add new testcase for access control

Signed-off-by: zhushujing <zhushujing@huawei.com>

tests/st/mix_precision/test_mix_precision.py

@@ -0,0 +1,89 @@
+# Copyright 2020 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.
+"""Test network turn on mix_precision."""
+
+import pytest
+import numpy as np
+from mindspore import nn
+from mindspore import ops
+from mindspore import amp
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+
+
+class Net(nn.Cell):
+    def __init__(self, in_c, out_c):
+        super().__init__()
+        self.relu = nn.ReLU()
+        self.bn1 = nn.BatchNorm2d(num_features=in_c,
+                                  gamma_init='ones',
+                                  beta_init='zeros',
+                                  moving_mean_init='zeros',
+                                  moving_var_init='ones')
+        self.bn2 = nn.BatchNorm2d(num_features=out_c,
+                                  gamma_init='ones',
+                                  beta_init='zeros',
+                                  moving_mean_init='zeros',
+                                  moving_var_init='ones')
+        self.conv = nn.Conv2d(in_channels=in_c,
+                              out_channels=out_c,
+                              kernel_size=3,
+                              stride=1,
+                              has_bias=True,
+                              pad_mode='same',
+                              weight_init='ones',
+                              bias_init='ones')
+        self.mean = ops.ReduceMean(keep_dims=False)
+
+    def construct(self, x):
+        x = self.relu(x)
+        x = self.bn1(x)
+        x = self.conv(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+        x = self.mean(x, (2, 3))
+        return x
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_auto_mix_precision():
+    input_data = np.random.randn(32, 3, 224, 224).astype(np.float64)
+    label_data = np.random.randn(32, 10).astype(np.float32)
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    net = Net(3, 10)
+    opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.001, momentum=0.0009, weight_decay=0.001,
+                      loss_scale=0.0001)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
+    train_network = amp.build_train_network(net, opt, loss, level="O3",
+                                            loss_scale_manager=FixedLossScaleManager(drop_overflow_update=False))
+    out = train_network(Tensor(input_data), Tensor(label_data))
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    net_pynative = Net(3, 10)
+    opt_pynative = nn.Momentum(params=net_pynative.trainable_params(), learning_rate=0.001, momentum=0.0009,
+                               weight_decay=0.001,
+                               loss_scale=0.0001)
+    loss_pynative = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
+    train_network_pynative = amp.build_train_network(net_pynative, opt_pynative, loss_pynative, level="O3",
+                                                     loss_scale_manager=FixedLossScaleManager(
+                                                         drop_overflow_update=False))
+    out_pynative = train_network_pynative(Tensor(input_data), Tensor(label_data))
+    assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)