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Repository: ildoonet/cutmix
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Directory structure:
gitextract_7nca5mpm/
├── .gitignore
├── LICENSE
├── README.md
├── __init__.py
├── ablations.md
├── autoaug/
│ ├── __init__.py
│ ├── archive.py
│ └── augmentations.py
├── conf/
│ ├── cifar100_pyramid200.yaml
│ ├── cifar100_pyramid272.yaml
│ ├── cifar100_wresnet28x10.yaml
│ ├── cifar100_wresnet40x2.yaml
│ ├── imagenet_resnet18.yaml
│ ├── imagenet_resnet200.yaml
│ ├── imagenet_resnet34.yaml
│ └── imagenet_resnet50.yaml
├── cutmix/
│ ├── __init__.py
│ ├── cutmix.py
│ └── utils.py
├── lr_scheduler.py
├── network/
│ ├── __init__.py
│ ├── pyramidnet.py
│ ├── resnet.py
│ ├── shakedrop.py
│ └── wideresnet.py
├── requirements.txt
├── setup.py
├── train.py
├── train_legacy.py
└── utils.py
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================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2019 Ildoo Kim
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
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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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
================================================
# cutmix
<img src="https://github.com/clovaai/CutMix-PyTorch/raw/master/img1.PNG" width=50% />
a Ready-to-use PyTorch Extension of Unofficial CutMix Implementations.
This re-implementation is improved in some parts,
- Fixing [issue #1](https://github.com/clovaai/CutMix-PyTorch/issues/1) in the original repository
- [issue #3](https://github.com/clovaai/CutMix-PyTorch/issues/3) : Random crop regions are randomly chosen, even within the same batch.
- [issue #4](https://github.com/clovaai/CutMix-PyTorch/issues/4) : Different lambda values(sizes of crop regions) are randomly chosen, even within the same batch.
- Images to be cropped are randomly chosen in the whole dataset. Original implementation selects images only inside the same batch(shuffling).
- Easy to install and use on your existing project.
- With additional augmentations(fast-autoaugment), the performances are improved further.
Hence, there may be **slightly-improved training results** also.
## Requirements
- python3
- torch >= 1.1.0
## Install
This repository is pip-installable,
```
$ pip install git+https://github.com/ildoonet/cutmix
```
or you can copy 'cutmix' folder to your project to use it.
## Usage
Our ```CutMix``` is inhereted from the PyTorch Dataset class so you can wrap your own dataset(eg. cifar10, imagenet, ...). Also we provide ```CutMixCrossEntropyLoss```, soft version of cross-entropy loss, which accept soft-labels required by cutmix.
```python
from cutmix.cutmix import CutMix
from cutmix.utils import CutMixCrossEntropyLoss
...
dataset = datasets.CIFAR100(args.cifarpath, train=True, download=True, transform=transform_train)
dataset = CutMix(dataset, num_class=100, beta=1.0, prob=0.5, num_mix=2) # this is paper's original setting for cifar.
...
criterion = CutMixCrossEntropyLoss(True)
for _ in range(num_epoch):
for input, target in loader: # input is cutmixed image's normalized tensor and target is soft-label which made by mixing 2 or more labels.
output = model(input)
loss = criterion(output, target)
loss.backward()
optimizer.step()
optimizer.zero_grad()
```
## Result
### PyramidNet-200 + ShakeDrop + *CutMix* \w CIFAR-100
| | Top-1 Error(@300epoch) | Top-1 Error(Best) | Model File |
|---------------------------------|------------:|------------|------------|
| Paper's Reported Result | N/A | 13.81 | N/A |
| Our Re-implementation | 13.68 | 13.15 | [Download(12.88)](https://www.dropbox.com/s/q4jsyvvhb4y8ys9/model_best.pth.tar?dl=0) |
| + Fast AutoAugment | 13.3 | 12.95 | |
We ran 6 indenpendent experiments with our re-implemented codes and got top-1 errors of 13.09, 13.29, 13.27, 13.24, 13.15 and 12.88, using below command.
(Converged at 300epoch with the top-1 errors of 13.55, 13.66, 13.95, 13.9, 13.8 and 13.32.)
```bash
$ python train.py -c conf/cifar100_pyramid200.yaml
```
### ResNet + **CutMix** \w ImageNet
| | | Top-1 Error<br/>(@300epoch) | Top-1 Error<br/>(Best) | Model File |
|------------|---------------------------------|------------:|----------:|-----------:|
| ResNet18 | Reported Result \wo CutMix | N/A | 30.43 |
| | Ours | 29.674 | 29.56 |
| ResNet34 | Reported Result \wo CutMix | N/A | 26.456 | |
| | Ours | 24.7 | 24.57 | [Download](https://www.dropbox.com/s/lcjfrcqmuoijig3/model_best.pth.tar?dl=0) |
| ResNet50 | Paper's Reported Result | N/A | 21.4 | N/A |
| | Author's Code(Our Re-run) | 21.768 | 21.586 | N/A |
| | Our Re-implementation | 21.524 | 21.340 | [Download(21.25)](https://www.dropbox.com/s/nqell4bh5oj68q1/model_best.pth.tar?dl=0) |
| ResNet200 | Our Re-implementation |
| | + Fast AutoAugment | 19.058 | 18.858 |
```bash
$ python train.py -c conf/imagenet_resnet50.yaml
```
We ran 5 independent experiments on ResNet50.
- Author's codes
- 300epoch : 21.762, 21.614, 21.762, 21.644, 21.810
- best : 21.56, 21.556, 21.666, 21.498, 21.648
- Our Re-implementation
- 300epoch : 21.53, 21.408, 21.55, 21.4, 21.73
- best : 21.392, 21.328, 21.386, 21.256, 21.34
## Reference
- Official
- Paper : https://arxiv.org/abs/1905.04899
- Implementation : https://github.com/clovaai/CutMix-PyTorch
- ShakeDrop
- https://github.com/owruby/shake-drop_pytorch
- Fast AutoAugment
- https://github.com/kakaobrain/fast-autoaugment
================================================
FILE: __init__.py
================================================
================================================
FILE: ablations.md
================================================
## cutmix_num
CutMix between more than two images. Test result show that there is no significant difference.
### cutmix_num=1 (original)
13.09, 13.29, 13.27, 13.24, 13.15, 12.88 avg= 13.15
13.55, 13.66, 13.95, 13.9, 13.8, 13.32 avg= 13.68
### cutmix_num=2
13.05, 13.35, 13.21, 13.13, 13.2 avg= 13.18
13.36, 13.81, 13.75, 13.59, 13.57 avg= 13.61
================================================
FILE: autoaug/__init__.py
================================================
================================================
FILE: autoaug/archive.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import defaultdict
from autoaug.augmentations import get_augment
def arsaug_policy():
exp0_0 = [
[('Solarize', 0.66, 0.34), ('Equalize', 0.56, 0.61)],
[('Equalize', 0.43, 0.06), ('AutoContrast', 0.66, 0.08)],
[('Color', 0.72, 0.47), ('Contrast', 0.88, 0.86)],
[('Brightness', 0.84, 0.71), ('Color', 0.31, 0.74)],
[('Rotate', 0.68, 0.26), ('TranslateX', 0.38, 0.88)]]
exp0_1 = [
[('TranslateY', 0.88, 0.96), ('TranslateY', 0.53, 0.79)],
[('AutoContrast', 0.44, 0.36), ('Solarize', 0.22, 0.48)],
[('AutoContrast', 0.93, 0.32), ('Solarize', 0.85, 0.26)],
[('Solarize', 0.55, 0.38), ('Equalize', 0.43, 0.48)],
[('TranslateY', 0.72, 0.93), ('AutoContrast', 0.83, 0.95)]]
exp0_2 = [
[('Solarize', 0.43, 0.58), ('AutoContrast', 0.82, 0.26)],
[('TranslateY', 0.71, 0.79), ('AutoContrast', 0.81, 0.94)],
[('AutoContrast', 0.92, 0.18), ('TranslateY', 0.77, 0.85)],
[('Equalize', 0.71, 0.69), ('Color', 0.23, 0.33)],
[('Sharpness', 0.36, 0.98), ('Brightness', 0.72, 0.78)]]
exp0_3 = [
[('Equalize', 0.74, 0.49), ('TranslateY', 0.86, 0.91)],
[('TranslateY', 0.82, 0.91), ('TranslateY', 0.96, 0.79)],
[('AutoContrast', 0.53, 0.37), ('Solarize', 0.39, 0.47)],
[('TranslateY', 0.22, 0.78), ('Color', 0.91, 0.65)],
[('Brightness', 0.82, 0.46), ('Color', 0.23, 0.91)]]
exp0_4 = [
[('Cutout', 0.27, 0.45), ('Equalize', 0.37, 0.21)],
[('Color', 0.43, 0.23), ('Brightness', 0.65, 0.71)],
[('ShearX', 0.49, 0.31), ('AutoContrast', 0.92, 0.28)],
[('Equalize', 0.62, 0.59), ('Equalize', 0.38, 0.91)],
[('Solarize', 0.57, 0.31), ('Equalize', 0.61, 0.51)]]
exp0_5 = [
[('TranslateY', 0.29, 0.35), ('Sharpness', 0.31, 0.64)],
[('Color', 0.73, 0.77), ('TranslateX', 0.65, 0.76)],
[('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)],
[('Color', 0.92, 0.79), ('Equalize', 0.68, 0.54)],
[('Sharpness', 0.87, 0.91), ('Sharpness', 0.93, 0.41)]]
exp0_6 = [
[('Solarize', 0.39, 0.35), ('Color', 0.31, 0.44)],
[('Color', 0.33, 0.77), ('Color', 0.25, 0.46)],
[('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)],
[('AutoContrast', 0.32, 0.79), ('Cutout', 0.68, 0.34)],
[('AutoContrast', 0.67, 0.91), ('AutoContrast', 0.73, 0.83)]]
return exp0_0 + exp0_1 + exp0_2 + exp0_3 + exp0_4 + exp0_5 + exp0_6
def autoaug2arsaug(f):
def autoaug():
mapper = defaultdict(lambda: lambda x: x)
mapper.update({
'ShearX': lambda x: float_parameter(x, 0.3),
'ShearY': lambda x: float_parameter(x, 0.3),
'TranslateX': lambda x: int_parameter(x, 10),
'TranslateY': lambda x: int_parameter(x, 10),
'Rotate': lambda x: int_parameter(x, 30),
'Solarize': lambda x: 256 - int_parameter(x, 256),
'Posterize2': lambda x: 4 - int_parameter(x, 4),
'Contrast': lambda x: float_parameter(x, 1.8) + .1,
'Color': lambda x: float_parameter(x, 1.8) + .1,
'Brightness': lambda x: float_parameter(x, 1.8) + .1,
'Sharpness': lambda x: float_parameter(x, 1.8) + .1,
'CutoutAbs': lambda x: int_parameter(x, 20)
})
def low_high(name, prev_value):
_, low, high = get_augment(name)
return float(prev_value - low) / (high - low)
policies = f()
new_policies = []
for policy in policies:
new_policies.append([(name, pr, low_high(name, mapper[name](level))) for name, pr, level in policy])
return new_policies
return autoaug
@autoaug2arsaug
def autoaug_paper_cifar10():
return [
[('Invert', 0.1, 7), ('Contrast', 0.2, 6)],
[('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)],
[('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)],
[('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)],
[('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)],
[('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)],
[('Color', 0.4, 3), ('Brightness', 0.6, 7)],
[('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)],
[('Equalize', 0.6, 5), ('Equalize', 0.5, 1)],
[('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)],
[('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)],
[('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)],
[('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)],
[('Brightness', 0.9, 6), ('Color', 0.2, 6)],
[('Solarize', 0.5, 2), ('Invert', 0.0, 3)],
[('Equalize', 0.2, 0), ('AutoContrast', 0.6, 0)],
[('Equalize', 0.2, 8), ('Equalize', 0.6, 4)],
[('Color', 0.9, 9), ('Equalize', 0.6, 6)],
[('AutoContrast', 0.8, 4), ('Solarize', 0.2, 8)],
[('Brightness', 0.1, 3), ('Color', 0.7, 0)],
[('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)],
[('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)],
[('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)],
[('Equalize', 0.8, 8), ('Invert', 0.1, 3)],
[('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)],
]
@autoaug2arsaug
def autoaug_policy():
"""AutoAugment policies found on Cifar."""
exp0_0 = [
[('Invert', 0.1, 7), ('Contrast', 0.2, 6)],
[('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)],
[('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)],
[('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)],
[('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)]]
exp0_1 = [
[('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)],
[('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)],
[('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)],
[('Equalize', 0.8, 8), ('Invert', 0.1, 3)],
[('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)]]
exp0_2 = [
[('Solarize', 0.4, 5), ('AutoContrast', 0.0, 2)],
[('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)],
[('AutoContrast', 0.9, 0), ('Solarize', 0.4, 3)],
[('Equalize', 0.7, 5), ('Invert', 0.1, 3)],
[('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)]]
exp0_3 = [
[('Solarize', 0.4, 5), ('AutoContrast', 0.9, 1)],
[('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.9, 9)],
[('AutoContrast', 0.8, 0), ('TranslateYAbs', 0.7, 9)],
[('TranslateYAbs', 0.2, 7), ('Color', 0.9, 6)],
[('Equalize', 0.7, 6), ('Color', 0.4, 9)]]
exp1_0 = [
[('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)],
[('Color', 0.4, 3), ('Brightness', 0.6, 7)],
[('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)],
[('Equalize', 0.6, 5), ('Equalize', 0.5, 1)],
[('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)]]
exp1_1 = [
[('Brightness', 0.3, 7), ('AutoContrast', 0.5, 8)],
[('AutoContrast', 0.9, 4), ('AutoContrast', 0.5, 6)],
[('Solarize', 0.3, 5), ('Equalize', 0.6, 5)],
[('TranslateYAbs', 0.2, 4), ('Sharpness', 0.3, 3)],
[('Brightness', 0.0, 8), ('Color', 0.8, 8)]]
exp1_2 = [
[('Solarize', 0.2, 6), ('Color', 0.8, 6)],
[('Solarize', 0.2, 6), ('AutoContrast', 0.8, 1)],
[('Solarize', 0.4, 1), ('Equalize', 0.6, 5)],
[('Brightness', 0.0, 0), ('Solarize', 0.5, 2)],
[('AutoContrast', 0.9, 5), ('Brightness', 0.5, 3)]]
exp1_3 = [
[('Contrast', 0.7, 5), ('Brightness', 0.0, 2)],
[('Solarize', 0.2, 8), ('Solarize', 0.1, 5)],
[('Contrast', 0.5, 1), ('TranslateYAbs', 0.2, 9)],
[('AutoContrast', 0.6, 5), ('TranslateYAbs', 0.0, 9)],
[('AutoContrast', 0.9, 4), ('Equalize', 0.8, 4)]]
exp1_4 = [
[('Brightness', 0.0, 7), ('Equalize', 0.4, 7)],
[('Solarize', 0.2, 5), ('Equalize', 0.7, 5)],
[('Equalize', 0.6, 8), ('Color', 0.6, 2)],
[('Color', 0.3, 7), ('Color', 0.2, 4)],
[('AutoContrast', 0.5, 2), ('Solarize', 0.7, 2)]]
exp1_5 = [
[('AutoContrast', 0.2, 0), ('Equalize', 0.1, 0)],
[('ShearY', 0.6, 5), ('Equalize', 0.6, 5)],
[('Brightness', 0.9, 3), ('AutoContrast', 0.4, 1)],
[('Equalize', 0.8, 8), ('Equalize', 0.7, 7)],
[('Equalize', 0.7, 7), ('Solarize', 0.5, 0)]]
exp1_6 = [
[('Equalize', 0.8, 4), ('TranslateYAbs', 0.8, 9)],
[('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.6, 9)],
[('TranslateYAbs', 0.9, 0), ('TranslateYAbs', 0.5, 9)],
[('AutoContrast', 0.5, 3), ('Solarize', 0.3, 4)],
[('Solarize', 0.5, 3), ('Equalize', 0.4, 4)]]
exp2_0 = [
[('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)],
[('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)],
[('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)],
[('Brightness', 0.9, 6), ('Color', 0.2, 8)],
[('Solarize', 0.5, 2), ('Invert', 0.0, 3)]]
exp2_1 = [
[('AutoContrast', 0.1, 5), ('Brightness', 0.0, 0)],
[('CutoutAbs', 0.2, 4), ('Equalize', 0.1, 1)],
[('Equalize', 0.7, 7), ('AutoContrast', 0.6, 4)],
[('Color', 0.1, 8), ('ShearY', 0.2, 3)],
[('ShearY', 0.4, 2), ('Rotate', 0.7, 0)]]
exp2_2 = [
[('ShearY', 0.1, 3), ('AutoContrast', 0.9, 5)],
[('TranslateYAbs', 0.3, 6), ('CutoutAbs', 0.3, 3)],
[('Equalize', 0.5, 0), ('Solarize', 0.6, 6)],
[('AutoContrast', 0.3, 5), ('Rotate', 0.2, 7)],
[('Equalize', 0.8, 2), ('Invert', 0.4, 0)]]
exp2_3 = [
[('Equalize', 0.9, 5), ('Color', 0.7, 0)],
[('Equalize', 0.1, 1), ('ShearY', 0.1, 3)],
[('AutoContrast', 0.7, 3), ('Equalize', 0.7, 0)],
[('Brightness', 0.5, 1), ('Contrast', 0.1, 7)],
[('Contrast', 0.1, 4), ('Solarize', 0.6, 5)]]
exp2_4 = [
[('Solarize', 0.2, 3), ('ShearX', 0.0, 0)],
[('TranslateXAbs', 0.3, 0), ('TranslateXAbs', 0.6, 0)],
[('Equalize', 0.5, 9), ('TranslateYAbs', 0.6, 7)],
[('ShearX', 0.1, 0), ('Sharpness', 0.5, 1)],
[('Equalize', 0.8, 6), ('Invert', 0.3, 6)]]
exp2_5 = [
[('AutoContrast', 0.3, 9), ('CutoutAbs', 0.5, 3)],
[('ShearX', 0.4, 4), ('AutoContrast', 0.9, 2)],
[('ShearX', 0.0, 3), ('Posterize2', 0.0, 3)],
[('Solarize', 0.4, 3), ('Color', 0.2, 4)],
[('Equalize', 0.1, 4), ('Equalize', 0.7, 6)]]
exp2_6 = [
[('Equalize', 0.3, 8), ('AutoContrast', 0.4, 3)],
[('Solarize', 0.6, 4), ('AutoContrast', 0.7, 6)],
[('AutoContrast', 0.2, 9), ('Brightness', 0.4, 8)],
[('Equalize', 0.1, 0), ('Equalize', 0.0, 6)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 4)]]
exp2_7 = [
[('Equalize', 0.5, 5), ('AutoContrast', 0.1, 2)],
[('Solarize', 0.5, 5), ('AutoContrast', 0.9, 5)],
[('AutoContrast', 0.6, 1), ('AutoContrast', 0.7, 8)],
[('Equalize', 0.2, 0), ('AutoContrast', 0.1, 2)],
[('Equalize', 0.6, 9), ('Equalize', 0.4, 4)]]
exp0s = exp0_0 + exp0_1 + exp0_2 + exp0_3
exp1s = exp1_0 + exp1_1 + exp1_2 + exp1_3 + exp1_4 + exp1_5 + exp1_6
exp2s = exp2_0 + exp2_1 + exp2_2 + exp2_3 + exp2_4 + exp2_5 + exp2_6 + exp2_7
return exp0s + exp1s + exp2s
PARAMETER_MAX = 10
def float_parameter(level, maxval):
return float(level) * maxval / PARAMETER_MAX
def int_parameter(level, maxval):
return int(float_parameter(level, maxval))
def random_search2048():
# cifar10
_policies_fold0 = [[[('Posterize', 0.709699990271369, 0.8236653036749833), ('Solarize', 0.9995791432489501, 0.895546498237044)], [('Cutout', 0.6831149863635602, 0.562498840188238), ('ShearX', 0.9189826133108392, 0.5251302162680564)], [('Contrast', 0.13358405061055256, 0.1952646403453232), ('Brightness', 0.7280409250762175, 0.4074824007813337)], [('Brightness', 0.5167734333379864, 0.2364143388929607), ('Cutout', 0.7707249841521517, 0.27251655306096945)], [('ShearX', 0.6033636441534456, 0.40143350276942125), ('Cutout', 0.601776421964206, 0.8309211575386521)]], [[('ShearY', 0.2647454506260575, 0.39702273362864104), ('TranslateY', 0.2832491627826961, 0.23292367395544888)], [('Sharpness', 0.0009080100005474101, 0.36415669560358954), ('Cutout', 0.5908461871814106, 0.25970426506860234)], [('Solarize', 0.18357214497294627, 0.9756079221974562), ('Posterize', 0.39949622410962027, 0.29477386092906754)], [('Brightness', 0.09429743375613386, 0.006386029532104098), ('AutoContrast', 0.9029329780551074, 0.618245983109469)], [('Brightness', 0.6805221664236891, 0.14520952319300118), ('AutoContrast', 0.9726893023125383, 0.8956889479129884)]], [[('AutoContrast', 0.5022944258939801, 0.7180484543995698), ('Sharpness', 0.5417189214004129, 0.6361117441801069)], [('Cutout', 0.4310015550851225, 0.6626254773281117), ('Sharpness', 0.9051744898059433, 0.29013044022529455)], [('Sharpness', 0.23402478143880234, 0.5771375764954312), ('TranslateX', 0.3042605080584019, 0.4831394209317993)], [('ShearY', 0.28294098744633145, 0.5117257776292635), ('TranslateX', 0.16098037819237088, 0.6787257524773109)], [('Invert', 0.2187145852935698, 0.45481197738805845), ('Sharpness', 0.6580451977055289, 0.4023285952188146)]], [[('Posterize', 0.016342472316007384, 0.8607494005505818), ('AutoContrast', 0.7262739271274912, 0.0313002073497044)], [('Rotate', 0.07179022433199145, 0.6118701886796194), ('Color', 0.36659463377601975, 0.5448457981737703)], [('Posterize', 0.8316355405301347, 0.8449372118629678), ('Equalize', 0.02532547691330711, 0.1864844447252464)], [('Brightness', 0.08459948578983079, 0.052197715510527876), ('Equalize', 0.22617068524447648, 0.13061858369152912)], [('TranslateX', 0.8845725642217469, 0.6060215475838564), ('Solarize', 0.6899395986026327, 0.9692836090269836)]], [[('Sharpness', 0.9392281799852268, 0.7363348197908195), ('ShearY', 0.9899980308449515, 0.5227266699999886)], [('Posterize', 0.5076864303680726, 0.6761552254345644), ('TranslateY', 0.1596282316962928, 0.45467456718727106)], [('AutoContrast', 0.06899059090029402, 0.9678821740286254), ('AutoContrast', 0.5649082625234694, 0.6699361749500335)], [('ShearY', 0.0026245862487058735, 0.34545210208272603), ('Solarize', 0.8649286616916771, 0.8331734284874224)], [('ShearX', 0.02935027589411332, 0.9061125355357449), ('ShearX', 0.9067387733443698, 0.44516017207290404)]], [[('AutoContrast', 0.8017337335962192, 0.9931376078313714), ('Sharpness', 0.8614521251468067, 0.40784078790560363)], [('ShearY', 0.36866522085599174, 0.6415594472314682), ('Contrast', 0.08403639928109341, 0.9873127512172337)], [('Posterize', 0.4511955515709096, 0.7760375562138506), ('Posterize', 0.5066707147413717, 0.9225458277522391)], [('ShearX', 0.049950630596731216, 0.04157438011541159), ('TranslateY', 0.31864874477508687, 0.3411553351449256)], [('Contrast', 0.2307344693281126, 0.19383778309110777), ('Posterize', 0.7381909885148881, 0.8539276575975397)]], [[('TranslateY', 0.041794855549646126, 0.061428527942731126), ('Contrast', 0.8835131198805206, 0.6685467353070597)], [('Contrast', 0.04328481505505355, 0.04680807461092151), ('ShearX', 0.1362639998937787, 0.8901316270067592)], [('Brightness',0.2476840359359921, 0.8572652665880937), ('AutoContrast', 0.6168863361077966, 0.412254955873945)], [('Color', 0.4185896280190774, 0.42581238727902926), ('Contrast', 0.676262138453488, 0.7286342378517439)], [('Sharpness', 0.07216253437820874, 0.4613083644362227), ('Posterize', 0.4357885702427907, 0.9647785625837578)]], [[('Color', 0.01786544266736767, 0.8928746945998216), ('Cutout', 0.5660736721008677, 0.002932078269684002)], [('Cutout', 0.9630847176870009, 0.20265802383570886), ('Rotate', 0.2806402950159874, 0.6976007178496048)], [('Sharpness', 0.651517303061078, 0.3034128051173922), ('AutoContrast', 0.8663667218653449, 0.9130351990575076)], [('Color', 0.4606739405468513, 0.712098372097414), ('AutoContrast', 0.7545177887601211, 0.6772226511796795)], [('ShearX', 0.2723880941865423, 0.7159971457667523), ('Contrast', 0.7996069939066458, 0.5178068595671571)]], [[('ShearX', 0.48583524508687137, 0.5824976712930959), ('TranslateY', 0.02240777363245261, 0.10001974537648883)], [('ShearX', 0.0533228175392777, 0.21303644191130733), ('ShearY', 0.71530338945374, 0.666026284260341)], [('Color', 0.20515761367736907, 0.904730172154942), ('ShearY', 0.19746474181370355, 0.31356086216669854)], [('ShearY', 0.21369214393927238, 0.24388686415873662), ('Cutout', 0.2369975830257155, 0.7007460791592609)], [('Equalize', 0.33276656113451064, 0.8256611755516485), ('Brightness', 0.1752554813246029, 0.41695603652164037)]], [[('ShearY', 0.16323689094509009, 0.8788167960053922), ('Cutout', 0.09298752419796497, 0.7809046279153092)], [('Posterize', 0.08031582077110178, 0.22385514283051144), ('Invert', 0.351272341605097, 0.6574507003533777)], [('Brightness', 0.00027528124162234935, 0.3296584353947595), ('Cutout', 0.7987019500020938, 0.6009588044991686)], [('AutoContrast', 0.6219271777794793, 0.8207128657190691), ('Color', 0.8716639494976303, 0.2259065727420193)], [('Invert', 0.27540185595836997, 0.7485135331456082), ('Cutout', 0.5029120629187204, 0.761906897331416)]]]
policies_fold0 = []
for p in _policies_fold0:
policies_fold0.extend(p)
policies = policies_fold0
return policies
def no_duplicates(f):
def wrap_remove_duplicates():
policies = f()
return remove_duplicates(policies)
return wrap_remove_duplicates
def remove_duplicates(policies):
s = set()
new_policies = []
for ops in policies:
key = []
for op in ops:
key.append(op[0])
key = '_'.join(key)
if key in s:
continue
else:
s.add(key)
new_policies.append(ops)
return new_policies
def fa_reduced_cifar10():
