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143 lines
4.9 KiB
143 lines
4.9 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test adam """
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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, Parameter
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import mindspore.common.dtype as mstype
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import AdamWeightDecay, AdamWeightDecayDynamicLR
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x):
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x = self.biasAdd(self.matmul(x, self.weight), self.bias)
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return x
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class NetWithoutWeight(nn.Cell):
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def __init__(self):
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super(NetWithoutWeight, self).__init__()
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self.matmul = P.MatMul()
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def construct(self, x):
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x = self.matmul(x, x)
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return x
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def test_adamwithoutparam():
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net = NetWithoutWeight()
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net.set_train()
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with pytest.raises(ValueError, match=r"Optimizer got an empty parameter list"):
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AdamWeightDecay(net.trainable_params(), learning_rate=0.1)
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def test_adamw_compile():
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""" test_adamw_compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = AdamWeightDecay(net.trainable_params(), learning_rate=0.1)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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def test_AdamWeightDecay_beta1():
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net = Net()
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print("**********", net.get_parameters())
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with pytest.raises(ValueError):
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AdamWeightDecay(net.get_parameters(), beta1=1.0, learning_rate=0.1)
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def test_AdamWeightDecay_beta2():
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net = Net()
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with pytest.raises(ValueError):
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AdamWeightDecay(net.get_parameters(), beta2=1.0, learning_rate=0.1)
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def test_AdamWeightDecay_e():
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net = Net()
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with pytest.raises(ValueError):
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AdamWeightDecay(net.get_parameters(), eps=-0.1, learning_rate=0.1)
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def test_AdamWeightDecayDynamicLR():
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""" test_AdamWeightDecayDynamicLR """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = AdamWeightDecayDynamicLR(net.trainable_params(), decay_steps=20, learning_rate=0.1)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_executor.compile(train_network, inputs, label)
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def test_adam_mindspore_with_empty_params():
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net = nn.Flatten()
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with pytest.raises(ValueError, match=r"Optimizer got an empty parameter list"):
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AdamWeightDecay(net.get_parameters())
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class TestSparseOps(nn.Cell):
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"""Define sparse operator"""
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def __init__(self, sparse_opt):
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super(TestSparseOps, self).__init__()
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self.sparse_apply_adam = sparse_opt
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self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="var")
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self.m = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="m")
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self.v = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="v")
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def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, indices):
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out = self.sparse_apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, epsilon,
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grad, indices)
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return out
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def test_sparse_adam():
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"""test sparse operator"""
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gradient = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
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indices = Tensor([0, 1, 2], mstype.int32)
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net = TestSparseOps(P.SparseApplyAdam())
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_executor.compile(net, 0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient, indices)
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def test_sparse_lazy_adam():
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"""test sparse operator"""
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gradient = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
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indices = Tensor([0, 1, 2], mstype.int32)
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net = TestSparseOps(P.SparseApplyLazyAdam())
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_executor.compile(net, 0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient, indices)
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