utils/flop_benchmark.py Normal file
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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
# modified from https://github.com/warmspringwinds/pytorch-segmentation-detection/blob/master/pytorch_segmentation_detection/utils/flops_benchmark.py
import copy, torch
def print_FLOPs(model, shape, logs):
print_log, log = logs
model = copy.deepcopy( model )
model = add_flops_counting_methods(model)
model = model.cuda()
model.eval()
cache_inputs = torch.zeros(*shape).cuda()
#print_log('In the calculating function : cache input size : {:}'.format(cache_inputs.size()), log)
_ = model(cache_inputs)
FLOPs = compute_average_flops_cost( model ) / 1e6
print_log('FLOPs : {:} MB'.format(FLOPs), log)
torch.cuda.empty_cache()
# ---- Public functions
def add_flops_counting_methods( model ):
model.__batch_counter__ = 0
add_batch_counter_hook_function( model )
model.apply( add_flops_counter_variable_or_reset )
model.apply( add_flops_counter_hook_function )
return model
def compute_average_flops_cost(model):
"""
A method that will be available after add_flops_counting_methods() is called on a desired net object.
Returns current mean flops consumption per image.
"""
batches_count = model.__batch_counter__
flops_sum = 0
for module in model.modules():
if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear):
flops_sum += module.__flops__
return flops_sum / batches_count
# ---- Internal functions
def pool_flops_counter_hook(pool_module, inputs, output):
batch_size = inputs[0].size(0)
kernel_size = pool_module.kernel_size
out_C, output_height, output_width = output.shape[1:]
assert out_C == inputs[0].size(1), '{:} vs. {:}'.format(out_C, inputs[0].size())
overall_flops = batch_size * out_C * output_height * output_width * kernel_size * kernel_size
pool_module.__flops__ += overall_flops
def fc_flops_counter_hook(fc_module, inputs, output):
batch_size = inputs[0].size(0)
xin, xout = fc_module.in_features, fc_module.out_features
assert xin == inputs[0].size(1) and xout == output.size(1), 'IO=({:}, {:})'.format(xin, xout)
overall_flops = batch_size * xin * xout
if fc_module.bias is not None:
overall_flops += batch_size * xout
fc_module.__flops__ += overall_flops
def conv_flops_counter_hook(conv_module, inputs, output):
batch_size = inputs[0].size(0)
output_height, output_width = output.shape[2:]
kernel_height, kernel_width = conv_module.kernel_size
in_channels = conv_module.in_channels
out_channels = conv_module.out_channels
groups = conv_module.groups
conv_per_position_flops = kernel_height * kernel_width * in_channels * out_channels / groups
active_elements_count = batch_size * output_height * output_width
overall_flops = conv_per_position_flops * active_elements_count
if conv_module.bias is not None:
overall_flops += out_channels * active_elements_count
conv_module.__flops__ += overall_flops
def batch_counter_hook(module, inputs, output):
# Can have multiple inputs, getting the first one
inputs = inputs[0]
batch_size = inputs.shape[0]
module.__batch_counter__ += batch_size
def add_batch_counter_hook_function(module):
if not hasattr(module, '__batch_counter_handle__'):
handle = module.register_forward_hook(batch_counter_hook)
module.__batch_counter_handle__ = handle
def add_flops_counter_variable_or_reset(module):
if isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear) \
or isinstance(module, torch.nn.AvgPool2d) or isinstance(module, torch.nn.MaxPool2d):
module.__flops__ = 0
def add_flops_counter_hook_function(module):
if isinstance(module, torch.nn.Conv2d):
if not hasattr(module, '__flops_handle__'):
handle = module.register_forward_hook(conv_flops_counter_hook)
module.__flops_handle__ = handle
elif isinstance(module, torch.nn.Linear):
if not hasattr(module, '__flops_handle__'):
handle = module.register_forward_hook(fc_flops_counter_hook)
module.__flops_handle__ = handle
elif isinstance(module, torch.nn.AvgPool2d) or isinstance(module, torch.nn.MaxPool2d):
if not hasattr(module, '__flops_handle__'):
handle = module.register_forward_hook(pool_flops_counter_hook)
module.__flops_handle__ = handle

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