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Repository: snu-mllab/PuzzleMix
Branch: master
Commit: e2dbf3a23710
Files: 47
Total size: 5.4 MB
Directory structure:
gitextract_j9jt4wru/
├── .gitignore
├── LICENSE
├── README.md
├── Visualization.ipynb
├── checkpoint/
│ ├── preactresnet18/
│ │ ├── log.pkl
│ │ └── log.txt
│ ├── test_robust.py
│ └── utils.py
├── figures/
│ └── sample.data
├── imagenet/
│ ├── LICENSE
│ ├── README.md
│ ├── mixup.py
│ ├── models/
│ │ ├── densenet.py
│ │ ├── preresnet.py
│ │ ├── pyramidnet.py
│ │ ├── resnet.py
│ │ ├── resnext.py
│ │ └── wide_resnet.py
│ ├── test.py
│ ├── train.py
│ └── utils.py
├── imagenet_fast/
│ ├── README.md
│ ├── configs/
│ │ └── puzzlemix/
│ │ ├── configs_fast_phase1.yml
│ │ ├── configs_fast_phase2.yml
│ │ └── configs_fast_phase3.yml
│ ├── init_paths.py
│ ├── lib/
│ │ ├── __init__.py
│ │ ├── utils.py
│ │ └── validation.py
│ ├── main_fast.py
│ ├── main_test.py
│ ├── models/
│ │ ├── __init__.py
│ │ └── imagenet_resnet.py
│ ├── requirements.txt
│ ├── resize.py
│ ├── run_fast.sh
│ └── run_test.sh
├── load_data.py
├── logger.py
├── main.py
├── mixup.py
├── models/
│ ├── __init__.py
│ ├── preresnet.py
│ └── wide_resnet.py
└── unit_test/
├── test_exact.py
├── test_graphcut.py
└── test_heuristic.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.par.*
*/*
*.pyc
*.swp
*/*.swp
*/*.pyc
!models/*
models/*.pyc
!unit_test/*
!checkpoint/*
saliency.py
!imagenet_fast/*
.DS_Store
!figures/*
figures/.DS_Store
!imagenet/*
imagenet/checkpoint/*
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2020 Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song
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.
================================================
FILE: README.md
================================================
# Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
This is the code for the paper "Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup" accepted at ICML'20 ([paper](https://arxiv.org/abs/2009.06962), [talk](https://icml.cc/virtual/2020/paper/6827), [blog](https://mllab.snu.ac.kr/kim-ICML2020.html)). Some parts of the codes are borrowed from manifold mixup ([link](https://github.com/vikasverma1077/manifold_mixup/tree/master/supervised)).

## Citing this Work
```
@inproceedings{kimICML20,
title= {Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup},
author = {Kim, Jang-Hyun and Choo, Wonho and Song, Hyun Oh},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2020}
}
```
## Updates
- (21.01.04) ImageNet training for 300 epochs is conducted! (Top-1 accuracy: **78.76%**, details are at [```./imagenet```](https://github.com/snu-mllab/PuzzleMix/tree/master/imagenet)).
- (20.12.01/ torch 1.7) We built a **multi-processing** code for graph-cut, which runs on CPUs. As a result, the Puzzle Mix implementation (50s/epoch) is only slower about 1.5 times than Vanilla training (34s/epoch) on CIFAR-100, PreActResNet-18.
To use the multi-processing, just simply add `--mp [n_procs]` in the command.
## Requirements
This code has been tested with
python 3.6.8
pytorch 1.1.0
torchvision 0.3.0
gco-wrapper (https://github.com/Borda/pyGCO)
matplotlib 2.1.0
numpy 1.13.3
six 1.12.0
## Download Checkpoints and Test
We provide a checkpoint of adversarial Puzzle Mix with PreActResNet18 trained on CIFAR-100. The model has 80.34% clean test accuracy and 42.89% accuracy against FGSM with 8/255 l-infinity epsilon-ball.
CIFAR-100 dataset will automatically be downloaded at ```[data_path]```. To test corruption robusetness, download the dataset at [here](https://github.com/hendrycks/robustness). Note that the corruption dataset should be downloaded at ```[data_path]``` with the folder name of Cifar100-C (for CIFAR100) and tiny-imagenet-200-C (for Tiny-ImageNet).
To test the model, run:
```
cd checkpoint
python test_robust.py --ckpt preactresnet18 --datapath [data_path]
```
The other models trained with Puzzle Mix can be also downloaded:
Dataset | Model | Method | Description | Model file
-- | -- | -- | -- | --
CIFAR-100 | WRN-28-10 | Puzzle Mix \[Table 2\] | 84.0% (top-1) | [drive](https://drive.google.com/drive/folders/1_vOVNYcoXFNCpTzSjmP6iKwM4I8kebNM?usp=sharing)
CIFAR-100 | WRN-28-10 | Puzzle Mix + Adv training [Table 2] | 84.0% (Top-1) / 52.8% (FGSM) | [drive](https://drive.google.com/drive/folders/1rk7pv4ov6zXjP83SmsdYFcm8J_FDJxI-?usp=sharing)
CIFAR-100 | WRN-28-10 | Puzzle Mix + Augmentation [Table 7] | 83.7% (Top-1) / 71.1% (CIFAR100-C) | [drive](https://drive.google.com/drive/folders/1G0ACJzfRGLS7-1jTbflLuMCW7QGDYrxs?usp=sharing)
CIFAR-100 | PreActResNet-18 | Puzzle Mix \[Table 3\] | 80.4% (Top-1) | [drive](https://drive.google.com/drive/folders/1qBLhcUsicVFZi5sxEWTXI0O07QObG5dH?usp=sharing)
CIFAR-100 | PreActResNet-18 | Puzzle Mix + Adv training [Table 3] | 80.2% (Top-1) / 42.9% (FGSM) | [drive](https://drive.google.com/drive/folders/159nUxYN58OYXtRnQSynt8iEJ0BmlXaj9?usp=sharing)
Tiny-ImageNet | PreActResNet-18 | Puzzle Mix [Table 4] | 63.9% (Top-1) | [drive](https://drive.google.com/drive/folders/1jxCib7eSoKBthNGyke7lQuVHgkl21mtZ?usp=sharing)
Also, we provide a jupyter notebook, **Visualization.ipynb**, by which users can visualize Puzzle Mix results with image samples.
