add cinn converge case

framework/e2e/cinn_converge/AutoEncoder_ad_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/AutoEncoder_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=128  -o Train.learning_rate=0.001 -o Train.epochs_iters=1 -o Global.output='./output/ts_anomaly_detection/AutoEncoder_ad_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_anomaly_detection/AutoEncoder_ad.yaml -o Global.mode=evaluate -o Global.dataset_dir="../PSM" -o Evaluate.weight_path='./output/ts_anomaly_detection/AutoEncoder_ad_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/AutoEncoder_ad_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/AutoEncoder_ad_dy.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/AutoEncoder_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=128  -o Train.learning_rate=0.001 -o Train.epochs_iters=1 -o Global.output='./output/ts_anomaly_detection/AutoEncoder_ad_dy'
+
+python main.py -c paddlex/configs/modules/ts_anomaly_detection/AutoEncoder_ad.yaml -o Global.mode=evaluate -o Global.dataset_dir="../PSM" -o Evaluate.weight_path='./output/ts_anomaly_detection/AutoEncoder_ad_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/AutoEncoder_ad_dy_eval'

framework/e2e/cinn_converge/ConvNeXt_base_224_CINN.sh

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+python main.py -c paddlex/configs/modules/image_classification/ConvNeXt_base_224.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=300 -o Train.batch_size=64 -o Train.learning_rate=0.004 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/ConvNeXt_base_224_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/ConvNeXt_base_224_dy.sh

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+python main.py -c paddlex/configs/modules/image_classification/ConvNeXt_base_224.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=300 -o Train.batch_size=64 -o Train.learning_rate=0.004 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/ConvNeXt_base_224_dy'

framework/e2e/cinn_converge/DLinear_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_forecast/DLinear.yaml -o Global.mode=train -o Global.dataset_dir=../Etth1/Etth1_train -o Train.target_cols="HUFL,HULL,MUFL,MULL,LUFL,LULL,OT" -o Train.batch_size=32 -o Train.learning_rate=0.005 -o Train.patience=3 -o Train.epochs_iters=10 -o Global.output='./output/ts_forecast/DLinear_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_forecast/DLinear.yaml -o Global.mode=evalute -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_forecast/DLinear_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_forecast/DLinear_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/DLinear_ad_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/DLinear_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=128  -o Train.learning_rate=0.001 -o Train.epochs_iters=1 -o Global.output='./output/ts_anomaly_detection/DLinear_ad_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_anomaly_detection/DLinear_ad.yaml -o Global.mode=evaluate -o Global.dataset_dir="../PSM" -o Evaluate.weight_path='./output/ts_anomaly_detection/DLinear_ad_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/DLinear_ad_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/DLinear_ad_dy.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/DLinear_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=128  -o Train.learning_rate=0.001 -o Train.epochs_iters=1 -o Global.output='./output/ts_anomaly_detection/DLinear_ad_dy'
+
+python main.py -c paddlex/configs/modules/ts_anomaly_detection/DLinear_ad.yaml -o Global.mode=evaluate -o Global.dataset_dir="../PSM" -o Evaluate.weight_path='./output/ts_anomaly_detection/DLinear_ad_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/DLinear_ad_dy_eval'

framework/e2e/cinn_converge/DLinear_dy.sh

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+python main.py -c paddlex/configs/modules/ts_forecast/DLinear.yaml -o Global.mode=train -o Global.dataset_dir=../Etth1/Etth1_train -o Train.target_cols="HUFL,HULL,MUFL,MULL,LUFL,LULL,OT" -o Train.batch_size=32 -o Train.learning_rate=0.005 -o Train.patience=3 -o Train.epochs_iters=10 -o Global.output='./output/ts_forecast/DLinear_dy'
+
+python main.py -c paddlex/configs/modules/ts_forecast/DLinear.yaml -o Global.mode=evalute -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_forecast/DLinear_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_forecast/DLinear_dy_eval'

framework/e2e/cinn_converge/Deeplabv3-R50_CINN.sh

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+python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3-R50.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=0 -o Train.learning_rate=0.005 \ 
+    -o Global.device=gpu:0,1,2,3,4,5,6,7
+    -o Global.output='./output/semantic_segmentation/Deeplabv3-R50_CINN'
+    -o Train.dy2st=True

framework/e2e/cinn_converge/Deeplabv3-R50_dy.sh

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+python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3-R50.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=0 -o Train.learning_rate=0.005 \ 
+    -o Global.device=gpu:0,1,2,3,4,5,6,7
+    -o Global.output='./output/semantic_segmentation/Deeplabv3-R50_dy'

framework/e2e/cinn_converge/Deeplabv3_Plus-R50_CINN.sh

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+python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3_Plus-R50.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=0 -o Train.learning_rate=0.005 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.output='./output/semantic_segmentation/Deeplabv3_Plus-R50_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/Deeplabv3_Plus-R50_dy.sh

