Upload folder using huggingface_hub (part 11)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/custom_dataset/yolov5_s-v61_syncbn_fast_1xb32-100e_cat.py

@@ -0,0 +1,76 @@
+_base_ = '../yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+
+max_epochs = 100
+data_root = './data/cat/'
+# data_root = '/root/workspace/mmyolo/data/cat/'  # Docker
+
+work_dir = './work_dirs/yolov5_s-v61_syncbn_fast_1xb32-100e_cat'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'  # noqa
+
+train_batch_size_per_gpu = 32
+train_num_workers = 4
+
+save_epoch_intervals = 2
+
+# base_lr_default * (your_bs / default_bs)
+base_lr = _base_.base_lr / 4
+
+anchors = [
+    [(68, 69), (154, 91), (143, 162)],  # P3/8
+    [(242, 160), (189, 287), (391, 207)],  # P4/16
+    [(353, 337), (539, 341), (443, 432)]  # P5/32
+]
+
+class_name = ('cat', )
+num_classes = len(class_name)
+metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
+
+train_cfg = dict(
+    max_epochs=max_epochs, val_begin=20, val_interval=save_epoch_intervals)
+
+model = dict(
+    bbox_head=dict(
+        head_module=dict(num_classes=num_classes),
+        prior_generator=dict(base_sizes=anchors),
+        loss_cls=dict(loss_weight=0.5 *
+                      (num_classes / 80 * 3 / _base_.num_det_layers))))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=_base_.dataset_type,
+            data_root=data_root,
+            metainfo=metainfo,
+            ann_file='annotations/trainval.json',
+            data_prefix=dict(img='images/'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file='annotations/trainval.json',
+        data_prefix=dict(img='images/')))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
+test_evaluator = val_evaluator
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=5,
+        save_best='auto'),
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=10))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/custom_dataset/yolov6_s_syncbn_fast_1xb32-100e_cat.py

@@ -0,0 +1,85 @@
+_base_ = '../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py'
+
+max_epochs = 100
+data_root = './data/cat/'
+
+work_dir = './work_dirs/yolov6_s_syncbn_fast_1xb32-100e_cat'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth'  # noqa
+
+train_batch_size_per_gpu = 32
+train_num_workers = 4  # train_num_workers = nGPU x 4
+
+save_epoch_intervals = 2
+
+# base_lr_default * (your_bs / default_bs)
+base_lr = _base_.base_lr / 8
+
+class_name = ('cat', )
+num_classes = len(class_name)
+metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
+
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_begin=20,
+    val_interval=save_epoch_intervals,
+    dynamic_intervals=[(max_epochs - _base_.num_last_epochs, 1)])
+
+model = dict(
+    bbox_head=dict(head_module=dict(num_classes=num_classes)),
+    train_cfg=dict(
+        initial_assigner=dict(num_classes=num_classes),
+        assigner=dict(num_classes=num_classes)))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=_base_.dataset_type,
+            data_root=data_root,
+            metainfo=metainfo,
+            ann_file='annotations/trainval.json',
+            data_prefix=dict(img='images/'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file='annotations/trainval.json',
+        data_prefix=dict(img='images/')))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
+test_evaluator = val_evaluator
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=5,
+        save_best='auto'),
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=10))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - _base_.num_last_epochs,
+        switch_pipeline=_base_.train_pipeline_stage2)
+]

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/custom_dataset/yolov7_tiny_syncbn_fast_1xb32-100e_cat.py

@@ -0,0 +1,78 @@
+_base_ = '../yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco.py'
+
+max_epochs = 100
+data_root = './data/cat/'
+
+work_dir = './work_dirs/yolov7_tiny_syncbn_fast_1xb32-100e_cat'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719-0ee5bbdf.pth'  # noqa
+
+train_batch_size_per_gpu = 32
+train_num_workers = 4  # train_num_workers = nGPU x 4
+
+save_epoch_intervals = 2
+
+# base_lr_default * (your_bs / default_bs)
+base_lr = 0.01 / 4
+
+anchors = [
+    [(68, 69), (154, 91), (143, 162)],  # P3/8
+    [(242, 160), (189, 287), (391, 207)],  # P4/16
+    [(353, 337), (539, 341), (443, 432)]  # P5/32
+]
+
+class_name = ('cat', )
+num_classes = len(class_name)
+metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
+
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_begin=20,
+    val_interval=save_epoch_intervals,
+    dynamic_intervals=[(max_epochs - 10, 1)])
+
+model = dict(
+    bbox_head=dict(
+        head_module=dict(num_classes=num_classes),
+        prior_generator=dict(base_sizes=anchors),
+        loss_cls=dict(loss_weight=0.5 *
+                      (num_classes / 80 * 3 / _base_.num_det_layers))))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=_base_.dataset_type,
+            data_root=data_root,
+            metainfo=metainfo,
+            ann_file='annotations/trainval.json',
+            data_prefix=dict(img='images/'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file='annotations/trainval.json',
+        data_prefix=dict(img='images/')))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
+test_evaluator = val_evaluator
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=2,
+        save_best='auto'),
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=10))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/README.md

@@ -0,0 +1,3 @@
+Tips: 这是 MMYOLO 应用范例的配置文件，请结合 [基于 MMYOLO 的频高图实时目标检测 benchmark](/docs/zh_cn/recommended_topics/application_examples/ionogram_detection.md) 来使用。
+
+Tips: This is the config file of the MMYOLO application examples. Please use it in combination with [A Benchmark for Ionogram Detection Based on MMYOLO](/docs/en/recommended_topics/application_examples/ionogram_detection.md).

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/rtmdet/rtmdet_l_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,107 @@
+_base_ = 'mmyolo::rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
+
+# ======================== Modified parameters ======================
+# -----data related-----
+data_root = './Iono4311/'
+train_ann_file = 'annotations/train.json'
+train_data_prefix = 'train_images/'
+val_ann_file = 'annotations/val.json'
+val_data_prefix = 'val_images/'
+test_ann_file = 'annotations/test.json'
+test_data_prefix = 'test_images/'
+
+class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
+num_classes = len(class_name)
+metainfo = dict(
+    classes=class_name,
+    palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
+             (130, 20, 12), (120, 121, 80)])
+
+train_batch_size_per_gpu = 32
+train_num_workers = 8
+val_batch_size_per_gpu = train_batch_size_per_gpu
+
+# Config of batch shapes. Only on val.
+batch_shapes_cfg = dict(batch_size=val_batch_size_per_gpu)
+
+# -----train val related-----
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco/rtmdet_l_syncbn_fast_8xb32-300e_coco_20230102_135928-ee3abdc4.pth'  # noqa
+
+# default hooks
+save_epoch_intervals = 10
+max_epochs = 100
+max_keep_ckpts = 1
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0,
+        end=300),
+    dict(
+        # use cosine lr from 20 to 100 epoch
+        type='CosineAnnealingLR',
+        eta_min=_base_.base_lr * 0.05,
+        begin=max_epochs // 5,
+        end=max_epochs,
+        T_max=max_epochs * 4 // 5,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# train_cfg
+val_interval = 2
+val_begin = 20
+
+tta_model = None
+tta_pipeline = None
+
+visualizer = dict(
+    vis_backends=[dict(type='LocalVisBackend'),
+                  dict(type='WandbVisBackend')])
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    bbox_head=dict(head_module=dict(num_classes=num_classes)),
+    train_cfg=dict(assigner=dict(num_classes=num_classes)))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix)))
+
+val_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file))
+
+test_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=test_data_prefix),
+        ann_file=test_ann_file))
+
+default_hooks = dict(
+    checkpoint=dict(
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'))
+
+val_evaluator = dict(ann_file=data_root + val_ann_file)
+test_evaluator = dict(ann_file=data_root + test_ann_file)
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_begin=val_begin,
+    val_interval=val_interval)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/rtmdet/rtmdet_s_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,83 @@
+_base_ = './rtmdet_l_fast_1xb32-100e_ionogram.py'
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco/rtmdet_s_syncbn_fast_8xb32-300e_coco_20221230_182329-0a8c901a.pth'  # noqa
+
+# ======================= Modified parameters =====================
+deepen_factor = 0.33
+widen_factor = 0.5
+img_scale = _base_.img_scale
+
+# ratio range for random resize
+random_resize_ratio_range = (0.5, 2.0)
+# Number of cached images in mosaic
+mosaic_max_cached_images = 40
+# Number of cached images in mixup
+mixup_max_cached_images = 20
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=mosaic_max_cached_images,
+        pad_val=114.0),
+    dict(
+        type='mmdet.RandomResize',
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='YOLOv5MixUp',
+        use_cached=True,
+        max_cached_images=mixup_max_cached_images),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='mmdet.RandomResize',
+        scale=img_scale,
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=_base_.max_epochs - _base_.num_epochs_stage2,
+        switch_pipeline=train_pipeline_stage2)
+]

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/rtmdet/rtmdet_tiny_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,62 @@
+_base_ = './rtmdet_s_fast_1xb32-100e_ionogram.py'
+
+# ======================= Modified parameters ======================
+deepen_factor = 0.167
+widen_factor = 0.375
+img_scale = _base_.img_scale
+
+load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco/rtmdet_tiny_syncbn_fast_8xb32-300e_coco_20230102_140117-dbb1dc83.pth'  # noqa
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1.0e-5, by_epoch=False, begin=0,
+        end=300),
+    dict(
+        # use cosine lr from 50 to 100 epoch
+        type='CosineAnnealingLR',
+        eta_min=_base_.base_lr * 0.05,
+        begin=_base_.max_epochs // 2,
+        end=_base_.max_epochs,
+        T_max=_base_.max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# =======================Unmodified in most cases==================
+model = dict(
+    backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=20,  # note
+        random_pop=False,  # note
+        pad_val=114.0),
+    dict(
+        type='mmdet.RandomResize',
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=(0.5, 2.0),
+        resize_type='mmdet.Resize',
+        keep_ratio=True),
+    dict(type='mmdet.RandomCrop', crop_size=img_scale),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(type='mmdet.Pad', size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='YOLOv5MixUp',
+        use_cached=True,
+        random_pop=False,
+        max_cached_images=10,
+        prob=0.5),
+    dict(type='mmdet.PackDetInputs')
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_m-v61_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,95 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# Copied from '../../yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py'
+deepen_factor = 0.67
+widen_factor = 0.75
+lr_factor = 0.1
+affine_scale = 0.9
+loss_cls_weight = 0.3
+loss_obj_weight = 0.7
+mixup_prob = 0.1
+
+# -----data related-----
+train_batch_size_per_gpu = 32
+
+# -----train val related-----
+# Scale lr for SGD
+base_lr = _base_.base_lr * train_batch_size_per_gpu \
+    / _base_.train_batch_size_per_gpu
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth'  # noqa
+
+# ===================== Unmodified in most cases ==================
+num_classes = _base_.num_classes
+num_det_layers = _base_.num_det_layers
+img_scale = _base_.img_scale
+
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+    ),
+    bbox_head=dict(
+        head_module=dict(widen_factor=widen_factor),
+        loss_cls=dict(loss_weight=loss_cls_weight *
+                      (num_classes / 80 * 3 / num_det_layers)),
+        loss_obj=dict(loss_weight=loss_obj_weight *
+                      ((img_scale[0] / 640)**2 * 3 / num_det_layers))))
+
+pre_transform = _base_.pre_transform
+albu_train_transforms = _base_.albu_train_transforms
+
+mosaic_affine_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114))
+]
+
+# enable mixup
+train_pipeline = [
+    *pre_transform, *mosaic_affine_pipeline,
+    dict(
+        type='YOLOv5MixUp',
+        prob=mixup_prob,
+        pre_transform=[*pre_transform, *mosaic_affine_pipeline]),
+    dict(
+        type='mmdet.Albu',
+        transforms=albu_train_transforms,
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
+        keymap={
+            'img': 'image',
+            'gt_bboxes': 'bboxes'
+        }),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    dataset=dict(dataset=dict(pipeline=train_pipeline)))
+
+val_dataloader = dict(batch_size=train_batch_size_per_gpu)
+test_dataloader = dict(batch_size=train_batch_size_per_gpu)
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb32-100e_ionogram_mosaic.py

