Fix seed and add statistics

test_tipc/configs/cd/_base_/airchange.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of AirChange dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: CDDataset

test_tipc/configs/cd/_base_/levircd.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of LEVIR-CD dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: CDDataset

test_tipc/configs/clas/_base_/ucmerced.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of UCMerced dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: ClasDataset

test_tipc/configs/clas/condensenetv2/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python
 gpu_list:0|0,1
 use_gpu:null|null
 --precision:null
---num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=10
+--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20
 --save_dir:adaptive
 --train_batch_size:lite_train_lite_infer=16|lite_train_whole_infer=16|whole_train_whole_infer=16
 --model_path:null

test_tipc/configs/det/_base_/rsod.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of RSOD dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: VOCDetDataset

test_tipc/configs/det/_base_/sarship.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of SARShip dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: VOCDetDataset

test_tipc/configs/res/_base_/rssr.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of RSSR dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: ResDataset

test_tipc/configs/seg/_base_/rsseg.yaml

@@ -1,5 +1,7 @@
 # Basic configurations of RSSeg dataset
 
+seed: 1024
+
 datasets:
     train: !Node
         type: SegDataset
@@ -32,8 +34,8 @@ transforms:
         - !Node
           type: Normalize
           args:
-            mean: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
-            std: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
         - !Node
           type: ArrangeSegmenter
           args: ['train']
@@ -47,8 +49,8 @@ transforms:
         - !Node
           type: Normalize
           args:
-            mean: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
-            std: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
         - !Node
           type: ArrangeSegmenter
           args: ['eval']

test_tipc/configs/seg/bisenetv2/bisenetv2_rsseg.yaml

@@ -5,7 +5,7 @@ _base_: ../_base_/rsseg.yaml
 save_dir: ./test_tipc/output/seg/bisenetv2/
 
 model: !Node
-    type: BiSeNet V2
+    type: BiSeNetV2
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/configs/seg/bisenetv2/train_infer_python.txt

@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/deeplabv3p/deeplabv3p_rsseg.yaml

@@ -7,5 +7,5 @@ save_dir: ./test_tipc/output/seg/deeplabv3p/
 model: !Node
     type: DeepLabV3P
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/configs/seg/deeplabv3p/train_infer_python.txt

@@ -4,7 +4,7 @@ python:python
 gpu_list:0|0,1
 use_gpu:null|null
 --precision:null
---num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=30
+--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20
 --save_dir:adaptive
 --train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4
 --model_path:null
@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/farseg/farseg_rsseg.yaml

@@ -7,5 +7,5 @@ save_dir: ./test_tipc/output/seg/farseg/
 model: !Node
     type: FarSeg
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/configs/seg/farseg/train_infer_python.txt

@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/fast_scnn/fast_scnn_rsseg.yaml

@@ -5,7 +5,7 @@ _base_: ../_base_/rsseg.yaml
 save_dir: ./test_tipc/output/seg/fast_scnn/
 
 model: !Node
-    type: Fast-SCNN
+    type: FastSCNN
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/configs/seg/fast_scnn/train_infer_python.txt

@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/hrnet/hrnet_rsseg.yaml

@@ -7,5 +7,5 @@ save_dir: ./test_tipc/output/seg/hrnet/
 model: !Node
     type: HRNet
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/configs/seg/hrnet/train_infer_python.txt

@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/unet/train_infer_python.txt

@@ -27,7 +27,7 @@ null:null
 ===========================export_params===========================
 --save_dir:adaptive
 --model_dir:adaptive
---fixed_input_shape:[-1,10,512,512]
+--fixed_input_shape:[-1,3,512,512]
 norm_export:deploy/export/export_model.py
 quant_export:null
 fpgm_export:null

test_tipc/configs/seg/unet/unet_rsseg.yaml

@@ -7,5 +7,5 @@ save_dir: ./test_tipc/output/seg/unet/
 model: !Node
     type: UNet
     args:
-        in_channels: 10
+        in_channels: 3
         num_classes: 5

test_tipc/docs/test_train_inference_python.md

@@ -19,7 +19,7 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho
 |  变化检测  | FC-Siam-conc | 正常训练 | 正常训练 | IoU=65.79% |
 |  变化检测  | FC-Siam-diff | 正常训练 | 正常训练 | IoU=61.23% |
 |  变化检测  | FCCDN | 正常训练 | 正常训练 | IoU=24.42% |
-|  场景分类  | CondenseNet V2 | 正常训练 | 正常训练 | Acc(top1)= |
+|  场景分类  | CondenseNet V2 | 正常训练 | 正常训练 | Acc(top1)=60.42% |
 |  场景分类  | HRNet | 正常训练 | 正常训练 | Acc(top1)=99.37% |
 |  场景分类  | MobileNetV3 | 正常训练 | 正常训练 | Acc(top1)=99.58% |
 |  场景分类  | ResNet50-vd | 正常训练 | 正常训练 | Acc(top1)=99.26% |
@@ -31,12 +31,12 @@ Linux GPU/CPU 基础训练推理测试的主程序为`test_train_inference_pytho
 |  目标检测  | PP-YOLO Tiny | 正常训练 | 正常训练 | mAP=44.27% |
 |  目标检测  | PP-YOLOv2 | 正常训练 | 正常训练 | mAP=59.37% |
 |  目标检测  | YOLOv3 | 正常训练 | 正常训练 | mAP=47.33% |
-|  图像分割  | BiSeNet V2 | 正常训练 | 正常训练 | mIoU= |
-|  图像分割  | DeepLab V3+ | 正常训练 | 正常训练 | mIoU=56.05% |
-|  图像分割  | FarSeg | 正常训练 | 正常训练 | mIoU=49.58% |
-|  图像分割  | Fast-SCNN | 正常训练 | 正常训练 | mIoU= |
-|  图像分割  | HRNet | 正常训练 | 正常训练 | mIoU= |
-|  图像分割  | UNet | 正常训练 | 正常训练 | mIoU=55.50% |
+|  图像分割  | BiSeNet V2 | 正常训练 | 正常训练 | mIoU=70.20 |
+|  图像分割  | DeepLab V3+ | 正常训练 | 正常训练 | mIoU=64.59% |
+|  图像分割  | FarSeg | 正常训练 | 正常训练 | mIoU=50.45% |
+|  图像分割  | Fast-SCNN | 正常训练 | 正常训练 | mIoU=48.97% |
+|  图像分割  | HRNet | 正常训练 | 正常训练 | mIoU=33.49% |
+|  图像分割  | UNet | 正常训练 | 正常训练 | mIoU=72.64% |
 
 *注：参考预测精度为whole_train_whole_infer模式下单卡训练汇报的精度数据。*