feat(projects): add yolox_multitask model

projects/YOLOX_opt_elan/configs/cityscapes/YOLOX_opt-S-DynamicRecognition/yolox-s-opt-elan-semseg_960x960_300e_cityscapes.py

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+_base_ = [
+    "../../../../../autoware_ml/configs/detection2d/default_runtime.py",
+    "../../../../../autoware_ml/configs/detection2d/schedules/schedule_1x.py",
+]
+
+custom_imports = dict(
+    imports=[
+        "projects.YOLOX_opt_elan.yolox",
+        "autoware_ml.detection2d.metrics",
+        "autoware_ml.detection2d.datasets",
+        "projects.YOLOX_opt_elan.yolox.models",
+        "projects.YOLOX_opt_elan.yolox.models.yolox_multitask",
+        "projects.YOLOX_opt_elan.yolox.transforms",
+        "mmseg.evaluation.metrics",  # 引入分割评估指标
+    ],
+    allow_failed_imports=False,
+)
+
+# parameter settings
+# IMG_SCALE = (960, 960)
+IMG_SCALE = (1024, 512)
+max_epochs = 300
+num_last_epochs = 15
+resume_from = None
+interval = 1
+batch_size = 16
+activation = "ReLU6"
+num_workers = 4
+base_lr = 0.001
+
+
+classes = ("person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle")
+palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32)]
+
+seg_classes = (
+    "road",
+    "sidewalk",
+    "building",
+    "wall",
+    "fence",
+    "pole",
+    "traffic light",
+    "traffic sign",
+    "vegetation",
+    "terrain",
+    "sky",
+    "person",
+    "rider",
+    "car",
+    "truck",
+    "bus",
+    "train",
+    "motorcycle",
+    "bicycle",
+)
+seg_palette = [
+    (128, 64, 128),
+    (244, 35, 232),
+    (70, 70, 70),
+    (102, 102, 156),
+    (190, 153, 153),
+    (153, 153, 153),
+    (250, 170, 30),
+    (220, 220, 0),
+    (107, 142, 35),
+    (152, 251, 152),
+    (70, 130, 180),
+    (220, 20, 60),
+    (255, 0, 0),
+    (0, 0, 142),
+    (0, 0, 70),
+    (0, 60, 100),
+    (0, 80, 100),
+    (0, 0, 230),
+    (119, 11, 32),
+]
+
+# metainfo = dict(classes=classes, palette=palette)
+metainfo = dict(classes=seg_classes, palette=seg_palette)
+
+model = dict(
+    type="YOLOXMultiTask",
+    data_preprocessor=dict(
+        type="DetDataPreprocessor",
+        pad_size_divisor=32,
+        # batch_augments=[
+        #     dict(
+        #         type="BatchSyncRandomResize",
+        #         random_size_range=(480, 800),
+        #         size_divisor=32,
+        #         interval=10,
+        #     )
+        # ],
+    ),
+    backbone=dict(
+        type="ELANDarknet",
+        deepen_factor=2,
+        widen_factor=1,
+        out_indices=(2, 3, 4),
+        act_cfg=dict(type=activation),
+    ),
+    neck=dict(
+        type="YOLOXPAFPN_ELAN",
+        in_channels=[128, 256, 512],
+        out_channels=128,
+        num_elan_blocks=2,
+        act_cfg=dict(type=activation),
+    ),
+    bbox_head=dict(
+        type="YOLOXHead",
+        num_classes=8,
+        in_channels=128,
+        feat_channels=128,
+        act_cfg=dict(type=activation),
+        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=0.0),
+        loss_bbox=dict(type="IoULoss", loss_weight=0.0),
+        loss_obj=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=0.0),
+        loss_l1=dict(type="L1Loss", loss_weight=0.0),
+    ),
+    mask_head=dict(
+        type="YOLOXSegHead",
+        in_channels=[128, 128, 128],
+        feat_channels=128,
+        num_classes=19,
+        act_cfg=dict(type=activation),
+        loss=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
+    ),
+    train_cfg=dict(assigner=dict(type="SimOTAAssigner", center_radius=2.5)),
+    test_cfg=dict(score_thr=0.01, nms=dict(type="nms", iou_threshold=0.65)),
+)
+
+dataset_type = "CocoDataset"
+data_root = "data/cityscapes/"
+backend_args = None
+
+train_pipeline = [
+    # dict(type="Mosaic", img_scale=IMG_SCALE, pad_val=114.0),
+    # dict(type="MixUp", img_scale=IMG_SCALE, ratio_range=(0.8, 1.6), pad_val=114.0),
+    dict(type="YOLOXHSVRandomAug"),
+    dict(type="RandomFlip", prob=0.5),
+    dict(type="Resize", scale=IMG_SCALE, keep_ratio=False),
+    dict(
+        type="Pad",
+        pad_to_square=False,
+        size_divisor=32,
+        pad_val=dict(img=(114.0, 114.0, 114.0), seg=255),
