configs: Create a new rtmdet folder, starting with nano size

Author: baorepo

…figs/rtmdet_nano_8xb32_300e_coco_relu.py …mdet/rtmdet_nano_8xb32_300e_coco_relu.pyconfigs/rtmdet_nano_8xb32_300e_coco_relu.py renamed to configs/rtmdet/rtmdet_nano_8xb32_300e_coco_relu.py configs/rtmdet_nano_8xb32_300e_coco_relu.py renamed to configs/rtmdet/rtmdet_nano_8xb32_300e_coco_relu.py

@@ -1,9 +1,9 @@
 from mmengine.config import read_base
 
 with read_base():
-    from ._base_.default_runtime import *
-    from ._base_.schedules.schedule_1x import *
-    from .datasets.coco_detection import *
+    from .._base_.default_runtime import *
+    from .._base_.schedules.schedule_1x import *
+    from ..datasets.coco_detection import *
 
 from torchvision.ops import nms
 from torch.nn import ReLU, BatchNorm2d
@@ -45,7 +45,7 @@
 from sscma.quantizer import RtmdetQuantModel
 
 default_hooks.visualization = dict(
-    type=DetVisualizationHook, draw=True, test_out_dir="works"
+    type=DetVisualizationHook, draw=False, test_out_dir="works"
 )
 
 visualizer = dict(type=DetLocalVisualizer, vis_backends=vis_backends, name="visualizer")
@@ -73,8 +73,8 @@
     type=RTMDet,
     data_preprocessor=dict(
         type=DetDataPreprocessor,
-        mean=[0, 0, 0],
-        std=[255, 255, 255],
+        mean=[103.53, 116.28, 123.675],
+        std=[57.375, 57.12, 58.395],
         bgr_to_rgb=False,
         batch_augments=None,
     ),
@@ -160,7 +160,7 @@
     bbox_head=model["bbox_head"],
     data_preprocessor=model["data_preprocessor"],  # data_preprocessor,
 )
-imdecode_backend = "torch"
+imdecode_backend = "pillow"
 train_pipeline = [
     dict(
         type=LoadImageFromFile,
@@ -219,7 +219,7 @@
 ]
 
 test_pipeline = [
-    dict(type=LoadImageFromFile,imdecode_backend=imdecode_backend, backend_args=backend_args),
+    dict(type=LoadImageFromFile,imdecode_backend=imdecode_backend, backend_args=None),
     dict(type=LoadAnnotations,imdecode_backend=imdecode_backend, with_bbox=True),
     dict(type=Resize, scale=imgsz, keep_ratio=True),
     dict(type=Pad, size=imgsz, pad_val=dict(img=(114, 114, 114))),
@@ -254,7 +254,7 @@
 
 
 val_dataloader = dict(
-    batch_size=16,
+    batch_size=32,
     num_workers=8,
     persistent_workers=True,
     pin_memory=True,