@@ -0,0 +1,35 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# -----data related-----
+train_batch_size_per_gpu = 32
+
+# -----train val related-----
+base_lr = _base_.base_lr * train_batch_size_per_gpu \
+    / _base_.train_batch_size_per_gpu / 2
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=(640, 640),
+        pad_val=114.0,
+        pre_transform=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
+]
+
+# ===================== Unmodified in most cases ==================
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    dataset=dict(dataset=dict(pipeline=train_pipeline)))
+
+val_dataloader = dict(batch_size=train_batch_size_per_gpu)
+
+test_dataloader = dict(batch_size=train_batch_size_per_gpu)
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb96-100e_ionogram.py

@@ -0,0 +1,108 @@
+_base_ = 'mmyolo::yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
+
+# ======================= Modified parameters =====================
+# -----data related-----
+data_root = './Iono4311/'
+train_ann_file = 'annotations/train.json'
+train_data_prefix = 'train_images/'
+val_ann_file = 'annotations/val.json'
+val_data_prefix = 'val_images/'
+test_ann_file = 'annotations/test.json'
+test_data_prefix = 'test_images/'
+class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
+num_classes = len(class_name)
+metainfo = dict(
+    classes=class_name,
+    palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
+             (130, 20, 12), (120, 121, 80)])
+# Batch size of a single GPU during training
+train_batch_size_per_gpu = 96
+# Worker to pre-fetch data for each single GPU during training
+train_num_workers = 8
+
+# -----model related-----
+# Basic size of multi-scale prior box
+anchors = [[[8, 6], [24, 4], [19, 9]], [[22, 19], [17, 49], [29, 45]],
+           [[44, 66], [96, 76], [126, 59]]]
+
+# -----train val related-----
+# base_lr_default * (your_bs / default_bs (8x16)) for SGD
+base_lr = _base_.base_lr * train_batch_size_per_gpu / (8 * 16)
+max_epochs = 100
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'  # noqa
+
+# default_hooks
+save_epoch_intervals = 10
+logger_interval = 20
+max_keep_ckpts = 1
+
+# train_cfg
+val_interval = 2
+val_begin = 20
+
+tta_model = None
+tta_pipeline = None
+
+visualizer = dict(
+    vis_backends=[dict(type='LocalVisBackend'),
+                  dict(type='WandbVisBackend')])
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    bbox_head=dict(
+        head_module=dict(num_classes=num_classes),
+        prior_generator=dict(base_sizes=anchors),
+        loss_cls=dict(loss_weight=0.5 *
+                      (num_classes / 80 * 3 / _base_.num_det_layers))))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=1,
+        dataset=dict(
+            type=_base_.dataset_type,
+            data_root=data_root,
+            metainfo=metainfo,
+            ann_file=train_ann_file,
+            data_prefix=dict(img=train_data_prefix),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=val_ann_file,
+        data_prefix=dict(img=val_data_prefix)))
+
+test_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=test_ann_file,
+        data_prefix=dict(img=test_data_prefix)))
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        save_param_scheduler=None,  # for yolov5
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'),
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=logger_interval))
+
+val_evaluator = dict(ann_file=data_root + val_ann_file)
+test_evaluator = dict(ann_file=data_root + test_ann_file)
+
+train_cfg = dict(
+    max_epochs=max_epochs, val_begin=val_begin, val_interval=val_interval)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb96-100e_ionogram_aug0.py

@@ -0,0 +1,21 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# -----train val related-----
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
+    dict(
+        type='LetterResize',
+        scale=(640, 640),
+        allow_scale_up=False,
+        pad_val=dict(img=114)),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param'))
+]
+
+# ===================== Unmodified in most cases ==================
+train_dataloader = dict(dataset=dict(dataset=dict(pipeline=train_pipeline)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb96-100e_ionogram_mosaic_affine.py

@@ -0,0 +1,29 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# -----train val related-----
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=(640, 640),
+        pad_val=114.0,
+        pre_transform=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-320, -320),
+        border_val=(114, 114, 114)),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
+]
+
+# ===================== Unmodified in most cases ==================
+train_dataloader = dict(dataset=dict(dataset=dict(pipeline=train_pipeline)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb96-100e_ionogram_mosaic_affine_albu_hsv.py

@@ -0,0 +1,44 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# -----train val related-----
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Mosaic',
+        img_scale=(640, 640),
+        pad_val=114.0,
+        pre_transform=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-320, -320),
+        border_val=(114, 114, 114)),
+    dict(
+        type='mmdet.Albu',
+        transforms=[
+            dict(type='Blur', p=0.01),
+            dict(type='MedianBlur', p=0.01),
+            dict(type='ToGray', p=0.01),
+            dict(type='CLAHE', p=0.01)
+        ],
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
+        keymap=dict(img='image', gt_bboxes='bboxes')),
+    dict(type='YOLOv5HSVRandomAug'),
+    # dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
+]
+
+# ===================== Unmodified in most cases ==================
+train_dataloader = dict(dataset=dict(dataset=dict(pipeline=train_pipeline)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov5/yolov5_s-v61_fast_1xb96-200e_ionogram_pre0.py

@@ -0,0 +1,17 @@
+_base_ = './yolov5_s-v61_fast_1xb96-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+# -----train val related-----
+base_lr = _base_.base_lr * 4
+max_epochs = 200
+load_from = None
+logger_interval = 50
+
+train_cfg = dict(max_epochs=max_epochs, )
+
+# ===================== Unmodified in most cases ==================
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=logger_interval))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov6/yolov6_l_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,29 @@
+_base_ = './yolov6_m_fast_1xb32-100e_ionogram.py'
+
+# ======================= Modified parameters =======================
+# -----model related-----
+deepen_factor = 1
+widen_factor = 1
+
+# -----train val related-----
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco/yolov6_l_syncbn_fast_8xb32-300e_coco_20221109_183156-91e3c447.pth'  # noqa
+
+# ====================== Unmodified in most cases ===================
+model = dict(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=1. / 2,
+        block_cfg=dict(
+            type='ConvWrapper',
+            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001)),
+        act_cfg=dict(type='SiLU', inplace=True)),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=1. / 2,
+        block_cfg=dict(
+            type='ConvWrapper',
+            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001)),
+        block_act_cfg=dict(type='SiLU', inplace=True)),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov6/yolov6_m_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,63 @@
+_base_ = './yolov6_s_fast_1xb32-100e_ionogram.py'
+
+# ======================= Modified parameters =======================
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 0.6
+# The scaling factor that controls the width of the network structure
+widen_factor = 0.75
+
+# -----train val related-----
+affine_scale = 0.9  # YOLOv5RandomAffine scaling ratio
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_m_syncbn_fast_8xb32-300e_coco/yolov6_m_syncbn_fast_8xb32-300e_coco_20221109_182658-85bda3f4.pth'  # noqa
+
+# ====================== Unmodified in most cases ===================
+model = dict(
+    backbone=dict(
+        type='YOLOv6CSPBep',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        hidden_ratio=2. / 3,
+        block_cfg=dict(type='RepVGGBlock'),
+        act_cfg=dict(type='ReLU', inplace=True)),
+    neck=dict(
+        type='YOLOv6CSPRepPAFPN',
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        block_cfg=dict(type='RepVGGBlock'),
+        hidden_ratio=2. / 3,
+        block_act_cfg=dict(type='ReLU', inplace=True)),
+    bbox_head=dict(
+        type='YOLOv6Head', head_module=dict(widen_factor=widen_factor)))
+
+mosaic_affine_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=_base_.img_scale,
+        pad_val=114.0,
+        pre_transform=_base_.pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
+        # img_scale is (width, height)
+        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
+        border_val=(114, 114, 114))
+]
+
+train_pipeline = [
+    *_base_.pre_transform, *mosaic_affine_pipeline,
+    dict(
+        type='YOLOv5MixUp',
+        prob=0.1,
+        pre_transform=[*_base_.pre_transform, *mosaic_affine_pipeline]),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(dataset=dict(dataset=dict(pipeline=train_pipeline)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov6/yolov6_s_fast_1xb32-100e_ionogram.py

@@ -0,0 +1,108 @@
+_base_ = 'mmyolo::yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py'
+
+# ======================= Modified parameters =====================
+# -----data related-----
+data_root = './Iono4311/'
+train_ann_file = 'annotations/train.json'
+train_data_prefix = 'train_images/'
+val_ann_file = 'annotations/val.json'
+val_data_prefix = 'val_images/'
+test_ann_file = 'annotations/test.json'
+test_data_prefix = 'test_images/'
+
+class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
+num_classes = len(class_name)
+metainfo = dict(
+    classes=class_name,
+    palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
+             (130, 20, 12), (120, 121, 80)])
+
+train_batch_size_per_gpu = 32
+train_num_workers = 8
+
+tta_model = None
+tta_pipeline = None
+
+# -----train val related-----
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth'  # noqa
+#  base_lr_default * (your_bs 32 / default_bs (8 x 32))
+base_lr = _base_.base_lr * train_batch_size_per_gpu / (8 * 32)
+max_epochs = 100
+save_epoch_intervals = 10
+val_begin = 20
+max_keep_ckpts = 1
+log_interval = 50
+visualizer = dict(
+    vis_backends=[dict(type='LocalVisBackend'),
+                  dict(type='WandbVisBackend')])
+
+# ==================== Unmodified in most cases ===================
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_begin=val_begin,
+    val_interval=save_epoch_intervals,
+    dynamic_intervals=None)
+
+model = dict(
+    bbox_head=dict(head_module=dict(num_classes=num_classes)),
+    train_cfg=dict(
+        initial_assigner=dict(num_classes=num_classes),
+        assigner=dict(num_classes=num_classes)))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=1,
+        dataset=dict(
+            type=_base_.dataset_type,
+            data_root=data_root,
+            metainfo=metainfo,
+            ann_file=train_ann_file,
+            data_prefix=dict(img=train_data_prefix),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=_base_.train_pipeline)))
+
+val_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=val_ann_file,
+        data_prefix=dict(img=val_data_prefix)))
+
+test_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=test_ann_file,
+        data_prefix=dict(img=test_data_prefix)))
+
+val_evaluator = dict(ann_file=data_root + val_data_prefix)
+test_evaluator = dict(ann_file=data_root + test_data_prefix)
+
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'),
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=log_interval))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - _base_.num_last_epochs,
+        switch_pipeline=_base_.train_pipeline_stage2)
+]