## Reproducing the results
Detailed descriptions of arguments are provided in ```main.py```. Below are some of the examples for reproducing the experimental results.
### ImageNet
To test with ImageNet, please refer to [```./imagenet_fast```](https://github.com/snu-mllab/PuzzleMix/tree/master/imagenet_fast) or [```./imagenet```](https://github.com/snu-mllab/PuzzleMix/tree/master/imagenet) (for 300 epochs training). ```./imagenet``` contains the most concise version of Puzzle Mix training code.
### CIFAR-100
Dataset will be downloaded at ```[data_path]``` and the results will be saved at ```[save_path]```. If you want to run codes without saving results, please set ```--log_off True```.
- To reproduce **Puzzle Mix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_size 4 --t_eps 0.8
```
- To reproduce **Puzzle Mix with PreActResNet18 for 600 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 600 --schedule 350 500 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_size 4 --t_eps 0.8
```
- To reproduce **adversarial Puzzle Mix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_size 4 --t_eps 0.8 --adv_p 0.1 --adv_eps 10.0
```
Below are commands to reproduce baselines.
- To reproduce **Vanilla with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train vanilla
```
- To reproduce **input mixup with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0
```
- To reproduce **manifold mixup with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup_hidden --mixup_alpha 2.0
```
- To reproduce **CutMix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset cifar100 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.1 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 400 800 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --box True
```
For **WRN28_10 with 400 epoch**, set ```--arch wrn28_10```, ```--epochs 400```, and ```--schedule 200 300```. For **WRN28_10 with 200 epoch**, set ```--epochs 200```, ```--schedule 120 170```, and ```--learning_rate 0.2```.
### Tiny-Imagenet-200
#### Download dataset
The following process is forked from ([link](https://github.com/vikasverma1077/manifold_mixup/tree/master/supervised)).
1. Download the zipped data from https://tiny-imagenet.herokuapp.com/
2. If not already exiting, create a subfolder "data" in root folder "PuzzleMix"
3. Extract the zipped data in folder PuzzleMix/data
4. Run the following script (This will arange the validation data in the format required by the pytorch loader)
```
python load_data.py
```
- To reproduce **Puzzle Mix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_eps 0.8 --clean_lam 1
```
- To reproduce **Puzzle Mix with PreActResNet18 for 600 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 600 --schedule 300 450 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_eps 0.8 --clean_lam 1
```
- To reproduce **adversarial Puzzle Mix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_eps 0.8 --adv_p 0.15 --adv_eps 10.0 --clean_lam 1
```
- To reproduce **adversarial Puzzle Mix with PreActResNet18 for 600 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 600 --schedule 300 450 --gammas 0.1 0.1 --train mixup --mixup_alpha 1.0 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_eps 0.8 --adv_p 0.15 --adv_eps 10.0 --clean_lam 1
```
Below are commands to reproduce baselines.
- To reproduce **Vanilla with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train vanilla
```
- To reproduce **input mixup with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --mixup_alpha 0.2
```
- To reproduce **manifold mixup with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup_hidden --mixup_alpha 0.2
```
- To reproduce **CutMix with PreActResNet18 for 1200 epochs**, run:
```
python main.py --dataset tiny-imagenet-200 --data_dir [data_path] --root_dir [save_path] --labels_per_class 500 --arch preactresnet18 --learning_rate 0.2 --momentum 0.9 --decay 0.0001 --epochs 1200 --schedule 600 900 --gammas 0.1 0.1 --train mixup --mixup_alpha 0.2 --box True
```
## License
MIT License
================================================
FILE: Visualization.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Packages"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torchvision\n",
"import torchvision.datasets as datasets\n",
"import torchvision.transforms as transforms\n",
"import torchvision.models as models\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import pickle\n",
"\n",
"from mixup import mixup_graph\n",
"\n",
"os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def print_fig(input, target=None, title=None, save_dir=None):\n",
" fig, axes = plt.subplots(1,len(input),figsize=(3*len(input),3))\n",
" if title:\n",
" fig.suptitle(title, size=16)\n",
" if len(input) == 1 :\n",
" axes = [axes]\n",
" \n",
" for i, ax in enumerate(axes):\n",
" if len(input.shape) == 4:\n",