p = [[["Contrast", 0.8320659688593578, 0.49884310562180767], ["TranslateX", 0.41849883971249136, 0.394023086494538]], [["Color", 0.3500483749890918, 0.43355143929883955], ["Color", 0.5120716140300229, 0.7508299643325016]], [["Rotate", 0.9447932604389472, 0.29723465088990375], ["Sharpness", 0.1564936149799504, 0.47169309978091745]], [["Rotate", 0.5430015349185097, 0.6518626678905443], ["Color", 0.5694844928020679, 0.3494533005430269]], [["AutoContrast", 0.5558922032451064, 0.783136004977799], ["TranslateY", 0.683914191471972, 0.7597025305860181]], [["TranslateX", 0.03489224481658926, 0.021025488042663354], ["Equalize", 0.4788637403857401, 0.3535481281496117]], [["Sharpness", 0.6428916269794158, 0.22791511918580576], ["Contrast", 0.016014045073950323, 0.26811312269487575]], [["Rotate", 0.2972727228410451, 0.7654251516829896], ["AutoContrast", 0.16005809254943348, 0.5380523650108116]], [["Contrast", 0.5823671057717301, 0.7521166301398389], ["TranslateY", 0.9949449214751978, 0.9612671341689751]], [["Equalize", 0.8372126687702321, 0.6944127225621206], ["Rotate", 0.25393282929784755, 0.3261658365286546]], [["Invert", 0.8222011603194572, 0.6597915864008403], ["Posterize", 0.31858707654447327, 0.9541013715579584]], [["Sharpness", 0.41314621282107045, 0.9437344470879956], ["Cutout", 0.6610495837889337, 0.674411664255093]], [["Contrast", 0.780121736705407, 0.40826152397463156], ["Color", 0.344019192125256, 0.1942922781355767]], [["Rotate", 0.17153139555621344, 0.798745732456474], ["Invert", 0.6010555860501262, 0.320742172554767]], [["Invert", 0.26816063450777416, 0.27152062163148327], ["Equalize", 0.6786829200236982, 0.7469412443514213]], [["Contrast", 0.3920564414367518, 0.7493644582838497], ["TranslateY", 0.8941657805606704, 0.6580846856375955]], [["Equalize", 0.875509207399372, 0.9061130537645283], ["Cutout", 0.4940280679087308, 0.7896229623628276]], [["Contrast", 0.3331423298065147, 0.7170041362529597], ["ShearX", 0.7425484291842793, 0.5285117152426109]], [["Equalize", 0.97344237365026, 0.4745759720473106], ["TranslateY", 0.055863458430295276, 0.9625142022954672]], [["TranslateX", 0.6810614083109192, 0.7509937355495521], ["TranslateY", 0.3866463019475701, 0.5185481505576112]], [["Sharpness", 0.4751529944753671, 0.550464012488733], ["Cutout", 0.9472914750534814, 0.5584925992985023]], [["Contrast", 0.054606784909375095, 0.17257080196712182], ["Cutout", 0.6077026782754803, 0.7996504165944938]], [["ShearX", 0.328798428243695, 0.2769563264079157], ["Cutout", 0.9037632437023772, 0.4915809476763595]], [["Cutout", 0.6891202672363478, 0.9951490996172914], ["Posterize", 0.06532762462628705, 0.4005246609075227]], [["TranslateY", 0.6908583592523334, 0.725612120376128], ["Rotate", 0.39907735501746666, 0.36505798032223147]], [["TranslateX", 0.10398364107399072, 0.5913918470536627], ["Rotate", 0.7169811539340365, 0.8283850670648724]], [["ShearY", 0.9526373530768361, 0.4482347365639251], ["Contrast", 0.4203947336351471, 0.41526799558953864]], [["Contrast", 0.24894431199700073, 0.09578870500994707], ["Solarize", 0.2273713345927395, 0.6214942914963707]], [["TranslateX", 0.06331228870032912, 0.8961907489444944], ["Cutout", 0.5110007859958743, 0.23704875994050723]], [["Cutout", 0.3769183548846172, 0.6560944580253987], ["TranslateY", 0.7201924599434143, 0.4132476526938319]], [["Invert", 0.6707431156338866, 0.11622795952464149], ["Posterize", 0.12075972752370845, 0.18024933294172307]], [["Color", 0.5010057264087142, 0.5277767327434318], ["Rotate", 0.9486115946366559, 0.31485546630220784]], [["ShearX", 0.31741302466630406, 0.1991215806270692], ["Invert", 0.3744727015523084, 0.6914113986757578]], [["Brightness", 0.40348479064392617, 0.8924182735724888], ["Brightness", 0.1973098763857779, 0.3939288933689655]], [["Color", 0.01208688664030888, 0.6055693000885217], ["Equalize", 0.433259451147881, 0.420711137966155]], [["Cutout", 0.2620018360076487, 0.11594468278143644], ["Rotate", 0.1310401567856766, 0.7244318146544101]], [["ShearX", 0.15249651845933576, 0.35277277071866986], ["Contrast", 0.28221794032094016, 0.42036586509397444]], [["Brightness", 0.8492912150468908, 0.26386920887886056], ["Solarize", 0.8764208056263386, 0.1258195122766067]], [["ShearX", 0.8537058239675831, 0.8415101816171269], ["AutoContrast", 0.23958568830416294, 0.9889049529564014]], [["Rotate", 0.6463207930684552, 0.8750192129056532], ["Contrast", 0.6865032211768652, 0.8564981333033417]], [["Equalize", 0.8877190311811044, 0.7370995897848609], ["TranslateX", 0.9979660314391368, 0.005683998913244781]], [["Color", 0.6420017551677819, 0.6225337265571229], ["Solarize", 0.8344504978566362, 0.8332856969941151]], [["ShearX", 0.7439332981992567, 0.9747608698582039], ["Equalize", 0.6259189804002959, 0.028017478098245174]], [["TranslateY", 0.39794770293366843, 0.8482966537902709], ["Rotate", 0.9312935630405351, 0.5300586925826072]], [["Cutout", 0.8904075572021911, 0.3522934742068766], ["Equalize", 0.6431186289473937, 0.9930577962126151]], [["Contrast", 0.9183553386089476, 0.44974266209396685], ["TranslateY", 0.8193684583123862, 0.9633741156526566]], [["ShearY", 0.616078299924283, 0.19219314358924766], ["Solarize", 0.1480945914138868, 0.05922109541654652]], [["Solarize", 0.25332455064128157, 0.18853037431947994], ["ShearY", 0.9518390093954243, 0.14603930044061142]], [["Color", 0.8094378664335412, 0.37029830225408433], ["Contrast", 0.29504113617467465, 0.065096365468442]], [["AutoContrast", 0.7075167558685455, 0.7084621693458267], ["Sharpness", 0.03555539453323875, 0.5651948313888351]], [["TranslateY", 0.5969982600930229, 0.9857264201029572], ["Rotate", 0.9898628564873607, 0.1985685534926911]], [["Invert", 0.14915939942810352, 0.6595839632446547], ["Posterize", 0.768535289994361, 0.5997358684618563]], [["Equalize", 0.9162691815967111, 0.3331035307653627], ["Color", 0.8169118187605557, 0.7653910258006366]], [["Rotate", 0.43489185299530897, 0.752215269135173], ["Brightness", 0.1569828560334806, 0.8002808712857853]], [["Invert", 0.931876215328345, 0.029428644395760872], ["Equalize", 0.6330036052674145, 0.7235531014288485]], [["ShearX", 0.5216138393704968, 0.849272958911589], ["AutoContrast", 0.19572688655120263, 0.9786551568639575]], [["ShearX", 0.9899586208275011, 0.22580547500610293], ["Brightness", 0.9831311903178727, 0.5055159610855606]], [["Brightness", 0.29179117009211486, 0.48003584672937294], ["Solarize", 0.7544252317330058, 0.05806581735063043]], [["AutoContrast", 0.8919800329537786, 0.8511261613698553], ["Contrast", 0.49199446084551035, 0.7302297140181429]], [["Cutout", 0.7079723710644835, 0.032565015538375874], ["AutoContrast", 0.8259782090388609, 0.7860708789468442]], [["Posterize", 0.9980262659801914, 0.6725084224935673], ["ShearY", 0.6195568269664682, 0.5444170291816751]], [["Posterize", 0.8687351834713217, 0.9978004914422602], ["Equalize", 0.4532646848325955, 0.6486748015710573]], [["Contrast", 0.2713928776950594, 0.15255249557027806], ["ShearY", 0.9276834387970199, 0.5266542862333478]], [["AutoContrast", 0.5240786618055582, 0.9325642258930253], ["Cutout", 0.38448627892037357, 0.21219415055662394]], [["TranslateX", 0.4299517937295352, 0.20133751201386152], ["TranslateX", 0.6753468310276597, 0.6985621035400441]], [["Rotate", 0.4006472499103597, 0.6704748473357586], ["Equalize", 0.674161668148079, 0.6528530101705237]], [["Equalize", 0.9139902833674455, 0.9015103149680278], ["Sharpness", 0.7289667720691948, 0.7623606352376232]], [["Cutout", 0.5911267429414259, 0.5953141187177585], ["Rotate", 0.5219064817468504, 0.11085141355857986]], [["TranslateX", 0.3620095133946267, 0.26194039409492476], ["Rotate", 0.3929841359545597, 0.4913406720338047]], [["Invert", 0.5175298901458896, 0.001661410821811482], ["Invert", 0.004656581318332242, 0.8157622192213624]], [["AutoContrast", 0.013609693335051465, 0.9318651749409604], ["Invert", 0.8980844358979592, 0.2268511862780368]], [["ShearY", 0.7717126261142194, 0.09975547983707711], ["Equalize", 0.7808494401429572, 0.4141412091009955]], [["TranslateX", 0.5878675721341552, 0.29813268038163376], ["Posterize", 0.21257276051591356, 0.2837285296666412]], [["Brightness", 0.4268335108566488, 0.4723784991635417], ["Cutout", 0.9386262901570471, 0.6597686851494288]], [["ShearX", 0.8259423807590159, 0.6215304795389204], ["Invert", 0.6663365779667443, 0.7729669184580387]], [["ShearY", 0.4801338723951297, 0.5220145420100984], ["Solarize", 0.9165803796596582, 0.04299335502862134]], [["Color", 0.17621114853558817, 0.7092601754635434], ["ShearX", 0.9014406936728542, 0.6028711944367818]], [["Rotate", 0.13073284972300658, 0.9088831512880851], ["ShearX", 0.4228105332316806, 0.7985249783662675]], [["Brightness", 0.9182753692730031, 0.0063635477774044436], ["Color", 0.4279825602663798, 0.28727149118585327]], [["Equalize", 0.578218285372267, 0.9611758542158054], ["Contrast", 0.5471552264150691, 0.8819635504027596]], [["Brightness", 0.3208589067274543, 0.45324733565167497], ["Solarize", 0.5218455808633233, 0.5946097503647126]], [["Equalize", 0.3790381278653, 0.8796082535775276], ["Solarize", 0.4875526773149246, 0.5186585878052613]], [["ShearY", 0.12026461479557571, 0.1336953429068397], ["Posterize", 0.34373988646025766, 0.8557727670803785]], [["Cutout", 0.2396745247507467, 0.8123036135209865], ["Equalize", 0.05022807681008945, 0.6648492261984383]], [["Brightness", 0.35226676470748264, 0.5950011514888855], ["Rotate", 0.27555076067000894, 0.9170063321486026]], [["ShearX", 0.320224630647278, 0.9683584649071976], ["Invert", 0.6905585196648905, 0.5929115667894518]], [["Color", 0.9941395717559652, 0.7474441679798101], ["Sharpness", 0.7559998478658021, 0.6656052889626682]], [["ShearY", 0.4004220568345669, 0.5737646992826074], ["Equalize", 0.9983495213746147, 0.8307907033362303]], [["Color", 0.13726809242038207, 0.9378850119950549], ["Equalize", 0.9853362454752445, 0.42670264496554156]], [["Invert", 0.13514636153298576, 0.13516363849081958], ["Sharpness", 0.2031189356693901, 0.6110226359872745]], [["TranslateX", 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[["ShearY", 0.3137466057521987, 0.6747433496011368], ["Rotate", 0.42118828936218133, 0.980121180104441]], [["Solarize", 0.8470375049950615, 0.15287589264139223], ["Cutout", 0.14438435054693055, 0.24296463267973512]], [["TranslateY", 0.08822241792224905, 0.36163911974799356], ["TranslateY", 0.11729726813270003, 0.6230889726445291]], [["ShearX", 0.7720112337718541, 0.2773292905760122], ["Sharpness", 0.756290929398613, 0.27830353710507705]], [["Color", 0.33825031007968287, 0.4657590047522816], ["ShearY", 0.3566628994713067, 0.859750504071925]], [["TranslateY", 0.06830147433378053, 0.9348778582086664], ["TranslateX", 0.15509346516378553, 0.26320778885339435]], [["Posterize", 0.20266751150740858, 0.008351463842578233], ["Sharpness", 0.06506971109417259, 0.7294471760284555]], [["TranslateY", 0.6278911394418829, 0.8702181892620695], ["Invert", 0.9367073860264247, 0.9219230428944211]], [["Sharpness", 0.1553425337673321, 0.17601557714491345], ["Solarize", 0.7040449681338888, 0.08764313147327729]], [["Equalize", 0.6082233904624664, 0.4177428549911376], ["AutoContrast", 0.04987405274618151, 0.34516208204700916]], [["Brightness", 0.9616085936167699, 0.14561237331885468], ["Solarize", 0.8927707736296572, 0.31176907850205704]], [["Brightness", 0.6707778304730988, 0.9046457117525516], ["Brightness", 0.6801448953060988, 0.20015313057149042]], [["Color", 0.8292680845499386, 0.5181603879593888], ["Brightness", 0.08549161770369762, 0.6567870536463203]], [["ShearY", 0.267802208078051, 0.8388133819588173], ["Sharpness", 0.13453409120796123, 0.10028351311149486]], [["Posterize", 0.775796593610272, 0.05359034561289766], ["Cutout", 0.5067360625733027, 0.054451986840317934]], [["TranslateX", 0.5845238647690084, 0.7507147553486293], ["Brightness", 0.2642051786121197, 0.2578358927056452]], [["Cutout", 0.10787517610922692, 0.8147986902794228], ["Contrast", 0.2190149206329539, 0.902210615462459]], [["TranslateX", 0.5663614214181296, 0.05309965916414028], ["ShearX", 0.9682797885154938, 0.41791929533938466]], [["ShearX", 0.2345325577621098, 0.383780128037189], ["TranslateX", 0.7298083748149163, 0.644325797667087]], [["Posterize", 0.5138725709682734, 0.7901809917259563], ["AutoContrast", 0.7966018627776853, 0.14529337543427345]], [["Invert", 0.5973031989249785, 0.417399314592829], ["Solarize", 0.9147539948653116, 0.8221272315548086]], [["Posterize", 0.601596043336383, 0.18969646160963938], ["Color", 0.7527275484079655, 0.431793831326888]], [["Equalize", 0.6731483454430538, 0.7866786558207602], ["TranslateX", 0.97574396899191, 0.5970255778044692]], [["Cutout", 0.15919495850169718, 0.8916094305850562], ["Invert", 0.8351348834751027, 0.4029937360314928]], [["Invert", 0.5894085405226027, 0.7283806854157764], ["Brightness", 0.3973976860470554, 0.949681121498567]], [["AutoContrast", 0.3707914135327408, 0.21192068592079616], ["ShearX", 