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config/dagr-l-dsec.yaml

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+dataset_directory: "/data/storage/daniel/aegnn/"
+output_directory: "/data/storage/daniel/aegnn/logs"
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+task: detection
+dataset: dsec
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+# network
+radius: 0.01
+time_window_us: 1000000
+max_neighbors: 16
+n_nodes: 50000
+
+batch_size: 64
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+activation: relu
+edge_attr_dim: 2
+aggr: sum
+kernel_size: 5
+pooling_aggr: max
+
+base_width: 0.5
+after_pool_width: 1
+net_stem_width: 1
+yolo_stem_width: 1
+num_scales: 2
+
+# learning
+weight_decay: 0.00001
+clip: 0.1
+
+pooling_dim_at_output: 5x7
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+aug_trans: 0.1
+aug_zoom: 1.5
+aug_p_flip: 0.5
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+img_net: resnet18
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+l_r: 0.0002
+tot_num_epochs: 801
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config/dagr-m-dsec.yaml

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+dataset_directory: "/data/storage/daniel/aegnn/"
+output_directory: "/data/storage/daniel/aegnn/logs"
+
+task: detection
+dataset: dsec
+
+# network
+radius: 0.01
+time_window_us: 1000000
+max_neighbors: 16
+n_nodes: 50000
+
+batch_size: 64
+
+activation: relu
+edge_attr_dim: 2
+aggr: sum
+kernel_size: 5
+pooling_aggr: max
+
+base_width: 0.5
+after_pool_width: 1
+net_stem_width: 0.75
+yolo_stem_width: 0.75
+num_scales: 2
+
+# learning
+weight_decay: 0.00001
+clip: 0.1
+
+pooling_dim_at_output: 5x7
+
+aug_trans: 0.1
+aug_zoom: 1.5
+aug_p_flip: 0.5
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+img_net: resnet18
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+l_r: 0.0002
+tot_num_epochs: 801
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config/dagr-n-dsec.yaml

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+dataset_directory: "/data/storage/daniel/aegnn/"
+output_directory: "/data/storage/daniel/aegnn/logs"
+
+task: detection
+dataset: dsec
+
+# network
+radius: 0.01
+time_window_us: 1000000
+max_neighbors: 16
+n_nodes: 50000
+
+batch_size: 64
+
+activation: relu
+edge_attr_dim: 2
+aggr: sum
+kernel_size: 5
+pooling_aggr: max
+
+base_width: 0.5
+after_pool_width: 1
+net_stem_width: 0.25
+yolo_stem_width: 0.25
+num_scales: 2
+
+# learning
+weight_decay: 0.00001
+clip: 0.1
+
+pooling_dim_at_output: 5x7
+
+aug_trans: 0.1
+aug_zoom: 1.5
+aug_p_flip: 0.5
+
+img_net: resnet18
+
+l_r: 0.0002
+tot_num_epochs: 801
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config/dagr-s-dsec.yaml

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+dataset_directory: "/data/storage/daniel/aegnn/"
+output_directory: "/data/storage/daniel/aegnn/logs"
+
+task: detection
+dataset: dsec
+
+# network
+radius: 0.01
+time_window_us: 1000000
+max_neighbors: 16
+n_nodes: 50000
+
+batch_size: 64
+
+activation: relu
+edge_attr_dim: 2
+aggr: sum
+kernel_size: 5
+pooling_aggr: max
+
+base_width: 0.5
+after_pool_width: 1
+net_stem_width: 0.5
+yolo_stem_width: 0.5
+num_scales: 2
+
+# learning
+weight_decay: 0.00001
+clip: 0.1
+
+pooling_dim_at_output: 5x7
+
+aug_trans: 0.1
+aug_zoom: 1.5
+aug_p_flip: 0.5
+
+img_net: resnet18
+
+l_r: 0.0002
+tot_num_epochs: 801
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download_and_install_dependencies.sh

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+#! /usr/bin/env bash
+DAGR_DIR=$(pwd)
+
+# Download detectron2 for its fast mAP calculation function
+mkdir $DAGR_DIR/libs
+cd $DAGR_DIR/libs
+git clone --no-checkout [email protected]:facebookresearch/detectron2.git
+cd $DAGR_DIR/libs/detectron2/
+git checkout 32bd159d7263683e39bf4e87e5c4ac88bad2fd73
+
+# Download YOLOX
+cd $DAGR_DIR/libs
+git clone --no-checkout [email protected]:Megvii-BaseDetection/YOLOX.git
+cd $DAGR_DIR/libs/YOLOX
+git checkout 618fd8c08b2bc5fac9ffbb19a3b7e039ea0d5b9a
+
+# Download dsec-det
+cd $DAGR_DIR/libs
+git clone [email protected]:uzh-rpg/dsec-det.git
+cd $DAGR_DIR/libs/dsec-det
+git checkout f3ea48b0eebef93b2052396fd23b3d40e6ff0363
+
+pip install -e $DAGR_DIR/libs/dsec-det
+pip install -e $DAGR_DIR/libs/detectron2
+pip install -e $DAGR_DIR/libs/YOLOX

download_example_data.sh

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+#! /usr/bin/env bash
+DAGR_DIR=$(pwd)
+DATA_DIR=$DAGR_DIR/data
+
+mkdir $DATA_DIR
+wget https://download.ifi.uzh.ch/rpg/dagr/data/dagr_s_50.pth -O $DATA_DIR/dagr_s_50.pth
+
+wget https://download.ifi.uzh.ch/rpg/dagr/data/DSEC_fragment.zip -O $DATA_DIR/DSEC_fragment.zip
+unzip $DATA_DIR/DSEC_fragment.zip -d $DATA_DIR
+rm -rf $DATA_DIR/DSEC_fragment.zip

install_env.sh

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+#! /usr/bin/env bash
+
+TORCH=$(python -c "import torch; print(torch.__version__)")
+CUDA=$(python -c "import torch; print(torch.version.cuda)")
+URL=https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
+
+pip install --no-cache-dir torch-scatter -f $URL;
+pip install --no-cache-dir torch-cluster -f $URL;
+pip install --no-cache-dir torch-spline-conv -f $URL;
+pip install --no-cache-dir torch-sparse -f $URL;
+pip install torch-geometric;
+pip install wandb numba hdf5plugin plotly matplotlib pycocotools opencv-python scikit-video pandas ruamel.yaml