[Feature] New config type

mmyolo/configs/_base_/default_runtime.py

@@ -0,0 +1,39 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.engine.hooks import DetVisualizationHook
+from mmdet.visualization import DetLocalVisualizer
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.runner import LogProcessor
+from mmengine.visualization import LocalVisBackend
+
+default_scope = None
+default_hooks = dict(
+    timer=dict(type=IterTimerHook),
+    logger=dict(type=LoggerHook, interval=50),
+    param_scheduler=dict(type=ParamSchedulerHook),
+    checkpoint=dict(type=CheckpointHook, interval=1),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=DetVisualizationHook))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+vis_backends = [dict(type=LocalVisBackend)]
+visualizer = dict(
+    type=DetLocalVisualizer, vis_backends=vis_backends, name='visualizer')
+log_processor = dict(type=LogProcessor, window_size=50, by_epoch=True)
+
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# file_client_args = dict(
+#         backend='petrel',
+#         path_mapping=dict({
+#             './data/': 's3://openmmlab/datasets/detection/',
+#             'data/': 's3://openmmlab/datasets/detection/'
+#         }))
+file_client_args = dict(backend='disk')

mmyolo/configs/_base_/det_p5_tta.py

@@ -0,0 +1,65 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.ops import nms
+from mmcv.transforms import Compose, LoadImageFromFile, TestTimeAug
+from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
+                                       RandomFlip)
+from mmdet.models import DetTTAModel
+
+from mmyolo.datasets.transforms import LetterResize, YOLOv5KeepRatioResize
+
+# TODO: Need to solve the problem of multiple file_client_args parameters
+# _file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+_file_client_args = dict(backend='disk')
+
+tta_model = dict(
+    type=DetTTAModel,
+    tta_cfg=dict(nms=dict(type=nms, iou_threshold=0.65), max_per_img=300))
+
+img_scales = [(640, 640), (320, 320), (960, 960)]
+
+#                                LoadImageFromFile
+#                     /                 |                     \
+# (RatioResize,LetterResize) (RatioResize,LetterResize) (RatioResize,LetterResize) # noqa
+#        /      \                    /      \                    /        \
+#  RandomFlip RandomFlip      RandomFlip RandomFlip        RandomFlip RandomFlip # noqa
+#      |          |                |         |                  |         |
+#  LoadAnn    LoadAnn           LoadAnn    LoadAnn           LoadAnn    LoadAnn
+#      |          |                |         |                  |         |
+#  PackDetIn  PackDetIn         PackDetIn  PackDetIn        PackDetIn  PackDetIn # noqa
+
+_multiscale_resize_transforms = [
+    dict(
+        type=Compose,
+        transforms=[
+            dict(type=YOLOv5KeepRatioResize, scale=s),
+            dict(
+                type=LetterResize,
+                scale=s,
+                allow_scale_up=False,
+                pad_val=dict(img=114))
+        ]) for s in img_scales
+]
+
+tta_pipeline = [
+    dict(type=LoadImageFromFile, file_client_args=_file_client_args),
+    dict(
+        type=TestTimeAug,
+        transforms=[
+            _multiscale_resize_transforms,
+            [dict(type=RandomFlip, prob=1.),
+             dict(type=RandomFlip, prob=0.)],
+            [dict(type=LoadAnnotations, with_bbox=True)],
+            [
+                dict(
+                    type=PackDetInputs,
+                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                               'scale_factor', 'pad_param', 'flip',
+                               'flip_direction'))
+            ]
+        ])
+]

mmyolo/configs/rtmdet/rtmdet_l_syncbn_fast_8xb32_300e_coco.py

@@ -0,0 +1,328 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+if '_base_':
+    from .._base_.default_runtime import *
+    from .._base_.det_p5_tta import *
+
+from mmcv.transforms import RandomResize
+from mmdet.datasets.transforms import (PackDetInputs, Pad, RandomCrop,
+                                       RandomFlip, Resize, YOLOXHSVRandomAug)
+from mmdet.engine.hooks import PipelineSwitchHook
+from mmdet.evaluation import CocoMetric
+from mmdet.models import GIoULoss, QualityFocalLoss
+from mmdet.models.task_modules import BboxOverlaps2D, MlvlPointGenerator
+from mmengine.dataset import DefaultSampler
+from mmengine.hooks import EMAHook
+from mmengine.optim import CosineAnnealingLR, LinearLR, OptimWrapper
+from mmengine.runner import EpochBasedTrainLoop, TestLoop, ValLoop
+from torch.nn import BatchNorm2d, SiLU
+from torch.optim import AdamW
+
+from mmyolo.datasets import (BatchShapePolicy, Mosaic, YOLOv5CocoDataset,
+                             yolov5_collate)
+from mmyolo.datasets.transforms import LoadAnnotations, YOLOv5MixUp
+from mmyolo.models import (CSPNeXt, CSPNeXtPAFPN, ExpMomentumEMA, RTMDetHead,
+                           RTMDetSepBNHeadModule, YOLODetector,
+                           YOLOv5DetDataPreprocessor)
+from mmyolo.models.task_modules.assigners import BatchDynamicSoftLabelAssigner
+from mmyolo.models.task_modules.coders import DistancePointBBoxCoder
+
+# -----data related-----
+data_root = 'data/coco/'
+# Path of train annotation file
+train_ann_file = 'annotations/instances_train2017.json'
+train_data_prefix = 'train2017/'  # Prefix of train image path
+# Path of val annotation file
+val_ann_file = 'annotations/instances_val2017.json'
+val_data_prefix = 'val2017/'  # Prefix of val image path
+
+num_classes = 80  # Number of classes for classification
+# Batch size of a single GPU during training
+train_batch_size_per_gpu = 32
+# Worker to pre-fetch data for each single GPU during training
+train_num_workers = 10
+# persistent_workers must be False if num_workers is 0.
+persistent_workers = True
+
+# -----train val related-----
+# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
+base_lr = 0.004
+max_epochs = 300  # Maximum training epochs
+# Change train_pipeline for final 20 epochs (stage 2)
+num_epochs_stage2 = 20
+model_test_cfg = dict(
+    # The config of multi-label for multi-class prediction.
+    multi_label=True,
+    # The number of boxes before NMS
+    nms_pre=30000,
+    score_thr=0.001,  # Threshold to filter out boxes.
+    nms=dict(type=nms, iou_threshold=0.65),  # NMS type and threshold
+    max_per_img=300)  # Max number of detections of each image
+
+# ========================Possible modified parameters========================
+# -----data related-----
+img_scale = (640, 640)  # width, height
+# ratio range for random resize
+random_resize_ratio_range = (0.1, 2.0)
+# Cached images number in mosaic
+mosaic_max_cached_images = 40
+# Number of cached images in mixup
+mixup_max_cached_images = 20
+# Dataset type, this will be used to define the dataset
+dataset_type = YOLOv5CocoDataset
+# Batch size of a single GPU during validation
+val_batch_size_per_gpu = 32
+# Worker to pre-fetch data for each single GPU during validation
+val_num_workers = 10
+
+# Config of batch shapes. Only on val.
+batch_shapes_cfg = dict(
+    type=BatchShapePolicy,
+    batch_size=val_batch_size_per_gpu,
+    img_size=img_scale[0],
+    size_divisor=32,
+    extra_pad_ratio=0.5)
+
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 1.0
+# The scaling factor that controls the width of the network structure
+widen_factor = 1.0
+# Strides of multi-scale prior box
+strides = [8, 16, 32]
+
+norm_cfg = dict(type=BatchNorm2d)  # Normalization config
+
+# -----train val related-----
+lr_start_factor = 1.0e-5
+dsl_topk = 13  # Number of bbox selected in each level
+loss_cls_weight = 1.0
+loss_bbox_weight = 2.0
+qfl_beta = 2.0  # beta of QualityFocalLoss
+weight_decay = 0.05
+
+# Save model checkpoint and validation intervals
+save_checkpoint_intervals = 10
+# validation intervals in stage 2
+val_interval_stage2 = 1
+# The maximum checkpoints to keep.
+max_keep_ckpts = 3
+# single-scale training is recommended to
+# be turned on, which can speed up training.
+env_cfg = dict(cudnn_benchmark=True)
+
+# ===============================Unmodified in most cases====================
+model = dict(
+    type=YOLODetector,
+    data_preprocessor=dict(
+        type=YOLOv5DetDataPreprocessor,
+        mean=[103.53, 116.28, 123.675],
+        std=[57.375, 57.12, 58.395],
+        bgr_to_rgb=False),
+    backbone=dict(
+        type=CSPNeXt,
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        channel_attention=True,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type=SiLU, inplace=True)),
+    neck=dict(
+        type=CSPNeXtPAFPN,
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        in_channels=[256, 512, 1024],
+        out_channels=256,
+        num_csp_blocks=3,
+        expand_ratio=0.5,
+        norm_cfg=norm_cfg,
+        act_cfg=dict(type=SiLU, inplace=True)),
+    bbox_head=dict(
+        type=RTMDetHead,
+        head_module=dict(
+            type=RTMDetSepBNHeadModule,
+            num_classes=num_classes,
+            in_channels=256,
+            stacked_convs=2,
+            feat_channels=256,
+            norm_cfg=norm_cfg,
+            act_cfg=dict(type=SiLU, inplace=True),
+            share_conv=True,
+            pred_kernel_size=1,
+            featmap_strides=strides),
+        prior_generator=dict(
+            type=MlvlPointGenerator, offset=0, strides=strides),
+        bbox_coder=dict(type=DistancePointBBoxCoder),
+        loss_cls=dict(
+            type=QualityFocalLoss,
+            use_sigmoid=True,
+            beta=qfl_beta,
+            loss_weight=loss_cls_weight),
+        loss_bbox=dict(type=GIoULoss, loss_weight=loss_bbox_weight)),
+    train_cfg=dict(
+        assigner=dict(
+            type=BatchDynamicSoftLabelAssigner,
+            num_classes=num_classes,
+            topk=dsl_topk,
+            iou_calculator=dict(type=BboxOverlaps2D)),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=model_test_cfg,
+)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile, file_client_args=file_client_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=RandomResize,
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomCrop, crop_size=img_scale),
+    dict(type=YOLOXHSVRandomAug),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=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=PackDetInputs)
+]
+
+train_pipeline_stage2 = [
+    dict(type=LoadImageFromFile, file_client_args=file_client_args),
+    dict(type=LoadAnnotations, with_bbox=True),
+    dict(
+        type=RandomResize,
+        scale=img_scale,
+        ratio_range=random_resize_ratio_range,
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomCrop, crop_size=img_scale),
+    dict(type=YOLOXHSVRandomAug),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=Pad, size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(type=PackDetInputs)
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile, file_client_args=file_client_args),
+    dict(type=YOLOv5KeepRatioResize, scale=img_scale),
+    dict(
+        type=LetterResize,
+        scale=img_scale,
+        allow_scale_up=False,
+        pad_val=dict(img=114)),
+    dict(type=LoadAnnotations, with_bbox=True, _scope_='mmdet'),
+    dict(
+        type=PackDetInputs,
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor', 'pad_param'))
+]
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    persistent_workers=persistent_workers,
+    pin_memory=True,
+    collate_fn=dict(type=yolov5_collate),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline))
+
+val_dataloader = dict(
+    batch_size=val_batch_size_per_gpu,
+    num_workers=val_num_workers,
+    persistent_workers=persistent_workers,
+    pin_memory=True,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=val_ann_file,
+        data_prefix=dict(img=val_data_prefix),
+        test_mode=True,
+        batch_shapes_cfg=batch_shapes_cfg,
+        pipeline=test_pipeline))
+
+test_dataloader = val_dataloader
+
+# Reduce evaluation time
+val_evaluator = dict(
+    type=CocoMetric,
+    proposal_nums=(100, 1, 10),
+    ann_file=data_root + val_ann_file,
+    metric='bbox')
+test_evaluator = val_evaluator
+
+# optimizer
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(type=AdamW, lr=base_lr, weight_decay=weight_decay),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type=LinearLR,
+        start_factor=lr_start_factor,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type=CosineAnnealingLR,
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        type=CheckpointHook,
+        interval=save_checkpoint_intervals,
+        max_keep_ckpts=max_keep_ckpts  # only keep latest 3 checkpoints
+    ))
+
+custom_hooks = [
+    dict(
+        type=EMAHook,
+        ema_type=ExpMomentumEMA,
+        momentum=0.0002,
+        update_buffers=True,
+        strict_load=False,
+        priority=49),
+    dict(
+        type=PipelineSwitchHook,
+        switch_epoch=max_epochs - num_epochs_stage2,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+train_cfg = dict(
+    type=EpochBasedTrainLoop,
+    max_epochs=max_epochs,
+    val_interval=save_checkpoint_intervals,
+    dynamic_intervals=[(max_epochs - num_epochs_stage2, val_interval_stage2)])
+
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)