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov6/yolov6_s_fast_1xb32-200e_ionogram_pre0.py

@@ -0,0 +1,17 @@
+_base_ = './yolov6_s_fast_1xb32-100e_ionogram.py'
+
+# ======================= Modified parameters =====================
+base_lr = _base_.base_lr * 4
+optim_wrapper = dict(optimizer=dict(lr=base_lr))
+max_epochs = 200
+load_from = None
+
+# ==================== Unmodified in most cases ===================
+train_cfg = dict(
+    max_epochs=max_epochs,
+    val_begin=20,
+)
+
+default_hooks = dict(
+    param_scheduler=dict(max_epochs=max_epochs),
+    logger=dict(type='LoggerHook', interval=50))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov7/yolov7_l_fast_1xb16-100e_ionogram.py

@@ -0,0 +1,98 @@
+_base_ = 'mmyolo::yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py'
+
+# ======================== Modified parameters ======================
+# -----data related-----
+data_root = './Iono4311/'
+train_ann_file = 'annotations/train.json'
+train_data_prefix = 'train_images/'
+val_ann_file = 'annotations/val.json'
+val_data_prefix = 'val_images/'
+test_ann_file = 'annotations/test.json'
+test_data_prefix = 'test_images/'
+
+class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
+num_classes = len(class_name)
+metainfo = dict(
+    classes=class_name,
+    palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
+             (130, 20, 12), (120, 121, 80)])
+
+train_batch_size_per_gpu = 16
+train_num_workers = 8
+
+# -----model related-----
+anchors = [[[14, 14], [35, 6], [32, 18]], [[32, 45], [28, 97], [52, 80]],
+           [[71, 122], [185, 94], [164, 134]]]
+
+# -----train val related-----
+#  base_lr_default * (your_bs 32 / default_bs (8 x 16))
+base_lr = _base_.base_lr * train_batch_size_per_gpu / (8 * 16)
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth'  # noqa
+
+# default hooks
+save_epoch_intervals = 10
+max_epochs = 100
+max_keep_ckpts = 1
+
+# train_cfg
+val_interval = 2
+val_begin = 20
+
+tta_model = None
+tta_pipeline = None
+
+visualizer = dict(
+    vis_backends=[dict(type='LocalVisBackend'),
+                  dict(type='WandbVisBackend')])
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    bbox_head=dict(
+        head_module=dict(num_classes=num_classes),
+        prior_generator=dict(base_sizes=anchors),
+        loss_cls=dict(loss_weight=_base_.loss_cls_weight *
+                      (num_classes / 80 * 3 / _base_.num_det_layers))))
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix)))
+
+val_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file))
+
+test_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        data_prefix=dict(img=test_data_prefix),
+        ann_file=test_ann_file))
+
+optim_wrapper = dict(
+    optimizer=dict(lr=base_lr, batch_size_per_gpu=train_batch_size_per_gpu))
+
+default_hooks = dict(
+    param_scheduler=dict(max_epochs=max_epochs),
+    checkpoint=dict(
+        interval=save_epoch_intervals, max_keep_ckpts=max_keep_ckpts))
+
+val_evaluator = dict(ann_file=data_root + val_ann_file)
+test_evaluator = dict(ann_file=data_root + test_ann_file)
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_begin=val_begin,
+    val_interval=val_interval)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov7/yolov7_tiny_fast_1xb16-100e_ionogram.py

@@ -0,0 +1,101 @@
+_base_ = './yolov7_l_fast_1xb16-100e_ionogram.py'
+
+# ======================== Modified parameters =======================
+# pre-train
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719-0ee5bbdf.pth'  # noqa
+
+# -----model related-----
+# Data augmentation
+max_translate_ratio = 0.1  # YOLOv5RandomAffine
+scaling_ratio_range = (0.5, 1.6)  # YOLOv5RandomAffine
+mixup_prob = 0.05  # YOLOv5MixUp
+randchoice_mosaic_prob = [0.8, 0.2]
+mixup_alpha = 8.0  # YOLOv5MixUp
+mixup_beta = 8.0  # YOLOv5MixUp
+
+# -----train val related-----
+loss_cls_weight = 0.5
+loss_obj_weight = 1.0
+
+lr_factor = 0.01  # Learning rate scaling factor
+
+# ====================== Unmodified in most cases ====================
+num_classes = _base_.num_classes
+num_det_layers = _base_.num_det_layers
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+model = dict(
+    backbone=dict(
+        arch='Tiny', act_cfg=dict(type='LeakyReLU', negative_slope=0.1)),
+    neck=dict(
+        is_tiny_version=True,
+        in_channels=[128, 256, 512],
+        out_channels=[64, 128, 256],
+        block_cfg=dict(
+            _delete_=True, type='TinyDownSampleBlock', middle_ratio=0.25),
+        act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+        use_repconv_outs=False),
+    bbox_head=dict(
+        head_module=dict(in_channels=[128, 256, 512]),
+        loss_cls=dict(loss_weight=loss_cls_weight *
+                      (num_classes / 80 * 3 / num_det_layers)),
+        loss_obj=dict(loss_weight=loss_obj_weight *
+                      ((img_scale[0] / 640)**2 * 3 / num_det_layers))))
+
+mosiac4_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_translate_ratio=max_translate_ratio,  # change
+        scaling_ratio_range=scaling_ratio_range,  # change
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+]
+
+mosiac9_pipeline = [
+    dict(
+        type='Mosaic9',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='YOLOv5RandomAffine',
+        max_rotate_degree=0.0,
+        max_shear_degree=0.0,
+        max_translate_ratio=max_translate_ratio,  # change
+        scaling_ratio_range=scaling_ratio_range,  # change
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        border_val=(114, 114, 114)),
+]
+
+randchoice_mosaic_pipeline = dict(
+    type='RandomChoice',
+    transforms=[mosiac4_pipeline, mosiac9_pipeline],
+    prob=randchoice_mosaic_prob)
+
+train_pipeline = [
+    *pre_transform,
+    randchoice_mosaic_pipeline,
+    dict(
+        type='YOLOv5MixUp',
+        alpha=mixup_alpha,
+        beta=mixup_beta,
+        prob=mixup_prob,  # change
+        pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
+    dict(type='YOLOv5HSVRandomAug'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/projects/misc/ionogram_detection/yolov7/yolov7_x_fast_1xb16-100e_ionogram.py

@@ -0,0 +1,19 @@
+_base_ = './yolov7_l_fast_1xb16-100e_ionogram.py'
+
+# ======================== Modified parameters =======================
+load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco/yolov7_x_syncbn_fast_8x16b-300e_coco_20221124_215331-ef949a68.pth'  # noqa
+
+# ===================== Unmodified in most cases ==================
+model = dict(
+    backbone=dict(arch='X'),
+    neck=dict(
+        in_channels=[640, 1280, 1280],
+        out_channels=[160, 320, 640],
+        block_cfg=dict(
+            type='ELANBlock',
+            middle_ratio=0.4,
+            block_ratio=0.4,
+            num_blocks=3,
+            num_convs_in_block=2),
+        use_repconv_outs=False),
+    bbox_head=dict(head_module=dict(in_channels=[320, 640, 1280])))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/pytest.ini

@@ -0,0 +1,7 @@
+[pytest]
+addopts = --xdoctest --xdoctest-style=auto
+norecursedirs = .git ignore build __pycache__ data docker docs .eggs
+
+filterwarnings= default
+                ignore:.*No cfgstr given in Cacher constructor or call.*:Warning
+                ignore:.*Define the __nice__ method for.*:Warning

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements.txt

@@ -0,0 +1,3 @@
+-r requirements/build.txt
+-r requirements/runtime.txt
+-r requirements/tests.txt

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/albu.txt

@@ -0,0 +1 @@
+albumentations --no-binary qudida,albumentations

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/build.txt

@@ -0,0 +1,3 @@
+# These must be installed before building mmyolo
+cython
+numpy

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/docs.txt

@@ -0,0 +1,13 @@
+docutils==0.16.0
+mmcv>=2.0.0rc4,<=2.1.0
+mmdet>=3.0.0
+mmengine>=0.7.1
+myst-parser
+-e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx==4.0.2
+sphinx-copybutton
+sphinx_markdown_tables
+sphinx_rtd_theme==0.5.2
+torch
+torchvision
+urllib3<2.0.0

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/mminstall.txt

@@ -0,0 +1,3 @@
+mmcv>=2.0.0rc4,<=2.1.0
+mmdet>=3.0.0
+mmengine>=0.7.1

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/mmpose.txt

@@ -0,0 +1 @@
+mmpose>=1.0.0

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/mmrotate.txt

@@ -0,0 +1 @@
+mmrotate>=1.0.0rc1

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/runtime.txt

@@ -0,0 +1,2 @@
+numpy
+prettytable

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/sahi.txt

@@ -0,0 +1 @@
+sahi>=0.11.4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/requirements/tests.txt

@@ -0,0 +1,17 @@
+flake8
+interrogate
+isort==4.3.21
+# Note: used for kwarray.group_items, this may be ported to mmcv in the future.
+kwarray
+memory_profiler
+mmcls>=1.0.0rc4
+mmpose>=1.0.0
+mmrazor>=1.0.0rc2
+mmrotate>=1.0.0rc1
+parameterized
+protobuf<=3.20.1
+psutil
+pytest
+ubelt
+xdoctest>=0.10.0
+yapf

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/setup.cfg

@@ -0,0 +1,21 @@
+[isort]
+line_length = 79
+multi_line_output = 0
+extra_standard_library = setuptools
+known_first_party = mmyolo
+known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,parameterized,pytorch_sphinx_theme,requests,scipy,seaborn,six,terminaltables,torch,ts,yaml,mmengine,mmdet,mmdeploy
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY
+
+[yapf]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+
+# ignore-words-list needs to be lowercase format. For example, if we want to
+# ignore word "BA", then we need to append "ba" to ignore-words-list rather
+# than "BA"
+[codespell]
+skip = *.ipynb
+quiet-level = 3
+ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,tood,ba,warmup,elease,dota

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/setup.py

@@ -0,0 +1,191 @@
+#!/usr/bin/env python
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import platform
+import shutil
+import sys
+import warnings
+from setuptools import find_packages, setup
+
+from torch.utils.cpp_extension import BuildExtension
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+version_file = 'mmyolo/version.py'
+
+
+def get_version():
+    with open(version_file) as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def parse_requirements(fname='requirements.txt', with_version=True):
+    """Parse the package dependencies listed in a requirements file but strips
+    specific versioning information.
+
+    Args:
+        fname (str): path to requirements file
+        with_version (bool, default=False): if True include version specs
+
+    Returns:
+        List[str]: list of requirements items
+
+    CommandLine:
+        python -c "import setup; print(setup.parse_requirements())"
+    """
+    import re
+    import sys
+    from os.path import exists
+    require_fpath = fname
+
+    def parse_line(line):
+        """Parse information from a line in a requirements text file."""
+        if line.startswith('-r '):
+            # Allow specifying requirements in other files
+            target = line.split(' ')[1]
+            for info in parse_require_file(target):
+                yield info
+        else:
+            info = {'line': line}
+            if line.startswith('-e '):
+                info['package'] = line.split('#egg=')[1]
+            elif '@git+' in line:
+                info['package'] = line
+            else:
+                # Remove versioning from the package
+                pat = '(' + '|'.join(['>=', '==', '>']) + ')'
+                parts = re.split(pat, line, maxsplit=1)
+                parts = [p.strip() for p in parts]
+
+                info['package'] = parts[0]
+                if len(parts) > 1:
+                    op, rest = parts[1:]
+                    if ';' in rest:
+                        # Handle platform specific dependencies
+                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
+                        version, platform_deps = map(str.strip,
+                                                     rest.split(';'))
+                        info['platform_deps'] = platform_deps
+                    else:
+                        version = rest  # NOQA
+                    info['version'] = (op, version)
+            yield info
+
+    def parse_require_file(fpath):
+        with open(fpath) as f:
+            for line in f.readlines():
+                line = line.strip()
+                if line and not line.startswith('#'):
+                    yield from parse_line(line)
+
+    def gen_packages_items():
+        if exists(require_fpath):
+            for info in parse_require_file(require_fpath):
+                parts = [info['package']]
+                if with_version and 'version' in info:
+                    parts.extend(info['version'])
+                if not sys.version.startswith('3.4'):
+                    # apparently package_deps are broken in 3.4
+                    platform_deps = info.get('platform_deps')
+                    if platform_deps is not None:
+                        parts.append(';' + platform_deps)
+                item = ''.join(parts)
+                yield item
+
+    packages = list(gen_packages_items())
+    return packages
+
+
+def add_mim_extension():
+    """Add extra files that are required to support MIM into the package.
+
+    These files will be added by creating a symlink to the originals if the
+    package is installed in `editable` mode (e.g. pip install -e .), or by
+    copying from the originals otherwise.
+    """
+
+    # parse installment mode
+    if 'develop' in sys.argv:
+        # installed by `pip install -e .`
+        if platform.system() == 'Windows':
+            # set `copy` mode here since symlink fails on Windows.
+            mode = 'copy'
+        else:
+            mode = 'symlink'
+    elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
+        # installed by `pip install .`
+        # or create source distribution by `python setup.py sdist`
+        mode = 'copy'
+    else:
+        return
+
+    filenames = ['tools', 'configs', 'demo', 'model-index.yml']
+    repo_path = osp.dirname(__file__)
+    mim_path = osp.join(repo_path, 'mmyolo', '.mim')
+    os.makedirs(mim_path, exist_ok=True)
+
+    for filename in filenames:
+        if osp.exists(filename):
+            src_path = osp.join(repo_path, filename)
+            tar_path = osp.join(mim_path, filename)
+
+            if osp.isfile(tar_path) or osp.islink(tar_path):
+                os.remove(tar_path)
+            elif osp.isdir(tar_path):
+                shutil.rmtree(tar_path)
+
+            if mode == 'symlink':
+                src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
+                os.symlink(src_relpath, tar_path)
+            elif mode == 'copy':
+                if osp.isfile(src_path):
+                    shutil.copyfile(src_path, tar_path)
+                elif osp.isdir(src_path):
+                    shutil.copytree(src_path, tar_path)
+                else:
+                    warnings.warn(f'Cannot copy file {src_path}.')
+            else:
+                raise ValueError(f'Invalid mode {mode}')
+
+
+if __name__ == '__main__':
+    add_mim_extension()
+    setup(
+        name='mmyolo',
+        version=get_version(),
+        description='OpenMMLab Toolbox of YOLO',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='MMYOLO Contributors',
+        author_email='openmmlab@gmail.com',
+        keywords='computer vision, object detection',
+        url='https://github.com/open-mmlab/mmyolo',
+        packages=find_packages(exclude=('configs', 'tools', 'demo')),
+        include_package_data=True,
+        classifiers=[
+            'Development Status :: 5 - Production/Stable',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9',
+        ],
+        license='GPL License 3.0',
+        install_requires=parse_requirements('requirements/runtime.txt'),
+        extras_require={
+            'all': parse_requirements('requirements.txt'),
+            'tests': parse_requirements('requirements/tests.txt'),
+            'build': parse_requirements('requirements/build.txt'),
+            'mim': parse_requirements('requirements/mminstall.txt'),
+        },
+        ext_modules=[],
+        cmdclass={'build_ext': BuildExtension},
+        zip_safe=False)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/regression/mmyolo.yml

@@ -0,0 +1,81 @@
+globals:
+  codebase_dir: ../mmyolo
+  checkpoint_force_download: False
+  images:
+    input_img: &input_img ../mmyolo/demo/demo.jpg
+    test_img: &test_img ./tests/data/tiger.jpeg
+  metric_info: &metric_info
+    box AP: # named after metafile.Results.Metrics
+      metric_key: coco/bbox_mAP # eval OrderedDict key name
+      tolerance: 1 # metric ±n%
+      multi_value: 100
+  convert_image: &convert_image
+    input_img: *input_img
+    test_img: *test_img
+  backend_test: &default_backend_test True
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmyolo/detection_onnxruntime_static.py
+
+  pipeline_ort_dynamic_fp32: &pipeline_ort_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmyolo/detection_onnxruntime_dynamic.py
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32_640x640
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmyolo/detection_tensorrt_static-640x640.py
+
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16_640x640
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmyolo/detection_tensorrt-fp16_static-640x640.py
+
+  pipeline_trt_dynamic_fp32: &pipeline_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmyolo/detection_tensorrt_dynamic-192x192-960x960.py
+
+  pipeline_trt_dynamic_fp16: &pipeline_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmyolo/detection_tensorrt-fp16_dynamic-64x64-1344x1344.py
+
+models:
+  - name: YOLOv5
+    metafile: configs/yolov5/metafile.yml
+    model_configs:
+      - configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+
+  - name: YOLOv6
+    metafile: configs/yolov6/metafile.yml
+    model_configs:
+      - configs/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+
+  - name: YOLOX
+    metafile: configs/yolox/metafile.yml
+    model_configs:
+      - configs/yolox/yolox_s_8xb8-300e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+
+
+  - name: RTMDet
+    metafile: configs/rtmdet/metafile.yml
+    model_configs:
+      - configs/rtmdet/rtmdet_s_syncbn_8xb32-300e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) OpenMMLab. All rights reserved.

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_transforms/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) OpenMMLab. All rights reserved.

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_transforms/test_formatting.py

@@ -0,0 +1,119 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+import unittest
+
+import numpy as np
+from mmdet.structures import DetDataSample
+from mmdet.structures.mask import BitmapMasks
+from mmengine.structures import InstanceData, PixelData
+
+from mmyolo.datasets.transforms import PackDetInputs
+
+
+class TestPackDetInputs(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the model and optimizer which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        data_prefix = osp.join(osp.dirname(__file__), '../../data')
+        img_path = osp.join(data_prefix, 'color.jpg')
+        rng = np.random.RandomState(0)
+        self.results1 = {
+            'img_id': 1,
+            'img_path': img_path,
+            'ori_shape': (300, 400),
+            'img_shape': (600, 800),
+            'scale_factor': 2.0,
+            'flip': False,
+            'img': rng.rand(300, 400),
+            'gt_seg_map': rng.rand(300, 400),
+            'gt_masks':
+            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
+            'gt_bboxes_labels': rng.rand(3, ),
+            'gt_ignore_flags': np.array([0, 0, 1], dtype=bool),
+            'proposals': rng.rand(2, 4),
+            'proposals_scores': rng.rand(2, )
+        }
+        self.results2 = {
+            'img_id': 1,
+            'img_path': img_path,
+            'ori_shape': (300, 400),
+            'img_shape': (600, 800),
+            'scale_factor': 2.0,
+            'flip': False,
+            'img': rng.rand(300, 400),
+            'gt_seg_map': rng.rand(300, 400),
+            'gt_masks':
+            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
+            'gt_bboxes_labels': rng.rand(3, ),
+            'proposals': rng.rand(2, 4),
+            'proposals_scores': rng.rand(2, )
+        }
+        self.results3 = {
+            'img_id': 1,
+            'img_path': img_path,
+            'ori_shape': (300, 400),
+            'img_shape': (600, 800),
+            'scale_factor': 2.0,
+            'flip': False,
+            'img': rng.rand(300, 400),
+            'gt_seg_map': rng.rand(300, 400),
+            'gt_masks':
+            BitmapMasks(rng.rand(3, 300, 400), height=300, width=400),
+            'gt_panoptic_seg': rng.rand(1, 300, 400),
+            'gt_bboxes_labels': rng.rand(3, ),
+            'proposals': rng.rand(2, 4),
+            'proposals_scores': rng.rand(2, )
+        }
+        self.meta_keys = ('img_id', 'img_path', 'ori_shape', 'scale_factor',
+                          'flip')
+
+    def test_transform(self):
+        transform = PackDetInputs(meta_keys=self.meta_keys)
+        results = transform(copy.deepcopy(self.results1))
+        self.assertIn('data_samples', results)
+        self.assertIsInstance(results['data_samples'], DetDataSample)
+        self.assertIsInstance(results['data_samples'].gt_instances,
+                              InstanceData)
+        self.assertIsInstance(results['data_samples'].ignored_instances,
+                              InstanceData)
+        self.assertEqual(len(results['data_samples'].gt_instances), 2)
+        self.assertEqual(len(results['data_samples'].ignored_instances), 1)
+        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)
+
+    def test_transform_without_ignore(self):
+        transform = PackDetInputs(meta_keys=self.meta_keys)
+        results = transform(copy.deepcopy(self.results2))
+        self.assertIn('data_samples', results)
+        self.assertIsInstance(results['data_samples'], DetDataSample)
+        self.assertIsInstance(results['data_samples'].gt_instances,
+                              InstanceData)
+        self.assertIsInstance(results['data_samples'].ignored_instances,
+                              InstanceData)
+        self.assertEqual(len(results['data_samples'].gt_instances), 3)
+        self.assertEqual(len(results['data_samples'].ignored_instances), 0)
+        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)
+
+    def test_transform_with_panoptic_seg(self):
+        transform = PackDetInputs(meta_keys=self.meta_keys)
+        results = transform(copy.deepcopy(self.results3))
+        self.assertIn('data_samples', results)
+        self.assertIsInstance(results['data_samples'], DetDataSample)
+        self.assertIsInstance(results['data_samples'].gt_instances,
+                              InstanceData)
+        self.assertIsInstance(results['data_samples'].ignored_instances,
+                              InstanceData)
+        self.assertEqual(len(results['data_samples'].gt_instances), 3)
+        self.assertEqual(len(results['data_samples'].ignored_instances), 0)
+        self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData)
+        self.assertIsInstance(results['data_samples'].gt_panoptic_seg,
+                              PixelData)
+
+    def test_repr(self):
+        transform = PackDetInputs(meta_keys=self.meta_keys)
+        self.assertEqual(
+            repr(transform), f'PackDetInputs(meta_keys={self.meta_keys})')

