[Enchance] Optimize and accelerate YOLOX with RTMDet hyps (#542)

* enchance yolox * update * update * fix * fix * fix lint
2023-02-17 11:27:03 +08:00 · 2023-02-17 11:27:03 +08:00 · 6400fba1af
parent 8cdc741fd3
commit 6400fba1af
9 changed files with 309 additions and 20 deletions
--- a/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py
+++ b/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py
@ -26,7 +26,7 @@ anchors = [
    [(142, 110), (192, 243), (459, 401)]  # P5/32
 ]
 # -----train val related-----
-# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
+# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs
 base_lr = 0.01
 max_epochs = 300  # Maximum training epochs

--- a/configs/yolox/README.md
+++ b/configs/yolox/README.md
@ -17,12 +17,28 @@ In this report, we present some experienced improvements to YOLO series, forming
 YOLOX-l model structure
 </div>

-## Results and Models
+## 🥳 🚀 Results and Models

-|  Backbone  | size | Mem (GB) | box AP |                                                  Config                                                  |                                                                                                                                    Download                                                                                                                                    |
-| :--------: | :--: | :------: | :----: | :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| YOLOX-tiny | 416  |   2.8    |  32.7  | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json) |
-|  YOLOX-s   | 640  |   5.6    |  40.8  |  [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolox/yolox_s_fast_8xb8-300e_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738.log.json)       |
+|  Backbone  | Size | Batch Size | AMP | RTMDet-Hyp | Mem (GB) |   Box AP    |                                                       Config                                                        |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :--------: | :--: | :--------: | :-: | :--------: | :------: | :---------: | :-----------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOX-tiny | 416  |    8xb8    | No  |     No     |   2.8    |    32.7     |       [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py)       |                                   [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json)                                   |
+| YOLOX-tiny | 416  |   8xb32    | Yes |    Yes     |   4.9    | 34.3 (+1.6) | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637.log.json) |
+|  YOLOX-s   | 640  |    8xb8    | Yes |     No     |   2.9    |    40.7     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600.log.json)                               |
+|  YOLOX-s   | 640  |   8xb32    | Yes |    Yes     |   9.8    | 41.9 (+1.2) |  [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645.log.json)       |
+|  YOLOX-m   | 640  |    8xb8    | Yes |     No     |   4.9    |    46.9     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218.log.json)                               |
+|  YOLOX-m   | 640  |   8xb32    | Yes |    Yes     |   17.6   | 47.5 (+0.6) |  [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py)   |       [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328.log.json)       |
+|  YOLOX-l   | 640  |    8xb8    | Yes |     No     |   8.0    |    50.1     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_l_fast_8xb8-300e_coco.py)         |                              [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast__8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715.log.json)                               |
+|  YOLOX-x   | 640  |    8xb8    | Yes |     No     |   9.8    |    51.4     |        [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_x_fast_8xb8-300e_coco.py)         |                               [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950.log.json)                               |
+
+YOLOX uses a default training configuration of `8xbs8` which results in a long training time, we expect it to use `8xbs32` to speed up the training and not cause a decrease in mAP. We modified `train_batch_size_per_gpu` from 8 to 32, `batch_augments_interval` from 10 to 1 and `base_lr` from 0.01 to 0.04 under YOLOX-s default configuration based on the linear scaling rule, which resulted in mAP degradation. Finally, I found that using RTMDet's training hyperparameter can improve performance in YOLOX Tiny/S/M, which also validates the superiority of RTMDet's training hyperparameter.
+
+The modified training parameters are as follows：
+
+1. train_batch_size_per_gpu: 8 -> 32
+2. batch_augments_interval: 10 -> 1
+3. num_last_epochs: 15 -> 20
+4. optim cfg: SGD -> AdamW, base_lr 0.01 -> 0.004, weight_decay 0.0005 -> 0.05
+5. ema momentum: 0.0001 -> 0.0002