0.28040127351140676, 0.6754553511344856]], [["Solarize", 0.07955132378694896, 0.15073572961927306], ["ShearY", 0.5735850168851625, 0.27147326850217746]], [["Equalize", 0.678653949549764, 0.8097796067861455], ["Contrast", 0.2283048527510083, 0.15507804874474185]], [["Equalize", 0.286013868374536, 0.186785848694501], ["Posterize", 0.16319021740810458, 0.1201304443285659]], [["Sharpness", 0.9601590830563757, 0.06267915026513238], ["AutoContrast", 0.3813920685124327, 0.294224403296912]], [["Brightness", 0.2703246632402241, 0.9168405377492277], ["ShearX", 0.6156009855831097, 0.4955986055846403]], [["Color", 0.9065504424987322, 0.03393612216080133], ["ShearY", 0.6768595880405884, 0.9981068127818191]], [["Equalize", 0.28812842368483904, 0.300387487349145], ["ShearY", 0.28812248704858345, 0.27105076231533964]], [["Brightness", 0.6864882730513477, 0.8205553299102412], ["Cutout", 0.45995236371265424, 0.5422030370297759]], [["Color", 0.34941404877084326, 0.25857961830158516], ["AutoContrast", 0.3451390878441899, 0.5000938249040454]], [["Invert", 0.8268247541815854, 0.6691380821226468], ["Cutout", 0.46489193601530476, 0.22620873109485895]], [["Rotate", 0.17879730528062376, 0.22670425330593935], ["Sharpness", 0.8692795688221834, 0.36586055020855723]], [["Brightness", 0.31203975139659634, 0.6934046293010939], ["Cutout", 0.31649437872271236, 0.08078625004157935]], [["Cutout", 0.3119482836150119, 0.6397160035509996], ["Contrast", 0.8311248624784223, 0.22897510169718616]], [["TranslateX", 0.7631157841429582, 0.6482890521284557], ["Brightness", 0.12681196272427664, 0.3669813784257344]], [["TranslateX", 0.06027722649179801, 0.3101104512201861], ["Sharpness", 0.5652076706249394, 0.05210008400968136]], [["AutoContrast", 0.39213552101583127, 0.5047021194355596], ["ShearY", 0.7164003055682187, 0.8063370761002899]], [["Solarize", 0.9574307011238342, 0.21472064809226854], ["AutoContrast", 0.8102612285047174, 0.716870148067014]], [["Rotate", 0.3592634277567387, 0.6452602893051465], ["AutoContrast", 0.27188430331411506, 0.06003099168464854]], [["Cutout", 0.9529536554825503, 0.5285505311027461], ["Solarize", 0.08478231903311029, 0.15986449762728216]], [["TranslateY", 0.31176130458018936, 0.5642853506158253], ["Equalize", 0.008890883901317648, 0.5146121040955942]], [["Color", 0.40773645085566157, 0.7110398926612682], ["Color", 0.18233100156439364, 0.7830036002758337]], [["Posterize", 0.5793809197821732, 0.043748553135581236], ["Invert", 0.4479962016131668, 0.7349663010359488]], [["TranslateX", 0.1994882312299382, 0.05216859488899439], ["Rotate", 0.48288726352035416, 0.44713829026777585]], [["Posterize", 0.22122838185154603, 0.5034546841241283], ["TranslateX", 0.2538745835410222, 0.6129055170893385]], [["Color", 0.6786559960640814, 0.4529749369803212], ["Equalize", 0.30215879674415336, 0.8733394611096772]], [["Contrast", 0.47316062430673456, 0.46669538897311447], ["Invert", 0.6514906551984854, 0.3053339444067804]], [["Equalize", 0.6443202625334524, 0.8689731394616441], ["Color", 0.7549183794057628, 0.8889001426329578]], [["Solarize", 0.616709740662654, 0.7792180816399313], ["ShearX", 0.9659155537406062, 0.39436937531179495]], [["Equalize", 0.23694011299406226, 0.027711152164392128], ["TranslateY", 0.1677339686527083, 0.3482126536808231]], [["Solarize", 0.15234175951790285, 0.7893840414281341], ["TranslateX", 0.2396395768284183, 0.27727219214979715]], [["Contrast", 0.3792017455380605, 0.32323660409845334], ["Contrast", 0.1356037413846466, 0.9127772969992305]], [["ShearX", 0.02642732222284716, 0.9184662576502115], ["Equalize", 0.11504884472142995, 0.8957638893097964]], [["TranslateY", 0.3193812913345325, 0.8828100030493128], ["ShearY", 0.9374975727563528, 0.09909415611083694]], [["AutoContrast", 0.025840721736048122, 0.7941037581373024], ["TranslateY", 0.498518003323313, 0.5777122846572548]], [["ShearY", 0.6042199307830248, 0.44809668754508836], ["Cutout", 0.3243978207701482, 0.9379740926294765]], [["ShearY", 0.6858549297583574, 0.9993252035788924], ["Sharpness", 0.04682428732773203, 0.21698099707915652]], [["ShearY", 0.7737469436637263, 0.8810127181224531], ["ShearY", 0.8995655445246451, 0.4312416220354539]], [["TranslateY", 0.4953094136709374, 0.8144161580138571], ["Solarize", 0.26301211718928097, 0.518345311180405]], [["Brightness", 0.8820246486031275, 0.571075863786249], ["ShearX", 0.8586669146703955, 0.0060476383595142735]], [["Sharpness", 0.20519233710982254, 0.6144574759149729], ["Posterize", 0.07976625267460813, 0.7480145046726968]], [["ShearY", 0.374075419680195, 0.3386105402023202], ["ShearX", 0.8228083637082115, 0.5885174783155361]], [["Brightness", 0.3528780713814561, 0.6999884884306623], ["Sharpness", 0.3680348120526238, 0.16953358258959617]], [["Brightness", 0.24891223104442084, 0.7973853494920095], ["TranslateX", 0.004256803835524736, 0.0470216343108546]], [["Posterize", 0.1947344282646012, 0.7694802711054367], ["Cutout", 0.9594385534844785, 0.5469744140592429]], [["Invert", 0.19012504762806026, 0.7816140211434693], ["TranslateY", 0.17479746932338402, 0.024249345245078602]], [["Rotate", 0.9669262055946796, 0.510166180775991], ["TranslateX", 0.8990602034610352, 0.6657802719304693]], [["ShearY", 0.5453049050407278, 0.8476872739603525], ["Cutout", 0.14226529093962592, 0.15756960661106634]], [["Equalize", 0.5895291156113004, 0.6797218994447763], ["TranslateY", 0.3541442192192753, 0.05166001155849864]], [["Equalize", 0.39530681662726097, 0.8448335365081087], ["Brightness", 0.6785483272734143, 0.8805568647038574]], [["Cutout", 0.28633258271917905, 0.7750870268336066], ["Equalize", 0.7221097824537182, 0.5865506280531162]], [["Posterize", 0.9044429629421187, 0.4620266401793388], ["Invert", 0.1803008045494473, 0.8073190766288534]], [["Sharpness", 0.7054649148075851, 0.3877207948962055], ["TranslateX", 0.49260224225927285, 0.8987462620731029]], [["Sharpness", 0.11196934729294483, 0.5953704422694938], ["Contrast", 0.13969334315069737, 0.19310569898434204]], [["Posterize", 0.5484346101051778, 0.7914140118600685], ["Brightness", 0.6428044691630473, 0.18811316670808076]], [["Invert", 0.22294834094984717, 0.05173157689962704], ["Cutout", 0.6091129168510456, 0.6280845506243643]], [["AutoContrast", 0.5726444076195267, 0.2799840903601295], ["Cutout", 0.3055752727786235, 0.591639807512993]], [["Brightness", 0.3707116723204462, 0.4049175910826627], ["Rotate", 0.4811601625588309, 0.2710760253723644]], [["ShearY", 0.627791719653608, 0.6877498291550205], ["TranslateX", 0.8751753308366824, 0.011164650018719358]], [["Posterize", 0.33832547954522263, 0.7087039872581657], ["Posterize", 0.6247474435007484, 0.7707784192114796]], [["Contrast", 0.17620186308493468, 0.9946224854942095], ["Solarize", 0.5431896088395964, 0.5867904203742308]], [["ShearX", 0.4667959516719652, 0.8938082224109446], ["TranslateY", 0.7311343008292865, 0.6829842246020277]], [["ShearX", 0.6130281467237769, 0.9924010909612302], ["Brightness", 0.41039241699696916, 0.9753218875311392]], [["TranslateY", 0.0747250386427123, 0.34602725521067534], ["Rotate", 0.5902597465515901, 0.361094672021087]], [["Invert", 0.05234890878959486, 0.36914978664919407], ["Sharpness", 0.42140532878231374, 0.19204058551048275]], [["ShearY", 0.11590485361909497, 0.6518540857972316], ["Invert", 0.6482444740361704, 0.48256237896163945]], [["Rotate", 0.4931329446923608, 0.037076242417301675], ["Contrast", 0.9097939772412852, 0.5619594905306389]], [["Posterize", 0.7311032479626216, 0.4796364593912915], ["Color", 0.13912123993932402, 0.03997286439663705]], [["AutoContrast", 0.6196602944085344, 0.2531430457527588], ["Rotate", 0.5583937060431972, 0.9893379795224023]], [["AutoContrast", 0.8847753125072959, 0.19123028952580057], ["TranslateY", 0.494361716097206, 0.14232297727461696]], [["Invert", 0.6212360716340707, 0.033898871473033165], ["AutoContrast", 0.30839896957008295, 0.23603569542166247]], [["Equalize", 0.8255583546605049, 0.613736933157845], ["AutoContrast", 0.6357166629525485, 0.7894617347709095]], [["Brightness", 0.33840706322846814, 0.07917167871493658], ["ShearY", 0.15693175752528676, 0.6282773652129153]], [["Cutout", 0.7550520024859294, 0.08982367300605598], ["ShearX", 0.5844942417320858, 0.36051195083380105]]]
return p
def fa_reduced_imagenet():
p = [[["ShearY", 0.14143816458479197, 0.513124791615952], ["Sharpness", 0.9290316227291179, 0.9788406212603302]], [["Color", 0.21502874228385338, 0.3698477943880306], ["TranslateY", 0.49865058747734736, 0.4352676987103321]], [["Brightness", 0.6603452126485386, 0.6990174510500261], ["Cutout", 0.7742953773992511, 0.8362550883640804]], [["Posterize", 0.5188375788270497, 0.9863648925446865], ["TranslateY", 0.8365230108655313, 0.6000972236440252]], [["ShearY", 0.9714994964711299, 0.2563663552809896], ["Equalize", 0.8987567223581153, 0.1181761775609772]], [["Sharpness", 0.14346409304565366, 0.5342189791746006], ["Sharpness", 0.1219714162835897, 0.44746801278319975]], [["TranslateX", 0.08089260772173967, 0.028011721602479833], ["TranslateX", 0.34767877352421406, 0.45131294688688794]], [["Brightness", 0.9191164585327378, 0.5143232242627864], ["Color", 0.9235247849934283, 0.30604586249462173]], [["Contrast", 0.4584173187505879, 0.40314219914942756], ["Rotate", 0.550289356406774, 0.38419022293237126]], [["Posterize", 0.37046156420799325, 0.052693291117634544], ["Cutout", 0.7597581409366909, 0.7535799791937421]], [["Color", 0.42583964114658746, 0.6776641859552079], ["ShearY", 0.2864805671096011, 0.07580175477739545]], [["Brightness", 0.5065952125552232, 0.5508640233704984], ["Brightness", 0.4760021616081475, 0.3544313318097987]], [["Posterize", 0.5169630851995185, 0.9466018906715961], ["Posterize", 0.5390336503396841, 0.1171015788193209]], [["Posterize", 0.41153170909576176, 0.7213063942615204], ["Rotate", 0.6232230424824348, 0.7291984098675746]], [["Color", 0.06704687234714028, 0.5278429246040438], ["Sharpness", 0.9146652195810183, 0.4581415618941407]], [["ShearX", 0.22404644446773492, 0.6508620171913467], ["Brightness", 0.06421961538672451, 0.06859528721039095]], [["Rotate", 0.29864103693134797, 0.5244313199644495], ["Sharpness", 0.4006161706584276, 0.5203708477368657]], [["AutoContrast", 0.5748186910788027, 0.8185482599354216], ["Posterize", 0.9571441684265188, 0.1921474117448481]], [["ShearY", 0.5214786760436251, 0.8375629059785009], ["Invert", 0.6872393349333636, 0.9307694335024579]], [["Contrast", 0.47219838080793364, 0.8228524484275648], ["TranslateY", 0.7435518856840543, 0.5888865560614439]], [["Posterize", 0.10773482839638836, 0.6597021018893648], ["Contrast", 0.5218466423129691, 0.562985661685268]], [["Rotate", 0.4401753067886466, 0.055198255925702475], ["Rotate", 0.3702153509335602, 0.5821574425474759]], [["TranslateY", 0.6714729117832363, 0.7145542887432927], ["Equalize", 0.0023263758097700205, 0.25837341854887885]], [["Cutout", 0.3159707561240235, 0.19539664199170742], ["TranslateY", 0.8702824829864558, 0.5832348977243467]], [["AutoContrast", 0.24800812729140026, 0.08017301277245716], ["Brightness", 0.5775505849482201, 0.4905904775616114]], [["Color", 0.4143517886294533, 0.8445937742921498], ["ShearY", 0.28688910858536587, 0.17539366839474402]], [["Brightness", 0.6341134194059947, 0.43683815933640435], 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["Color", 0.3586283093530607, 0.3696246850445087]], [["Posterize", 0.2052935984046965, 0.16796913860308244], ["ShearX", 0.4807226832843722, 0.11296747254563266]], [["Cutout", 0.2016411266364791, 0.2765295444084803], ["Brightness", 0.3054112810424313, 0.695924264931216]], [["Rotate", 0.8405872184910479, 0.5434142541450815], ["Cutout", 0.4493615138203356, 0.893453735250007]], [["Contrast", 0.8433310507685494, 0.4915423577963278], ["ShearX", 0.22567799557913246, 0.20129892537008834]], [["Contrast", 0.045954277103674224, 0.5043900167190442], ["Cutout", 0.5552992473054611, 0.14436447810888237]], [["AutoContrast", 0.7719296115130478, 0.4440417544621306], ["Sharpness", 0.13992809206158283, 0.7988278670709781]], [["Color", 0.7838574233513952, 0.5971351401625151], ["TranslateY", 0.13562290583925385, 0.2253039635819158]], [["Cutout", 0.24870301109385806, 0.6937886690381568], ["TranslateY", 0.4033400068952813, 0.06253378991880915]], [["TranslateX", 