Setup 0. Install python3.6 and pytorch 3. I recommend the [Anaconda distribution](https://repo.continuum.io/archive/). To install PyTorch if you haven't already, use ```conda install pytorch=0.3.0 torchvision=0.2.0 cuda90 -c pytorch```. 在win在下的conda是找不到这种低版本的依赖库的. 1. Update the config file with the dataset paths. Specifically: - Visual Genome (the VG_100K folder, image_data.json, VG-SGG.h5, and VG-SGG-dicts.json). See data/stanford_filtered/README.md for the steps I used to download these. - You'll also need to fix your PYTHONPATH: ```export PYTHONPATH=/home/rowan/code/scene-graph``` 数据集的问题:可以直接下载除去.json文件的其他数据,但自己下载的.json文件的格式是不可以用的. 预训练模型的网址是google的,https://drive.google.com/open?id=11zKRr2OF5oclFL47kjFYBOxScotQzArX 2. Compile everything. run ```make``` in the main directory: this compiles the Bilinear Interpolation operation for the RoIs as well as the Highway LSTM. 此处make文件只能linux环境下执行 3. Pretrain VG detection. The old version involved pretraining COCO as well, but we got rid of that for simplicity. Run ./scripts/pretrain_detector.sh Note: You might have to modify the learning rate and batch size, particularly if you don't have 3 Titan X GPUs (which is what I used). [You can also download the pretrained detector checkpoint here.](https://drive.google.com/open?id=11zKRr2OF5oclFL47kjFYBOxScotQzArX) 4. Train VG scene graph classification: run ./scripts/train_models_sgcls.sh 2 (will run on GPU 2). OR, download the MotifNet-cls checkpoint here: [Motifnet-SGCls/PredCls](https://drive.google.com/open?id=12qziGKYjFD3LAnoy4zDT3bcg5QLC0qN6). 5. Refine for detection: run ./scripts/refine_for_detection.sh 2 or download the [Motifnet-SGDet](https://drive.google.com/open?id=1thd_5uSamJQaXAPVGVOUZGAOfGCYZYmb) checkpoint. 6. Evaluate: Refer to the scripts ./scripts/eval_models_sg[cls/det].sh.

 

 

出处:

@inproceedings{zellers2018scenegraphs, title={Neural Motifs: Scene Graph Parsing with Global Context}, author={Zellers, Rowan and Yatskar, Mark and Thomson, Sam and Choi, Yejin}, booktitle = "Conference on Computer Vision and Pattern Recognition", year={2018} }