Pytorch implementation of FiLM: Visual Reasoning with a General Conditioning Layer
- Python3
- Pytorch 1.0.0
- TensorBoardX
The original implementation used some of the pretrained layers in resnet, or four convolution layers with kernel size = 4 and stride = 2 when starting from scratch.
However, in this implementation, considering that the target is a Sort-of-CLEVR, I reduced the number of layers with stride = 2 to three and added two layers with stride = 1 to increase the size of the feature map.
Initial convolution layer configuration for this implementation is:
(Kernel size = 5, stride = 2, padding = 2)
(Kernel size = 3, stride = 2, padding = 1)
(Kernel size = 3, stride = 2, padding = 1)
(Kernel size = 3, stride = 1, padding = 1)
(Kernel size = 3, stride = 1, padding = 1)
generate sort-of-clevr dataset
python soc_generator.py
train
python train.py
--batch_size={64}
--n_epoch={120}
--lr={1e-4}
--weight_decay={1e-4}
--save_dir={model}
--dataset={data/sort-of-clevr.pickle}
--init={kaiming}
--n_res={6}
--seed={12345}
--n_cpu={4}
[--resume={}]
test
python test.py
--n_res
--dataset
--model
visualize
python visualize.py
--n_res
--dataset
--model
--save_dir [features]
Example of a visualized feature map image
| Sort-of-CLEVR | n_res = 6 |
|---|---|
| Accuracy | 98% |
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent