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https://github.com/HiKapok/tf.extra_losses
Large-Margin Softmax Loss, Angular Softmax Loss, Additive Margin Softmax, ArcFaceLoss And FocalLoss In Tensorflow
https://github.com/HiKapok/tf.extra_losses
angular-softmax arcface focalloss large-margin-softmax sphereface tensorflow
Last synced: 9 days ago
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Large-Margin Softmax Loss, Angular Softmax Loss, Additive Margin Softmax, ArcFaceLoss And FocalLoss In Tensorflow
- Host: GitHub
- URL: https://github.com/HiKapok/tf.extra_losses
- Owner: HiKapok
- License: mit
- Created: 2018-04-24T03:23:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-25T02:21:15.000Z (over 5 years ago)
- Last Synced: 2024-08-01T13:19:35.556Z (3 months ago)
- Topics: angular-softmax, arcface, focalloss, large-margin-softmax, sphereface, tensorflow
- Language: C++
- Homepage:
- Size: 22.5 KB
- Stars: 105
- Watchers: 11
- Forks: 25
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Large-Margin Softmax Loss, Angular Softmax Loss, Additive Margin Softmax, ArcFaceLoss And FocalLoss In Tensorflow
This repository contains core codes of the reimplementation of the following papers in TensorFlow:
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://arxiv.org/abs/1612.02295)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063)
- [Additive Margin Softmax for Face Verification](https://arxiv.org/abs/1801.05599) or [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://arxiv.org/abs/1801.09414)
- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)
If your goal is to reproduce the results in the original paper, please use the official codes:
- [Large Margin Softmax Loss in ICML 2016](https://github.com/wy1iu/LargeMargin_Softmax_Loss)
- [Angular Softmax Loss in CVPR 2017](https://github.com/wy1iu/sphereface)
- [Additive Margin Softmax](https://github.com/happynear/AMSoftmax)
- [ArcFace: Additive Angular Margin Loss](https://github.com/deepinsight/insightface)
- [Focal Loss in ICCV 2017](https://github.com/facebookresearch/Detectron)
## ##
For using these Ops on your own machine:
- copy the header file "cuda\_config.h" from "your\_python\_path/site-packages/external/local\_config\_cuda/cuda/cuda/cuda\_config.h" to "your\_python\_path/site-packages/tensorflow/include/tensorflow/stream\_executor/cuda/cuda\_config.h".
- run the following script:
```sh
mkdir build
cd build && cmake ..
make
```
- run "test\_op.py" and check the numeric errors to test your install
- follow the below codes snippet to integrate this Op into your own code:
- For Large Margin Softmax Loss:
```python
op_module = tf.load_op_library(so_lib_path)
large_margin_softmax = op_module.large_margin_softmax
@ops.RegisterGradient("LargeMarginSoftmax")
def _large_margin_softmax_grad(op, grad, _):
'''The gradients for `LargeMarginSoftmax`.
'''
inputs_features = op.inputs[0]
inputs_weights = op.inputs[1]
inputs_labels = op.inputs[2]
cur_lambda = op.outputs[1]
margin_order = op.get_attr('margin_order')
grads = op_module.large_margin_softmax_grad(inputs_features, inputs_weights, inputs_labels, grad, cur_lambda[0], margin_order)
return [grads[0], grads[1], None, None]
var_weights = tf.Variable(initial_value, trainable=True, name='lsoftmax_weights')
result = large_margin_softmax(features, var_weights, labels, global_step, 4, 1000., 0.000025, 35., 0.)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=result[0]))
```
- For Angular Softmax Loss:
```python
op_module = tf.load_op_library(so_lib_path)
angular_softmax = op_module.angular_softmax
@ops.RegisterGradient("AngularSoftmax")
def _angular_softmax_grad(op, grad, _):
'''The gradients for `AngularSoftmax`.
'''
inputs_features = op.inputs[0]
inputs_weights = op.inputs[1]
inputs_labels = op.inputs[2]
cur_lambda = op.outputs[1]
margin_order = op.get_attr('margin_order')
grads = op_module.angular_softmax_grad(inputs_features, inputs_weights, inputs_labels, grad, cur_lambda[0], margin_order)
return [grads[0], grads[1], None, None]
var_weights = tf.Variable(initial_value, trainable=True, name='asoftmax_weights')
normed_var_weights = tf.nn.l2_normalize(var_weights, 1, 1e-10, name='weights_normed')
result = angular_softmax(features, normed_var_weights, labels, global_step, 4, 1000., 0.000025, 35., 0.)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=result[0]))
```
- For others just refer to this [script](https://github.com/HiKapok/tf.extra_losses/blob/master/py_loss.py).
All the codes was tested under TensorFlow 1.6, Python 3.5, Ubuntu 16.04 with CUDA 8.0. The outputs of these Ops in C++ had been compared with the original caffe codes' outputs, and the bias could be ignored. The gradients of this Op had been checked using [tf.test.compute\_gradient\_error](https://www.tensorflow.org/api_docs/python/tf/test/compute_gradient_error) and [tf.test.compute\_gradient](https://www.tensorflow.org/api_docs/python/tf/test/compute_gradient). While the others are implemented following the official implementation in Python Ops.
If you encountered some linkage problem when generating or loading *.so, you are highly recommended to read this section in the [official tourial](https://www.tensorflow.org/extend/adding_an_op#compile_the_op_using_your_system_compiler_tensorflow_binary_installation) to make sure you were using the same C++ ABI version.
Any contributions to this repo is welcomed.
## ##
MIT License