update network testcase

tests/st/model_zoo_tests/bgcf/test_BGCF_amazon_beauty.py

@@ -18,7 +18,7 @@ import pytest
 from tests.st.model_zoo_tests import utils
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_onecard

tests/st/model_zoo_tests/centernet/test_centernet.py

@@ -18,7 +18,7 @@ import pytest
 from tests.st.model_zoo_tests import utils
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_single

tests/st/model_zoo_tests/face_detection/test_FaceDetection_WIDER.py

@@ -18,7 +18,7 @@ import pytest
 from tests.st.model_zoo_tests import utils
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_onecard

tests/st/model_zoo_tests/resnet50/test_resnet50_cifar10.py

@@ -50,7 +50,7 @@ def test_resnet50_cifar10_ascend():
     assert sum(loss_list) / len(loss_list) < 0.70
 
 
-@pytest.mark.level0
+@pytest.mark.level2
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_single

tests/st/model_zoo_tests/transformer/test_transformer.py

@@ -145,7 +145,7 @@ class TimeMonitor(Callback):
         self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
@@ -217,7 +217,7 @@ def test_transformer():
     assert per_step_mseconds <= expect_per_step_mseconds + 10
 
 
-@pytest.mark.level0
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard

tests/st/model_zoo_tests/yolov3/test_yolov3.py

@@ -84,7 +84,8 @@ class TimeMonitor(Callback):
 
 DATA_DIR = "/home/workspace/mindspore_dataset/coco/coco2017/mindrecord_train/yolov3"
 
-@pytest.mark.level1
+
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_single

tests/st/networks/models/bert/bert_precision/test_bert_tdt_lossscale.py

@@ -245,7 +245,7 @@ def test_bert_precision(enable_graph_kernel=False):
     assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
@@ -253,6 +253,10 @@ def test_bert_precision_graph_kernel_off():
     test_bert_precision(enable_graph_kernel=False)
 
 
+@pytest.mark.level2
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_bert_precision_graph_kernel_on():
     test_bert_precision(enable_graph_kernel=True)
 

tests/st/networks/models/deeplabv3/test_deeplabv3.py

@@ -69,7 +69,8 @@ def model_fine_tune(train_net, fix_weight_layer):
         if fix_weight_layer in para.name:
             para.requires_grad = False
 
-@pytest.mark.level0
+
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard

tests/st/networks/test_ascend_lenet_enable_rdr.py

@@ -23,7 +23,7 @@ def test_train(device_type):
         test_lenet()
 
 @security_off_wrap
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.env_onecard
 def test_train_with_Ascend():

tests/st/networks/test_gpu_alexnet.py

@@ -68,7 +68,7 @@ class AlexNet(nn.Cell):
         return x
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_trainTensor(num_classes=10, epoch=15, batch_size=32):
@@ -89,7 +89,7 @@ def test_trainTensor(num_classes=10, epoch=15, batch_size=32):
     assert losses[-1] < 0.01
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_train_tensor_memory_opt(num_classes=10, epoch=15, batch_size=32):

tests/st/networks/test_gpu_lenet.py

@@ -216,7 +216,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
     return mnist_ds
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_train_and_eval_lenet():

tests/st/networks/test_gpu_lenet_enable_rdr.py

@@ -31,14 +31,14 @@ def test_train_and_eval():
         test_train_and_eval_lenet()
 
 @security_off_wrap
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_train_with_GPU():
     test_train("GPU")
 
 @security_off_wrap
-@pytest.mark.level0
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_train_and_eval_with_GPU():

tests/st/networks/test_gpu_resnet.py

@@ -328,7 +328,7 @@ def resnet50(num_classes):
     return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes)
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_trainTensor(num_classes=10, epoch=8, batch_size=1):
@@ -352,7 +352,7 @@ def test_trainTensor(num_classes=10, epoch=8, batch_size=1):
     assert (losses[-1].asnumpy() < 1)
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_train_tensor_memory_opt(num_classes=10, epoch=8, batch_size=1):
@@ -406,7 +406,7 @@ def test_trainTensor_big_batchSize(num_classes=10, epoch=8, batch_size=338):
     assert (losses[-1].asnumpy() < 1)
 
 
-@pytest.mark.level0
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_trainTensor_amp(num_classes=10, epoch=18, batch_size=16):

tests/st/networks/test_gradient_accumulation.py

@@ -205,7 +205,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
     return mnist_ds
 
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard