add cpu st lenet

tests/st/networks/test_cpu_lenet.py

@@ -78,4 +78,4 @@ def test_lenet():
     data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([32]).astype(np.int32))
     net = LeNet()
-    train(net, data, label)
+    train(net, data, label)

tests/st/networks/test_network_main.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+<<<<<<< HEAD:tests/st/networks/test_network_main.py
 """
 Function: 
     test network
@@ -31,6 +32,47 @@ from models.lenet import LeNet
 from models.resnetv1_5 import resnet50
 from models.alexnet import AlexNet
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+=======
+import pytest
+from mindspore.nn import TrainOneStepCell, WithLossCell
+import mindspore.context as context
+from mindspore.nn.optim import Momentum
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore import Tensor
+
+class LeNet(nn.Cell):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.relu = P.ReLU()
+        self.batch_size = 32
+
+        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.reshape = P.Reshape()
+        self.fc1 = nn.Dense(400, 120)
+        self.fc2 = nn.Dense(120, 84)
+        self.fc3 = nn.Dense(84, 10)
+
+    def construct(self, input_x):
+        output = self.conv1(input_x)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.conv2(output)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.reshape(output, (self.batch_size, -1))
+        output = self.fc1(output)
+        output = self.relu(output)
+        output = self.fc2(output)
+        output = self.relu(output)
+        output = self.fc3(output)
+        return output
+
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py
 
 def train(net, data, label):
     learning_rate = 0.01
@@ -47,17 +89,24 @@ def train(net, data, label):
     print("+++++++++++++++++++++++++++")
     assert res
 
+<<<<<<< HEAD:tests/st/networks/test_network_main.py
 def test_resnet50():
     data = Tensor(np.ones([32, 3 ,224, 224]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([32]).astype(np.int32))
     net = resnet50(32, 10)
     train(net, data, label)
 
+=======
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py
 def test_lenet():
     data = Tensor(np.ones([32, 1 ,32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([32]).astype(np.int32))
     net = LeNet()
     train(net, data, label)
+<<<<<<< HEAD:tests/st/networks/test_network_main.py
 
 def test_alexnet():
     data = Tensor(np.ones([32, 3 ,227, 227]).astype(np.float32) * 0.01)
@@ -79,3 +128,5 @@ if __name__ == "__main__":
         test_alexnet()
     else:
         print("Please add net name like --net lenet")
+=======
+>>>>>>> add cpu st lenet:tests/st/networks/test_cpu_lenet.py