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_transforms/test_mix_img_transforms.py

@@ -0,0 +1,416 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+import unittest
+
+import numpy as np
+import torch
+from mmdet.structures.bbox import HorizontalBoxes
+from mmdet.structures.mask import BitmapMasks, PolygonMasks
+
+from mmyolo.datasets import YOLOv5CocoDataset
+from mmyolo.datasets.transforms import Mosaic, Mosaic9, YOLOv5MixUp, YOLOXMixUp
+from mmyolo.utils import register_all_modules
+
+register_all_modules()
+
+
+class TestMosaic(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]
+
+        self.dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'dataset':
+            self.dataset
+        }
+
+    def test_transform(self):
+        # test assertion for invalid img_scale
+        with self.assertRaises(AssertionError):
+            transform = Mosaic(img_scale=640)
+
+        # test assertion for invalid probability
+        with self.assertRaises(AssertionError):
+            transform = Mosaic(prob=1.5)
+
+        # test assertion for invalid max_cached_images
+        with self.assertRaises(AssertionError):
+            transform = Mosaic(use_cached=True, max_cached_images=1)
+
+        transform = Mosaic(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_no_gt(self):
+        self.results['gt_bboxes'] = np.empty((0, 4), dtype=np.float32)
+        self.results['gt_bboxes_labels'] = np.empty((0, ), dtype=np.int64)
+        self.results['gt_ignore_flags'] = np.empty((0, ), dtype=bool)
+        transform = Mosaic(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = transform(copy.deepcopy(self.results))
+        self.assertIsInstance(results, dict)
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(
+            results['gt_bboxes_labels'].shape[0] == results['gt_bboxes'].
+            shape[0] == results['gt_ignore_flags'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_box_list(self):
+        transform = Mosaic(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+        results = transform(results)
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_mask(self):
+        rng = np.random.RandomState(0)
+        pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=True)
+        ]
+
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+        results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'gt_masks':
+            PolygonMasks.random(num_masks=3, height=224, width=224, rng=rng),
+            'dataset':
+            dataset
+        }
+        transform = Mosaic(img_scale=(12, 10), pre_transform=pre_transform)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+        results = transform(results)
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestMosaic9(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        rng = np.random.RandomState(0)
+        self.pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]
+
+        self.dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'gt_masks':
+            BitmapMasks(rng.rand(3, 224, 224), height=224, width=224),
+            'dataset':
+            self.dataset
+        }
+
+    def test_transform(self):
+        # test assertion for invalid img_scale
+        with self.assertRaises(AssertionError):
+            transform = Mosaic9(img_scale=640)
+
+        # test assertion for invalid probability
+        with self.assertRaises(AssertionError):
+            transform = Mosaic9(prob=1.5)
+
+        # test assertion for invalid max_cached_images
+        with self.assertRaises(AssertionError):
+            transform = Mosaic9(use_cached=True, max_cached_images=1)
+
+        transform = Mosaic9(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_no_gt(self):
+        self.results['gt_bboxes'] = np.empty((0, 4), dtype=np.float32)
+        self.results['gt_bboxes_labels'] = np.empty((0, ), dtype=np.int64)
+        self.results['gt_ignore_flags'] = np.empty((0, ), dtype=bool)
+        transform = Mosaic9(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = transform(copy.deepcopy(self.results))
+        self.assertIsInstance(results, dict)
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(
+            results['gt_bboxes_labels'].shape[0] == results['gt_bboxes'].
+            shape[0] == results['gt_ignore_flags'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_box_list(self):
+        transform = Mosaic9(
+            img_scale=(12, 10), pre_transform=self.pre_transform)
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+        results = transform(results)
+        self.assertTrue(results['img'].shape[:2] == (20, 24))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestYOLOv5MixUp(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]
+        self.dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+
+        self.results = {
+            'img':
+            np.random.random((288, 512, 3)),
+            'img_shape': (288, 512),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'dataset':
+            self.dataset
+        }
+
+    def test_transform(self):
+        transform = YOLOv5MixUp(pre_transform=self.pre_transform)
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (288, 512))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+        # test assertion for invalid max_cached_images
+        with self.assertRaises(AssertionError):
+            transform = YOLOv5MixUp(use_cached=True, max_cached_images=1)
+
+    def test_transform_with_box_list(self):
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+
+        transform = YOLOv5MixUp(pre_transform=self.pre_transform)
+        results = transform(results)
+        self.assertTrue(results['img'].shape[:2] == (288, 512))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_mask(self):
+        rng = np.random.RandomState(0)
+        pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=True)
+        ]
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+
+        results = {
+            'img':
+            np.random.random((288, 512, 3)),
+            'img_shape': (288, 512),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'gt_masks':
+            PolygonMasks.random(num_masks=3, height=288, width=512, rng=rng),
+            'dataset':
+            dataset
+        }
+
+        transform = YOLOv5MixUp(pre_transform=pre_transform)
+        results = transform(copy.deepcopy(results))
+        self.assertTrue(results['img'].shape[:2] == (288, 512))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestYOLOXMixUp(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        rng = np.random.RandomState(0)
+        self.pre_transform = [
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ]
+        self.dataset = YOLOv5CocoDataset(
+            data_prefix=dict(
+                img=osp.join(osp.dirname(__file__), '../../data')),
+            ann_file=osp.join(
+                osp.dirname(__file__), '../../data/coco_sample_color.json'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=32),
+            pipeline=[])
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+            'gt_masks':
+            BitmapMasks(rng.rand(3, 224, 224), height=224, width=224),
+            'dataset':
+            self.dataset
+        }
+
+    def test_transform(self):
+        # test assertion for invalid img_scale
+        with self.assertRaises(AssertionError):
+            transform = YOLOXMixUp(img_scale=640)
+
+        # test assertion for invalid max_cached_images
+        with self.assertRaises(AssertionError):
+            transform = YOLOXMixUp(use_cached=True, max_cached_images=1)
+
+        transform = YOLOXMixUp(
+            img_scale=(10, 12),
+            ratio_range=(0.8, 1.6),
+            pad_val=114.0,
+            pre_transform=self.pre_transform)
+
+        # self.results['mix_results'] = [copy.deepcopy(self.results)]
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_boxlist(self):
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+
+        transform = YOLOXMixUp(
+            img_scale=(10, 12),
+            ratio_range=(0.8, 1.6),
+            pad_val=114.0,
+            pre_transform=self.pre_transform)
+        results = transform(results)
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_transforms/test_transforms.py

@@ -0,0 +1,493 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+import unittest
+
+import mmcv
+import numpy as np
+import torch
+from mmdet.structures.bbox import HorizontalBoxes
+from mmdet.structures.mask import BitmapMasks, PolygonMasks
+
+from mmyolo.datasets.transforms import (LetterResize, LoadAnnotations,
+                                        YOLOv5HSVRandomAug,
+                                        YOLOv5KeepRatioResize,
+                                        YOLOv5RandomAffine)
+from mmyolo.datasets.transforms.transforms import (PPYOLOERandomCrop,
+                                                   PPYOLOERandomDistort,
+                                                   YOLOv5CopyPaste)
+
+
+class TestLetterResize(unittest.TestCase):
+
+    def setUp(self):
+        """Set up the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        rng = np.random.RandomState(0)
+        self.data_info1 = dict(
+            img=np.random.random((300, 400, 3)),
+            gt_bboxes=np.array([[0, 0, 150, 150]], dtype=np.float32),
+            batch_shape=np.array([192, 672], dtype=np.int64),
+            gt_masks=PolygonMasks.random(1, height=300, width=400, rng=rng))
+        self.data_info2 = dict(
+            img=np.random.random((300, 400, 3)),
+            gt_bboxes=np.array([[0, 0, 150, 150]], dtype=np.float32))
+        self.data_info3 = dict(
+            img=np.random.random((300, 400, 3)),
+            batch_shape=np.array([192, 672], dtype=np.int64))
+        self.data_info4 = dict(img=np.random.random((300, 400, 3)))
+
+    def test_letter_resize(self):
+        # Test allow_scale_up
+        transform = LetterResize(scale=(640, 640), allow_scale_up=False)
+        results = transform(copy.deepcopy(self.data_info1))
+        self.assertEqual(results['img_shape'], (192, 672, 3))
+        self.assertTrue(
+            (results['gt_bboxes'] == np.array([[208., 0., 304., 96.]])).all())
+        self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
+        self.assertTrue((results['pad_param'] == np.array([0., 0., 208.,
+                                                           208.])).all())
+        self.assertTrue(
+            (np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
+
+        # Test pad_val
+        transform = LetterResize(scale=(640, 640), pad_val=dict(img=144))
+        results = transform(copy.deepcopy(self.data_info1))
+        self.assertEqual(results['img_shape'], (192, 672, 3))
+        self.assertTrue(
+            (results['gt_bboxes'] == np.array([[208., 0., 304., 96.]])).all())
+        self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
+        self.assertTrue((results['pad_param'] == np.array([0., 0., 208.,
+                                                           208.])).all())
+        self.assertTrue(
+            (np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
+
+        # Test use_mini_pad
+        transform = LetterResize(scale=(640, 640), use_mini_pad=True)
+        results = transform(copy.deepcopy(self.data_info1))
+        self.assertEqual(results['img_shape'], (192, 256, 3))
+        self.assertTrue((results['gt_bboxes'] == np.array([[0., 0., 96.,
+                                                            96.]])).all())
+        self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
+        self.assertTrue((results['pad_param'] == np.array([0., 0., 0.,
+                                                           0.])).all())
+        self.assertTrue(
+            (np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
+
+        # Test stretch_only
+        transform = LetterResize(scale=(640, 640), stretch_only=True)
+        results = transform(copy.deepcopy(self.data_info1))
+        self.assertEqual(results['img_shape'], (192, 672, 3))
+        self.assertTrue((results['gt_bboxes'] == np.array(
+            [[0., 0., 251.99998474121094, 96.]])).all())
+        self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
+        self.assertTrue((results['pad_param'] == np.array([0., 0., 0.,
+                                                           0.])).all())
+
+        # Test
+        transform = LetterResize(scale=(640, 640), pad_val=dict(img=144))
+        for _ in range(5):
+            input_h, input_w = np.random.randint(100, 700), np.random.randint(
+                100, 700)
+            output_h, output_w = np.random.randint(100,
+                                                   700), np.random.randint(
+                                                       100, 700)
+            data_info = dict(
+                img=np.random.random((input_h, input_w, 3)),
+                gt_bboxes=np.array([[0, 0, 10, 10]], dtype=np.float32),
+                batch_shape=np.array([output_h, output_w], dtype=np.int64),
+                gt_masks=PolygonMasks(
+                    [[np.array([0., 0., 0., 10., 10., 10., 10., 0.])]],
+                    height=input_h,
+                    width=input_w))
+            results = transform(data_info)
+            self.assertEqual(results['img_shape'], (output_h, output_w, 3))
+            self.assertTrue(
+                (results['batch_shape'] == np.array([output_h,
+                                                     output_w])).all())
+
+        # Test without batchshape
+        transform = LetterResize(scale=(640, 640), pad_val=dict(img=144))
+        for _ in range(5):
+            input_h, input_w = np.random.randint(100, 700), np.random.randint(
+                100, 700)
+            data_info = dict(
+                img=np.random.random((input_h, input_w, 3)),
+                gt_bboxes=np.array([[0, 0, 10, 10]], dtype=np.float32),
+                gt_masks=PolygonMasks(
+                    [[np.array([0., 0., 0., 10., 10., 10., 10., 0.])]],
+                    height=input_h,
+                    width=input_w))
+            results = transform(data_info)
+            self.assertEqual(results['img_shape'], (640, 640, 3))
+
+        # TODO: Testing the existence of multiple scale_factor and pad_param
+        transform = [
+            YOLOv5KeepRatioResize(scale=(32, 32)),
+            LetterResize(scale=(64, 68), pad_val=dict(img=144))
+        ]
+        for _ in range(5):
+            input_h, input_w = np.random.randint(100, 700), np.random.randint(
+                100, 700)
+            output_h, output_w = np.random.randint(100,
+                                                   700), np.random.randint(
+                                                       100, 700)
+            data_info = dict(
+                img=np.random.random((input_h, input_w, 3)),
+                gt_bboxes=np.array([[0, 0, 5, 5]], dtype=np.float32),
+                batch_shape=np.array([output_h, output_w], dtype=np.int64))
+            for t in transform:
+                data_info = t(data_info)
+            # because of the "math.round" operation,
+            # it is unable to strictly restore the original input shape
+            # we just validate the correctness of scale_factor and pad_param
+            self.assertIn('scale_factor', data_info)
+            self.assertIn('pad_param', data_info)
+            pad_param = data_info['pad_param'].reshape(-1, 2).sum(
+                1)  # (top, b, l, r) -> (h, w)
+            scale_factor = np.asarray(data_info['scale_factor'])  # (w, h)
+
+            max_long_edge = max((32, 32))
+            max_short_edge = min((32, 32))
+            scale_factor_keepratio = min(
+                max_long_edge / max(input_h, input_w),
+                max_short_edge / min(input_h, input_w))
+            validate_shape = np.asarray(
+                (int(input_h * scale_factor_keepratio),
+                 int(input_w * scale_factor_keepratio)))
+            scale_factor_keepratio = np.asarray(
+                (validate_shape[1] / input_w, validate_shape[0] / input_h))
+
+            scale_factor_letter = ((np.asarray(
+                (output_h, output_w)) - pad_param) / validate_shape)[::-1]
+            self.assertTrue(data_info['img_shape'][:2] == (output_h, output_w))
+            self.assertTrue((scale_factor == (scale_factor_keepratio *
+                                              scale_factor_letter)).all())
+
+
+class TestYOLOv5KeepRatioResize(unittest.TestCase):
+
+    def setUp(self):
+        """Set up the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        rng = np.random.RandomState(0)
+        self.data_info1 = dict(
+            img=np.random.random((300, 400, 3)),
+            gt_bboxes=np.array([[0, 0, 150, 150]], dtype=np.float32),
+            gt_masks=PolygonMasks.random(
+                num_masks=1, height=300, width=400, rng=rng))
+        self.data_info2 = dict(img=np.random.random((300, 400, 3)))
+
+    def test_yolov5_keep_ratio_resize(self):
+        # test assertion for invalid keep_ratio
+        with self.assertRaises(AssertionError):
+            transform = YOLOv5KeepRatioResize(scale=(640, 640))
+            transform.keep_ratio = False
+            results = transform(copy.deepcopy(self.data_info1))
+
+        # Test with gt_bboxes
+        transform = YOLOv5KeepRatioResize(scale=(640, 640))
+        results = transform(copy.deepcopy(self.data_info1))
+        self.assertTrue(transform.keep_ratio, True)
+        self.assertEqual(results['img_shape'], (480, 640))
+        self.assertTrue(
+            (results['gt_bboxes'] == np.array([[0., 0., 240., 240.]])).all())
+        self.assertTrue((np.array(results['scale_factor'],
+                                  dtype=np.float32) == 1.6).all())
+
+        # Test only img
+        transform = YOLOv5KeepRatioResize(scale=(640, 640))
+        results = transform(copy.deepcopy(self.data_info2))
+        self.assertEqual(results['img_shape'], (480, 640))
+        self.assertTrue((np.array(results['scale_factor'],
+                                  dtype=np.float32) == 1.6).all())
+
+
+class TestYOLOv5HSVRandomAug(unittest.TestCase):
+
+    def setUp(self):
+        """Set up the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.data_info = dict(
+            img=mmcv.imread(
+                osp.join(osp.dirname(__file__), '../../data/color.jpg'),
+                'color'))
+
+    def test_yolov5_hsv_random_aug(self):
+        # Test with gt_bboxes
+        transform = YOLOv5HSVRandomAug(
+            hue_delta=0.015, saturation_delta=0.7, value_delta=0.4)
+        results = transform(copy.deepcopy(self.data_info))
+        self.assertTrue(
+            results['img'].shape[:2] == self.data_info['img'].shape[:2])
+
+
+class TestLoadAnnotations(unittest.TestCase):
+
+    def setUp(self):
+        """Set up the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        data_prefix = osp.join(osp.dirname(__file__), '../../data')
+        seg_map = osp.join(data_prefix, 'gray.jpg')
+        self.results = {
+            'ori_shape': (300, 400),
+            'seg_map_path':
+            seg_map,
+            'instances': [{
+                'bbox': [0, 0, 10, 20],
+                'bbox_label': 1,
+                'mask': [[0, 0, 0, 20, 10, 20, 10, 0]],
+                'ignore_flag': 0
+            }, {
+                'bbox': [10, 10, 110, 120],
+                'bbox_label': 2,
+                'mask': [[10, 10, 110, 10, 110, 120, 110, 10]],
+                'ignore_flag': 0
+            }, {
+                'bbox': [50, 50, 60, 80],
+                'bbox_label': 2,
+                'mask': [[50, 50, 60, 50, 60, 80, 50, 80]],
+                'ignore_flag': 1
+            }]
+        }
+
+    def test_load_bboxes(self):
+        transform = LoadAnnotations(
+            with_bbox=True,
+            with_label=False,
+            with_seg=False,
+            with_mask=False,
+            box_type=None)
+        results = transform(copy.deepcopy(self.results))
+        self.assertIn('gt_bboxes', results)
+        self.assertTrue((results['gt_bboxes'] == np.array([[0, 0, 10, 20],
+                                                           [10, 10, 110,
+                                                            120]])).all())
+        self.assertEqual(results['gt_bboxes'].dtype, np.float32)
+        self.assertTrue(
+            (results['gt_ignore_flags'] == np.array([False, False])).all())
+        self.assertEqual(results['gt_ignore_flags'].dtype, bool)
+
+        # test empty instance
+        results = transform({})
+        self.assertIn('gt_bboxes', results)
+        self.assertTrue(results['gt_bboxes'].shape == (0, 4))
+        self.assertIn('gt_ignore_flags', results)
+        self.assertTrue(results['gt_ignore_flags'].shape == (0, ))
+
+    def test_load_labels(self):
+        transform = LoadAnnotations(
+            with_bbox=False,
+            with_label=True,
+            with_seg=False,
+            with_mask=False,
+        )
+        results = transform(copy.deepcopy(self.results))
+        self.assertIn('gt_bboxes_labels', results)
+        self.assertTrue((results['gt_bboxes_labels'] == np.array([1,
+                                                                  2])).all())
+        self.assertEqual(results['gt_bboxes_labels'].dtype, np.int64)
+
+        # test empty instance
+        results = transform({})
+        self.assertIn('gt_bboxes_labels', results)
+        self.assertTrue(results['gt_bboxes_labels'].shape == (0, ))
+
+
+class TestYOLOv5RandomAffine(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+        }
+
+    def test_transform(self):
+        # test assertion for invalid translate_ratio
+        with self.assertRaises(AssertionError):
+            transform = YOLOv5RandomAffine(max_translate_ratio=1.5)
+
+        # test assertion for invalid scaling_ratio_range
+        with self.assertRaises(AssertionError):
+            transform = YOLOv5RandomAffine(scaling_ratio_range=(1.5, 0.5))
+
+        with self.assertRaises(AssertionError):
+            transform = YOLOv5RandomAffine(scaling_ratio_range=(0, 0.5))
+
+        transform = YOLOv5RandomAffine()
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_boxlist(self):
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+
+        transform = YOLOv5RandomAffine()
+        results = transform(copy.deepcopy(results))
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestPPYOLOERandomCrop(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+        }
+
+    def test_transform(self):
+        transform = PPYOLOERandomCrop()
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_boxlist(self):
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+
+        transform = PPYOLOERandomCrop()
+        results = transform(copy.deepcopy(results))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestPPYOLOERandomDistort(unittest.TestCase):
+
+    def setUp(self):
+        """Setup the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.results = {
+            'img':
+            np.random.random((224, 224, 3)),
+            'img_shape': (224, 224),
+            'gt_bboxes_labels':
+            np.array([1, 2, 3], dtype=np.int64),
+            'gt_bboxes':
+            np.array([[10, 10, 20, 20], [20, 20, 40, 40], [40, 40, 80, 80]],
+                     dtype=np.float32),
+            'gt_ignore_flags':
+            np.array([0, 0, 1], dtype=bool),
+        }
+
+    def test_transform(self):
+        # test assertion for invalid prob
+        with self.assertRaises(AssertionError):
+            transform = PPYOLOERandomDistort(
+                hue_cfg=dict(min=-18, max=18, prob=1.5))
+
+        # test assertion for invalid num_distort_func
+        with self.assertRaises(AssertionError):
+            transform = PPYOLOERandomDistort(num_distort_func=5)
+
+        transform = PPYOLOERandomDistort()
+        results = transform(copy.deepcopy(self.results))
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == np.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+    def test_transform_with_boxlist(self):
+        results = copy.deepcopy(self.results)
+        results['gt_bboxes'] = HorizontalBoxes(results['gt_bboxes'])
+
+        transform = PPYOLOERandomDistort()
+        results = transform(copy.deepcopy(results))
+        self.assertTrue(results['img'].shape[:2] == (224, 224))
+        self.assertTrue(results['gt_bboxes_labels'].shape[0] ==
+                        results['gt_bboxes'].shape[0])
+        self.assertTrue(results['gt_bboxes_labels'].dtype == np.int64)
+        self.assertTrue(results['gt_bboxes'].dtype == torch.float32)
+        self.assertTrue(results['gt_ignore_flags'].dtype == bool)
+
+
+class TestYOLOv5CopyPaste(unittest.TestCase):
+
+    def setUp(self):
+        """Set up the data info which are used in every test method.
+
+        TestCase calls functions in this order: setUp() -> testMethod() ->
+        tearDown() -> cleanUp()
+        """
+        self.data_info = dict(
+            img=np.random.random((300, 400, 3)),
+            gt_bboxes=np.array([[0, 0, 10, 10]], dtype=np.float32),
+            gt_masks=PolygonMasks(
+                [[np.array([0., 0., 0., 10., 10., 10., 10., 0.])]],
+                height=300,
+                width=400))
+
+    def test_transform(self):
+        # test transform
+        transform = YOLOv5CopyPaste(prob=1.0)
+        results = transform(copy.deepcopy(self.data_info))
+        self.assertTrue(len(results['gt_bboxes']) == 2)
+        self.assertTrue(len(results['gt_masks']) == 2)
+
+        rng = np.random.RandomState(0)
+        # test with bitmap
+        with self.assertRaises(AssertionError):
+            results = transform(
+                dict(
+                    img=np.random.random((300, 400, 3)),
+                    gt_bboxes=np.array([[0, 0, 10, 10]], dtype=np.float32),
+                    gt_masks=BitmapMasks(
+                        rng.rand(1, 300, 400), height=300, width=400)))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_utils.py

