Upload rtmdet-m.py

rtmdet-m.py

@@ -1,7 +1,181 @@
-_base_ = "./rtmdet-l.py"
-
+_base_ = [
+    "mmdet::_base_/default_runtime.py",
+    "mmdet::_base_/schedules/schedule_1x.py",
+    "mmdet::_base_/datasets/coco_detection.py",
+    "mmdet::rtmdet/rtmdet_tta.py",
+]
 model = dict(
-    backbone=dict(deepen_factor=0.67, widen_factor=0.75),
-    neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
-    bbox_head=dict(in_channels=192, feat_channels=192),
+    type="RTMDet",
+    data_preprocessor=dict(
+        type="DetDataPreprocessor",
+        mean=[103.53, 116.28, 123.675],
+        std=[57.375, 57.12, 58.395],
+        bgr_to_rgb=False,
+        batch_augments=None,
+    ),
+    backbone=dict(
+        type="CSPNeXt",
+        arch="P5",
+        expand_ratio=0.5,
+        deepen_factor=0.67,
+        widen_factor=0.75,
+        channel_attention=True,
+        norm_cfg=dict(type="SyncBN"),
+        act_cfg=dict(type="SiLU", inplace=True),
+    ),
+    neck=dict(
+        type="CSPNeXtPAFPN",
+        in_channels=[192, 384, 768],
+        out_channels=192,
+        num_csp_blocks=2,
+        expand_ratio=0.5,
+        norm_cfg=dict(type="SyncBN"),
+        act_cfg=dict(type="SiLU", inplace=True),
+    ),
+    bbox_head=dict(
+        type="RTMDetSepBNHead",
+        num_classes=80,
+        in_channels=192,
+        stacked_convs=2,
+        feat_channels=192,
+        anchor_generator=dict(type="MlvlPointGenerator", offset=0, strides=[8, 16, 32]),
+        bbox_coder=dict(type="DistancePointBBoxCoder"),
+        loss_cls=dict(
+            type="QualityFocalLoss", use_sigmoid=True, beta=2.0, loss_weight=1.0
+        ),
+        loss_bbox=dict(type="GIoULoss", loss_weight=2.0),
+        with_objectness=False,
+        exp_on_reg=True,
+        share_conv=True,
+        pred_kernel_size=1,
+        norm_cfg=dict(type="SyncBN"),
+        act_cfg=dict(type="SiLU", inplace=True),
+    ),
+    train_cfg=dict(
+        assigner=dict(type="DynamicSoftLabelAssigner", topk=13),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False,
+    ),
+    test_cfg=dict(
+        nms_pre=30000,
+        min_bbox_size=0,
+        score_thr=0.001,
+        nms=dict(type="nms", iou_threshold=0.65),
+        max_per_img=300,
+    ),
+)
+
+train_pipeline = [
+    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
+    dict(type="LoadAnnotations", with_bbox=True),
+    dict(type="CachedMosaic", img_scale=(640, 640), pad_val=114.0),
+    dict(
+        type="RandomResize", scale=(1280, 1280), ratio_range=(0.1, 2.0), keep_ratio=True
+    ),
+    dict(type="RandomCrop", crop_size=(640, 640)),
+    dict(type="YOLOXHSVRandomAug"),
+    dict(type="RandomFlip", prob=0.5),
+    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type="CachedMixUp",
+        img_scale=(640, 640),
+        ratio_range=(1.0, 1.0),
+        max_cached_images=20,
+        pad_val=(114, 114, 114),
+    ),
+    dict(type="mmdet.PackDetInputs"),
+]
+
+train_pipeline_stage2 = [
+    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
+    dict(type="LoadAnnotations", with_bbox=True),
+    dict(
+        type="RandomResize", scale=(640, 640), ratio_range=(0.1, 2.0), keep_ratio=True
+    ),
+    dict(type="RandomCrop", crop_size=(640, 640)),
+    dict(type="YOLOXHSVRandomAug"),
+    dict(type="RandomFlip", prob=0.5),
+    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
+    dict(type="mmdet.PackDetInputs"),
+]
+
+test_pipeline = [
+    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
+    dict(type="Resize", scale=(640, 640), keep_ratio=True),
+    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type="mmdet.PackDetInputs",
+        meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
+    ),
+]
+
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=10,
+    batch_sampler=None,
+    pin_memory=True,
+    dataset=dict(pipeline=train_pipeline),
+)
+val_dataloader = dict(
+    batch_size=5, num_workers=10, dataset=dict(pipeline=test_pipeline)
+)
+test_dataloader = val_dataloader
+
+max_epochs = 300
+stage2_num_epochs = 20
+base_lr = 0.004
+interval = 10
+
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_interval=interval,
+    dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)],
+)
+
+val_evaluator = dict(proposal_nums=(100, 1, 10))
+test_evaluator = val_evaluator
+
+# optimizer
+optim_wrapper = dict(
+    _delete_=True,
+    type="OptimWrapper",
+    optimizer=dict(type="AdamW", lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),
+)
+
+# learning rate
+param_scheduler = [
+    dict(type="LinearLR", start_factor=1.0e-5, by_epoch=False, begin=0, end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type="CosineAnnealingLR",
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True,
+    ),
+]
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        interval=interval, max_keep_ckpts=3  # only keep latest 3 checkpoints
+    )
 )
+custom_hooks = [
+    dict(
+        type="EMAHook",
+        ema_type="ExpMomentumEMA",
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49,
+    ),
+    dict(
+        type="PipelineSwitchHook",
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2,
+    ),
+]