GAN design
Created by: zchen0211
GAN is an important concept to support in our refactoring. Based on the current support of our framework, it is better to implement GANs with following concepts:
- Dependency analysis and sub-graph running;
- Gradient with respect to some certain variables;
- Multiple loss functions design.
Operators and functions required to build GAN:
Important building blocks | People in Charge | Required |
---|---|---|
convolution 2d (done) | Chengduo | Y |
cudnn conv 2d (missing) | Chengduo | N |
deconv 2d (missing) | Zhuoyuan, Zhihong | Y |
cudnn deconv 2d (missing) | Zhuoyuan, Zhihong | N |
batch norm (missing) | Zhuoyuan, Jiayi | Y |
cudnn batch norm (missing) | Zhuoyuan, Jiayi | N |
max-pooling (done) | ? | Y |
fc (done) | ? | Y |
softmax loss (done) | ? | Y |
reshape op (done) | ? | Y |
Dependency Engine (done) | Jiayi | Y * |
Python API (done) | Longfei, Jiayi | Y * |
Executor (done) | Tony | Y * |
Multi optimizer (woking) | Longfei | Y * |
Optimizer with any para | ? | Y * |
Concat op (done) | ? | N (Cond) |
Repmat op (done) |