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+python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3_Plus-R50.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=0 -o Train.learning_rate=0.005 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.output='./output/semantic_segmentation/Deeplabv3_Plus-R50_dy'

framework/e2e/cinn_converge/FasterNet-S_CINN.sh

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+python main.py -c paddlex/configs/modules/image_classification/FasterNet-S.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=300 -o Train.batch_size=256 -o Train.learning_rate=0.004 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/FasterNet-S_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/FasterNet-S_dy.sh

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+python main.py -c paddlex/configs/modules/image_classification/FasterNet-S.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=300 -o Train.batch_size=256 -o Train.learning_rate=0.004 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/FasterNet-S_dy'

framework/e2e/cinn_converge/Mask-RT-DETR-L_CINN.sh

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+python main.py -c paddlex/configs/modules/instance_segmentation/Mask-RT-DETR-L.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../coco \
+    -o Train.num_classes=80 -o Train.epochs_iters=72 -o Train.batch_size=2 \
+    -o Train.warmup_steps=2000 -o Train.learning_rate=0.0001 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 \
+    -o Global.output='./output/instance_segmentation/Mask-RT-DETR-L_CINN' \
+    -o Train.dy2st=True

framework/e2e/cinn_converge/Mask-RT-DETR-L_dy.sh

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+python main.py -c paddlex/configs/modules/instance_segmentation/Mask-RT-DETR-L.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../coco \
+    -o Train.num_classes=80 -o Train.epochs_iters=72 -o Train.batch_size=2 \
+    -o Train.warmup_steps=2000 -o Train.learning_rate=0.0001 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 \
+    -o Global.output='./output/instance_segmentation/Mask-RT-DETR-L_dy'

framework/e2e/cinn_converge/MaskRCNN-ResNet50-vd-FPN_CINN.sh

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+python main.py -c paddlex/configs/modules/instance_segmentation/MaskRCNN-ResNet50-vd-FPN.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../coco \
+    -o Train.num_classes=80 -o Train.epochs_iters=12 -o Train.batch_size=1 \
+    -o Train.warmup_steps=1000 -o Train.learning_rate=0.01 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 \
+    -o Global.output='./output/instance_segmentation/MaskRCNN-ResNet50-vd-FPN_CINN' \
+    -o Train.dy2st=True

framework/e2e/cinn_converge/MaskRCNN-ResNet50-vd-FPN_dy.sh

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+python main.py -c paddlex/configs/modules/instance_segmentation/MaskRCNN-ResNet50-vd-FPN.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../coco \
+    -o Train.num_classes=80 -o Train.epochs_iters=12 -o Train.batch_size=1 \
+    -o Train.warmup_steps=1000 -o Train.learning_rate=0.01 \
+    -o Global.device=gpu:0,1,2,3,4,5,6,7 \
+    -o Global.output='./output/instance_segmentation/MaskRCNN-ResNet50-vd-FPN_dy'

framework/e2e/cinn_converge/MobileNetV1_x1_0_CINN.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV1_x1_0.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=120 -o Train.batch_size=32 -o Train.learning_rate=0.1 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV1_x1_0_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/MobileNetV1_x1_0_dy.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV1_x1_0.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=120 -o Train.batch_size=32 -o Train.learning_rate=0.1 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV1_x1_0_dy'

framework/e2e/cinn_converge/MobileNetV3_large_x1_0_CINN.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV3_large_x1_0.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=360 -o Train.batch_size=128 -o Train.learning_rate=0.65 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV3_large_x1_0_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/MobileNetV3_large_x1_0_dy.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV3_large_x1_0.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=360 -o Train.batch_size=128 -o Train.learning_rate=0.65 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV3_large_x1_0_dy'

framework/e2e/cinn_converge/MobileNetV4_conv_small_CINN.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV4_conv_small.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=500 -o Train.batch_size=64 -o Train.learning_rate=0.001 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV4_conv_small_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/MobileNetV4_conv_small_dy.sh

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+python main.py -c paddlex/configs/modules/image_classification/MobileNetV4_conv_small.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=500 -o Train.batch_size=64 -o Train.learning_rate=0.001 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/MobileNetV4_conv_small_dy'

framework/e2e/cinn_converge/NLinear_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_forecast/NLinear.yaml -o Global.mode=train -o Global.dataset_dir=../Etth1/Etth1_train -o Train.target_cols="HUFL,HULL,MUFL,MULL,LUFL,LULL,OT" -o Train.batch_size=32 -o Train.learning_rate=0.005 -o Train.patience=3 -o Train.epochs_iters=10 -o Global.output='./output/ts_forecast/NLinear_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_forecast/NLinear.yaml -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_forecast/NLinear_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_forecast/NLinear_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/NLinear_dy.sh

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+python main.py -c paddlex/configs/modules/ts_forecast/NLinear.yaml -o Global.mode=train -o Global.dataset_dir=../Etth1/Etth1_train -o Train.target_cols="HUFL,HULL,MUFL,MULL,LUFL,LULL,OT" -o Train.batch_size=32 -o Train.learning_rate=0.005 -o Train.patience=3 -o Train.epochs_iters=10 -o Global.output='./output/ts_forecast/NLinear_dy'
+
+python main.py -c paddlex/configs/modules/ts_forecast/NLinear.yaml -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_forecast/NLinear_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_forecast/NLinear_dy_eval'

framework/e2e/cinn_converge/Nonstationary_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/Nonstationary.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=32  -o Train.learning_rate=0.001 -o Train.epochs_iters=3 -o Global.output='./output/ts_anomaly_detection/Nonstationary_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_forecast/Nonstationary.yaml  -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_anomaly_detection/Nonstationary_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/Nonstationary_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/Nonstationary_ad_CINN.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/Nonstationary_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=32  -o Train.learning_rate=0.001 -o Train.epochs_iters=3 -o Global.output='./output/ts_anomaly_detection/Nonstationary_ad_CINN' -o Train.dy2st=True
+
+python main.py -c paddlex/configs/modules/ts_forecast/Nonstationary_ad.yaml  -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_anomaly_detection/Nonstationary_ad_CINN/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/Nonstationary_ad_CINN_eval' -o Train.dy2st=True

framework/e2e/cinn_converge/Nonstationary_ad_dy.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/Nonstationary_ad.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=32  -o Train.learning_rate=0.001 -o Train.epochs_iters=3 -o Global.output='./output/ts_anomaly_detection/Nonstationary_ad_dy'
+
+python main.py -c paddlex/configs/modules/ts_forecast/Nonstationary_ad.yaml  -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_anomaly_detection/Nonstationary_ad_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/Nonstationary_ad_dy_eval'

framework/e2e/cinn_converge/Nonstationary_dy.sh

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+python main.py -c paddlex/configs/modules/ts_anomaly_detection/Nonstationary.yaml -o Global.mode=train -o Train.feature_cols="feature_0,feature_1,feature_2,feature_3,feature_4,feature_5,feature_6,feature_7,feature_8,feature_9,feature_10,feature_11,feature_12,feature_13,feature_14,feature_15,feature_16,feature_17,feature_18,feature_19,feature_20,feature_21,feature_22,feature_23,feature_24" -o Global.dataset_dir="../PSM" -o Train.input_len=100 -o Train.batch_size=32  -o Train.learning_rate=0.001 -o Train.epochs_iters=3 -o Global.output='./output/ts_anomaly_detection/Nonstationary_dy'
+
+python main.py -c paddlex/configs/modules/ts_forecast/Nonstationary.yaml  -o Global.mode=evaluate -o Global.dataset_dir=../Etth1/Etth1_val -o Evaluate.weight_path='./output/ts_anomaly_detection/Nonstationary_dy/best_accuracy.pdparams.tar' -o Global.output='./output/ts_anomaly_detection/Nonstationary_dy_eval'

framework/e2e/cinn_converge/OCRNet_HRNet-W48_CINN.sh

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+python main.py -c paddlex/configs/modules/semantic_segmentation/OCRNet_HRNet-W48.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=1000 -o Train.learning_rate=0.01 \ 
+    -o Global.device=gpu:0,1,2,3,4,5,6,7
+    -o Global.output='./output/semantic_segmentation/OCRNet_HRNet-W48_CINN'
+    -o Train.dy2st=True

framework/e2e/cinn_converge/OCRNet_HRNet-W48_dy.sh

@@ -0,0 +1,6 @@
+python main.py -c paddlex/configs/modules/semantic_segmentation/OCRNet_HRNet-W48.yaml \
+    -o Global.mode=train -o Global.dataset_dir=../cityscapes \
+    -o Train.num_classes=19 -o Train.epochs_iters=160000 -o Train.batch_size=1 \
+    -o Train.warmup_steps=1000 -o Train.learning_rate=0.01 \ 
+    -o Global.device=gpu:0,1,2,3,4,5,6,7
+    -o Global.output='./output/semantic_segmentation/OCRNet_HRNet-W48_dy'

framework/e2e/cinn_converge/PP-HGNetV2-B4_CINN.sh

@@ -0,0 +1 @@
+python main.py -c paddlex/configs/modules/image_classification/PP-HGNetV2-B4.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=200 -o Train.batch_size=64 -o Train.learning_rate=0.5 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/PP-HGNetV2-B4_CINN' -o Train.dy2st=True

framework/e2e/cinn_converge/PP-HGNetV2-B4_dy.sh

@@ -0,0 +1 @@
+python main.py -c paddlex/configs/modules/image_classification/PP-HGNetV2-B4.yaml -o Global.mode=train -o Train.num_classes=1000 -o Train.epochs_iters=200 -o Train.batch_size=64 -o Train.learning_rate=0.5 -o Train.pretrain_weight_path=None -o Global.device=gpu:0,1,2,3,4,5,6,7 -o Global.dataset_dir=../ILSVRC2012/ -o Global.output='./output/image_classification/PP-HGNetV2-B4_dy'