@@ -0,0 +1,138 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import unittest
+
+import numpy as np
+import torch
+from mmdet.structures import DetDataSample
+from mmdet.structures.bbox import HorizontalBoxes
+from mmengine.structures import InstanceData
+
+from mmyolo.datasets import BatchShapePolicy, yolov5_collate
+
+
+def _rand_bboxes(rng, num_boxes, w, h):
+    cx, cy, bw, bh = rng.rand(num_boxes, 4).T
+
+    tl_x = ((cx * w) - (w * bw / 2)).clip(0, w)
+    tl_y = ((cy * h) - (h * bh / 2)).clip(0, h)
+    br_x = ((cx * w) + (w * bw / 2)).clip(0, w)
+    br_y = ((cy * h) + (h * bh / 2)).clip(0, h)
+
+    bboxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
+    return bboxes
+
+
+class TestYOLOv5Collate(unittest.TestCase):
+
+    def test_yolov5_collate(self):
+        rng = np.random.RandomState(0)
+
+        inputs = torch.randn((3, 10, 10))
+        data_samples = DetDataSample()
+        gt_instances = InstanceData()
+        bboxes = _rand_bboxes(rng, 4, 6, 8)
+        gt_instances.bboxes = HorizontalBoxes(bboxes, dtype=torch.float32)
+        labels = rng.randint(1, 2, size=len(bboxes))
+        gt_instances.labels = torch.LongTensor(labels)
+        data_samples.gt_instances = gt_instances
+
+        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)])
+        self.assertIsInstance(out, dict)
+        self.assertTrue(out['inputs'].shape == (1, 3, 10, 10))
+        self.assertTrue(out['data_samples'], dict)
+        self.assertTrue(out['data_samples']['bboxes_labels'].shape == (4, 6))
+
+        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)] *
+                             2)
+        self.assertIsInstance(out, dict)
+        self.assertTrue(out['inputs'].shape == (2, 3, 10, 10))
+        self.assertTrue(out['data_samples'], dict)
+        self.assertTrue(out['data_samples']['bboxes_labels'].shape == (8, 6))
+
+    def test_yolov5_collate_with_multi_scale(self):
+        rng = np.random.RandomState(0)
+
+        inputs = torch.randn((3, 10, 10))
+        data_samples = DetDataSample()
+        gt_instances = InstanceData()
+        bboxes = _rand_bboxes(rng, 4, 6, 8)
+        gt_instances.bboxes = HorizontalBoxes(bboxes, dtype=torch.float32)
+        labels = rng.randint(1, 2, size=len(bboxes))
+        gt_instances.labels = torch.LongTensor(labels)
+        data_samples.gt_instances = gt_instances
+
+        out = yolov5_collate([dict(inputs=inputs, data_samples=data_samples)],
+                             use_ms_training=True)
+        self.assertIsInstance(out, dict)
+        self.assertTrue(out['inputs'][0].shape == (3, 10, 10))
+        self.assertTrue(out['data_samples'], dict)
+        self.assertTrue(out['data_samples']['bboxes_labels'].shape == (4, 6))
+        self.assertIsInstance(out['inputs'], list)
+        self.assertIsInstance(out['data_samples']['bboxes_labels'],
+                              torch.Tensor)
+
+        out = yolov5_collate(
+            [dict(inputs=inputs, data_samples=data_samples)] * 2,
+            use_ms_training=True)
+        self.assertIsInstance(out, dict)
+        self.assertTrue(out['inputs'][0].shape == (3, 10, 10))
+        self.assertTrue(out['data_samples'], dict)
+        self.assertTrue(out['data_samples']['bboxes_labels'].shape == (8, 6))
+        self.assertIsInstance(out['inputs'], list)
+        self.assertIsInstance(out['data_samples']['bboxes_labels'],
+                              torch.Tensor)
+
+
+class TestBatchShapePolicy(unittest.TestCase):
+
+    def test_batch_shape_policy(self):
+        src_data_infos = [{
+            'height': 20,
+            'width': 100,
+        }, {
+            'height': 11,
+            'width': 100,
+        }, {
+            'height': 21,
+            'width': 100,
+        }, {
+            'height': 30,
+            'width': 100,
+        }, {
+            'height': 10,
+            'width': 100,
+        }]
+
+        expected_data_infos = [{
+            'height': 10,
+            'width': 100,
+            'batch_shape': np.array([96, 672])
+        }, {
+            'height': 11,
+            'width': 100,
+            'batch_shape': np.array([96, 672])
+        }, {
+            'height': 20,
+            'width': 100,
+            'batch_shape': np.array([160, 672])
+        }, {
+            'height': 21,
+            'width': 100,
+            'batch_shape': np.array([160, 672])
+        }, {
+            'height': 30,
+            'width': 100,
+            'batch_shape': np.array([224, 672])
+        }]
+
+        batch_shapes_policy = BatchShapePolicy(batch_size=2)
+        out_data_infos = batch_shapes_policy(src_data_infos)
+
+        for i in range(5):
+            self.assertEqual(
+                (expected_data_infos[i]['height'],
+                 expected_data_infos[i]['width']),
+                (out_data_infos[i]['height'], out_data_infos[i]['width']))
+            self.assertTrue(
+                np.allclose(expected_data_infos[i]['batch_shape'],
+                            out_data_infos[i]['batch_shape']))

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_yolov5_coco.py

@@ -0,0 +1,71 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import unittest
+
+from mmyolo.datasets import YOLOv5CocoDataset
+
+
+class TestYOLOv5CocoDataset(unittest.TestCase):
+
+    def test_batch_shapes_cfg(self):
+        batch_shapes_cfg = dict(
+            type='BatchShapePolicy',
+            batch_size=2,
+            img_size=640,
+            size_divisor=32,
+            extra_pad_ratio=0.5)
+
+        # test serialize_data=True
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(img='imgs'),
+            ann_file='tests/data/coco_sample.json',
+            filter_cfg=dict(filter_empty_gt=False, min_size=0),
+            pipeline=[],
+            serialize_data=True,
+            batch_shapes_cfg=batch_shapes_cfg,
+        )
+
+        expected_img_ids = [3, 0, 2, 1]
+        expected_batch_shapes = [[512, 672], [512, 672], [672, 672],
+                                 [672, 672]]
+        for i, data in enumerate(dataset):
+            assert data['img_id'] == expected_img_ids[i]
+            assert data['batch_shape'].tolist() == expected_batch_shapes[i]
+
+        # test serialize_data=True
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(img='imgs'),
+            ann_file='tests/data/coco_sample.json',
+            filter_cfg=dict(filter_empty_gt=False, min_size=0),
+            pipeline=[],
+            serialize_data=False,
+            batch_shapes_cfg=batch_shapes_cfg,
+        )
+
+        expected_img_ids = [3, 0, 2, 1]
+        expected_batch_shapes = [[512, 672], [512, 672], [672, 672],
+                                 [672, 672]]
+        for i, data in enumerate(dataset):
+            assert data['img_id'] == expected_img_ids[i]
+            assert data['batch_shape'].tolist() == expected_batch_shapes[i]
+
+    def test_prepare_data(self):
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(img='imgs'),
+            ann_file='tests/data/coco_sample.json',
+            filter_cfg=dict(filter_empty_gt=False, min_size=0),
+            pipeline=[],
+            serialize_data=True,
+            batch_shapes_cfg=None,
+        )
+        for data in dataset:
+            assert 'dataset' in data
+
+        # test with test_mode = True
+        dataset = YOLOv5CocoDataset(
+            data_prefix=dict(img='imgs'),
+            ann_file='tests/data/coco_sample.json',
+            test_mode=True,
+            pipeline=[])
+
+        for data in dataset:
+            assert 'dataset' not in data

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_datasets/test_yolov5_voc.py

@@ -0,0 +1,86 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import unittest
+
+from mmengine.dataset import ConcatDataset
+
+from mmyolo.datasets import YOLOv5VOCDataset
+from mmyolo.utils import register_all_modules
+
+register_all_modules()
+
+
+class TestYOLOv5VocDataset(unittest.TestCase):
+
+    def test_batch_shapes_cfg(self):
+        batch_shapes_cfg = dict(
+            type='BatchShapePolicy',
+            batch_size=2,
+            img_size=640,
+            size_divisor=32,
+            extra_pad_ratio=0.5)
+
+        # test serialize_data=True
+        dataset = YOLOv5VOCDataset(
+            data_root='tests/data/VOCdevkit/',
+            ann_file='VOC2007/ImageSets/Main/trainval.txt',
+            data_prefix=dict(sub_data_root='VOC2007/'),
+            test_mode=True,
+            pipeline=[],
+            batch_shapes_cfg=batch_shapes_cfg,
+        )
+
+        expected_img_ids = ['000001']
+        expected_batch_shapes = [[672, 480]]
+        for i, data in enumerate(dataset):
+            assert data['img_id'] == expected_img_ids[i]
+            assert data['batch_shape'].tolist() == expected_batch_shapes[i]
+
+    def test_prepare_data(self):
+        dataset = YOLOv5VOCDataset(
+            data_root='tests/data/VOCdevkit/',
+            ann_file='VOC2007/ImageSets/Main/trainval.txt',
+            data_prefix=dict(sub_data_root='VOC2007/'),
+            filter_cfg=dict(filter_empty_gt=False, min_size=0),
+            pipeline=[],
+            serialize_data=True,
+            batch_shapes_cfg=None,
+        )
+        for data in dataset:
+            assert 'dataset' in data
+
+        # test with test_mode = True
+        dataset = YOLOv5VOCDataset(
+            data_root='tests/data/VOCdevkit/',
+            ann_file='VOC2007/ImageSets/Main/trainval.txt',
+            data_prefix=dict(sub_data_root='VOC2007/'),
+            filter_cfg=dict(
+                filter_empty_gt=True, min_size=32, bbox_min_size=None),
+            pipeline=[],
+            test_mode=True,
+            batch_shapes_cfg=None)
+
+        for data in dataset:
+            assert 'dataset' not in data
+
+    def test_concat_dataset(self):
+        dataset = ConcatDataset(
+            datasets=[
+                dict(
+                    type='YOLOv5VOCDataset',
+                    data_root='tests/data/VOCdevkit/',
+                    ann_file='VOC2007/ImageSets/Main/trainval.txt',
+                    data_prefix=dict(sub_data_root='VOC2007/'),
+                    filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                    pipeline=[]),
+                dict(
+                    type='YOLOv5VOCDataset',
+                    data_root='tests/data/VOCdevkit/',
+                    ann_file='VOC2012/ImageSets/Main/trainval.txt',
+                    data_prefix=dict(sub_data_root='VOC2012/'),
+                    filter_cfg=dict(filter_empty_gt=False, min_size=32),
+                    pipeline=[])
+            ],
+            ignore_keys='dataset_type')
+
+        dataset.full_init()
+        self.assertEqual(len(dataset), 2)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_deploy/conftest.py