+    ),
+    dict(type="FilterAnnotations", min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type="PackDetInputs"),
+]
+
+test_pipeline = [
+    dict(type="LoadImageFromFile", backend_args=backend_args),
+    dict(type="FixCityscapesPath", data_root=data_root, split="val"),
+    dict(type="LoadAnnotations", with_bbox=True, with_seg=True),
+    dict(type="Resize", scale=IMG_SCALE, keep_ratio=False),
+    dict(type="Pad", pad_to_square=False, size_divisor=32, pad_val=dict(img=(114.0, 114.0, 114.0), seg=255)),
+    dict(
+        type="PackDetInputs",
+        meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
+    ),
+]
+
+train_dataset = dict(
+    type="MultiImageMixDataset",
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img="leftImg8bit/train", seg_map_path="gtFine/train"),
+        ann_file="annotations/instancesonly_filtered_gtFine_train.json",
+        pipeline=[
+            dict(type="LoadImageFromFile", backend_args=backend_args),
+            dict(type="FixCityscapesPath", data_root=data_root, split="train"),
+            dict(type="LoadAnnotations", with_bbox=True, with_seg=True),
+        ],
+        filter_cfg=dict(filter_empty_gt=False, min_size=8),
+        backend_args=backend_args,
+        metainfo=metainfo,
+    ),
+    pipeline=train_pipeline,
+)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=num_workers,
+    persistent_workers=True,
+    sampler=dict(type="DefaultSampler", shuffle=True),
+    dataset=train_dataset,
+)
+
+val_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=num_workers,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type="DefaultSampler", shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img="leftImg8bit/val", seg_map_path="gtFine/val"),
+        ann_file="annotations/instancesonly_filtered_gtFine_val.json",
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args,
+        metainfo=metainfo,
+    ),
+)
+test_dataloader = val_dataloader
+
+val_evaluator = [
+    dict(type="mmseg.IoUMetric", ignore_index=255, iou_metrics=["mIoU"], prefix="seg", classes=seg_classes)
+]
+test_evaluator = val_evaluator
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=interval)
+
+optimizer = dict(
+    type="OptimWrapper",
+    optimizer=dict(type="SGD", lr=base_lr, momentum=0.9, weight_decay=5e-4, nesterov=True),
+    paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0),
+)
+
+if max_epochs > 5:
+    param_scheduler = [
+        dict(
+            type="mmdet.QuadraticWarmupLR",
+            by_epoch=True,
+            begin=0,
+            end=5,
+            convert_to_iter_based=True,
+        ),
+        dict(
+            type="CosineAnnealingLR",
+            eta_min=base_lr * 0.05,
+            begin=5,
+            T_max=max_epochs - num_last_epochs,
+            end=max_epochs - num_last_epochs,
+            by_epoch=True,
+            convert_to_iter_based=True,
+        ),
+        dict(
+            type="ConstantLR",
+            by_epoch=True,
+            factor=1,
+            begin=max_epochs - num_last_epochs,
+            end=max_epochs,
+        ),
+    ]
+else:
+    param_scheduler = []
+
+log_config = dict(
+    interval=1,
+    hooks=[dict(type="TextLoggerHook"), dict(type="TensorboardLoggerHook")],
+)
+
+default_hooks = dict(
+    checkpoint=dict(interval=interval, max_keep_ckpts=3, save_best="seg/mIoU", rule="greater"),
+    visualization=dict(
+        type="DetVisualizationHook", draw=False, interval=50, show=False, wait_time=2, test_out_dir="vis_data"
+    ),
+)
+
+custom_hooks = [
+    dict(type="YOLOXModeSwitchHook", num_last_epochs=num_last_epochs, priority=48),
+    dict(type="SyncNormHook", priority=48),
+    dict(
+        type="EMAHook",
+        ema_type="ExpMomentumEMA",
+        momentum=0.0001,
+        update_buffers=True,
+        priority=4,
+    ),
+]
+
+auto_scale_lr = dict(base_batch_size=batch_size)
+
+vis_backends = [
+    dict(type="LocalVisBackend"),
+    dict(type="TensorboardVisBackend"),
+]
+
+visualizer = dict(
+    type="DetLocalVisualizer",
+    vis_backends=[dict(type="LocalVisBackend"), dict(type="TensorboardVisBackend")],
+    name="visualizer",
+    alpha=0.5,
+)