ssib test

.gitignore

@@ -9,6 +9,7 @@ config/tc128*
 config/tlp*
 config/trackingnet*
 config/vfs*
+config/ssib*
 
 weights/
 results/

config/ssib_s3_womotion.yaml

@@ -0,0 +1,133 @@
+common:
+    exp_name: ssib_s3_womotion
+
+    # Model related
+    model_type: ssib 
+    remove_layers: ['layer4']
+    im_mean: [0.485, 0.456, 0.406]
+    im_std: [0.229, 0.224, 0.225]
+    nopadding: False
+    head_depth: -1
+    resume: 'weights/ssib_200ep.pth.tar'
+
+    # Misc
+    down_factor: 8
+    infer2D: True 
+    workers: 4
+    gpu_id: 0
+    device: cuda
+
+sot:
+    dataset: 'OTB2015'
+    dataroot: '/home/wangzd/datasets/GOT/OTB100/'
+    epoch_test: False
+
+vos:
+    davisroot: '/home/wangzd/datasets/uvc/DAVIS/'
+    split: 'val'
+    temperature: 0.05
+    topk: 10
+    radius: 12
+    videoLen: 5 
+    cropSize: -1
+    head_depth: -1
+    no_l2: False
+    long_mem: [0]
+    infer2D: False
+    norm_mask: False
+
+mot:
+    obid: 'FairMOT'
+    mot_root: '/home/wangzd/datasets/MOT/MOT16'
+    feat_size: [4,10]
+    save_videos: True
+    save_images: False
+    test_mot16: False
+    track_buffer: 30
+    min_box_area: 200
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    dup_iou_thres: 0.15
+    confirm_iou_thres: 0.7
+    img_size: [1088, 608]
+    prop_flag: False
+    use_kalman: True 
+    asso_with_motion: False 
+    motion_lambda: 1
+    motion_gated: False
+
+mots:
+    obid: 'COSTA'
+    mots_root: '/home/wangzd/datasets/GOT/MOTS'
+    save_videos: False
+    save_images: True
+    test: False 
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    prop_flag: False
+    max_mask_area: 200
+    dup_iou_thres: 0.15
+    confirm_iou_thres: 0.7 
+    first_stage_thres: 0.7
+    feat_size: [4,10]
+    use_kalman: True 
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+
+posetrack:
+    obid: 'lighttrack_MSRA152'
+    data_root: '/home/wangzd/datasets/GOT/Posetrack2018'
+    split: 'val'
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    frame_rate: 6
+    save_videos: False
+    save_images: True
+    prop_flag: False
+    feat_size: [4,10]
+    max_mask_area: 400
+    dup_iou_thres: 0.2
+    confirm_iou_thres: 0.6
+    first_stage_thres: 0.7
+    use_kalman: True
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+    only_position: True
+
+vis:
+    obid: 'MaskTrackRCNN'
+    data_root: '/home/wangzd/datasets/GOT/YoutubeVIS/'
+    split: 'val'
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    frame_rate: 6
+    save_videos: False
+    save_images: True
+    prop_flag: False
+    feat_size: [12,12]
+    max_mask_area: 1000
+    dup_iou_thres: 0.2
+    confirm_iou_thres: 0.6
+    first_stage_thres: 0.9
+    use_kalman: True
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+
+
+
+
+
+
+
+
+

config/ssib_s4_womotion.yaml

@@ -0,0 +1,133 @@
+common:
+    exp_name: ssib_s4_womotion
+
+    # Model related
+    model_type: ssib 
+    remove_layers: []
+    im_mean: [0.485, 0.456, 0.406]
+    im_std: [0.229, 0.224, 0.225]
+    nopadding: False
+    head_depth: -1
+    resume: 'weights/ssib_200ep.pth.tar'
+
+    # Misc
+    down_factor: 8
+    infer2D: True 
+    workers: 4
+    gpu_id: 0
+    device: cuda
+
+sot:
+    dataset: 'OTB2015'
+    dataroot: '/home/wangzd/datasets/GOT/OTB100/'
+    epoch_test: False
+
+vos:
+    davisroot: '/home/wangzd/datasets/uvc/DAVIS/'
+    split: 'val'
+    temperature: 0.05
+    topk: 10
+    radius: 12
+    videoLen: 5 
+    cropSize: -1
+    head_depth: -1
+    no_l2: False
+    long_mem: [0]
+    infer2D: False
+    norm_mask: False
+
+mot:
+    obid: 'FairMOT'
+    mot_root: '/home/wangzd/datasets/MOT/MOT16'
+    feat_size: [4,10]
+    save_videos: True
+    save_images: False
+    test_mot16: False
+    track_buffer: 30
+    min_box_area: 200
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    dup_iou_thres: 0.15
+    confirm_iou_thres: 0.7
+    img_size: [1088, 608]
+    prop_flag: False
+    use_kalman: True 
+    asso_with_motion: False 
+    motion_lambda: 1
+    motion_gated: False
+
+mots:
+    obid: 'COSTA'
+    mots_root: '/home/wangzd/datasets/GOT/MOTS'
+    save_videos: False
+    save_images: True
+    test: False 
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    prop_flag: False
+    max_mask_area: 200
+    dup_iou_thres: 0.15
+    confirm_iou_thres: 0.7 
+    first_stage_thres: 0.7
+    feat_size: [4,10]
+    use_kalman: True 
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+
+posetrack:
+    obid: 'lighttrack_MSRA152'
+    data_root: '/home/wangzd/datasets/GOT/Posetrack2018'
+    split: 'val'
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    frame_rate: 6
+    save_videos: False
+    save_images: True
+    prop_flag: False
+    feat_size: [4,10]
+    max_mask_area: 200
+    dup_iou_thres: 0.2
+    confirm_iou_thres: 0.6
+    first_stage_thres: 0.7
+    use_kalman: True
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+    only_position: True
+
+vis:
+    obid: 'MaskTrackRCNN'
+    data_root: '/home/wangzd/datasets/GOT/YoutubeVIS/'
+    split: 'val'
+    track_buffer: 30
+    nms_thres: 0.4
+    conf_thres: 0.5
+    iou_thres: 0.5
+    frame_rate: 6
+    save_videos: False
+    save_images: True
+    prop_flag: False
+    feat_size: [12,12]
+    max_mask_area: 1000 
+    dup_iou_thres: 0.2
+    confirm_iou_thres: 0.6
+    first_stage_thres: 0.9
+    use_kalman: True
+    asso_with_motion: False
+    motion_lambda: 1 
+    motion_gated: False
+
+
+
+
+
+
+
+
+

eval.sh

@@ -10,9 +10,9 @@ EXP_NAME=$1
 CFG_PATH=config/${EXP_NAME}.yaml
 SMRY_ROOT=results/summary/${EXP_NAME}
 mkdir -p $SMRY_ROOT
-CUDA_VISIBLE_DEVICES=$2 python -u test/test_sot_siamfc.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/sot_siamfc.log 2>&1 
-CUDA_VISIBLE_DEVICES=$2 python -u test/test_sot_cfnet.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/sot_cfnet.log 2>&1
+#CUDA_VISIBLE_DEVICES=$2 python -u test/test_sot_siamfc.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/sot_siamfc.log 2>&1 
+#CUDA_VISIBLE_DEVICES=$2 python -u test/test_sot_cfnet.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/sot_cfnet.log 2>&1
 CUDA_VISIBLE_DEVICES=$2 python -u test/test_vos.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/vos.log 2>&1
-CUDA_VISIBLE_DEVICES=$2 python -u test/test_mot.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/mot.log 2>&1
-CUDA_VISIBLE_DEVICES=$2 python -u test/test_mots.py --config $CFG_PATH | tee  results/summary/${EXP_NAME}/mots.log 2>&1
-CUDA_VISIBLE_DEVICES=$2 python -u test/test_posetrack.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/posetrack.log 2>&1
+#CUDA_VISIBLE_DEVICES=$2 python -u test/test_mot.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/mot.log 2>&1
+#CUDA_VISIBLE_DEVICES=$2 python -u test/test_mots.py --config $CFG_PATH | tee  results/summary/${EXP_NAME}/mots.log 2>&1
+#CUDA_VISIBLE_DEVICES=$2 python -u test/test_posetrack.py --config $CFG_PATH | tee results/summary/${EXP_NAME}/posetrack.log 2>&1

model/model.py

@@ -129,6 +129,12 @@ def make_encoder(args):
         net_state = {k.replace('module.encoder_q.', ''):v for k,v in net_ckpt['state_dict'].items() \
                 if 'module.encoder_q' in k}
         partial_load(net_state, net)
+    elif model_type == 'ssib':
+        net = resnet.resnet50(pretrained=False)
+        net_ckpt = torch.load(args.resume)
+        net_state = {k.replace('module.encoder.', ''):v for k,v in net_ckpt.items() \
+                if 'module.encoder' in k}
+        partial_load(net_state, net)
     elif model_type == 'uvc':
         net = load_uvc_model(args.resume)
     elif model_type == 'timecycle':