add the pynative test case for dcn and yolov5 on gpu

tests/st/dynamic_shape/test_dcn_dynamic_gpu.py

@@ -209,12 +209,11 @@ def get_train_loss(numeric_columns, sparse_columns, data_list, mode):
     loss_callback = LossCallback()
     model = Model(train_net)
     sink_step = dataset.get_dataset_size()
-    model.train(sink_step, dataset, callbacks=loss_callback, sink_size=1)
+    model.train(sink_step, dataset, callbacks=loss_callback, sink_size=1, dataset_sink_mode=True)
     loss_list = loss_callback.loss_list
     return loss_list
 
 
-@pytest.mark.skip(reason="Do not support yet.")
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -230,8 +229,8 @@ def test_train():
     SparseFeature = namedtuple("SparseFeature", ['name', 'voc_size', 'embed_size'])
     sparse_columns = [SparseFeature('a', 7, 6), SparseFeature('b', 136, 18), SparseFeature('c', 3, 6)]
     data_list = gen_data(numeric_columns, sparse_columns, batch_size_list)
-    # For graph mode
+    # GRAPH_MODE is temporarily not supported due to some new features that are not completely complete
     set_seed(0)
-    graph_loss = get_train_loss(numeric_columns, sparse_columns, data_list, context.GRAPH_MODE)
+    graph_loss = get_train_loss(numeric_columns, sparse_columns, data_list, context.PYNATIVE_MODE)
     expect_loss = [6.687461, 2928.5852, 8715.267]
     assert np.allclose(graph_loss, expect_loss, 1e-3, 1e-3)

tests/st/dynamic_shape/yolov5_dynamic/run_yolov5_dynamic.py

@@ -46,7 +46,11 @@ def train_preprocess():
     if config.lr_scheduler == 'cosine_annealing' and config.max_epoch > config.T_max:
         config.T_max = config.max_epoch
 
-    ms.set_context(mode=ms.GRAPH_MODE, device_target=config.device_target)
+    ms.set_context(device_target=config.device_target)
+    if config.mode_name == "GRAPH":
+        ms.set_context(mode=ms.GRAPH_MODE)
+    else:
+        ms.set_context(mode=ms.PYNATIVE_MODE)
 
     if config.is_distributed:
         # init distributed
@@ -106,7 +110,7 @@ def run_train():
     loss_callback = LossMonitor(1)
     model = Model(network)
     sink_step = dataset.get_dataset_size()
-    model.train(sink_step, dataset, callbacks=loss_callback, sink_size=1)
+    model.train(sink_step, dataset, callbacks=loss_callback, sink_size=1, dataset_sink_mode=True)
 
 
 if __name__ == "__main__":

tests/st/dynamic_shape/yolov5_dynamic/test_yolov5_dynamic.py

@@ -20,7 +20,7 @@ from mindspore import log as logger
 from tests.st.model_zoo_tests import utils
 
 
-def run_yolov5_dynamic_case(device_target):
+def run_yolov5_dynamic_case(device_target, mode="GRAPH"):
     cur_path = os.getcwd()
     model_path = "{}/../../../../tests/models/official/cv".format(cur_path)
     model_name = "yolov5"
@@ -31,9 +31,10 @@ def run_yolov5_dynamic_case(device_target):
     # pylint: disable=anomalous-backslash-in-string
     new_list = ["-1"]
     utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "src/yolo.py"))
+    os.system("""sed -i '1i\mode_name: "GRAPH"' {}""".format(os.path.join(cur_model_path, "default_config.yaml")))
     os.system("cp -r {} {}".format(os.path.join(cur_path, "run_yolov5_dynamic.py"), cur_model_path))
-    exec_network_shell = "cd {}; python run_yolov5_dynamic.py --device_target={} > log &".format(
-        cur_model_path, device_target)
+    exec_network_shell = "cd {}; python run_yolov5_dynamic.py --device_target={} --mode_name={} > log &".format(
+        cur_model_path, device_target, mode)
     logger.warning("cmd [{}] is running...".format(exec_network_shell))
     os.system(exec_network_shell)
     cmd = "ps -ef | grep python | grep run_yolov5_dynamic.py | grep -v grep"
@@ -49,6 +50,22 @@ def run_yolov5_dynamic_case(device_target):
     return loss_list
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_yolov5_dynamic_gpu_pynative():
+    """
+    Feature: yolov5_dynamic
+    Description: test yolov5_dynamic run
+    Expectation: loss is same with the expect on PYNATIVE_MODE
+    """
+    loss_list = run_yolov5_dynamic_case("GPU", "PYNATIVE")
+    expect_loss = [7200.505, 544.873, 600.88]
+    # Different gpu device (such as V100 and 3090) lead to some differences
+    # in the calculation results, so atol and rtol is large
+    assert np.allclose(loss_list, expect_loss, 1e-2, 1e-2)
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard