[Improve] Beautify the YOLOv7 configuration

configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py

@@ -1,42 +1,103 @@
 _base_ = '../_base_/default_runtime.py'
 
-# dataset settings
-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 val image path
 
-# parameters that often need to be modified
-img_scale = (640, 640)  # width, height
-max_epochs = 300
-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 = 16
+# Worker to pre-fetch data for each single GPU during training
 train_num_workers = 8
-# persistent_workers must be False if num_workers is 0.
+# persistent_workers must be False if num_workers is 0
 persistent_workers = True
+
+# -----model related-----
+# Basic size of multi-scale prior box
+anchors = [
+    [(12, 16), (19, 36), (40, 28)],  # P3/8
+    [(36, 75), (76, 55), (72, 146)],  # P4/16
+    [(142, 110), (192, 243), (459, 401)]  # P5/32
+]
+# -----train val related-----
+# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
+base_lr = 0.01
+max_epochs = 300  # Maximum training epochs
+
+num_epoch_stage2 = 30  # The last 30 epochs switch evaluation interval
+val_interval_stage2 = 1  # Evaluation interval
+
+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
+# 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
 
-# only on Val
+# Config of batch shapes. Only on val.
+# It means not used if batch_shapes_cfg is None.
 batch_shapes_cfg = dict(
     type='BatchShapePolicy',
     batch_size=val_batch_size_per_gpu,
     img_size=img_scale[0],
+    # The image scale of padding should be divided by pad_size_divisor
     size_divisor=32,
+    # Additional paddings for pixel scale
     extra_pad_ratio=0.5)
 
-# different from yolov5
-anchors = [
-    [(12, 16), (19, 36), (40, 28)],  # P3/8
-    [(36, 75), (76, 55), (72, 146)],  # P4/16
-    [(142, 110), (192, 243), (459, 401)]  # P5/32
-]
-strides = [8, 16, 32]
-num_det_layers = 3
-num_classes = 80
+# -----model related-----
+strides = [8, 16, 32]  # Strides of multi-scale prior box
+num_det_layers = 3  # The number of model output scales
+norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
+
+# Data augmentation
+max_translate_ratio = 0.2  # YOLOv5RandomAffine
+scaling_ratio_range = (0.1, 2.0)  # YOLOv5RandomAffine
+mixup_prob = 0.15  # YOLOv5MixUp
+randchoice_mosaic_prob = [0.8, 0.2]
+mixup_alpha = 8.0  # YOLOv5MixUp
+mixup_beta = 8.0  # YOLOv5MixUp
+
+# -----train val related-----
+loss_cls_weight = 0.3
+loss_bbox_weight = 0.05
+loss_obj_weight = 0.7
+# BatchYOLOv7Assigner params
+simota_candidate_topk = 10
+simota_iou_weight = 3.0
+simota_cls_weight = 1.0
+prior_match_thr = 4.  # Priori box matching threshold
+obj_level_weights = [4., 1.,
+                     0.4]  # The obj loss weights of the three output layers
 
-# single-scale training is recommended to
+lr_factor = 0.1  # Learning rate scaling factor
+weight_decay = 0.0005
+save_epoch_intervals = 1  # Save model checkpoint and validation intervals
+max_keep_ckpts = 3  # The maximum checkpoints to keep.
+
+# 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(
@@ -47,7 +108,7 @@
     backbone=dict(
         type='YOLOv7Backbone',
         arch='L',
-        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        norm_cfg=norm_cfg,
         act_cfg=dict(type='SiLU', inplace=True)),
     neck=dict(
         type='YOLOv7PAFPN',
@@ -61,7 +122,7 @@
         in_channels=[512, 1024, 1024],
         # The real output channel will be multiplied by 2
         out_channels=[128, 256, 512],
-        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='YOLOv7Head',
@@ -80,31 +141,28 @@
             type='mmdet.CrossEntropyLoss',
             use_sigmoid=True,
             reduction='mean',
-            loss_weight=0.3 * (num_classes / 80 * 3 / num_det_layers)),
+            loss_weight=loss_cls_weight *
+            (num_classes / 80 * 3 / num_det_layers)),
         loss_bbox=dict(
             type='IoULoss',
             iou_mode='ciou',
             bbox_format='xywh',
             reduction='mean',
-            loss_weight=0.05 * (3 / num_det_layers),
+            loss_weight=loss_bbox_weight * (3 / num_det_layers),
             return_iou=True),
         loss_obj=dict(
             type='mmdet.CrossEntropyLoss',
             use_sigmoid=True,
             reduction='mean',
-            loss_weight=0.7 * ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
-        obj_level_weights=[4., 1., 0.4],
+            loss_weight=loss_obj_weight *
+            ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
+        prior_match_thr=prior_match_thr,
+        obj_level_weights=obj_level_weights,
         # BatchYOLOv7Assigner params
-        prior_match_thr=4.,
-        simota_candidate_topk=10,
-        simota_iou_weight=3.0,
-        simota_cls_weight=1.0),
-    test_cfg=dict(
-        multi_label=True,
-        nms_pre=30000,
-        score_thr=0.001,
-        nms=dict(type='nms', iou_threshold=0.65),
-        max_per_img=300))
+        simota_candidate_topk=simota_candidate_topk,
+        simota_iou_weight=simota_iou_weight,
+        simota_cls_weight=simota_cls_weight),
+    test_cfg=model_test_cfg)
 
 pre_transform = [
     dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
@@ -121,8 +179,8 @@
         type='YOLOv5RandomAffine',
         max_rotate_degree=0.0,
         max_shear_degree=0.0,
-        max_translate_ratio=0.2,  # note
-        scaling_ratio_range=(0.1, 2.0),  # note
+        max_translate_ratio=max_translate_ratio,  # note
+        scaling_ratio_range=scaling_ratio_range,  # note
         # img_scale is (width, height)
         border=(-img_scale[0] // 2, -img_scale[1] // 2),
         border_val=(114, 114, 114)),
@@ -138,8 +196,8 @@
         type='YOLOv5RandomAffine',
         max_rotate_degree=0.0,
         max_shear_degree=0.0,
-        max_translate_ratio=0.2,  # note
-        scaling_ratio_range=(0.1, 2.0),  # note
+        max_translate_ratio=max_translate_ratio,  # note
+        scaling_ratio_range=scaling_ratio_range,  # note
         # img_scale is (width, height)
         border=(-img_scale[0] // 2, -img_scale[1] // 2),
         border_val=(114, 114, 114)),
@@ -148,16 +206,16 @@
 randchoice_mosaic_pipeline = dict(
     type='RandomChoice',
     transforms=[mosiac4_pipeline, mosiac9_pipeline],
-    prob=[0.8, 0.2])
+    prob=randchoice_mosaic_prob)
 
 train_pipeline = [
     *pre_transform,
     randchoice_mosaic_pipeline,
     dict(
         type='YOLOv5MixUp',
-        alpha=8.0,  # note
-        beta=8.0,  # note
-        prob=0.15,
+        alpha=mixup_alpha,  # note
+        beta=mixup_beta,  # note
+        prob=mixup_prob,
         pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
     dict(type='YOLOv5HSVRandomAug'),
     dict(type='mmdet.RandomFlip', prob=0.5),
@@ -177,8 +235,8 @@
     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))
 
@@ -208,8 +266,8 @@
         type=dataset_type,
         data_root=data_root,
         test_mode=True,
-        data_prefix=dict(img='val2017/'),
-        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img=val_data_prefix),
+        ann_file=val_ann_file,
         pipeline=test_pipeline,
         batch_shapes_cfg=batch_shapes_cfg))
 
@@ -220,9 +278,9 @@
     type='OptimWrapper',
     optimizer=dict(
         type='SGD',
-        lr=0.01,
+        lr=base_lr,
         momentum=0.937,
-        weight_decay=0.0005,
+        weight_decay=weight_decay,
         nesterov=True,
         batch_size_per_gpu=train_batch_size_per_gpu),
     constructor='YOLOv7OptimWrapperConstructor')
@@ -231,27 +289,14 @@
     param_scheduler=dict(
         type='YOLOv5ParamSchedulerHook',
         scheduler_type='cosine',
-        lr_factor=0.1,  # note
+        lr_factor=lr_factor,  # note
         max_epochs=max_epochs),
     checkpoint=dict(
         type='CheckpointHook',
         save_param_scheduler=False,
-        interval=1,
+        interval=save_epoch_intervals,
         save_best='auto',
-        max_keep_ckpts=3))
-
-val_evaluator = dict(
-    type='mmdet.CocoMetric',
-    proposal_nums=(100, 1, 10),  # Can be accelerated
-    ann_file=data_root + 'annotations/instances_val2017.json',
-    metric='bbox')
-test_evaluator = val_evaluator
-
-train_cfg = dict(
-    type='EpochBasedTrainLoop',
-    max_epochs=max_epochs,
-    val_interval=save_epoch_intervals,
-    dynamic_intervals=[(270, 1)])
+        max_keep_ckpts=max_keep_ckpts))
 
 custom_hooks = [
     dict(
@@ -263,7 +308,17 @@
         priority=49)
 ]
 
+val_evaluator = dict(
+    type='mmdet.CocoMetric',
+    proposal_nums=(100, 1, 10),  # Can be accelerated
+    ann_file=data_root + val_ann_file,
+    metric='bbox')
+test_evaluator = val_evaluator
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop',
+    max_epochs=max_epochs,
+    val_interval=save_epoch_intervals,
+    dynamic_intervals=[(max_epochs - num_epoch_stage2, val_interval_stage2)])
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
-
-# randomness = dict(seed=1, deterministic=True)