baihuawei
4 years ago
parent
66dd2730b5
commit
cff87d6f65
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.context as context
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from mindspore.nn import Dense
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import Momentum
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.bias_add = P.BiasAdd()
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self.bias_add1 = P.BiasAdd()
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def construct(self, x, b, c):
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return self.bias_add1(self.bias_add(x, b), c)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_bias_add1():
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x = np.ones([2, 2]).astype(np.float16)
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b = np.array([1, 1]).astype(np.float16)
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c = np.array([1, 1]).astype(np.float16)
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bias_add = Net()
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output = bias_add(Tensor(x), Tensor(b), Tensor(c))
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expect_output = np.ones([2, 2]).astype(np.float16) * 3
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assert np.all(output.asnumpy() == expect_output)
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class Net1(nn.Cell):
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def __init__(self):
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super(Net1, self).__init__()
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self.bias_add = P.BiasAdd()
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self.mul = P.Mul()
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def construct(self, x, a, b):
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p1 = self.bias_add(x, b)
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p2 = self.bias_add(x, a)
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p3 = self.mul(p1, p2)
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return p3
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class Net2(nn.Cell):
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def __init__(self):
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super(Net2, self).__init__()
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self.bias_add = P.BiasAdd()
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self.bias_add1 = P.BiasAdd()
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def construct(self, x, b, c):
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return self.bias_add1(self.bias_add(x, b), c)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_bias_add2():
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x = np.ones([2, 2]).astype(np.float32)
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a = np.array([1, 1]).astype(np.float32)
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b = np.array([1, 1]).astype(np.float32)
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c = np.array([1, 1]).astype(np.float32)
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bias_add = Net1()
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output = bias_add(Tensor(x), Tensor(a), Tensor(b))
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print(output)
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net2 = Net2()
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output2 = net2(Tensor(x), Tensor(b), Tensor(c))
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print(output2)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class MomentumNet(nn.Cell):
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def __init__(self):
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super(MomentumNet, self).__init__()
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self.batch_size = 1
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self.reshape = P.Reshape()
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weight = Tensor(np.ones([10, 16]).astype(np.float32) * 0.01)
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self.fc1 = Dense(16, 10, weight_init=weight)
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def construct(self, input_x):
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output = self.reshape(input_x, (self.batch_size, -1))
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output = self.fc1(output)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_momentum():
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epoch = 1
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net = MomentumNet()
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learning_rate = (0.1, 0.2)
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momentum = 0.9
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
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train_network.set_train()
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losses = []
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for _ in range(epoch):
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data = Tensor(np.arange(0, 16).reshape(1, 1, 4, 4).astype(np.float32) * 0.01)
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label = Tensor(np.array([0]).astype(np.int32))
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loss = train_network(data, label)
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losses.append(loss)
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print("================================")
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print(losses)
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return losses
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