0.0036059390486775644, 0.5234723884081843], ["Solarize", 0.42724862530733526, 0.8697702564187633]], [["Equalize", 0.5446026737834311, 0.9367992979112202], ["ShearY", 0.5943478903735789, 0.42345889214100046]], [["ShearX", 0.18611885697957506, 0.7320849092947314], ["ShearX", 0.3796416430900566, 0.03817761920009881]], [["Posterize", 0.37636778506979124, 0.26807924785236537], ["Brightness", 0.4317372554383255, 0.5473346211870932]], [["Brightness", 0.8100436240916665, 0.3817612088285007], ["Brightness", 0.4193974619003253, 0.9685902764026623]], [["Contrast", 0.701776402197012, 0.6612786008858009], ["Color", 0.19882787177960912, 0.17275597188875483]], [["Color", 0.9538303302832989, 0.48362384535228686], ["ShearY", 0.2179980837345602, 0.37027290936457313]], [["TranslateY", 0.6068028691503798, 0.3919346523454841], ["Cutout", 0.8228303342563138, 0.18372280287814613]], [["Equalize", 0.016416758802906828, 0.642838949194916], ["Cutout", 0.5761717838655257, 0.7600661153497648]], [["Color", 0.9417761826818639, 0.9916074035986558], ["Equalize", 0.2524209308597042, 0.6373703468715077]], [["Brightness", 0.75512589439513, 0.6155072321007569], ["Contrast", 0.32413476940254515, 0.4194739830159837]], [["Sharpness", 0.3339450765586968, 0.9973297539194967], ["AutoContrast", 0.6523930242124429, 0.1053482471037186]], [["ShearX", 0.2961391955838801, 0.9870036064904368], ["ShearY", 0.18705025965909403, 0.4550895821154484]], [["TranslateY", 0.36956447983807883, 0.36371471767143543], ["Sharpness", 0.6860051967688487, 0.2850190720087796]], [["Cutout", 0.13017742151902967, 0.47316674150067195], ["Invert", 0.28923829959551883, 0.9295585654924601]], [["Contrast", 0.7302368472279086, 0.7178974949876642], ["TranslateY", 0.12589674152030433, 0.7485392909494947]], [["Color", 0.6474693117772619, 0.5518269515590674], ["Contrast", 0.24643004970708016, 0.3435581358079418]], [["Contrast", 0.5650327855750835, 0.4843031798040887], ["Brightness", 0.3526684005761239, 0.3005305004600969]], [["Rotate", 0.09822284968122225, 0.13172798244520356], ["Equalize", 0.38135066977857157, 0.5135129123554154]], [["Contrast", 0.5902590645585712, 0.2196062383730596], ["ShearY", 0.14188379126120954, 0.1582612142182743]], [["Cutout", 0.8529913814417812, 0.89734031211874], ["Color", 0.07293767043078672, 0.32577659205278897]], [["Equalize", 0.21401668971453247, 0.040015259500028266], ["ShearY", 0.5126400895338797, 0.4726484828276388]], [["Brightness", 0.8269430025954498, 0.9678362841865166], ["ShearY", 0.17142069814830432, 0.4726727848289514]], [["Brightness", 0.699707089334018, 0.2795501395789335], ["ShearX", 0.5308818178242845, 0.10581814221896294]], [["Equalize", 0.32519644258946145, 0.15763390340309183], ["TranslateX", 0.6149090364414208, 0.7454832565718259]], [["AutoContrast", 0.5404508567155423, 0.7472387762067986], ["Equalize", 0.05649876539221024, 0.5628180219887216]]]
return p
================================================
FILE: autoaug/augmentations.py
================================================
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
random_mirror = True
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random_mirror and random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
def ShearY(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random_mirror and random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random_mirror and random.random() > 0.5:
v = -v
v = v * img.size[0]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random_mirror and random.random() > 0.5:
v = -v
v = v * img.size[1]
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def TranslateXAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v <= 10
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
def TranslateYAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v <= 10
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
def Rotate(img, v): # [-30, 30]
assert -30 <= v <= 30
if random_mirror and random.random() > 0.5:
v = -v
return img.rotate(v)
def AutoContrast(img, _):
return PIL.ImageOps.autocontrast(img)
def Invert(img, _):
return PIL.ImageOps.invert(img)
def Equalize(img, _):
return PIL.ImageOps.equalize(img)
def Flip(img, _): # not from the paper
return PIL.ImageOps.mirror(img)
def Solarize(img, v): # [0, 256]
assert 0 <= v <= 256
return PIL.ImageOps.solarize(img, v)
def Posterize(img, v): # [4, 8]
assert 4 <= v <= 8
v = int(v)
return PIL.ImageOps.posterize(img, v)
def Posterize2(img, v): # [0, 4]
assert 0 <= v <= 4
v = int(v)
return PIL.ImageOps.posterize(img, v)
def Contrast(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Contrast(img).enhance(v)
def Color(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Color(img).enhance(v)
def Brightness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Brightness(img).enhance(v)
def Sharpness(img, v): # [0.1,1.9]
assert 0.1 <= v <= 1.9
return PIL.ImageEnhance.Sharpness(img).enhance(v)
def Cutout(img, v): # [0, 60] => percentage: [0, 0.2]
assert 0.0 <= v <= 0.2
if v <= 0.:
return img
v = v * img.size[0]
return CutoutAbs(img, v)
# x0 = np.random.uniform(w - v)
# y0 = np.random.uniform(h - v)
# xy = (x0, y0, x0 + v, y0 + v)
# color = (127, 127, 127)
# img = img.copy()
# PIL.ImageDraw.Draw(img).rectangle(xy, color)
# return img
def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
# assert 0 <= v <= 20
if v < 0:
return img
w, h = img.size
x0 = np.random.uniform(w)
y0 = np.random.uniform(h)
x0 = int(max(0, x0 - v / 2.))
y0 = int(max(0, y0 - v / 2.))
x1 = min(w, x0 + v)
y1 = min(h, y0 + v)
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
PIL.ImageDraw.Draw(img).rectangle(xy, color)
return img
def SamplePairing(imgs): # [0, 0.4]
def f(img1, v):
i = np.random.choice(len(imgs))
img2 = PIL.Image.fromarray(imgs[i])
return PIL.Image.blend(img1, img2, v)
return f
def augment_list(for_autoaug=True): # 16 operations and their ranges
l = [
(ShearX, -0.3, 0.3), # 0
(ShearY, -0.3, 0.3), # 1
(TranslateX, -0.45, 0.45), # 2
(TranslateY, -0.45, 0.45), # 3
(Rotate, -30, 30), # 4
(AutoContrast, 0, 1), # 5
(Invert, 0, 1), # 6
(Equalize, 0, 1), # 7
(Solarize, 0, 256), # 8
(Posterize, 4, 8), # 9
(Contrast, 0.1, 1.9), # 10
(Color, 0.1, 1.9), # 11
(Brightness, 0.1, 1.9), # 12
(Sharpness, 0.1, 1.9), # 13
(Cutout, 0, 0.2), # 14
# (SamplePairing(imgs), 0, 0.4), # 15
]
if for_autoaug:
l += [
(CutoutAbs, 0, 20), # compatible with auto-augment
(Posterize2, 0, 4), # 9
(TranslateXAbs, 0, 10), # 9
(TranslateYAbs, 0, 10), # 9
]
return l
augment_dict = {fn.__name__: (fn, v1, v2) for fn, v1, v2 in augment_list()}
def get_augment(name):
return augment_dict[name]
def apply_augment(img, name, level):
augment_fn, low, high = get_augment(name)
return augment_fn(img.copy(), level * (high - low) + low)
class Augmentation(object):
def __init__(self, policies):
self.policies = policies
def __call__(self, img):
for _ in range(1):
policy = random.choice(self.policies)
for name, pr, level in policy:
if random.random() > pr:
continue
img = apply_augment(img, name, level)
return img
================================================
FILE: conf/cifar100_pyramid200.yaml
================================================
dataset: cifar100
net_type: pyramidnet
depth: 200
alpha: 240
epochs: 300
batch_size: 64
lr: 0.25
momentum: 0.9
lr_schedule:
type: 'pyramid'
gradient_clip: 0
weight_decay: 0.0001
cutout: 0
cutmix:
beta: 1.0
prob: 0.5
num: 1
================================================
FILE: conf/cifar100_pyramid272.yaml
================================================
dataset: cifar100
net_type: pyramidnet
depth: 272
alpha: 200
epochs: 1800
batch_size: 64
lr: 0.05
momentum: 0.9
lr_schedule:
type: 'pyramid'
gradient_clip: 5
weight_decay: 0.00005
cutmix:
beta: 1.0
prob: 0.5
num: 1
================================================
FILE: conf/cifar100_wresnet28x10.yaml
================================================
dataset: cifar100
net_type: wresnet28_10
depth: 28
alpha: 10
epochs: 300
batch_size: 128
lr: 0.025
momentum: 0.9
lr_schedule:
type: 'cosine'
gradient_clip: 5
weight_decay: 0.0002
cutout: 16
cutmix:
beta: 1.0
prob: 0.5
num: 1
================================================
FILE: conf/cifar100_wresnet40x2.yaml
================================================
dataset: cifar100
net_type: wresnet40_2
depth: 40
alpha: 2
epochs: 300
batch_size: 128
lr: 0.025
momentum: 0.9
lr_schedule:
type: 'cosine'
gradient_clip: 5
weight_decay: 0.0002
cutout: 16
cutmix:
beta: 1.0
prob: 0.5
num: 1
================================================
FILE: conf/imagenet_resnet18.yaml
================================================
dataset: imagenet
net_type: resnet
depth: 18
epochs: 300
batch_size: 256
lr: 0.1
momentum: 0.9
weight_decay: 0.0001
cutmix:
beta: 1.0
prob: 1.0
num: 1
================================================
FILE: conf/imagenet_resnet200.yaml
================================================
dataset: imagenet
net_type: resnet
depth: 200
epochs: 300
batch_size: 256
lr: 0.1
momentum: 0.9
weight_decay: 0.0001
cutmix:
beta: 1.0
prob: 1.0
num: 1
================================================
FILE: conf/imagenet_resnet34.yaml
================================================
dataset: imagenet
net_type: resnet
depth: 34
epochs: 300
batch_size: 256
lr: 0.1
momentum: 0.9
weight_decay: 0.0001
cutmix:
beta: 1.0
prob: 1.0
num: 1
================================================
FILE: conf/imagenet_resnet50.yaml
================================================
dataset: imagenet
net_type: resnet
depth: 50
epochs: 300
batch_size: 256
lr: 0.1
momentum: 0.9
weight_decay: 0.0001
cutmix:
beta: 1.0
prob: 1.0
num: 1
================================================
FILE: cutmix/__init__.py
================================================
from cutmix.cutmix import CutMix
================================================
FILE: cutmix/cutmix.py
================================================
import numpy as np
import random
from torch.utils.data.dataset import Dataset
from cutmix.utils import onehot, rand_bbox
class CutMix(Dataset):
def __init__(self, dataset, num_class, num_mix=1, beta=1., prob=1.0):
self.dataset = dataset
self.num_class = num_class
self.num_mix = num_mix
self.beta = beta
self.prob = prob
def __getitem__(self, index):
img, lb = self.dataset[index]
lb_onehot = onehot(self.num_class, lb)
for _ in range(self.num_mix):
r = np.random.rand(1)
if self.beta <= 0 or r > self.prob:
continue
# generate mixed sample
lam = np.random.beta(self.beta, self.beta)
rand_index = random.choice(range(len(self)))
img2, lb2 = self.dataset[rand_index]
lb2_onehot = onehot(self.num_class, lb2)
bbx1, bby1, bbx2, bby2 = rand_bbox(img.size(), lam)
img[:, bbx1:bbx2, bby1:bby2] = img2[:, bbx1:bbx2, bby1:bby2]
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (img.size()[-1] * img.size()[-2]))
lb_onehot = lb_onehot * lam + lb2_onehot * (1. - lam)
return img, lb_onehot
def __len__(self):
return len(self.dataset)
================================================
FILE: cutmix/utils.py
================================================
import numpy as np
import torch
from torch.nn.modules.module import Module
class CutMixCrossEntropyLoss(Module):
def __init__(self, size_average=True):
super().__init__()
self.size_average = size_average
def forward(self, input, target):
if len(target.size()) == 1:
target = torch.nn.functional.one_hot(target, num_classes=input.size(-1))
target = target.float().cuda()
return cross_entropy(input, target, self.size_average)
def cross_entropy(input, target, size_average=True):
""" Cross entropy that accepts soft targets
Args:
pred: predictions for neural network
targets: targets, can be soft
size_average: if false, sum is returned instead of mean
Examples::
input = torch.FloatTensor([[1.1, 2.8, 1.3], [1.1, 2.1, 4.8]])
input = torch.autograd.Variable(out, requires_grad=True)
target = torch.FloatTensor([[0.05, 0.9, 0.05], [0.05, 0.05, 0.9]])
target = torch.autograd.Variable(y1)
loss = cross_entropy(input, target)
loss.backward()
"""
logsoftmax = torch.nn.LogSoftmax(dim=1)
if size_average:
return torch.mean(torch.sum(-target * logsoftmax(input), dim=1))
else:
return torch.sum(torch.sum(-target * logsoftmax(input), dim=1))
def onehot(size, target):
vec = torch.zeros(size, dtype=torch.float32)
vec[target] = 1.
