From 2bd37b79c865a80ec173cd0b8aeceb9c003b0524 Mon Sep 17 00:00:00 2001 From: zhushujing Date: Sat, 21 Nov 2020 15:45:45 +0800 Subject: [PATCH] add new testcase for access control Signed-off-by: zhushujing --- tests/st/mix_precision/test_mix_precision.py | 89 ++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 tests/st/mix_precision/test_mix_precision.py diff --git a/tests/st/mix_precision/test_mix_precision.py b/tests/st/mix_precision/test_mix_precision.py new file mode 100644 index 00000000000..2182f4ccd20 --- /dev/null +++ b/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)