[Improve] Beautify the YOLOX configuration (#529)

* Beautify the YOLOX configuration

* fix checks

* Update configs/yolox/yolox_s_fast_8xb8-300e_coco.py

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>

* fix letter case problem

* beauty yolox configs except yolox_s's config

* fix lint

* Update configs/yolox/yolox_s_fast_8xb8-300e_coco.py

Co-authored-by: Haian Huang(深度眸) <1286304229@qq.com>

* fix yolox_s yolox_tiny

* fix tiny

* fix tiny

* simple tiny

---------

Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>
Co-authored-by: Haian Huang(深度眸) <1286304229@qq.com>

configs/yolox/yolox_l_fast_8xb8-300e_coco.py

@@ -1,8 +1,10 @@
 _base_ = './yolox_s_fast_8xb8-300e_coco.py'
 
+# ========================modified parameters======================
 deepen_factor = 1.0
 widen_factor = 1.0
 
+# =======================Unmodified in most cases==================
 # model settings
 model = dict(
     backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),

configs/yolox/yolox_m_fast_8xb8-300e_coco.py

@@ -1,8 +1,10 @@
 _base_ = './yolox_s_fast_8xb8-300e_coco.py'
 
+# ========================modified parameters======================
 deepen_factor = 0.67
 widen_factor = 0.75
 
+# =======================Unmodified in most cases==================
 # model settings
 model = dict(
     backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),

configs/yolox/yolox_nano_fast_8xb8-300e_coco.py

@@ -1,9 +1,11 @@
 _base_ = './yolox_tiny_fast_8xb8-300e_coco.py'
 
+# ========================modified parameters======================
 deepen_factor = 0.33
 widen_factor = 0.25
 use_depthwise = True
 
+# =======================Unmodified in most cases==================
 # model settings
 model = dict(
     backbone=dict(

configs/yolox/yolox_s_fast_8xb8-300e_coco.py

@@ -1,21 +1,64 @@
 _base_ = '../_base_/default_runtime.py'
 
-data_root = 'data/coco/'
-dataset_type = 'YOLOv5CocoDataset'
+# ========================Frequently modified parameters======================
+# -----data related-----
+data_root = 'data/coco/'  # Root path of data
+# 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 train image path
 
-img_scale = (640, 640)  # width, height
-deepen_factor = 0.33
-widen_factor = 0.5
-
-save_epoch_intervals = 10
+num_classes = 80  # Number of classes for classification
+# Batch size of a single GPU during training
 train_batch_size_per_gpu = 8
+# Worker to pre-fetch data for each single GPU during tarining
 train_num_workers = 8
+# Presistent_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 = 0.01
+max_epochs = 300  # Maximum training epochs
+
+model_test_cfg = dict(
+    yolox_style=True,  # better
+    # The config of multi-label for multi-class prediction
+    multi_label=True,  # 40.5 -> 40.7
+    score_thr=0.001,  # Threshold to filter out boxes
+    max_per_img=300,  # Max number of detections of each image
+    nms=dict(type='nms', iou_threshold=0.65))  # NMS type and threshold
+
+# ========================Possible modified parameters========================
+# -----data related-----
+img_scale = (640, 640)  # width, height
+# 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 = 1
+# Worker to pre-fetch data for each single GPU during validation
 val_num_workers = 2
 
-max_epochs = 300
-num_last_epochs = 15
+# -----model related-----
+# The scaling factor that controls the depth of the network structure
+deepen_factor = 0.33
+# The scaling factor that controls the width of the network structure
+widen_factor = 0.5
+norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
 
+# -----train val related-----
+weight_decay = 0.0005
+num_last_epochs = 15
+random_affine_scaling_ratio_range = (0.1, 2)
+mixup_ratio_range = (0.8, 1.6)
+# Save model checkpoint and validation intervals
+save_epoch_intervals = 10
+# The maximum checkpoints to keep.
+max_keep_ckpts = 3
+
+# ===============================Unmodified in most cases====================
 # model settings
 model = dict(
     type='YOLODetector',
@@ -44,7 +87,7 @@ model = dict(
         widen_factor=widen_factor,
         out_indices=(2, 3, 4),
         spp_kernal_sizes=(5, 9, 13),
-        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        norm_cfg=norm_cfg,
         act_cfg=dict(type='SiLU', inplace=True),
     ),
     neck=dict(
@@ -53,20 +96,20 @@ model = dict(
         widen_factor=widen_factor,
         in_channels=[256, 512, 1024],
         out_channels=256,
-        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        norm_cfg=norm_cfg,
         act_cfg=dict(type='SiLU', inplace=True)),
     bbox_head=dict(
         type='YOLOXHead',
         head_module=dict(
             type='YOLOXHeadModule',
-            num_classes=80,
+            num_classes=num_classes,
             in_channels=256,
             feat_channels=256,
             widen_factor=widen_factor,
             stacked_convs=2,
             featmap_strides=(8, 16, 32),
             use_depthwise=False,
-            norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+            norm_cfg=norm_cfg,
             act_cfg=dict(type='SiLU', inplace=True),
         ),
         loss_cls=dict(
@@ -92,12 +135,7 @@ model = dict(
             type='mmdet.SimOTAAssigner',
             center_radius=2.5,
             iou_calculator=dict(type='mmdet.BboxOverlaps2D'))),
-    test_cfg=dict(
-        yolox_style=True,  # better
-        multi_label=True,  # 40.5 -> 40.7
-        score_thr=0.001,
-        max_per_img=300,
-        nms=dict(type='nms', iou_threshold=0.65)))
+    test_cfg=model_test_cfg)
 
 pre_transform = [
     dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
@@ -113,13 +151,13 @@ train_pipeline_stage1 = [
         pre_transform=pre_transform),
     dict(
         type='mmdet.RandomAffine',
-        scaling_ratio_range=(0.1, 2),
+        scaling_ratio_range=random_affine_scaling_ratio_range,
         # img_scale is (width, height)
         border=(-img_scale[0] // 2, -img_scale[1] // 2)),
     dict(
         type='YOLOXMixUp',
         img_scale=img_scale,
-        ratio_range=(0.8, 1.6),
+        ratio_range=mixup_ratio_range,
         pad_val=114.0,
         pre_transform=pre_transform),
     dict(type='mmdet.YOLOXHSVRandomAug'),
@@ -155,15 +193,15 @@ train_pipeline_stage2 = [
 train_dataloader = dict(
     batch_size=train_batch_size_per_gpu,
     num_workers=train_num_workers,
-    persistent_workers=True,
+    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='annotations/instances_train2017.json',
-        data_prefix=dict(img='train2017/'),
+        ann_file=train_ann_file,
+        data_prefix=dict(img=train_data_prefix),
         filter_cfg=dict(filter_empty_gt=False, min_size=32),
         pipeline=train_pipeline_stage1))
 
@@ -184,15 +222,15 @@ test_pipeline = [
 val_dataloader = dict(
     batch_size=val_batch_size_per_gpu,
     num_workers=val_num_workers,
-    persistent_workers=True,
+    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='annotations/instances_val2017.json',
-        data_prefix=dict(img='val2017/'),
+        ann_file=val_ann_file,
+        data_prefix=dict(img=val_data_prefix),
         test_mode=True,
         pipeline=test_pipeline))
 test_dataloader = val_dataloader
@@ -201,18 +239,20 @@ test_dataloader = val_dataloader
 val_evaluator = dict(
     type='mmdet.CocoMetric',
     proposal_nums=(100, 1, 10),
-    ann_file=data_root + 'annotations/instances_val2017.json',
+    ann_file=data_root + val_ann_file,
     metric='bbox')
 
 test_evaluator = val_evaluator
 
 # optimizer
 # default 8 gpu
-base_lr = 0.01
 optim_wrapper = dict(
     type='OptimWrapper',
     optimizer=dict(
-        type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4,
+        type='SGD',
+        lr=base_lr,
+        momentum=0.9,
+        weight_decay=weight_decay,
         nesterov=True),
     paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
 
@@ -248,7 +288,10 @@ param_scheduler = [
 
 default_hooks = dict(
     checkpoint=dict(
-        type='CheckpointHook', interval=1, max_keep_ckpts=3, save_best='auto'))
+        type='CheckpointHook',
+        interval=save_epoch_intervals,
+        max_keep_ckpts=max_keep_ckpts,
+        save_best='auto'))
 
 custom_hooks = [
     dict(

configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py

@@ -1,14 +1,20 @@
 _base_ = './yolox_s_fast_8xb8-300e_coco.py'
 
+# ========================modified parameters======================
 deepen_factor = 0.33
 widen_factor = 0.375
 
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+scaling_ratio_range = (0.5, 1.5)
+
+# =======================Unmodified in most cases==================
 # model settings
 model = dict(
     data_preprocessor=dict(batch_augments=[
         dict(
-            type='mmdet.BatchSyncRandomResize',
-            random_size_range=(320, 640),  # note
+            type='YOLOXBatchSyncRandomResize',
+            random_size_range=(320, 640),
             size_divisor=32,
             interval=10)
     ]),
@@ -16,9 +22,6 @@ model = dict(
     neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
     bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
 
-img_scale = _base_.img_scale
-pre_transform = _base_.pre_transform
-
 train_pipeline_stage1 = [
     *pre_transform,
     dict(
@@ -28,7 +31,7 @@ train_pipeline_stage1 = [
         pre_transform=pre_transform),
     dict(
         type='mmdet.RandomAffine',
-        scaling_ratio_range=(0.5, 1.5),  # note
+        scaling_ratio_range=scaling_ratio_range,  # note
         # img_scale is (width, height)
         border=(-img_scale[0] // 2, -img_scale[1] // 2)),
     dict(type='mmdet.YOLOXHSVRandomAug'),

configs/yolox/yolox_x_fast_8xb8-300e_coco.py

@@ -1,8 +1,10 @@
 _base_ = './yolox_s_fast_8xb8-300e_coco.py'
 
+# ========================modified parameters======================
 deepen_factor = 1.33
 widen_factor = 1.25
 
+# =======================Unmodified in most cases==================
 # model settings
 model = dict(
     backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),