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COMMANDS.md

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Input

  1. Image
  2. Image RoI
  3. Use common mask
  4. Use spatial encoder
  5. Upsample mask

Train Time

  1. Train for 8 epochs, batch_size=24, LR=1E-4
  2. Optimize binary and unary predictions together after 4 epochs

Output

  1. Unaries (Translation, Scale, Rotation)
  2. Binaries (Relative Translation, Relative Scale, Relative Direction)

Train SunCG

Box3D base model

nice -n 20 python -m relative3d.experiments.suncg.box3d --plot_scalars --save_epoch_freq=4 --batch_size=24 --name=box3d_base_spatial_mask_common_upsample --use_context --pred_voxels=False --classify_rot --shape_loss_wt=10 --n_data_workers=8 --num_epochs=8 --suncg_dir /nvme-scratch/nileshk/suncg/ --pred_relative=True --visdom --display_port=8094 --display_id=1 --rel_opt=True --display_freq=100 --display_visuals  --auto_rel_opt=5  --use_spatial_map=True --use_mask_in_common=True  --upsample_mask=True

DWR model

Finetune predictions on detections

python -m relative3d.experiments.suncg.dwr --plot_scalars --save_epoch_freq=1 --batch_size=24 --name=dwr_base_spatial_mask_common_upsample --use_context --pred_voxels=False --classify_rot --box3d_ft --box3d_pretrain_name=box3d_base_spatial_mask_common_upsample --shape_loss_wt=10 --n_data_workers=8 --num_epochs=1 --suncg_dir /nvme-scratch/nileshk/suncg/ --pred_relative=True --visdom --display_port=8094 --display_id=1 --rel_opt=True --display_freq=100 --display_visuals  --use_spatial_map=True --use_mask_in_common=True  --upsample_mask=True

DWR FT

Fineture shape decoder

python -m relative3d.experiments.suncg.dwr --name=dwr_base_spatial_mask_common_upsample_ft --classify_rot --shape_dec_ft --use_context --plot_scalars --display_visuals --save_epoch_freq=1 --display_freq=1000 --display_id=202 --shape_loss_wt=2 --label_loss_wt=10 --batch_size=24 --num_epochs=1 --ft_pretrain_epoch=1 --ft_pretrain_name=dwr_base_spatial_mask_common_upsample --split_size=1.0 --display_port=8094 --suncg_dir=/nvme-scratch/nileshk/suncg/ --n_data_workers=4 --visdom=True --use_spatial_map=True --use_mask_in_common=True --upsample_mask=True --pred_relative=True --rel_opt=True

Evaluation Scipts

Evaluate results on SunCG with GT bboxes

python -m relative3d.benchmark.suncg.box3d --num_train_epoch=4 --name=box3d_base_spatial_mask_common_upsample --classify_rot --pred_voxels=False --use_context --save_visuals --visuals_freq=50 --eval_set=val --pred_relative=True --suncg_dir=/nvme-scratch/nileshk/suncg/ --preload_stats=False --results_name=box3d_base_spatial_mask_common_upsample --do_updates=True --save_predictions_to_disk=True

Evaluate results on SunCG in detection setting to report mAP scores

python -m relative3d.benchmark.suncg.dwr --num_train_epoch=1 --name=dwr_base_spatial_mask_common_upsample --classify_rot --pred_voxels=True --use_context --save_visuals --visuals_freq=50 --eval_set=val --pred_relative=True --suncg_dir=/nvme-scratch/nileshk/suncg/ --preload_stats=False  --results_name=dwr_base_spatial_mask_common_upsample --do_updates=True  --save_predictions_to_disk=True --use_spatial_map=True --use_mask_in_common=True --upsample_mask=True

Train on NYU

Box3D base model

We are going to fine tune the model trained on SunCG

python -m relative3d.experiments.nyu.box3d --plot_scalars --save_epoch_freq=4 --batch_size=24 --name=nyu_box3d_base_spatial_mask_common_upsample --use_context --pred_voxels=False --classify_rot --shape_loss_wt=10 --n_data_workers=8 --num_epochs=16 --nyu_dir /nfs.yoda/imisra/nileshk/nyud2/ --pred_relative=True --visdom --display_port=8094 --display_id=1 --rel_opt=True --display_freq=100 --display_visuals   --use_spatial_map=True --use_mask_in_common=True  --upsample_mask=True --ft_pretrain_name=box3d_base_spatial_mask_common_upsample --ft_pretrain_epoch=8

DWR base model

We are going to fine tune the model trained on SunCG for detection. We do not fine tune the shape decoder on NYUv2 as the dataset has very few CAD models.

python -m relative3d.experiments.nyu.dwr --plot_scalars --save_epoch_freq=1 --batch_size=8 --name=nyu_dwr_base_spatial_mask_common_upsample --use_context --pred_voxels=False --classify_rot --ft_pretrain_name=dwr_base_spatial_mask_common_upsample --ft_pretrain_epoch=1 --shape_loss_wt=10 --n_data_workers=0 --num_epochs=16 --nyu_dir /nfs.yoda/imisra/nileshk/nyud2/ --pred_relative=True --visdom --display_port=8094 --display_id=1 --rel_opt=True --display_freq=100 --display_visuals  --use_spatial_map=True --use_mask_in_common=True  --upsample_mask=True