" ax.imshow(input[i].permute(1,2,0).numpy())\n",
" else :\n",
" ax.imshow(input[i].numpy(), cmap='gray', vmin=0., vmax=1.)\n",
" \n",
" if target is not None:\n",
" output = net((input[i].unsqueeze(0) - mean)/std)\n",
" loss = criterion(output, target[i:i+1])\n",
" ax.set_title(\"loss: {:.3f}\\n pred: {}\\n true : {}\".format(loss, CIFAR100_LABELS_LIST[output.max(1)[1][0]], CIFAR100_LABELS_LIST[target[i]]))\n",
" ax.axis('off')\n",
" plt.subplots_adjust(wspace = 0.1)\n",
" \n",
" if save_dir is not None:\n",
" plt.savefig(save_dir, bbox_inches = 'tight', pad_inches = 0)\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model, Data, Saliency"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"''' Model '''\n",
"resnet = models.resnet18(pretrained=True)\n",
"mean = torch.tensor([0.485, 0.456, 0.406])\n",
"std = torch.tensor([0.229, 0.224, 0.225])\n",
"mean_torch = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)\n",
"std_torch = std.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)\n",
"\n",
"resnet.eval()\n",
"criterion = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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
gitextract_j9jt4wru/
├── .gitignore
├── LICENSE
├── README.md
├── Visualization.ipynb
├── checkpoint/
│ ├── preactresnet18/
│ │ ├── log.pkl
│ │ └── log.txt
│ ├── test_robust.py
│ └── utils.py
├── figures/
│ └── sample.data
├── imagenet/
│ ├── LICENSE
│ ├── README.md
│ ├── mixup.py
│ ├── models/
│ │ ├── densenet.py
│ │ ├── preresnet.py
│ │ ├── pyramidnet.py
│ │ ├── resnet.py
│ │ ├── resnext.py
│ │ └── wide_resnet.py
│ ├── test.py
│ ├── train.py
│ └── utils.py
├── imagenet_fast/
│ ├── README.md
│ ├── configs/
│ │ └── puzzlemix/
│ │ ├── configs_fast_phase1.yml
│ │ ├── configs_fast_phase2.yml
│ │ └── configs_fast_phase3.yml
│ ├── init_paths.py
│ ├── lib/
│ │ ├── __init__.py
│ │ ├── utils.py
│ │ └── validation.py
│ ├── main_fast.py
│ ├── main_test.py
│ ├── models/
│ │ ├── __init__.py
│ │ └── imagenet_resnet.py
│ ├── requirements.txt
│ ├── resize.py
│ ├── run_fast.sh
│ └── run_test.sh
├── load_data.py
├── logger.py
├── main.py
├── mixup.py
├── models/
│ ├── __init__.py
│ ├── preresnet.py
│ └── wide_resnet.py
└── unit_test/
├── test_exact.py
├── test_graphcut.py
└── test_heuristic.py
SYMBOL INDEX (282 symbols across 27 files)
FILE: checkpoint/test_robust.py
function accuracy (line 40) | def accuracy(output, target, topk=(1,)):
FILE: checkpoint/utils.py
class AverageMeter (line 11) | class AverageMeter(object):
method __init__ (line 13) | def __init__(self):
method reset (line 16) | def reset(self):
method update (line 22) | def update(self, val, n=1):
class RecorderMeter (line 29) | class RecorderMeter(object):
method __init__ (line 31) | def __init__(self, total_epoch):
method reset (line 34) | def reset(self, total_epoch):
method update (line 44) | def update(self, idx, train_loss, train_acc, val_loss, val_acc):
method max_accuracy (line 53) | def max_accuracy(self, istrain):
method plot_curve (line 58) | def plot_curve(self, save_path):
function time_string (line 104) | def time_string():
function convert_secs2time (line 109) | def convert_secs2time(epoch_time):
function time_file_str (line 115) | def time_file_str():
function to_one_hot (line 120) | def to_one_hot(inp,num_classes,device='cuda'):
function unsqueeze3 (line 127) | def unsqueeze3(tensor):
function cost_matrix (line 130) | def cost_matrix(width):
function K_prox (line 146) | def K_prox(x, H, transpose=False):
function K (line 154) | def K(x, xi1):
function nan_recover (line 157) | def nan_recover(tensor, thres=1e100):
function barycenter_conv2d (line 161) | def barycenter_conv2d(input1, input2, reg=2e-3, weights=None, numItermax...
function mixup_process (line 318) | def mixup_process(out, target_reweighted, mixup_alpha=1.0, loss_batch=No...
function mixup_data (line 387) | def mixup_data(x, y, alpha):
function get_lambda (line 400) | def get_lambda(alpha=1.0, alpha2=None):
class Cutout (line 412) | class Cutout(object):
method __init__ (line 418) | def __init__(self, n_holes, length):
method apply (line 422) | def apply(self, img):
function get_images_edges_cvh (line 452) | def get_images_edges_cvh(channel, height, width):
function cut_3d_graph (line 470) | def cut_3d_graph(unary_cost, pairwise_cost, cost_v, cost_h, cost_c, n_it...
function graphcut_multi (line 509) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
function graphcut_multi_float (line 545) | def graphcut_multi_float(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n...
function neigh_penalty (line 586) | def neigh_penalty(input1, input2, k, dim=2):
function mixup_box (line 603) | def mixup_box(input1, input2, grad1, grad2, method='random', alpha=0.5):
function mixup_graph (line 666) | def mixup_graph(input1, grad1, indices, block_num=2, method='random', al...