return vec
def rand_bbox(size, lam):
if len(size) == 4:
W = size[2]
H = size[3]
elif len(size) == 3:
W = size[1]
H = size[2]
else:
raise Exception
cut_rat = np.sqrt(1. - lam)
cut_w = np.int(W * cut_rat)
cut_h = np.int(H * cut_rat)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
return bbx1, bby1, bbx2, bby2
================================================
FILE: lr_scheduler.py
================================================
import torch
from theconf import Config as C
def adjust_learning_rate_pyramid(optimizer, max_epoch):
def __adjust_learning_rate_pyramid(epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
base_lr = C.get()['lr']
lr = base_lr * (0.1 ** (epoch // (max_epoch * 0.5))) * (0.1 ** (epoch // (max_epoch * 0.75)))
return lr
return torch.optim.lr_scheduler.LambdaLR(optimizer, __adjust_learning_rate_pyramid)
def adjust_learning_rate_resnet(optimizer):
"""
Sets the learning rate to the initial LR decayed by 10 on every predefined epochs
Ref: AutoAugment
"""
if C.get()['epoch'] == 90:
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [30, 60, 80])
elif C.get()['epoch'] == 270: # autoaugment
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [90, 180, 240])
elif C.get()['epoch'] == 300: # autoaugment
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [75, 150, 225])
else:
raise ValueError('invalid epoch=%d for resnet scheduler' % C.get()['epoch'])
================================================
FILE: network/__init__.py
================================================
================================================
FILE: network/pyramidnet.py
================================================
import torch
import torch.nn as nn
import math
from network.shakedrop import ShakeDrop
def conv3x3(in_planes, out_planes, stride=1):
"""
3x3 convolution with padding
"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
outchannel_ratio = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, p_shakedrop=1.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn3 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.shake_drop = ShakeDrop(p_shakedrop)
def forward(self, x):
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.shake_drop(out)
if self.downsample is not None:
shortcut = self.downsample(x)
featuremap_size = shortcut.size()[2:4]
else:
shortcut = x
featuremap_size = out.size()[2:4]
batch_size = out.size()[0]
residual_channel = out.size()[1]
shortcut_channel = shortcut.size()[1]
if residual_channel != shortcut_channel:
padding = torch.autograd.Variable(
torch.cuda.FloatTensor(batch_size, residual_channel - shortcut_channel, featuremap_size[0],
featuremap_size[1]).fill_(0))
out += torch.cat((shortcut, padding), 1)
else:
out += shortcut
return out
class Bottleneck(nn.Module):
outchannel_ratio = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, p_shakedrop=1.0):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, (planes * 1), kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn3 = nn.BatchNorm2d((planes * 1))
self.conv3 = nn.Conv2d((planes * 1), planes * Bottleneck.outchannel_ratio, kernel_size=1, bias=False)
self.bn4 = nn.BatchNorm2d(planes * Bottleneck.outchannel_ratio)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.shake_drop = ShakeDrop(p_shakedrop)
def forward(self, x):
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn3(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn4(out)
out = self.shake_drop(out)
if self.downsample is not None:
shortcut = self.downsample(x)
featuremap_size = shortcut.size()[2:4]
else:
shortcut = x
featuremap_size = out.size()[2:4]
batch_size = out.size()[0]
residual_channel = out.size()[1]
shortcut_channel = shortcut.size()[1]
if residual_channel != shortcut_channel:
padding = torch.autograd.Variable(
torch.cuda.FloatTensor(batch_size, residual_channel - shortcut_channel, featuremap_size[0],
featuremap_size[1]).fill_(0))
out += torch.cat((shortcut, padding), 1)
else:
out += shortcut
return out
class PyramidNet(nn.Module):
def __init__(self, dataset, depth, alpha, num_classes, bottleneck=True):
super(PyramidNet, self).__init__()
self.dataset = dataset
if self.dataset.startswith('cifar'):
self.inplanes = 16
if bottleneck:
n = int((depth - 2) / 9)
block = Bottleneck
else:
n = int((depth - 2) / 6)
block = BasicBlock
self.addrate = alpha / (3 * n * 1.0)
self.ps_shakedrop = [1. - (1.0 - (0.5 / (3 * n)) * (i + 1)) for i in range(3 * n)]
self.input_featuremap_dim = self.inplanes
self.conv1 = nn.Conv2d(3, self.input_featuremap_dim, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.input_featuremap_dim)
self.featuremap_dim = self.input_featuremap_dim
self.layer1 = self.pyramidal_make_layer(block, n)
self.layer2 = self.pyramidal_make_layer(block, n, stride=2)
self.layer3 = self.pyramidal_make_layer(block, n, stride=2)
self.final_featuremap_dim = self.input_featuremap_dim
self.bn_final = nn.BatchNorm2d(self.final_featuremap_dim)
self.relu_final = nn.ReLU(inplace=True)
self.avgpool = nn.AvgPool2d(8)
self.fc = nn.Linear(self.final_featuremap_dim, num_classes)
elif dataset == 'imagenet':
blocks = {18: BasicBlock, 34: BasicBlock, 50: Bottleneck, 101: Bottleneck, 152: Bottleneck, 200: Bottleneck}
layers = {18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3],
200: [3, 24, 36, 3]}
if layers.get(depth) is None:
if bottleneck == True:
blocks[depth] = Bottleneck
temp_cfg = int((depth - 2) / 12)
else:
blocks[depth] = BasicBlock
temp_cfg = int((depth - 2) / 8)
layers[depth] = [temp_cfg, temp_cfg, temp_cfg, temp_cfg]
print('=> the layer configuration for each stage is set to', layers[depth])
self.inplanes = 64
self.addrate = alpha / (sum(layers[depth]) * 1.0)
self.input_featuremap_dim = self.inplanes
self.conv1 = nn.Conv2d(3, self.input_featuremap_dim, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(self.input_featuremap_dim)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.featuremap_dim = self.input_featuremap_dim
self.layer1 = self.pyramidal_make_layer(blocks[depth], layers[depth][0])
self.layer2 = self.pyramidal_make_layer(blocks[depth], layers[depth][1], stride=2)
self.layer3 = self.pyramidal_make_layer(blocks[depth], layers[depth][2], stride=2)
self.layer4 = self.pyramidal_make_layer(blocks[depth], layers[depth][3], stride=2)
self.final_featuremap_dim = self.input_featuremap_dim
self.bn_final = nn.BatchNorm2d(self.final_featuremap_dim)
self.relu_final = nn.ReLU(inplace=True)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(self.final_featuremap_dim, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
assert len(self.ps_shakedrop) == 0, self.ps_shakedrop
def pyramidal_make_layer(self, block, block_depth, stride=1):
downsample = None
if stride != 1: # or self.inplanes != int(round(featuremap_dim_1st)) * block.outchannel_ratio:
downsample = nn.AvgPool2d((2, 2), stride=(2, 2), ceil_mode=True)
layers = []
self.featuremap_dim = self.featuremap_dim + self.addrate
layers.append(block(self.input_featuremap_dim, int(round(self.featuremap_dim)), stride, downsample, p_shakedrop=self.ps_shakedrop.pop(0)))
for i in range(1, block_depth):
temp_featuremap_dim = self.featuremap_dim + self.addrate
layers.append(
block(int(round(self.featuremap_dim)) * block.outchannel_ratio, int(round(temp_featuremap_dim)), 1, p_shakedrop=self.ps_shakedrop.pop(0)))
self.featuremap_dim = temp_featuremap_dim
self.input_featuremap_dim = int(round(self.featuremap_dim)) * block.outchannel_ratio
return nn.Sequential(*layers)
def forward(self, x):
if self.dataset == 'cifar10' or self.dataset == 'cifar100':
x = self.conv1(x)
x = self.bn1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.bn_final(x)
x = self.relu_final(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
elif self.dataset == 'imagenet':
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn_final(x)
x = self.relu_final(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
================================================
FILE: network/resnet.py
================================================
# Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * Bottleneck.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, dataset, depth, num_classes, bottleneck=False):
super(ResNet, self).__init__()
self.dataset = dataset
if self.dataset.startswith('cifar'):
self.inplanes = 16
print(bottleneck)
if bottleneck == True:
n = int((depth - 2) / 9)
block = Bottleneck
else:
n = int((depth - 2) / 6)
block = BasicBlock
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(8)
self.fc = nn.Linear(64 * block.expansion, num_classes)
elif dataset == 'imagenet':
blocks ={18: BasicBlock, 34: BasicBlock, 50: Bottleneck, 101: Bottleneck, 152: Bottleneck, 200: Bottleneck}
layers ={18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], 200: [3, 24, 36, 3]}
assert layers[depth], 'invalid detph for ResNet (depth should be one of 18, 34, 50, 101, 152, and 200)'
self.inplanes = 64
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(blocks[depth], 64, layers[depth][0])
self.layer2 = self._make_layer(blocks[depth], 128, layers[depth][1], stride=2)
self.layer3 = self._make_layer(blocks[depth], 256, layers[depth][2], stride=2)
self.layer4 = self._make_layer(blocks[depth], 512, layers[depth][3], stride=2)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(512 * blocks[depth].expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
if self.dataset == 'cifar10' or self.dataset == 'cifar100':
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
elif self.dataset == 'imagenet':
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
================================================
FILE: network/shakedrop.py
================================================
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeDropFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, training=True, p_drop=0.5, alpha_range=[-1, 1]):
if training:
gate = torch.cuda.FloatTensor([0]).bernoulli_(1 - p_drop)
ctx.save_for_backward(gate)
if gate.item() == 0:
alpha = torch.cuda.FloatTensor(x.size(0)).uniform_(*alpha_range)
alpha = alpha.view(alpha.size(0), 1, 1, 1).expand_as(x)
return alpha * x
else:
return x
else:
return (1 - p_drop) * x
@staticmethod
def backward(ctx, grad_output):
gate = ctx.saved_tensors[0]
if gate.item() == 0:
beta = torch.cuda.FloatTensor(grad_output.size(0)).uniform_(0, 1)
beta = beta.view(beta.size(0), 1, 1, 1).expand_as(grad_output)
beta = Variable(beta)
return beta * grad_output, None, None, None
else:
return grad_output, None, None, None
class ShakeDrop(nn.Module):
def __init__(self, p_drop=0.5, alpha_range=[-1, 1]):
super(ShakeDrop, self).__init__()
self.p_drop = p_drop
self.alpha_range = alpha_range
def forward(self, x):
return ShakeDropFunction.apply(x, self.training, self.p_drop, self.alpha_range)
================================================
FILE: network/wideresnet.py
================================================
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform_(m.weight, gain=np.sqrt(2))
init.constant_(m.bias, 0)
elif classname.find('BatchNorm') != -1:
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
class WideBasic(nn.Module):
def __init__(self, in_planes, planes, dropout_rate, stride=1):
super(WideBasic, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.9)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
self.dropout = nn.Dropout(p=dropout_rate)
self.bn2 = nn.BatchNorm2d(planes, momentum=0.9)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
)
def forward(self, x):
out = self.dropout(self.conv1(F.relu(self.bn1(x))))
out = self.conv2(F.relu(self.bn2(out)))
out += self.shortcut(x)
return out
class WideResNet(nn.Module):
def __init__(self, depth, widen_factor, dropout_rate, num_classes):
super(WideResNet, self).__init__()
self.in_planes = 16
assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
n = int((depth - 4) / 6)
k = widen_factor
nStages = [16, 16*k, 32*k, 64*k]
self.conv1 = conv3x3(3, nStages[0])
self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=1)
self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2)
self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2)
self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
self.linear = nn.Linear(nStages[3], num_classes)
# self.apply(conv_init)
def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, dropout_rate, stride))
self.in_planes = planes
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = F.relu(self.bn1(out))
# out = F.avg_pool2d(out, 8)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
================================================
FILE: requirements.txt
================================================
git+https://github.com/wbaek/theconf
sklearn
git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git
================================================
FILE: setup.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import setuptools
_VERSION = '0.1'
# 'opencv-python >= 3.3.1'
REQUIRED_PACKAGES = [
]
DEPENDENCY_LINKS = [
]
setuptools.setup(
name='cutmix',
version=_VERSION,
description='a Ready-to-use PyTorch Extension of Unofficial CutMix Implementations',
install_requires=REQUIRED_PACKAGES,
dependency_links=DEPENDENCY_LINKS,
url='https://github.com/ildoonet/cutmix/',
license='MIT License',
package_dir={},
packages=setuptools.find_packages(exclude=['run', 'autoaug', 'conf', 'network', 'tests']),
)
================================================
FILE: train.py
================================================
# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from sklearn.model_selection._split import StratifiedShuffleSplit
from theconf.argument_parser import ConfigArgumentParser
from torch.utils.data.dataset import Subset
from tqdm._tqdm import tqdm
from network import resnet as RN
import network.pyramidnet as PYRM
from network.wideresnet import WideResNet as WRN
import utils
import warnings
from cutmix.cutmix import CutMix
from cutmix.utils import CutMixCrossEntropyLoss
from autoaug.archive import fa_reduced_cifar10, fa_reduced_imagenet, autoaug_paper_cifar10, autoaug_policy
from autoaug.augmentations import Augmentation
warnings.filterwarnings("ignore")
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = ConfigArgumentParser(conflict_handler='resolve')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--expname', default='TEST', type=str, help='name of experiment')
parser.add_argument('--cifarpath', default='/data/private/pretrainedmodels/', type=str)
parser.add_argument('--imagenetpath', default='/data/private/pretrainedmodels/imagenet/', type=str)
parser.add_argument('--autoaug', default='', type=str)
parser.add_argument('--cv', default=-1, type=int)
parser.add_argument('--only-eval', action='store_true')
parser.add_argument('--checkpoint', default='', type=str)
parser.set_defaults(bottleneck=True)
parser.set_defaults(verbose=True)
best_err1 = 100
best_err5 = 100
def main():
global args, best_err1, best_err5
args = parser.parse_args()
if args.dataset.startswith('cifar'):
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]]