 **Note**:

--- a/configs/yolox/metafile.yml
+++ b/configs/yolox/metafile.yml
@ -36,11 +36,83 @@ Models:
    In Collection: YOLOX
    Config: configs/yolox/yolox_s_fast_8xb8-300e_coco.py
    Metadata:
-      Training Memory (GB): 5.6
+      Training Memory (GB): 2.9
      Epochs: 300
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
-          box AP: 40.8
-    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth
+  - Name: yolox_m_fast_8xb8-300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_m_fast_8xb8-300e_coco.py
+    Metadata:
+      Training Memory (GB): 4.9
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.9
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth
+  - Name: yolox_l_fast_8xb8-300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_l_fast_8xb8-300e_coco.py
+    Metadata:
+      Training Memory (GB): 8.0
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.1
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth
+  - Name: yolox_x_fast_8xb8-300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_x_fast_8xb8-300e_coco.py
+    Metadata:
+      Training Memory (GB): 9.8
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 51.4
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth
+  - Name: yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 4.9
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 34.3
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth
+  - Name: yolox_s_fast_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 9.8
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth
+  - Name: yolox_m_fast_8xb32-300e-rtmdet-hyp_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py
+    Metadata:
+      Training Memory (GB): 17.6
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+    Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth
--- a/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py
+++ b/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py
@ -0,0 +1,12 @@
+_base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_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),
+    neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
+    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
--- a/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py
+++ b/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py
@ -0,0 +1,21 @@
+_base_ = './yolox_tiny_fast_8xb32-300e-rtmdet-hyp_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(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        use_depthwise=use_depthwise),
+    neck=dict(
+        deepen_factor=deepen_factor,
+        widen_factor=widen_factor,
+        use_depthwise=use_depthwise),
+    bbox_head=dict(
+        head_module=dict(
+            widen_factor=widen_factor, use_depthwise=use_depthwise)))
--- a/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py
+++ b/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py
@ -0,0 +1,87 @@
+_base_ = './yolox_s_fast_8xb8-300e_coco.py'
+
+# ========================modified parameters======================
+# Batch size of a single GPU during training
+# 8 -> 32
+train_batch_size_per_gpu = 32
+
+# Multi-scale training intervals
+# 10 -> 1
+batch_augments_interval = 1
+
+# Last epoch number to switch training pipeline
+# 15 -> 20
+num_last_epochs = 20
+
+# Base learning rate for optim_wrapper. Corresponding to 8xb32=256 bs
+base_lr = 0.004
+
+# SGD -> AdamW
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# 0.0001 -> 0.0002
+ema_momentum = 0.0002
+
+# ============================== Unmodified in most cases ===================
+model = dict(
+    data_preprocessor=dict(batch_augments=[
+        dict(
+            type='YOLOXBatchSyncRandomResize',
+            random_size_range=(480, 800),
+            size_divisor=32,
+            interval=batch_augments_interval)
+    ]))
+
+param_scheduler = [
+    dict(
+        # use quadratic formula to warm up 5 epochs
+        # and lr is updated by iteration
+        # TODO: fix default scope in get function
+        type='mmdet.QuadraticWarmupLR',
+        by_epoch=True,
+        begin=0,
+        end=5,
+        convert_to_iter_based=True),
+    dict(
+        # use cosine lr from 5 to 285 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=5,
+        T_max=_base_.max_epochs - num_last_epochs,
+        end=_base_.max_epochs - num_last_epochs,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        # use fixed lr during last num_last_epochs epochs
+        type='ConstantLR',
+        by_epoch=True,
+        factor=1,
+        begin=_base_.max_epochs - num_last_epochs,
+        end=_base_.max_epochs,
+    )
+]
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        new_train_pipeline=_base_.train_pipeline_stage2,
+        priority=48),
+    dict(type='mmdet.SyncNormHook', priority=48),
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=ema_momentum,
+        update_buffers=True,
+        strict_load=False,
+        priority=49)
+]
+
+train_dataloader = dict(batch_size=train_batch_size_per_gpu)
+train_cfg = dict(dynamic_intervals=[(_base_.max_epochs - num_last_epochs, 1)])
+auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu)
--- a/configs/yolox/yolox_s_fast_8xb8-300e_coco.py
+++ b/configs/yolox/yolox_s_fast_8xb8-300e_coco.py
@ -47,9 +47,16 @@ 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)
+# generate new random resize shape interval
+batch_augments_interval = 10

 # -----train val related-----
 weight_decay = 0.0005
+loss_cls_weight = 1.0
+loss_bbox_weight = 5.0
+loss_obj_weight = 1.0
+loss_bbox_aux_weight = 1.0
+center_radius = 2.5  # SimOTAAssigner
 num_last_epochs = 15
 random_affine_scaling_ratio_range = (0.1, 2)
 mixup_ratio_range = (0.8, 1.6)
@ -58,6 +65,8 @@ save_epoch_intervals = 10
 # The maximum checkpoints to keep.
 max_keep_ckpts = 3

+ema_momentum = 0.0001
+
 # ===============================Unmodified in most cases====================
 # model settings
 model = dict(
@ -79,7 +88,7 @@ model = dict(
                type='YOLOXBatchSyncRandomResize',
                random_size_range=(480, 800),
                size_divisor=32,
-                interval=10)
+                interval=batch_augments_interval)
        ]),
    backbone=dict(
        type='YOLOXCSPDarknet',
@ -116,24 +125,26 @@ model = dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
-            loss_weight=1.0),
+            loss_weight=loss_cls_weight),
        loss_bbox=dict(
            type='mmdet.IoULoss',
            mode='square',
            eps=1e-16,
            reduction='sum',
-            loss_weight=5.0),
+            loss_weight=loss_bbox_weight),
        loss_obj=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
-            loss_weight=1.0),
+            loss_weight=loss_obj_weight),
        loss_bbox_aux=dict(
-            type='mmdet.L1Loss', reduction='sum', loss_weight=1.0)),
+            type='mmdet.L1Loss',
+            reduction='sum',
+            loss_weight=loss_bbox_aux_weight)),
    train_cfg=dict(
        assigner=dict(
            type='mmdet.SimOTAAssigner',
-            center_radius=2.5,
+            center_radius=center_radius,
            iou_calculator=dict(type='mmdet.BboxOverlaps2D'))),
    test_cfg=model_test_cfg)

@ -303,7 +314,7 @@ custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
-        momentum=0.0001,
+        momentum=ema_momentum,
        update_buffers=True,
        strict_load=False,
        priority=49)
@ -315,6 +326,6 @@ train_cfg = dict(
    val_interval=save_epoch_intervals,
    dynamic_intervals=[(max_epochs - num_last_epochs, 1)])

-auto_scale_lr = dict(base_batch_size=64)
+auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu)
 val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
--- a/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py
+++ b/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py
@ -0,0 +1,70 @@
+_base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py'
+
+# ========================modified parameters======================
+deepen_factor = 0.33
+widen_factor = 0.375
+
+# Multi-scale training intervals
+# 10 -> 1
+batch_augments_interval = 1
+
+scaling_ratio_range = (0.5, 1.5)
+
+# =======================Unmodified in most cases==================
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+
+# model settings
+model = dict(
+    data_preprocessor=dict(batch_augments=[
+        dict(
+            type='YOLOXBatchSyncRandomResize',
+            random_size_range=(320, 640),
+            size_divisor=32,
+            interval=batch_augments_interval)
+    ]),
+    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_stage1 = [
+    *pre_transform,
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        pre_transform=pre_transform),
+    dict(
+        type='mmdet.RandomAffine',
+        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'),
+    dict(type='mmdet.RandomFlip', prob=0.5),
+    dict(
+        type='mmdet.FilterAnnotations',
+        min_gt_bbox_wh=(1, 1),
+        keep_empty=False),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
+                   'flip_direction'))
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
+    dict(type='mmdet.Resize', scale=(416, 416), keep_ratio=True),  # note
+    dict(
+        type='mmdet.Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline_stage1))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = val_dataloader
--- a/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py
+++ b/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py
@ -3,12 +3,12 @@ _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==================
+img_scale = _base_.img_scale
+pre_transform = _base_.pre_transform
+
 # model settings
 model = dict(
    data_preprocessor=dict(batch_augments=[