function mask_transport (line 844) | def mask_transport(mask, grad_pool, eps=0.01, t_type='full'):
function transport_image (line 877) | def transport_image(img, plan, batch_size, block_num, block_size):
function create_val_folder (line 890) | def create_val_folder(data_set_path):
function aug (line 919) | def aug(image, preprocess):
FILE: imagenet/mixup.py
function cost_matrix (line 7) | def cost_matrix(width):
function graphcut_multi (line 30) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
function neigh_penalty (line 68) | def neigh_penalty(input1, input2, k):
function mixup_graph (line 82) | def mixup_graph(input1,
function mask_transport (line 200) | def mask_transport(mask, grad_pool, eps=0.01):
function transport_image (line 225) | def transport_image(img, plan, block_num, block_size):
FILE: imagenet/models/densenet.py
class Bottleneck (line 5) | class Bottleneck(nn.Module):
method __init__ (line 6) | def __init__(self, nChannels, growthRate):
method forward (line 14) | def forward(self, x):
class SingleLayer (line 20) | class SingleLayer(nn.Module):
method __init__ (line 21) | def __init__(self, nChannels, growthRate):
method forward (line 26) | def forward(self, x):
class Transition (line 31) | class Transition(nn.Module):
method __init__ (line 32) | def __init__(self, nChannels, nOutChannels):
method forward (line 37) | def forward(self, x):
class DenseNet (line 42) | class DenseNet(nn.Module):
method __init__ (line 43) | def __init__(self, growthRate, depth, reduction, nClasses, bottleneck):
method _make_dense (line 80) | def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck):
method forward (line 90) | def forward(self, x):
function densenet100_12 (line 99) | def densenet100_12(num_classes=10):
function densenet100_24 (line 104) | def densenet100_24(num_classes=10):
FILE: imagenet/models/preresnet.py
class PreActBlock (line 13) | class PreActBlock(nn.Module):
method __init__ (line 17) | def __init__(self, in_planes, planes, stride=1):
method forward (line 29) | def forward(self, x):
class PreActBottleneck (line 38) | class PreActBottleneck(nn.Module):
method __init__ (line 42) | def __init__(self, in_planes, planes, stride=1):
method forward (line 56) | def forward(self, x):
class PreActResNet (line 66) | class PreActResNet(nn.Module):
method __init__ (line 67) | def __init__(self, block, num_blocks, initial_channels, num_classes, s...
method _make_layer (line 79) | def _make_layer(self, block, planes, num_blocks, stride):
method compute_h1 (line 87) | def compute_h1(self,x):
method compute_h2 (line 93) | def compute_h2(self,x):
method forward (line 100) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, ar...
function preactresnet18 (line 145) | def preactresnet18(num_classes=10, dropout = False, stride=1):
function preactresnet34 (line 148) | def preactresnet34(num_classes=10, dropout = False, stride=1):
function preactresnet50 (line 151) | def preactresnet50(num_classes=10, dropout = False, stride=1):
function preactresnet101 (line 154) | def preactresnet101(num_classes=10, dropout = False, stride=1):
function preactresnet152 (line 157) | def preactresnet152(num_classes=10, dropout = False, stride=1):
function test (line 160) | def test():
FILE: imagenet/models/pyramidnet.py
function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
class BasicBlock (line 13) | class BasicBlock(nn.Module):
method __init__ (line 16) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 27) | def forward(self, x):
class Bottleneck (line 55) | class Bottleneck(nn.Module):
method __init__ (line 58) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct...
method forward (line 72) | def forward(self, x):
class PyramidNet (line 106) | class PyramidNet(nn.Module):
method __init__ (line 108) | def __init__(self, dataset, depth, alpha, num_classes, bottleneck=False):
method pyramidal_make_layer (line 181) | def pyramidal_make_layer(self, block, block_depth, stride=1):
method forward (line 197) | def forward(self, x):
FILE: imagenet/models/resnet.py
function conv3x3 (line 6) | def conv3x3(in_planes, out_planes, stride=1):
class BasicBlock (line 12) | class BasicBlock(nn.Module):
method __init__ (line 15) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 26) | def forward(self, x):
class Bottleneck (line 45) | class Bottleneck(nn.Module):
method __init__ (line 48) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 62) | def forward(self, x):
class ResNet (line 83) | class ResNet(nn.Module):
method __init__ (line 84) | def __init__(self, dataset, depth, num_classes, bottleneck=False):
method _make_layer (line 131) | def _make_layer(self, block, planes, blocks, stride=1):
method forward (line 148) | def forward(self, x):
FILE: imagenet/models/resnext.py
class ResNeXtBottleneck (line 14) | class ResNeXtBottleneck(nn.Module):
method __init__ (line 19) | def __init__(self, inplanes, planes, cardinality, base_width, stride=1...
method forward (line 36) | def forward(self, x):
class CifarResNeXt (line 54) | class CifarResNeXt(nn.Module):
method __init__ (line 59) | def __init__(self, block, depth, cardinality, base_width, num_classes,...
method _make_layer (line 92) | def _make_layer(self, block, planes, blocks, stride=1):
method forward (line 109) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, mi...
function resnext29_16_64 (line 161) | def resnext29_16_64(num_classes=10,dropout=False, per_img_std = False):
function resnext29_8_64 (line 170) | def resnext29_8_64(num_classes=10, dropout=False, per_img_std = False):
FILE: imagenet/models/wide_resnet.py
function conv3x3 (line 18) | def conv3x3(in_planes, out_planes, stride=1):
function conv_init (line 21) | def conv_init(m):
class wide_basic (line 30) | class wide_basic(nn.Module):
method __init__ (line 31) | def __init__(self, in_planes, planes, stride=1):
method forward (line 44) | def forward(self, x):
class Wide_ResNet (line 51) | class Wide_ResNet(nn.Module):
method __init__ (line 53) | def __init__(self, depth, widen_factor, num_classes, stride = 1):
method _wide_layer (line 72) | def _wide_layer(self, block, planes, num_blocks, stride):
method forward (line 83) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, ar...
function wrn28_10 (line 128) | def wrn28_10(num_classes=10, dropout = False, stride = 1):
function wrn28_2 (line 133) | def wrn28_2(num_classes=10, dropout = False, stride = 1):
FILE: imagenet/test.py
function main (line 80) | def main():
function validate (line 168) | def validate(val_loader, model, criterion):
class AverageMeter (line 211) | class AverageMeter(object):
method __init__ (line 213) | def __init__(self):
method reset (line 216) | def reset(self):
method update (line 222) | def update(self, val, n=1):
function accuracy (line 229) | def accuracy(output, target, topk=(1, )):
FILE: imagenet/train.py
function str2bool (line 34) | def str2bool(v):
function main (line 139) | def main():
function train (line 337) | def train(train_loader, model, criterion, criterion_batch, optimizer, ep...
function rand_bbox (line 462) | def rand_bbox(size, lam):
function validate (line 481) | def validate(val_loader, model, criterion, epoch):
function save_checkpoint (line 529) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 539) | class AverageMeter(object):
method __init__ (line 541) | def __init__(self):
method reset (line 544) | def reset(self):
method update (line 550) | def update(self, val, n=1):
function adjust_learning_rate (line 557) | def adjust_learning_rate(optimizer, epoch):
function get_learning_rate (line 572) | def get_learning_rate(optimizer):
function accuracy (line 579) | def accuracy(output, target, topk=(1, )):
FILE: imagenet/utils.py
class Compose (line 9) | class Compose(object):
method __init__ (line 21) | def __init__(self, transforms):
method __call__ (line 24) | def __call__(self, img):
method __repr__ (line 29) | def __repr__(self):
class Lighting (line 38) | class Lighting(object):
method __init__ (line 40) | def __init__(self, alphastd, eigval, eigvec):
method __call__ (line 45) | def __call__(self, img):
class Grayscale (line 58) | class Grayscale(object):
method __call__ (line 59) | def __call__(self, img):
class Saturation (line 67) | class Saturation(object):
method __init__ (line 68) | def __init__(self, var):
method __call__ (line 71) | def __call__(self, img):
class Brightness (line 77) | class Brightness(object):
method __init__ (line 78) | def __init__(self, var):
method __call__ (line 81) | def __call__(self, img):
class Contrast (line 87) | class Contrast(object):
method __init__ (line 88) | def __init__(self, var):
method __call__ (line 91) | def __call__(self, img):
class ColorJitter (line 98) | class ColorJitter(object):
method __init__ (line 99) | def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4):
method __call__ (line 104) | def __call__(self, img):
FILE: imagenet_fast/lib/utils.py
class AverageMeter (line 14) | class AverageMeter(object):
method __init__ (line 16) | def __init__(self):
method reset (line 19) | def reset(self):
method update (line 25) | def update(self, val, n=1):
function adjust_learning_rate (line 32) | def adjust_learning_rate(initial_lr, optimizer, epoch, n_repeats):
function fgsm (line 39) | def fgsm(gradz, step_size):
function accuracy (line 43) | def accuracy(output, target, topk=(1,)):
function initiate_logger (line 60) | def initiate_logger(output_path, evaluate):
function get_model_names (line 72) | def get_model_names():
function pad_str (line 77) | def pad_str(msg, total_len=70):
function parse_config_file (line 81) | def parse_config_file(args):
function save_checkpoint (line 94) | def save_checkpoint(state, is_best, filepath, epoch):
function cost_matrix (line 103) | def cost_matrix(width):
function graphcut_multi (line 118) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
function graphcut_multi_float (line 154) | def graphcut_multi_float(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n...
function neigh_penalty (line 188) | def neigh_penalty(input1, input2, k):
function get_mask (line 201) | def get_mask(input1, grad1, block_num, indices, alpha=0.5, beta=1.2, gam...
function transport (line 269) | def transport(input1, grad1, indices, block_num, mask, eps=0.8):
function mask_transport (line 294) | def mask_transport(mask, grad_pool, eps=0.01):
function transport_image (line 321) | def transport_image(img, plan, block_num, block_size):
function create_val_folder (line 336) | def create_val_folder(data_set_path):
FILE: imagenet_fast/lib/validation.py
function validate (line 8) | def validate(val_loader, model, criterion, configs, logger):
FILE: imagenet_fast/main_fast.py
function parse_args (line 30) | def parse_args():
function main (line 70) | def main():
function train (line 203) | def train(train_loader, model, optimizer, epoch, lr_schedule, clean_lam=...
FILE: imagenet_fast/main_test.py
function parse_args (line 29) | def parse_args():
function compute_mce (line 81) | def compute_mce(corruption_accs):
function main (line 91) | def main():
function train (line 173) | def train(train_loader, model, optimizer, epoch, lr_schedule, half=False):
function test (line 310) | def test(net, test_loader):
function test_c (line 334) | def test_c(net, test_transform):
FILE: imagenet_fast/models/imagenet_resnet.py
function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
class BasicBlock (line 13) | class BasicBlock(nn.Module):
method __init__ (line 16) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 26) | def forward(self, x):
class Bottleneck (line 45) | class Bottleneck(nn.Module):
method __init__ (line 48) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 61) | def forward(self, x):
class ResNet (line 84) | class ResNet(nn.Module):
method __init__ (line 86) | def __init__(self, block, layers, num_classes=1000):
method _make_layer (line 110) | def _make_layer(self, block, planes, blocks, stride=1):
method forward (line 127) | def forward(self, x, manifold=False, graphcut=False, lam=None, permute...
function resnet18 (line 171) | def resnet18(num_classes=1000):
function resnet34 (line 181) | def resnet34(num_classes=1000):
function resnet50 (line 191) | def resnet50(num_classes=1000, dropout = False, per_img_std = False, str...
function resnet101 (line 201) | def resnet101(num_classes=1000):
function resnet152 (line 211) | def resnet152(num_classes=1000):
FILE: imagenet_fast/resize.py
function resize_img (line 18) | def resize_img(p, im, fn, sz):
function resizes (line 28) | def resizes(p, fn):
function resize_imgs (line 34) | def resize_imgs(p):
FILE: load_data.py
function load_data_subset (line 10) | def load_data_subset(batch_size,
function create_val_folder (line 161) | def create_val_folder(data_set_path):
FILE: logger.py
function copy_script_to_folder (line 14) | def copy_script_to_folder(caller_path, folder):
function time_string (line 21) | def time_string():
function convert_secs2time (line 28) | def convert_secs2time(epoch_time):
class RecorderMeter (line 35) | class RecorderMeter(object):
method __init__ (line 37) | def __init__(self, total_epoch):
method reset (line 40) | def reset(self, total_epoch):
method update (line 51) | def update(self, idx, train_loss, train_acc, val_loss, val_acc):
method max_accuracy (line 61) | def max_accuracy(self, istrain):
method plot_curve (line 66) | def plot_curve(self, save_path):
class AverageMeter (line 111) | class AverageMeter(object):
method __init__ (line 113) | def __init__(self):
method reset (line 116) | def reset(self):
method update (line 122) | def update(self, val, n=1):
function plotting (line 129) | def plotting(exp_dir):
FILE: main.py
function str2bool (line 28) | def str2bool(v):
function experiment_name_non_mnist (line 181) | def experiment_name_non_mnist(dataset=args.dataset,
function print_log (line 242) | def print_log(print_string, log, end='\n'):
function save_checkpoint (line 253) | def save_checkpoint(state, is_best, save_path, filename):
function adjust_learning_rate (line 262) | def adjust_learning_rate(optimizer, epoch, gammas, schedule):
function accuracy (line 276) | def accuracy(output, target, topk=(1, )):
function train (line 299) | def train(train_loader, model, optimizer, epoch, args, log, mp=None):
function validate (line 424) | def validate(val_loader, model, log, fgsm=False, eps=4, rand_init=False,...
function main (line 480) | def main():
FILE: mixup.py
function to_one_hot (line 9) | def to_one_hot(inp, num_classes, device='cuda'):
function cost_matrix (line 16) | def cost_matrix(width, device='cuda'):
function mixup_process (line 44) | def mixup_process(out,
function get_lambda (line 120) | def get_lambda(alpha=1.0, alpha2=None):
function graphcut_multi (line 132) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
function neigh_penalty (line 170) | def neigh_penalty(input1, input2, k):
function mixup_box (line 184) | def mixup_box(input1, input2, alpha=0.5, device='cuda'):
function mixup_graph (line 207) | def mixup_graph(input1,
function mask_transport (line 340) | def mask_transport(mask, grad_pool, eps=0.01):
function transport_image (line 367) | def transport_image(img, plan, batch_size, block_num, block_size):
FILE: models/preresnet.py
class PreActBlock (line 14) | class PreActBlock(nn.Module):
method __init__ (line 18) | def __init__(self, in_planes, planes, stride=1):
method forward (line 38) | def forward(self, x):
class PreActBottleneck (line 47) | class PreActBottleneck(nn.Module):
method __init__ (line 51) | def __init__(self, in_planes, planes, stride=1):
method forward (line 68) | def forward(self, x):
class PreActResNet (line 78) | class PreActResNet(nn.Module):
method __init__ (line 79) | def __init__(self, block, num_blocks, initial_channels, num_classes, s...
