!22243 add Ascend and CPU ST for enabling RDR

Merge pull request !22243 from yuximiao/yuximiao_rdr

tests/st/networks/models/lenet.py

@@ -0,0 +1,50 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+class LeNet(nn.Cell):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.relu = P.ReLU()
+        self.batch_size = 1
+        weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
+        weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
+        self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
+
+        self.reshape = P.Reshape()
+        self.reshape1 = 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.fc2(output)
+        output = self.fc3(output)
+        return output

tests/st/networks/test_ascend_lenet_enable_rdr.py

@@ -0,0 +1,28 @@
+import os
+import tempfile
+import json
+import pytest
+
+import mindspore.context as context
+from .test_network_main import test_lenet
+
+# create config file for RDR
+def create_config_file(path):
+    data_dict = {'rdr': {'enable': True, 'path': path}}
+    filename = os.path.join(path, "mindspore_config.json")
+    with open(filename, "w") as f:
+        json.dump(data_dict, f)
+    return filename
+
+def test_train(device_type):
+    context.set_context(mode=context.GRAPH_MODE, device_target=device_type)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        config_file = create_config_file(tmpdir)
+        context.set_context(env_config_path=config_file)
+        test_lenet()
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_train_with_Ascend():
+    test_train("Ascend")

tests/st/networks/test_cpu_lenet_enable_rdr.py

@@ -0,0 +1,28 @@
+import os
+import tempfile
+import json
+import pytest
+
+import mindspore.context as context
+from .test_network_main import test_lenet
+
+# create config file for RDR
+def create_config_file(path):
+    data_dict = {'rdr': {'enable': True, 'path': path}}
+    filename = os.path.join(path, "mindspore_config.json")
+    with open(filename, "w") as f:
+        json.dump(data_dict, f)
+    return filename
+
+def test_train(device_type):
+    context.set_context(mode=context.GRAPH_MODE, device_target=device_type)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        config_file = create_config_file(tmpdir)
+        context.set_context(env_config_path=config_file)
+        test_lenet()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_train_with_CPU():
+    test_train("CPU")

tests/st/networks/test_network_main.py

@@ -56,9 +56,9 @@ def test_resnet50():
 
 
 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()
+    data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
+    label = Tensor(np.ones([net.batch_size]).astype(np.int32))
     train(net, data, label)