)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
autoaug = args.autoaug
if autoaug:
print('augmentation: %s' % autoaug)
if autoaug == 'fa_reduced_cifar10':
transform_train.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
elif autoaug == 'fa_reduced_imagenet':
transform_train.transforms.insert(0, Augmentation(fa_reduced_imagenet()))
elif autoaug == 'autoaug_cifar10':
transform_train.transforms.insert(0, Augmentation(autoaug_paper_cifar10()))
elif autoaug == 'autoaug_extend':
transform_train.transforms.insert(0, Augmentation(autoaug_policy()))
elif autoaug in ['default', 'inception', 'inception320']:
pass
else:
raise ValueError('not found augmentations. %s' % C.get()['aug'])
transform_test = transforms.Compose([
transforms.ToTensor(),
normalize
])
if args.dataset == 'cifar100':
ds_train = datasets.CIFAR100(args.cifarpath, train=True, download=True, transform=transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(ds_train))), ds_train.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
ds_valid = Subset(ds_train, valid_idx)
ds_train = Subset(ds_train, train_idx)
else:
ds_valid = Subset(ds_train, [])
ds_test = datasets.CIFAR100(args.cifarpath, train=False, transform=transform_test)
train_loader = torch.utils.data.DataLoader(
CutMix(ds_train, 100, beta=args.cutmix_beta, prob=args.cutmix_prob, num_mix=args.cutmix_num),
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
tval_loader = torch.utils.data.DataLoader(ds_valid,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(ds_test,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
numberofclass = 100
elif args.dataset == 'cifar10':
ds_train = datasets.CIFAR10(args.cifarpath, train=True, download=True, transform=transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(ds_train))), ds_train.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
ds_valid = Subset(ds_train, valid_idx)
ds_train = Subset(ds_train, train_idx)
else:
ds_valid = Subset(ds_train, [])
train_loader = torch.utils.data.DataLoader(
CutMix(ds_train, 10,
beta=args.cutmix_beta, prob=args.cutmix_prob, num_mix=args.cutmix_num),
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
tval_loader = torch.utils.data.DataLoader(ds_valid,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.cifarpath, train=False, transform=transform_test),
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
numberofclass = 10
else:
raise Exception('unknown dataset: {}'.format(args.dataset))
elif args.dataset == 'imagenet':
traindir = os.path.join(args.imagenetpath, 'train')
valdir = os.path.join(args.imagenetpath, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
jittering = utils.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4)
lighting = utils.Lighting(alphastd=0.1,
eigval=[0.2175, 0.0188, 0.0045],
eigvec=[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]])
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
jittering,
lighting,
normalize,
])
autoaug = args.autoaug
if autoaug:
print('augmentation: %s' % autoaug)
if autoaug == 'fa_reduced_cifar10':
transform_train.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
elif autoaug == 'fa_reduced_imagenet':
transform_train.transforms.insert(0, Augmentation(fa_reduced_imagenet()))
elif autoaug == 'autoaug_cifar10':
transform_train.transforms.insert(0, Augmentation(autoaug_paper_cifar10()))
elif autoaug == 'autoaug_extend':
transform_train.transforms.insert(0, Augmentation(autoaug_policy()))
elif autoaug in ['default', 'inception', 'inception320']:
pass
else:
raise ValueError('not found augmentations. %s' % C.get()['aug'])
train_dataset = datasets.ImageFolder(traindir, transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(train_dataset))), train_dataset.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
valid_dataset = Subset(train_dataset, valid_idx)
train_dataset = Subset(train_dataset, train_idx)
else:
valid_dataset = Subset(train_dataset, [])
train_dataset = CutMix(train_dataset, 1000, beta=args.cutmix_beta, prob=args.cutmix_prob, num_mix=args.cutmix_num)
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
tval_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
numberofclass = 1000
else:
raise Exception('unknown dataset: {}'.format(args.dataset))
print("=> creating model '{}'".format(args.net_type))
if args.net_type == 'resnet':
model = RN.ResNet(args.dataset, args.depth, numberofclass, True)
elif args.net_type == 'pyramidnet':
model = PYRM.PyramidNet(args.dataset, args.depth, args.alpha, numberofclass, True)
elif 'wresnet' in args.net_type:
model = WRN(args.depth, args.alpha, dropout_rate=0.0, num_classes=numberofclass)
else:
raise ValueError('unknown network architecture: {}'.format(args.net_type))
model = torch.nn.DataParallel(model).cuda()
print('the number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
# define loss function (criterion) and optimizer
criterion = CutMixCrossEntropyLoss(True)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=1e-4, nesterov=True)
cudnn.benchmark = True
for epoch in range(0, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
model.train()
err1, err5, train_loss = run_epoch(train_loader, model, criterion, optimizer, epoch, 'train')
train_err1 = err1
err1, err5, train_loss = run_epoch(tval_loader, model, criterion, None, epoch, 'train-val')
# evaluate on validation set
model.eval()
err1, err5, val_loss = run_epoch(val_loader, model, criterion, None, epoch, 'valid')
# remember best prec@1 and save checkpoint
is_best = err1 <= best_err1
best_err1 = min(err1, best_err1)
if is_best:
best_err5 = err5
print('Current Best (top-1 and 5 error):', best_err1, best_err5)
save_checkpoint({
'epoch': epoch,
'arch': args.net_type,
'state_dict': model.state_dict(),
'best_err1': best_err1,
'best_err5': best_err5,
'optimizer': optimizer.state_dict(),
}, is_best, filename='checkpoint_e%d_top1_%.3f_%.3f.pth' % (epoch, train_err1, err1))
print('Best(top-1 and 5 error):', best_err1, best_err5)
def run_epoch(loader, model, criterion, optimizer, epoch, tag):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
if optimizer:
current_lr = get_learning_rate(optimizer)[0]
else:
current_lr = None
tqdm_disable = bool(os.environ.get('TASK_NAME', '')) # for KakaoBrain
loader = tqdm(loader, disable=tqdm_disable)
loader.set_description('[%s %04d/%04d]' % (tag, epoch, args.epochs))
for i, (input, target) in enumerate(loader):
# measure data loading time
data_time.update(time.time() - end)
input, target = input.cuda(), target.cuda()
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
if len(target.size()) == 1:
err1, err5 = accuracy(output.data, target, topk=(1, 5))
top1.update(err1.item(), input.size(0))
top5.update(err5.item(), input.size(0))
if optimizer:
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
del loss, output
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
loader.set_postfix(lr=current_lr, batch_time=batch_time.avg, data_time=data_time.avg, loss=losses.avg, top1=top1.avg, top5=top5.avg)
if tqdm_disable:
print('[%s %03d/%03d] %s' % (tag, epoch, args.epochs, dict(lr=current_lr, batch_time=batch_time.avg, data_time=data_time.avg, loss=losses.avg, top1=top1.avg, top5=top5.avg)))
return top1.avg, top5.avg, losses.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
if not args.expname:
return
directory = "runs/%s/" % args.expname
if not os.path.exists(directory):
os.makedirs(directory)
filename = directory + filename
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, os.path.join('runs', args.expname, 'model_best.pth'))
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
if args.dataset.startswith('cifar'):
lr = args.lr * (0.1 ** (epoch // (args.epochs * 0.5))) * (0.1 ** (epoch // (args.epochs * 0.75)))
elif args.dataset == 'imagenet':
if args.epochs == 300:
lr = args.lr * (0.1 ** (epoch // 75))
else:
lr = args.lr * (0.1 ** (epoch // 30))
else:
raise ValueError(args.dataset)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_learning_rate(optimizer):
lr = []
for param_group in optimizer.param_groups:
lr += [param_group['lr']]
return lr
def 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)
wrong_k = batch_size - correct_k
res.append(wrong_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()
================================================
FILE: train_legacy.py
================================================
# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py
import os
import shutil
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from sklearn.model_selection._split import StratifiedShuffleSplit
from theconf.argument_parser import ConfigArgumentParser
from theconf import Config as C
from torch.utils.data.dataset import Subset
from tqdm._tqdm import tqdm
from warmup_scheduler.scheduler import GradualWarmupScheduler
from lr_scheduler import adjust_learning_rate_resnet, adjust_learning_rate_pyramid
from network import resnet as RN
import network.pyramidnet as PYRM
from network.wideresnet import WideResNet as WRN
import utils
import warnings
from autoaug.archive import fa_reduced_cifar10, fa_reduced_imagenet, autoaug_paper_cifar10, autoaug_policy
from autoaug.augmentations import Augmentation
warnings.filterwarnings("ignore")
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = ConfigArgumentParser(conflict_handler='resolve')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--expname', default='TEST', type=str, help='name of experiment')
parser.add_argument('--cifarpath', default='/data/private/pretrainedmodels/', type=str)
parser.add_argument('--imagenetpath', default='/data/private/pretrainedmodels/imagenet/', type=str)
parser.add_argument('--autoaug', default='', type=str)
parser.add_argument('--cv', default=-1, type=int)
parser.add_argument('--only-eval', action='store_true')
parser.add_argument('--checkpoint', default='', type=str)
parser.set_defaults(bottleneck=True)
parser.set_defaults(verbose=True)
best_err1 = 100
best_err5 = 100
def main():
global args, best_err1, best_err5
args = parser.parse_args()
if args.dataset.startswith('cifar'):
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]]
)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
autoaug = args.autoaug
if autoaug:
print('augmentation: %s' % autoaug)
if autoaug == 'fa_reduced_cifar10':
transform_train.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
elif autoaug == 'fa_reduced_imagenet':
transform_train.transforms.insert(0, Augmentation(fa_reduced_imagenet()))
elif autoaug == 'autoaug_cifar10':
transform_train.transforms.insert(0, Augmentation(autoaug_paper_cifar10()))
elif autoaug == 'autoaug_extend':
transform_train.transforms.insert(0, Augmentation(autoaug_policy()))
elif autoaug in ['default', 'inception', 'inception320']:
pass
else:
raise ValueError('not found augmentations. %s' % C.get()['aug'])
transform_test = transforms.Compose([
transforms.ToTensor(),
normalize
])
if args.dataset == 'cifar100':
ds_train = datasets.CIFAR100(args.cifarpath, train=True, download=True, transform=transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(ds_train))), ds_train.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
ds_valid = Subset(ds_train, valid_idx)
ds_train = Subset(ds_train, train_idx)
else:
ds_valid = Subset(ds_train, [])
ds_test = datasets.CIFAR100(args.cifarpath, train=False, transform=transform_test)
train_loader = torch.utils.data.DataLoader(
ds_train,
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
tval_loader = torch.utils.data.DataLoader(ds_valid,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(ds_test,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
numberofclass = 100
elif args.dataset == 'cifar10':
ds_train = datasets.CIFAR10(args.cifarpath, train=True, download=True, transform=transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(ds_train))), ds_train.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
ds_valid = Subset(ds_train, valid_idx)
ds_train = Subset(ds_train, train_idx)
else:
ds_valid = Subset(ds_train, [])
train_loader = torch.utils.data.DataLoader(
ds_train,
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
tval_loader = torch.utils.data.DataLoader(ds_valid,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.cifarpath, train=False, transform=transform_test),
batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True)
numberofclass = 10
else:
raise Exception('unknown dataset: {}'.format(args.dataset))
elif args.dataset == 'imagenet':
traindir = os.path.join(args.imagenetpath, 'train')
valdir = os.path.join(args.imagenetpath, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
jittering = utils.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4)
lighting = utils.Lighting(alphastd=0.1,
eigval=[0.2175, 0.0188, 0.0045],
eigvec=[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]])
transform_train = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
jittering,
lighting,
normalize,
])
autoaug = args.autoaug
if autoaug:
print('augmentation: %s' % autoaug)
if autoaug == 'fa_reduced_cifar10':
transform_train.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
elif autoaug == 'fa_reduced_imagenet':
transform_train.transforms.insert(0, Augmentation(fa_reduced_imagenet()))
elif autoaug == 'autoaug_cifar10':
transform_train.transforms.insert(0, Augmentation(autoaug_paper_cifar10()))
elif autoaug == 'autoaug_extend':
transform_train.transforms.insert(0, Augmentation(autoaug_policy()))
elif autoaug in ['default', 'inception', 'inception320']:
pass
else:
raise ValueError('not found augmentations. %s' % C.get()['aug'])
train_dataset = datasets.ImageFolder(traindir, transform_train)
if args.cv >= 0:
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
sss = sss.split(list(range(len(train_dataset))), train_dataset.targets)
for _ in range(args.cv + 1):
train_idx, valid_idx = next(sss)
valid_dataset = Subset(train_dataset, valid_idx)
train_dataset = Subset(train_dataset, train_idx)
else:
valid_dataset = Subset(train_dataset, [])
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
tval_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
numberofclass = 1000
else:
raise Exception('unknown dataset: {}'.format(args.dataset))
print("=> creating model '{}'".format(args.net_type))
if args.net_type == 'resnet':
model = RN.ResNet(args.dataset, args.depth, numberofclass, True)
elif args.net_type == 'pyramidnet':
model = PYRM.PyramidNet(args.dataset, args.depth, args.alpha, numberofclass, True)
elif 'wresnet' in args.net_type:
model = WRN(args.depth, args.alpha, dropout_rate=0.0, num_classes=numberofclass)
else:
raise ValueError('unknown network architecture: {}'.format(args.net_type))
model = torch.nn.DataParallel(model).cuda()
print('the number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=C.get()['weight_decay'], nesterov=True)
lr_scheduler_type = C.get()['lr_schedule'].get('type', 'cosine')
if lr_scheduler_type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=C.get()['epochs'], eta_min=0.)
