syn-code1

mindspore/ccsrc/utils/callbacks.h

@@ -40,7 +40,7 @@ const int kCallbackOk = 0;
 const int kCallbackFalied = 1;
 
 bool GetParameterShape(const FuncGraphPtr& anf_graph, const std::string& param_name,
-                       const std::shared_ptr<std::vector<int>>& shape)
+                       const std::shared_ptr<std::vector<int>>& shape);
 uint32_t SummarySaveCallback(uint32_t, const std::map<std::string, TensorPtr>&);
 
 }  // namespace callbacks

tests/st/networks/test_network_main.py

@@ -12,7 +12,6 @@
 # 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
@@ -32,47 +31,6 @@ 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
@@ -89,24 +47,17 @@ 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)
@@ -128,5 +79,3 @@ 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