method _make_layer (line 95) | def _make_layer(self, block, planes, num_blocks, stride):
method compute_h1 (line 103) | def compute_h1(self, x):
method compute_h2 (line 109) | def compute_h2(self, x):
method forward (line 116) | def forward(self,
function preactresnet18 (line 174) | def preactresnet18(num_classes=10, dropout=False, stride=1):
function preactresnet34 (line 178) | def preactresnet34(num_classes=10, dropout=False, stride=1):
function preactresnet50 (line 182) | def preactresnet50(num_classes=10, dropout=False, stride=1):
function preactresnet101 (line 186) | def preactresnet101(num_classes=10, dropout=False, stride=1):
function preactresnet152 (line 190) | def preactresnet152(num_classes=10, dropout=False, stride=1):
function test (line 194) | def test():
FILE: models/wide_resnet.py
function conv3x3 (line 18) | def conv3x3(in_planes, out_planes, stride=1):
function conv_init (line 22) | def conv_init(m):
class wide_basic (line 32) | class wide_basic(nn.Module):
method __init__ (line 33) | def __init__(self, in_planes, planes, stride=1):
method forward (line 45) | def forward(self, x):
class Wide_ResNet (line 53) | class Wide_ResNet(nn.Module):
method __init__ (line 54) | def __init__(self, depth, widen_factor, num_classes, stride=1):
method _wide_layer (line 73) | def _wide_layer(self, block, planes, num_blocks, stride):
method forward (line 83) | def forward(self,
function wrn28_10 (line 142) | def wrn28_10(num_classes=10, dropout=False, stride=1):
function wrn28_2 (line 147) | def wrn28_2(num_classes=10, dropout=False, stride=1):
FILE: unit_test/test_exact.py
function mask_transport (line 6) | def mask_transport(mask, grad_pool, eps=0.01, n_iter=5, t_type='full'):
function cost_matrix (line 49) | def cost_matrix(width=16, dist=2):
FILE: unit_test/test_graphcut.py
function test_graph (line 8) | def test_graph(unary1, unary2, pw_x, pw_y, beta, n_labels=2, verbose=Fal...
function graphcut_multi (line 63) | def graphcut_multi(unary1, unary2, pw_x, pw_y, beta, n_labels=2):
FILE: unit_test/test_heuristic.py
function cost_matrix (line 16) | def cost_matrix(width=16, dist=2, device='cuda'):
function mask_transport (line 32) | def mask_transport(mask, grad_pool, eps=0.01, n_iter=None, t_type='full'...
function transport_image (line 71) | def transport_image(img, plan, batch_size, block_num, block_size):
function transport_image_loop (line 85) | def transport_image_loop(img, plan, batch_size, block_num, block_size):
Condensed preview — 47 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (5,670K chars).
[
{
"path": ".gitignore",
"chars": 193,
"preview": "*.par.*\n*/*\n*.pyc\n*.swp\n*/*.swp\n*/*.pyc\n!models/*\nmodels/*.pyc\n!unit_test/*\n!checkpoint/*\nsaliency.py\n!imagenet_fast/*\n."
},
{
"path": "LICENSE",
"chars": 1100,
"preview": "MIT License\n\nCopyright (c) 2020 Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song\n\nPermission is hereby granted, free of charg"
},
{
"path": "README.md",
"chars": 11241,
"preview": "# Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup\n\nThis is the code for the paper \"Puzzle Mix: Ex"
},
{
"path": "Visualization.ipynb",
"chars": 5070826,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Packages\"\n ]\n },\n {\n \"cel"
},
{
"path": "checkpoint/preactresnet18/log.txt",
"chars": 296808,
"preview": "save path : mixup/experiments/cifar100_pre18/attack/cifar100_arch_preactresnet18_train_mixup_eph_1200_lr_0.1_m_alpha_1.0"
},
{
"path": "checkpoint/test_robust.py",
"chars": 8847,
"preview": "#!/usr/bin/env python\nfrom __future__ import division\n\nimport os, sys, shutil, time, random\nsys.path.append('..')\nfrom g"
},
{
"path": "checkpoint/utils.py",
"chars": 37348,
"preview": "import os, sys, time\nimport torch\nfrom torch.autograd import Variable\nimport numpy as np\nimport matplotlib\nmatplotlib.us"
},
{
"path": "imagenet/LICENSE",
"chars": 1063,
"preview": "Copyright (c) 2019-present NAVER Corp.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof "
},
{
"path": "imagenet/README.md",
"chars": 3029,
"preview": "## ImageNet for 300 epochs\n\nIn this repository, we test Puzzle Mix on the experimental setting of [CutMix](https://githu"
},
{
"path": "imagenet/mixup.py",
"chars": 9104,
"preview": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport gco\n\n\ndef cost_matrix(width):\n '''transport co"
},
{
"path": "imagenet/models/densenet.py",
"chars": 3629,
"preview": "import math, torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Bottleneck(nn.Module):\n def __init__(se"
},
{
"path": "imagenet/models/preresnet.py",
"chars": 6329,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\nimport sys,os\nim"
},
{
"path": "imagenet/models/pyramidnet.py",
"chars": 8902,
"preview": "# Original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/PyramidNet.py\n\nimport torch\nimport torch.nn"
},
{
"path": "imagenet/models/resnet.py",
"chars": 6230,
"preview": "# Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py\n\nimport torch.nn as nn\nimpor"
},
{
"path": "imagenet/models/resnext.py",
"chars": 6088,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\nimport math\nfrom torch.auto"
},
{
"path": "imagenet/models/wide_resnet.py",
"chars": 4907,
"preview": "### dropout has been removed in this code. original code had dropout#####\nimport torch\nimport torch.nn as nn\nimport torc"
},
{
"path": "imagenet/test.py",
"chars": 9436,
"preview": "# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py\n\nimport argparse\nimport os\nimport "
},
{
"path": "imagenet/train.py",
"chars": 24570,
"preview": "# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py\nimport argparse\nimport os\nimport s"
},
{
"path": "imagenet/utils.py",
"chars": 3173,
"preview": "# original code: https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py\n\nimport torch\nimport random\n\n__"
},
{
"path": "imagenet_fast/README.md",
"chars": 1531,
"preview": "# Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup (ImageNet)\nThis is the code for fast ImageNet t"
},
{
"path": "imagenet_fast/configs/puzzlemix/configs_fast_phase1.yml",
"chars": 1065,
"preview": "TRAIN:\n \n # Architecture name, see pytorch models package for\n # a list of possible architectures\n arch: 're"
},
{
"path": "imagenet_fast/configs/puzzlemix/configs_fast_phase2.yml",
"chars": 1040,
"preview": "TRAIN:\n \n # Architecture name, see pytorch models package for\n # a list of possible architectures\n arch: 're"
},
{
"path": "imagenet_fast/configs/puzzlemix/configs_fast_phase3.yml",
"chars": 1064,
"preview": "TRAIN:\n \n # Architecture name, see pytorch models package for\n # a list of possible architectures\n arch: 're"
},
{
"path": "imagenet_fast/init_paths.py",
"chars": 78,
"preview": "import sys\nimport matplotlib\n\nmatplotlib.use('Agg')\nsys.path.insert(0, 'lib')\n"
},
{
"path": "imagenet_fast/lib/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "imagenet_fast/lib/utils.py",
"chars": 14420,
"preview": "import logging\nimport os\nimport datetime\nimport torchvision.models as models\nimport math\nimport torch\nimport yaml\nfrom e"
},
{
"path": "imagenet_fast/lib/validation.py",
"chars": 2025,
"preview": "from utils import *\nimport torch\nimport sys\nimport numpy as np\nimport time\n\n\ndef validate(val_loader, model, criterion, "
},
{
"path": "imagenet_fast/main_fast.py",
"chars": 12464,
"preview": "# This module is adapted from https://github.com/mahyarnajibi/FreeAdversarialTraining/blob/master/main_free.py\n# Which i"
},
{
"path": "imagenet_fast/main_test.py",
"chars": 14296,
"preview": "# This module is adapted from https://github.com/mahyarnajibi/FreeAdversarialTraining/blob/master/main_free.py\n# Which i"
},
{
"path": "imagenet_fast/models/__init__.py",
"chars": 1153,
"preview": "\"\"\"The models subpackage contains definitions for the following model\narchitectures:\n- `ResNeXt` for CIFAR10 CIFAR100\nY"
},
{
"path": "imagenet_fast/models/imagenet_resnet.py",
"chars": 6476,
"preview": "import torch.nn as nn\nimport math\nimport torch.utils.model_zoo as model_zoo\nimport random\nfrom utils import *\n\ndef conv3"
},
{
"path": "imagenet_fast/requirements.txt",
"chars": 31,
"preview": "numpy\npyyaml\nEasyDict\nargparse\n"
},
{
"path": "imagenet_fast/resize.py",
"chars": 1206,
"preview": "from PIL import Image\nfrom pathlib import Path\nfrom concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\nfr"
},
{
"path": "imagenet_fast/run_fast.sh",
"chars": 1506,
"preview": "DATA160=/data_large/readonly/imagenet-sz/160\nDATA352=/data_large/readonly/imagenet-sz/352\n\nNAME=puzzlemix\n\nCONFIG1=confi"
},
{
"path": "imagenet_fast/run_test.sh",
"chars": 1541,
"preview": "DATA160=/data_large/readonly/ImageNet-Fast/imagenet-sz/160\nDATA352=/data_large/readonly/ImageNet-Fast/imagenet-sz/352\n\nN"
},
{
"path": "load_data.py",
"chars": 8521,
"preview": "import torch\nimport os\nfrom torchvision import datasets, transforms\nfrom torch.utils.data.sampler import SubsetRandomSam"
},
{
"path": "logger.py",
"chars": 5507,
"preview": "import argparse\nimport sys\nif sys.version_info[0] < 3:\n import cPickle as pickle\nelse:\n import _pickle as pickle\ni"
},
{
"path": "main.py",
"chars": 25207,
"preview": "from __future__ import division\n# Codes are borrowed from https://github.com/vikasverma1077/manifold_mixup/tree/master/s"
},
{
"path": "mixup.py",
"chars": 13415,
"preview": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport gco\n\ndevice = 'cuda' if torch.cuda.is_available()"
},
{
"path": "models/__init__.py",
"chars": 429,
"preview": "\"\"\"The models subpackage contains definitions for the following model\narchitectures:\n- `ResNeXt` for CIFAR10 CIFAR100\nY"
},
{
"path": "models/preresnet.py",
"chars": 7076,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\nimport sys, os\ni"
},
{
"path": "models/wide_resnet.py",
"chars": 5126,
"preview": "### dropout has been removed in this code. original code had dropout#####\nimport torch\nimport torch.nn as nn\nimport torc"
},
{
"path": "unit_test/test_exact.py",
"chars": 3603,
"preview": "import numpy as np\nimport torch\nimport itertools\nfrom lapjv import lapjv\n\ndef mask_transport(mask, grad_pool, eps=0.01, "
},
{
"path": "unit_test/test_graphcut.py",
"chars": 4211,
"preview": "import math\nimport numpy as np\nimport gco\n\n\n\n\"\"\"test Code for multi-label graph cut\"\"\"\ndef test_graph(unary1, unary2, pw"
},
{
"path": "unit_test/test_heuristic.py",
"chars": 6884,
"preview": "import os, sys, time\nimport torch\nfrom torch.autograd import Variable\nimport numpy as np\nimport matplotlib\nmatplotlib.us"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the snu-mllab/PuzzleMix GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 47 files (5.4 MB), approximately 1.4M tokens, and a symbol index with 282 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.