elif lr_scheduler_type == 'resnet':
scheduler = adjust_learning_rate_resnet(optimizer)
elif lr_scheduler_type == 'pyramid':
scheduler = adjust_learning_rate_pyramid(optimizer, C.get()['epochs'])
else:
raise ValueError('invalid lr_schduler=%s' % lr_scheduler_type)
if C.get()['lr_schedule'].get('warmup', None):
scheduler = GradualWarmupScheduler(
optimizer,
multiplier=C.get()['lr_schedule']['warmup']['multiplier'],
total_epoch=C.get()['lr_schedule']['warmup']['epoch'],
after_scheduler=scheduler
)
for epoch in range(0, args.epochs):
scheduler.step(epoch)
# train for one epoch
model.train()
err1, err5, train_loss = run_epoch(train_loader, model, criterion, optimizer, epoch, 'train')
train_err1 = err1
err1, err5, train_loss = run_epoch(tval_loader, model, criterion, None, epoch, 'train-val')
# evaluate on validation set
model.eval()
err1, err5, val_loss = run_epoch(val_loader, model, criterion, None, epoch, 'valid')
# remember best prec@1 and save checkpoint
is_best = err1 <= best_err1
best_err1 = min(err1, best_err1)
if is_best:
best_err5 = err5
print('Current Best (top-1 and 5 error):', best_err1, best_err5)
save_checkpoint({
'epoch': epoch,
'arch': args.net_type,
'state_dict': model.state_dict(),
'best_err1': best_err1,
'best_err5': best_err5,
'optimizer': optimizer.state_dict(),
}, is_best, filename='checkpoint_e%d_top1_%.3f_%.3f.pth' % (epoch, train_err1, err1))
print('Best(top-1 and 5 error):', best_err1, best_err5)
def run_epoch(loader, model, criterion, optimizer, epoch, tag):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
if optimizer:
current_lr = get_learning_rate(optimizer)[0]
else:
current_lr = None
tqdm_disable = bool(os.environ.get('TASK_NAME', '')) # for KakaoBrain
loader = tqdm(loader, disable=tqdm_disable)
loader.set_description('[%s %04d/%04d]' % (tag, epoch, args.epochs))
for i, (input, target) in enumerate(loader):
# measure data loading time
data_time.update(time.time() - end)
input, target = input.cuda(), target.cuda()
r = np.random.rand(1)
if args.cutmix_be
gitextract_7nca5mpm/ ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── ablations.md ├── autoaug/ │ ├── __init__.py │ ├── archive.py │ └── augmentations.py ├── conf/ │ ├── cifar100_pyramid200.yaml │ ├── cifar100_pyramid272.yaml │ ├── cifar100_wresnet28x10.yaml │ ├── cifar100_wresnet40x2.yaml │ ├── imagenet_resnet18.yaml │ ├── imagenet_resnet200.yaml │ ├── imagenet_resnet34.yaml │ └── imagenet_resnet50.yaml ├── cutmix/ │ ├── __init__.py │ ├── cutmix.py │ └── utils.py ├── lr_scheduler.py ├── network/ │ ├── __init__.py │ ├── pyramidnet.py │ ├── resnet.py │ ├── shakedrop.py │ └── wideresnet.py ├── requirements.txt ├── setup.py ├── train.py ├── train_legacy.py └── utils.py
SYMBOL INDEX (128 symbols across 12 files)
FILE: autoaug/archive.py
function arsaug_policy (line 10) | def arsaug_policy():
function autoaug2arsaug (line 58) | def autoaug2arsaug(f):
function autoaug_paper_cifar10 (line 90) | def autoaug_paper_cifar10():
function autoaug_policy (line 121) | def autoaug_policy():
function float_parameter (line 247) | def float_parameter(level, maxval):
function int_parameter (line 251) | def int_parameter(level, maxval):
function random_search2048 (line 255) | def random_search2048():
function no_duplicates (line 266) | def no_duplicates(f):
function remove_duplicates (line 274) | def remove_duplicates(policies):
function fa_reduced_cifar10 (line 291) | def fa_reduced_cifar10():
function fa_reduced_imagenet (line 296) | def fa_reduced_imagenet():
FILE: autoaug/augmentations.py
function ShearX (line 11) | def ShearX(img, v): # [-0.3, 0.3]
function ShearY (line 18) | def ShearY(img, v): # [-0.3, 0.3]
function TranslateX (line 25) | def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
function TranslateY (line 33) | def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
function TranslateXAbs (line 41) | def TranslateXAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
function TranslateYAbs (line 48) | def TranslateYAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
function Rotate (line 55) | def Rotate(img, v): # [-30, 30]
function AutoContrast (line 62) | def AutoContrast(img, _):
function Invert (line 66) | def Invert(img, _):
function Equalize (line 70) | def Equalize(img, _):
function Flip (line 74) | def Flip(img, _): # not from the paper
function Solarize (line 78) | def Solarize(img, v): # [0, 256]
function Posterize (line 83) | def Posterize(img, v): # [4, 8]
function Posterize2 (line 89) | def Posterize2(img, v): # [0, 4]
function Contrast (line 95) | def Contrast(img, v): # [0.1,1.9]
function Color (line 100) | def Color(img, v): # [0.1,1.9]
function Brightness (line 105) | def Brightness(img, v): # [0.1,1.9]
function Sharpness (line 110) | def Sharpness(img, v): # [0.1,1.9]
function Cutout (line 115) | def Cutout(img, v): # [0, 60] => percentage: [0, 0.2]
function CutoutAbs (line 133) | def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
function SamplePairing (line 154) | def SamplePairing(imgs): # [0, 0.4]
function augment_list (line 163) | def augment_list(for_autoaug=True): # 16 operations and their ranges
function get_augment (line 195) | def get_augment(name):
function apply_augment (line 199) | def apply_augment(img, name, level):
class Augmentation (line 204) | class Augmentation(object):
method __init__ (line 205) | def __init__(self, policies):
method __call__ (line 208) | def __call__(self, img):
FILE: cutmix/cutmix.py
class CutMix (line 8) | class CutMix(Dataset):
method __init__ (line 9) | def __init__(self, dataset, num_class, num_mix=1, beta=1., prob=1.0):
method __getitem__ (line 16) | def __getitem__(self, index):
method __len__ (line 39) | def __len__(self):
FILE: cutmix/utils.py
class CutMixCrossEntropyLoss (line 6) | class CutMixCrossEntropyLoss(Module):
method __init__ (line 7) | def __init__(self, size_average=True):
method forward (line 11) | def forward(self, input, target):
function cross_entropy (line 18) | def cross_entropy(input, target, size_average=True):
function onehot (line 42) | def onehot(size, target):
function rand_bbox (line 48) | def rand_bbox(size, lam):
FILE: lr_scheduler.py
function adjust_learning_rate_pyramid (line 6) | def adjust_learning_rate_pyramid(optimizer, max_epoch):
function adjust_learning_rate_resnet (line 17) | def adjust_learning_rate_resnet(optimizer):
FILE: network/pyramidnet.py
function conv3x3 (line 8) | def conv3x3(in_planes, out_planes, stride=1):
class BasicBlock (line 15) | class BasicBlock(nn.Module):
method __init__ (line 18) | def __init__(self, inplanes, planes, stride=1, downsample=None, p_shak...
method forward (line 30) | def forward(self, x):
class Bottleneck (line 63) | class Bottleneck(nn.Module):
method __init__ (line 66) | def __init__(self, inplanes, planes, stride=1, downsample=None, p_shak...
method forward (line 81) | def forward(self, x):
class PyramidNet (line 120) | class PyramidNet(nn.Module):
method __init__ (line 122) | def __init__(self, dataset, depth, alpha, num_classes, bottleneck=True):
method pyramidal_make_layer (line 199) | def pyramidal_make_layer(self, block, block_depth, stride=1):
method forward (line 216) | def forward(self, x):
FILE: network/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 27) | def forward(self, x):
class Bottleneck (line 46) | class Bottleneck(nn.Module):
method __init__ (line 49) | def __init__(self, inplanes, planes, stride=1, downsample=None):
method forward (line 63) | def forward(self, x):
class ResNet (line 84) | class ResNet(nn.Module):
method __init__ (line 85) | def __init__(self, dataset, depth, num_classes, bottleneck=False):
method _make_layer (line 132) | def _make_layer(self, block, planes, blocks, stride=1):
method forward (line 149) | def forward(self, x):
FILE: network/shakedrop.py
class ShakeDropFunction (line 9) | class ShakeDropFunction(torch.autograd.Function):
method forward (line 12) | def forward(ctx, x, training=True, p_drop=0.5, alpha_range=[-1, 1]):
method backward (line 26) | def backward(ctx, grad_output):
class ShakeDrop (line 37) | class ShakeDrop(nn.Module):
method __init__ (line 39) | def __init__(self, p_drop=0.5, alpha_range=[-1, 1]):
method forward (line 44) | def forward(self, x):
FILE: network/wideresnet.py
function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
function conv_init (line 11) | def conv_init(m):
class WideBasic (line 21) | class WideBasic(nn.Module):
method __init__ (line 22) | def __init__(self, in_planes, planes, dropout_rate, stride=1):
method forward (line 36) | def forward(self, x):
class WideResNet (line 44) | class WideResNet(nn.Module):
method __init__ (line 45) | def __init__(self, depth, widen_factor, dropout_rate, num_classes):
method _wide_layer (line 64) | def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
method forward (line 74) | def forward(self, x):
FILE: train.py
function main (line 57) | def main():
function run_epoch (line 264) | def run_epoch(loader, model, criterion, optimizer, epoch, tag):
function save_checkpoint (line 318) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 331) | class AverageMeter(object):
method __init__ (line 334) | def __init__(self):
method reset (line 337) | def reset(self):
method update (line 343) | def update(self, val, n=1):
function adjust_learning_rate (line 350) | def adjust_learning_rate(optimizer, epoch):
function get_learning_rate (line 366) | def get_learning_rate(optimizer):
function accuracy (line 373) | def accuracy(output, target, topk=(1,)):
FILE: train_legacy.py
function main (line 59) | def main():
function run_epoch (line 280) | def run_epoch(loader, model, criterion, optimizer, epoch, tag):
function save_checkpoint (line 352) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 365) | class AverageMeter(object):
method __init__ (line 368) | def __init__(self):
method reset (line 371) | def reset(self):
method update (line 377) | def update(self, val, n=1):
function get_learning_rate (line 384) | def get_learning_rate(optimizer):
function rand_bbox (line 391) | def rand_bbox(size, lam):
function accuracy (line 410) | def accuracy(output, target, topk=(1,)):
FILE: utils.py
class Compose (line 9) | class Compose(object):
method __init__ (line 22) | def __init__(self, transforms):
method __call__ (line 25) | def __call__(self, img):
method __repr__ (line 30) | def __repr__(self):
class Lighting (line 39) | class Lighting(object):
method __init__ (line 42) | def __init__(self, alphastd, eigval, eigvec):
method __call__ (line 47) | def __call__(self, img):
class Grayscale (line 60) | class Grayscale(object):
method __call__ (line 62) | def __call__(self, img):
class Saturation (line 70) | class Saturation(object):
method __init__ (line 72) | def __init__(self, var):
method __call__ (line 75) | def __call__(self, img):
class Brightness (line 81) | class Brightness(object):
method __init__ (line 83) | def __init__(self, var):
method __call__ (line 86) | def __call__(self, img):
class Contrast (line 92) | class Contrast(object):
method __init__ (line 94) | def __init__(self, var):
method __call__ (line 97) | def __call__(self, img):
class ColorJitter (line 104) | class ColorJitter(object):
method __init__ (line 106) | def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4):
method __call__ (line 111) | def __call__(self, img):
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (211K chars).
[
{
"path": ".gitignore",
"chars": 1203,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 1066,
"preview": "MIT License\n\nCopyright (c) 2019 Ildoo Kim\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\n"
},
{
"path": "README.md",
"chars": 4718,
"preview": "# cutmix\n\n<img src=\"https://github.com/clovaai/CutMix-PyTorch/raw/master/img1.PNG\" width=50% />\n\na Ready-to-use PyTorch "
},
{
"path": "__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "ablations.md",
"chars": 358,
"preview": "## cutmix_num\n\nCutMix between more than two images. Test result show that there is no significant difference.\n\n### cutmi"
},
{
"path": "autoaug/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "autoaug/archive.py",
"chars": 127963,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom colle"
},
{
"path": "autoaug/augmentations.py",
"chars": 5461,
"preview": "# code in this file is adpated from rpmcruz/autoaugment\n# https://github.com/rpmcruz/autoaugment/blob/master/transformat"
},
{
"path": "conf/cifar100_pyramid200.yaml",
"chars": 235,
"preview": "dataset: cifar100\nnet_type: pyramidnet\ndepth: 200\nalpha: 240\n\nepochs: 300\nbatch_size: 64\nlr: 0.25\nmomentum: 0.9\nlr_sched"
},
{
"path": "conf/cifar100_pyramid272.yaml",
"chars": 226,
"preview": "dataset: cifar100\nnet_type: pyramidnet\ndepth: 272\nalpha: 200\n\nepochs: 1800\nbatch_size: 64\nlr: 0.05\nmomentum: 0.9\nlr_sche"
},
{
"path": "conf/cifar100_wresnet28x10.yaml",
"chars": 237,
"preview": "dataset: cifar100\nnet_type: wresnet28_10\ndepth: 28\nalpha: 10\n\nepochs: 300\nbatch_size: 128\nlr: 0.025\nmomentum: 0.9\nlr_sch"
},
{
"path": "conf/cifar100_wresnet40x2.yaml",
"chars": 235,
"preview": "dataset: cifar100\nnet_type: wresnet40_2\ndepth: 40\nalpha: 2\n\nepochs: 300\nbatch_size: 128\nlr: 0.025\nmomentum: 0.9\nlr_sched"
},
{
"path": "conf/imagenet_resnet18.yaml",
"chars": 160,
"preview": "dataset: imagenet\nnet_type: resnet\ndepth: 18\n\nepochs: 300\nbatch_size: 256\nlr: 0.1\nmomentum: 0.9\n\nweight_decay: 0.0001\n\nc"
},
{
"path": "conf/imagenet_resnet200.yaml",
"chars": 161,
"preview": "dataset: imagenet\nnet_type: resnet\ndepth: 200\n\nepochs: 300\nbatch_size: 256\nlr: 0.1\nmomentum: 0.9\n\nweight_decay: 0.0001\n\n"
},
{
"path": "conf/imagenet_resnet34.yaml",
"chars": 160,
"preview": "dataset: imagenet\nnet_type: resnet\ndepth: 34\n\nepochs: 300\nbatch_size: 256\nlr: 0.1\nmomentum: 0.9\n\nweight_decay: 0.0001\n\nc"
},
{
"path": "conf/imagenet_resnet50.yaml",
"chars": 160,
"preview": "dataset: imagenet\nnet_type: resnet\ndepth: 50\n\nepochs: 300\nbatch_size: 256\nlr: 0.1\nmomentum: 0.9\n\nweight_decay: 0.0001\n\nc"
},
{
"path": "cutmix/__init__.py",
"chars": 32,
"preview": "from cutmix.cutmix import CutMix"
},
{
"path": "cutmix/cutmix.py",
"chars": 1268,
"preview": "import numpy as np\nimport random\nfrom torch.utils.data.dataset import Dataset\n\nfrom cutmix.utils import onehot, rand_bbo"
},
{
"path": "cutmix/utils.py",
"chars": 2003,
"preview": "import numpy as np\nimport torch\nfrom torch.nn.modules.module import Module\n\n\nclass CutMixCrossEntropyLoss(Module):\n d"
},
{
"path": "lr_scheduler.py",
"chars": 1109,
"preview": "import torch\n\nfrom theconf import Config as C\n\n\ndef adjust_learning_rate_pyramid(optimizer, max_epoch):\n def __adjust"
},
{
"path": "network/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "network/pyramidnet.py",
"chars": 9383,
"preview": "import torch\nimport torch.nn as nn\nimport math\n\nfrom network.shakedrop import ShakeDrop\n\n\ndef conv3x3(in_planes, out_pla"
},
{
"path": "network/resnet.py",
"chars": 6231,
"preview": "# Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py\n\nimport torch.nn as nn\nimpor"
},
{
"path": "network/shakedrop.py",
"chars": 1454,
"preview": "# -*- coding: utf-8 -*-\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import V"
},
{
"path": "network/wideresnet.py",
"chars": 2955,
"preview": "import torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\nimport numpy as np\n\n\ndef conv3x3(in_p"
},
{
"path": "requirements.txt",
"chars": 106,
"preview": "git+https://github.com/wbaek/theconf\nsklearn\ngit+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git"
},
{
"path": "setup.py",
"chars": 641,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport set"
},
{
"path": "train.py",
"chars": 15645,
"preview": "# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py\n\nimport os\nimport shutil\nimport ti"
},
{
"path": "train_legacy.py",
"chars": 17036,
"preview": "# original code: https://github.com/dyhan0920/PyramidNet-PyTorch/blob/master/train.py\n\nimport os\nimport shutil\nimport ti"
},
{
"path": "utils.py",
"chars": 3207,
"preview": "# original code: https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py\n\nimport torch\nimport random\n\n__"
}
]
About this extraction
This page contains the full source code of the ildoonet/cutmix GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (198.6 KB), approximately 75.2k tokens, and a symbol index with 128 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.