update rtts

BIGWangYuDong · Oct 23, 2023 · 0d0ee77 · 0d0ee77
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diff --git a/.dev_scripts/train_rtts.sh b/.dev_scripts/train_rtts.sh
@@ -0,0 +1,16 @@
+PARTITION=$1
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+log_dir="work_dirs/slurm_logs"
+mkdir -p "$log_dir"
+
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-atss_r50_1x             configs/detection/rtts_dataset/atss_r50_fpn_1x_rtts-coco.py                   work_dirs/rtts/atss_r50_fpn_1x_rtts-coco                    --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/atss_r50_fpn_1x_rtts-coco/eval_result                 > "$log_dir/rtts_atss_r50_fpn_1x_rtts-coco.log"                     &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-cascade_r50_1x          configs/detection/rtts_dataset/cascade-rcnn_r50_fpn_1x_rtts-coco.py           work_dirs/rtts/cascade-rcnn_r50_fpn_1x_rtts-coco            --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/cascade-rcnn_r50_fpn_1x_rtts-coco/eval_result         > "$log_dir/rtts_cascade-rcnn_r50_fpn_1x_rtts-coco.log"             &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-faster_r50_1x           configs/detection/rtts_dataset/faster-rcnn_r50_fpn_1x_rtts-coco.py            work_dirs/rtts/faster-rcnn_r50_fpn_1x_rtts-coco             --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/faster-rcnn_r50_fpn_1x_rtts-coco/eval_result          > "$log_dir/rtts_faster-rcnn_r50_fpn_1x_rtts-coco.log"              &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-fcos_r50_1x             configs/detection/rtts_dataset/fcos_r50-caffe_fpn_gn-head_1x_rtts-coco.py     work_dirs/rtts/fcos_r50-caffe_fpn_gn-head_1x_rtts-coco      --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/fcos_r50-caffe_fpn_gn-head_1x_rtts-coco/eval_result   > "$log_dir/rtts_fcos_r50-caffe_fpn_gn-head_1x_rtts-coco.log"       &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-paa_r50_1x              configs/detection/rtts_dataset/paa_r50_fpn_1x_rtts-coco.py                    work_dirs/rtts/paa_r50_fpn_1x_rtts-coco                     --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/paa_r50_fpn_1x_rtts-coco/eval_result                  > "$log_dir/rtts_paa_r50_fpn_1x_rtts-coco.log"                      &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-retinanet_r50_1x        configs/detection/rtts_dataset/retinanet_r50_fpn_1x_rtts-coco.py              work_dirs/rtts/retinanet_r50_fpn_1x_rtts-coco               --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/retinanet_r50_fpn_1x_rtts-coco/eval_result            > "$log_dir/rtts_retinanet_r50_fpn_1x_rtts-coco.log"                &
+SRUN_ARGS=${SRUN_ARGS}  GPUS=$GPUS  GPUS_PER_NODE=$GPUS_PER_NODE  CPUS_PER_TASK=$CPUS_PRE_TASK  ./tools/slurm_train.sh  llmit2  rtts-tood_r50_1x             configs/detection/rtts_dataset/tood_r50_fpn_1x_rtts-coco.py                   work_dirs/rtts/tood_r50_fpn_1x_rtts-coco                    --cfg-options default_hooks.checkpoint.max_keep_ckpts=1  randomness.seed=0  val_evaluator.outfile_prefix=work_dirs/rtts/tood_r50_fpn_1x_rtts-coco/eval_result                 > "$log_dir/rtts_tood_r50_fpn_1x_rtts-coco.log"                     &
diff --git a/configs/detection/rtts_dataset/README.md b/configs/detection/rtts_dataset/README.md
diff --git a/configs/detection/rtts_dataset/atss_r50_fpn_1x_rtts-coco.py b/configs/detection/rtts_dataset/atss_r50_fpn_1x_rtts-coco.py
@@ -0,0 +1,86 @@
+_base_ = [
+    '../_base_/datasets/rtts_coco.py', '../_base_/schedules/schedule_1x.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ATSS',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=5,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+# optimizer
+optim_wrapper = dict(
+    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
+
+# add WandbVisBackend
+# vis_backends = [
+#     dict(type='LocalVisBackend'),
+#     dict(type='WandbVisBackend',
+#          init_kwargs=dict(
+#             project='rtts_detection',
+#             name='atss_r50_fpn_1x_rtts',
+#             entity='lqit',
+#             )
+#         )
+# ]
+# visualizer = dict(
+#     type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
diff --git a/configs/detection/rtts_dataset/cascade-rcnn_r50_fpn_1x_rtts-coco.py b/configs/detection/rtts_dataset/cascade-rcnn_r50_fpn_1x_rtts-coco.py
@@ -0,0 +1,204 @@
+_base_ = [
+    '../_base_/datasets/rtts_coco.py', '../_base_/schedules/schedule_1x.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='CascadeRCNN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='CascadeRoIHead',
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=5,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=5,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=5,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ]),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)))
+
+# add WandbVisBackend
+# vis_backends = [
+#     dict(type='LocalVisBackend'),
+#     dict(type='WandbVisBackend',
+#          init_kwargs=dict(
+#             project='rtts_detection',
+#             name='cascade-rcnn_r50_fpn_1x_rtts',
+#             entity='lqit',
+#             )
+#         )
+# ]
+# visualizer = dict(
+#     type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')