https://github.com/CoinCheung/pytorch-loss

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
https://github.com/CoinCheung/pytorch-loss

amsoftmax cuda dice-loss ema focal-loss label-smoothing lovasz-softmax mish partial-fc pytorch triplet-loss

Last synced: 2 months ago
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label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

• Host: GitHub
• URL: https://github.com/CoinCheung/pytorch-loss
• Owner: CoinCheung
• License: mit
• Created: 2019-04-10T07:48:03.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-10-17T01:41:43.000Z (3 months ago)
• Last Synced: 2024-11-05T03:36:46.611Z (3 months ago)
• Topics: amsoftmax, cuda, dice-loss, ema, focal-loss, label-smoothing, lovasz-softmax, mish, partial-fc, pytorch, triplet-loss
• Language: Python
• Homepage:
• Size: 148 KB
• Stars: 2,175
• Watchers: 23
• Forks: 373
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pytorch-loss

My implementation of label-smooth, amsoftmax, partial-fc, focal-loss, dual-focal-loss, triplet-loss, giou/diou/ciou-loss/func, affinity-loss, pc_softmax_cross_entropy, ohem-loss(softmax based on line hard mining loss), large-margin-softmax(bmvc2019), lovasz-softmax-loss, and dice-loss(both generalized soft dice loss and batch soft dice loss). Maybe this is useful in my future work.

Also tried to implement swish, hard-swish(hswish) and mish activation functions.

Additionally, cuda based one-hot function is added (support label smooth).

Newly add an "Exponential Moving Average(EMA)" operator.

Add convolution ops, such as coord-conv2d, and dynamic-conv2d(dy-conv2d).

Some operators are implemented with pytorch cuda extension, so you need to compile it first: 

```

    $ python -m pip install .

```

After installing, now you can pick up what you need and use the losses or ops like one of thes: 

```python

from pytorch_loss import SwishV1, SwishV2, SwishV3

from pytorch_loss import HSwishV1, HSwishV2, HSwishV3

from pytorch_loss import MishV1, MishV2, MishV3

from pytorch_loss import convert_to_one_hot, convert_to_one_hot_cu, OnehotEncoder

from pytorch_loss import EMA

from pytorch_loss import TripletLoss

from pytorch_loss import SoftDiceLossV1, SoftDiceLossV2, SoftDiceLossV3

from pytorch_loss import PCSoftmaxCrossEntropyV1, PCSoftmaxCrossEntropyV2

from pytorch_loss import LargeMarginSoftmaxV1, LargeMarginSoftmaxV2, LargeMarginSoftmaxV3

from pytorch_loss import LabelSmoothSoftmaxCEV1, LabelSmoothSoftmaxCEV2, LabelSmoothSoftmaxCEV3

from pytorch_loss import GIOULoss, DIOULoss, CIOULoss

from pytorch_loss import iou_func, giou_func, diou_func, ciou_func

from pytorch_loss import FocalLossV1, FocalLossV2, FocalLossV3

from pytorch_loss import Dual_Focal_loss

from pytorch_loss import GeneralizedSoftDiceLoss, BatchSoftDiceLoss

from pytorch_loss import AMSoftmax

from pytorch_loss import AffinityFieldLoss, AffinityLoss

from pytorch_loss import OhemCELoss, OhemLargeMarginLoss

from pytorch_loss import LovaszSoftmaxV1, LovaszSoftmaxV3

from pytorch_loss import TaylorCrossEntropyLossV1, TaylorCrossEntropyLossV3

from pytorch_loss import InfoNceDist

from pytorch_loss import PartialFCAMSoftmax

from pytorch_loss import TaylorSoftmaxV1, TaylorSoftmaxV3

from pytorch_loss import LogTaylorSoftmaxV1, LogTaylorSoftmaxV3

from pytorch_loss import CoordConv2d, DY_Conv2d

```

Note that some losses or ops have 3 versions, like `LabelSmoothSoftmaxCEV1`, `LabelSmoothSoftmaxCEV2`, `LabelSmoothSoftmaxCEV3`, here `V1` means the implementation with pure pytorch ops and use `torch.autograd` for backward computation, `V2` means implementation with pure pytorch ops but use self-derived formula for backward computation, and `V3` means implementation with cuda extension. Generally speaking, the `V3` ops are faster and more memory efficient, since I have tried to squeeze everything in one cuda kernel function, which in most cases brings less overhead than a combination of pytorch ops.

For those who happen to find this repo, if you see errors in my code, feel free to open an issue to correct me.