@@ -0,0 +1,13 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def init_test():
+    # init default scope
+    from mmdet.utils import register_all_modules as register_det
+
+    from mmyolo.utils import register_all_modules as register_yolo
+
+    register_yolo(True)
+    register_det(False)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_deploy/test_mmyolo_models.py

@@ -0,0 +1,165 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import random
+
+import numpy as np
+import pytest
+import torch
+from mmengine import Config
+
+try:
+    import importlib
+    importlib.import_module('mmdeploy')
+except ImportError:
+    pytest.skip('mmdeploy is not installed.', allow_module_level=True)
+
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import Backend
+from mmdeploy.utils.config_utils import register_codebase
+from mmdeploy.utils.test import (WrapModel, check_backend, get_model_outputs,
+                                 get_rewrite_outputs)
+
+try:
+    codebase = register_codebase('mmyolo')
+    import_codebase(codebase, ['mmyolo.deploy'])
+except ImportError:
+    pytest.skip('mmyolo is not installed.', allow_module_level=True)
+
+
+def seed_everything(seed=1029):
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.enabled = False
+
+
+def get_yolov5_head_model():
+    """YOLOv5 Head Config."""
+    test_cfg = Config(
+        dict(
+            multi_label=True,
+            nms_pre=30000,
+            score_thr=0.001,
+            nms=dict(type='nms', iou_threshold=0.65),
+            max_per_img=300))
+
+    from mmyolo.models.dense_heads import YOLOv5Head
+    head_module = dict(
+        type='YOLOv5HeadModule',
+        num_classes=4,
+        in_channels=[2, 4, 8],
+        featmap_strides=[8, 16, 32],
+        num_base_priors=1)
+
+    model = YOLOv5Head(head_module, test_cfg=test_cfg)
+
+    model.requires_grad_(False)
+    return model
+
+
+@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
+def test_yolov5_head_predict_by_feat(backend_type: Backend):
+    """Test predict_by_feat rewrite of YOLOXHead."""
+    check_backend(backend_type)
+    yolov5_head = get_yolov5_head_model()
+    yolov5_head.cpu().eval()
+    s = 256
+    batch_img_metas = [{
+        'scale_factor': (1.0, 1.0),
+        'pad_shape': (s, s, 3),
+        'img_shape': (s, s, 3),
+        'ori_shape': (s, s, 3)
+    }]
+    output_names = ['dets', 'labels']
+    deploy_cfg = Config(
+        dict(
+            backend_config=dict(type=backend_type.value),
+            onnx_config=dict(output_names=output_names, input_shape=None),
+            codebase_config=dict(
+                type='mmyolo',
+                task='ObjectDetection',
+                post_processing=dict(
+                    score_threshold=0.05,
+                    iou_threshold=0.5,
+                    max_output_boxes_per_class=20,
+                    pre_top_k=-1,
+                    keep_top_k=10,
+                    background_label_id=-1,
+                ),
+                module=['mmyolo.deploy'])))
+    seed_everything(1234)
+    cls_scores = [
+        torch.rand(1, yolov5_head.num_classes * yolov5_head.num_base_priors,
+                   4 * pow(2, i), 4 * pow(2, i)) for i in range(3, 0, -1)
+    ]
+    seed_everything(5678)
+    bbox_preds = [
+        torch.rand(1, 4 * yolov5_head.num_base_priors, 4 * pow(2, i),
+                   4 * pow(2, i)) for i in range(3, 0, -1)
+    ]
+    seed_everything(9101)
+    objectnesses = [
+        torch.rand(1, 1 * yolov5_head.num_base_priors, 4 * pow(2, i),
+                   4 * pow(2, i)) for i in range(3, 0, -1)
+    ]
+
+    # to get outputs of pytorch model
+    model_inputs = {
+        'cls_scores': cls_scores,
+        'bbox_preds': bbox_preds,
+        'objectnesses': objectnesses,
+        'batch_img_metas': batch_img_metas,
+        'with_nms': True
+    }
+    model_outputs = get_model_outputs(yolov5_head, 'predict_by_feat',
+                                      model_inputs)
+
+    # to get outputs of onnx model after rewrite
+    wrapped_model = WrapModel(
+        yolov5_head,
+        'predict_by_feat',
+        batch_img_metas=batch_img_metas,
+        with_nms=True)
+    rewrite_inputs = {
+        'cls_scores': cls_scores,
+        'bbox_preds': bbox_preds,
+        'objectnesses': objectnesses,
+    }
+    rewrite_outputs, is_backend_output = get_rewrite_outputs(
+        wrapped_model=wrapped_model,
+        model_inputs=rewrite_inputs,
+        deploy_cfg=deploy_cfg)
+
+    if is_backend_output:
+        # hard code to make two tensors with the same shape
+        # rewrite and original codes applied different nms strategy
+        min_shape = min(model_outputs[0].bboxes.shape[0],
+                        rewrite_outputs[0].shape[1], 5)
+        for i in range(len(model_outputs)):
+            rewrite_outputs[0][i, :min_shape, 0::2] = \
+                rewrite_outputs[0][i, :min_shape, 0::2].clamp_(0, s)
+            rewrite_outputs[0][i, :min_shape, 1::2] = \
+                rewrite_outputs[0][i, :min_shape, 1::2].clamp_(0, s)
+            assert np.allclose(
+                model_outputs[i].bboxes[:min_shape],
+                rewrite_outputs[0][i, :min_shape, :4],
+                rtol=1e-03,
+                atol=1e-05)
+            assert np.allclose(
+                model_outputs[i].scores[:min_shape],
+                rewrite_outputs[0][i, :min_shape, 4],
+                rtol=1e-03,
+                atol=1e-05)
+            assert np.allclose(
+                model_outputs[i].labels[:min_shape],
+                rewrite_outputs[1][i, :min_shape],
+                rtol=1e-03,
+                atol=1e-05)
+    else:
+        assert rewrite_outputs is not None

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_deploy/test_object_detection.py

@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from tempfile import NamedTemporaryFile, TemporaryDirectory
+
+import numpy as np
+import pytest
+import torch
+from mmengine import Config
+
+try:
+    import importlib
+    importlib.import_module('mmdeploy')
+except ImportError:
+    pytest.skip('mmdeploy is not installed.', allow_module_level=True)
+
+import mmdeploy.backend.onnxruntime as ort_apis
+from mmdeploy.apis import build_task_processor
+from mmdeploy.codebase import import_codebase
+from mmdeploy.utils import load_config
+from mmdeploy.utils.config_utils import register_codebase
+from mmdeploy.utils.test import SwitchBackendWrapper
+
+try:
+    codebase = register_codebase('mmyolo')
+    import_codebase(codebase, ['mmyolo.deploy'])
+except ImportError:
+    pytest.skip('mmyolo is not installed.', allow_module_level=True)
+
+model_cfg_path = 'tests/test_deploy/data/model.py'
+model_cfg = load_config(model_cfg_path)[0]
+model_cfg.test_dataloader.dataset.data_root = \
+    'tests/data'
+model_cfg.test_dataloader.dataset.ann_file = 'coco_sample.json'
+model_cfg.test_evaluator.ann_file = \
+    'tests/coco_sample.json'
+deploy_cfg = Config(
+    dict(
+        backend_config=dict(type='onnxruntime'),
+        codebase_config=dict(
+            type='mmyolo',
+            task='ObjectDetection',
+            post_processing=dict(
+                score_threshold=0.05,
+                confidence_threshold=0.005,  # for YOLOv3
+                iou_threshold=0.5,
+                max_output_boxes_per_class=200,
+                pre_top_k=5000,
+                keep_top_k=100,
+                background_label_id=-1,
+            ),
+            module=['mmyolo.deploy']),
+        onnx_config=dict(
+            type='onnx',
+            export_params=True,
+            keep_initializers_as_inputs=False,
+            opset_version=11,
+            input_shape=None,
+            input_names=['input'],
+            output_names=['dets', 'labels'])))
+onnx_file = NamedTemporaryFile(suffix='.onnx').name
+task_processor = None
+img_shape = (32, 32)
+img = np.random.rand(*img_shape, 3)
+
+
+@pytest.fixture(autouse=True)
+def init_task_processor():
+    global task_processor
+    task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
+
+
+@pytest.fixture
+def backend_model():
+    from mmdeploy.backend.onnxruntime import ORTWrapper
+    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
+    wrapper = SwitchBackendWrapper(ORTWrapper)
+    wrapper.set(
+        outputs={
+            'dets': torch.rand(1, 10, 5).sort(2).values,
+            'labels': torch.randint(0, 10, (1, 10))
+        })
+
+    yield task_processor.build_backend_model([''])
+
+    wrapper.recover()
+
+
+def test_visualize(backend_model):
+    img_path = 'tests/data/color.jpg'
+    input_dict, _ = task_processor.create_input(
+        img_path, input_shape=img_shape)
+    results = backend_model.test_step(input_dict)[0]
+    with TemporaryDirectory() as dir:
+        filename = dir + 'tmp.jpg'
+        task_processor.visualize(img, results, filename, 'window')
+        assert os.path.exists(filename)

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/YOLO-World/mmyolo/tests/test_downstream/test_mmrazor.py

@@ -0,0 +1,21 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import pytest
+from mmcls.models.backbones.base_backbone import BaseBackbone
+
+from mmyolo.testing import get_detector_cfg
+
+
+@pytest.mark.parametrize('cfg_file', [
+    'razor/subnets/'
+    'yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py', 'razor/subnets/'
+    'rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco.py', 'razor/subnets/'
+    'yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco.py'
+])
+def test_razor_backbone_init(cfg_file):
+    model = get_detector_cfg(cfg_file)
+    model_cfg = copy.deepcopy(model.backbone)
+    from mmrazor.registry import MODELS
+    model = MODELS.build(model_cfg)
+    assert isinstance(model, BaseBackbone)