mmyolo/configs/rtmdet/rtmdet_s_syncbn_fast_8xb32_300e_coco.py

@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+if '_base_':
+    from .rtmdet_l_syncbn_fast_8xb32_300e_coco import *
+
+from mmengine.model import PretrainedInit
+
+checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth'  # noqa
+
+# ========================modified parameters======================
+deepen_factor = 0.33
+widen_factor = 0.5
+
+# 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.update(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        # Since the checkpoint includes CUDA:0 data,
+        # it must be forced to set map_location.
+        # Once checkpoint is fixed, it can be removed.
+        init_cfg=dict(
+            type=PretrainedInit,
+            prefix='backbone.',
+            checkpoint=checkpoint,
+            map_location='cpu')),
+    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, file_client_args=file_client_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=RandomResize,
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomCrop, crop_size=img_scale),
+    dict(type=YOLOXHSVRandomAug),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=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=PackDetInputs)
+]
+
+train_pipeline_stage2 = [
+    dict(type=LoadImageFromFile, file_client_args=file_client_args),
+    dict(type=LoadAnnotations, with_bbox=True),
+    dict(
+        type=RandomResize,
+        scale=img_scale,
+        ratio_range=random_resize_ratio_range,  # note
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomCrop, crop_size=img_scale),
+    dict(type=YOLOXHSVRandomAug),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=Pad, size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(type=PackDetInputs)
+]
+
+train_dataloader.update(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=PipelineSwitchHook,
+        switch_epoch=max_epochs - num_epochs_stage2,
+        switch_pipeline=train_pipeline_stage2)
+]

mmyolo/configs/rtmdet/rtmdet_tiny_syncbn_fast_8xb32_300e_coco.py

@@ -0,0 +1,60 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+if '_base_':
+    from .rtmdet_s_syncbn_fast_8xb32_300e_coco import *
+
+checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'  # noqa
+
+# ========================modified parameters======================
+deepen_factor = 0.167
+widen_factor = 0.375
+
+# ratio range for random resize
+random_resize_ratio_range = (0.5, 2.0)
+# Number of cached images in mosaic
+mosaic_max_cached_images = 20
+# Number of cached images in mixup
+mixup_max_cached_images = 10
+
+# =======================Unmodified in most cases==================
+model.update(
+    backbone=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        init_cfg=dict(checkpoint=checkpoint)),
+    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, file_client_args=file_client_args),
+    dict(type=LoadAnnotations, with_bbox=True),
+    dict(
+        type=Mosaic,
+        img_scale=img_scale,
+        use_cached=True,
+        max_cached_images=mosaic_max_cached_images,  # note
+        random_pop=False,  # note
+        pad_val=114.0),
+    dict(
+        type=RandomResize,
+        # img_scale is (width, height)
+        scale=(img_scale[0] * 2, img_scale[1] * 2),
+        ratio_range=random_resize_ratio_range,
+        resize_type=Resize,
+        keep_ratio=True),
+    dict(type=RandomCrop, crop_size=img_scale),
+    dict(type=YOLOXHSVRandomAug),
+    dict(type=RandomFlip, prob=0.5),
+    dict(type=Pad, size=img_scale, pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type=YOLOv5MixUp,
+        use_cached=True,
+        random_pop=False,
+        max_cached_images=mixup_max_cached_images,
+        prob=0.5),
+    dict(type=PackDetInputs)
+]
+
+train_dataloader.update(dataset=dict(pipeline=train_pipeline))

setup.cfg

@@ -19,3 +19,6 @@ SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
 skip = *.ipynb
 quiet-level = 3
 ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,tood,ba,warmup,elease,dota
+
+[flake8]
+per-file-ignores = mmyolo/configs/*: F401,F403,F405