forked from mindspore-Ecosystem/mindspore
149 lines
4.8 KiB
Python
149 lines
4.8 KiB
Python
# 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_pynative_model """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Parameter, ParameterTuple, Tensor
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from mindspore import context
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from mindspore.nn.optim import Momentum
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from ..ut_filter import non_graph_engine
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grad_by_list = C.GradOperation(get_by_list=True)
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def setup_module(module):
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context.set_context(mode=context.PYNATIVE_MODE)
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class GradWrap(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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self.weights = ParameterTuple(network.get_parameters())
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def construct(self, x, label):
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weights = self.weights
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return grad_by_list(self.network, weights)(x, label)
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@non_graph_engine
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def test_softmaxloss_grad():
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""" test_softmaxloss_grad """
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class NetWithLossClass(nn.Cell):
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""" NetWithLossClass definition """
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def __init__(self, network):
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super(NetWithLossClass, self).__init__()
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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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.fc = 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.fc(x, self.weight), self.bias)
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return x
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net = GradWrap(NetWithLossClass(Net()))
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predict = 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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print("pynative run")
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out = net.construct(predict, label)
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print("out:", out)
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print(out[0], (out[0]).asnumpy(), ":result")
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@non_graph_engine
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def test_lenet_grad():
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""" test_lenet_grad """
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class NetWithLossClass(nn.Cell):
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""" NetWithLossClass definition """
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def __init__(self, network):
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super(NetWithLossClass, self).__init__()
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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class LeNet5(nn.Cell):
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""" LeNet5 definition """
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def __init__(self):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = P.Flatten()
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def construct(self, x):
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x = self.max_pool2d(self.relu(self.conv1(x)))
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x = self.max_pool2d(self.relu(self.conv2(x)))
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x = self.flatten(x)
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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input_data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([1, 10]).astype(np.float32))
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iteration_num = 1
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verification_step = 0
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net = LeNet5()
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False)
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momen_opti = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = GradWrap(NetWithLossClass(net))
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train_net.set_train()
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for i in range(0, iteration_num):
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# get the gradients
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grads = train_net(input_data, label)
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# update parameters
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success = momen_opti(grads)
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if success is False:
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print("fail to run optimizer")
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# verification
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if i == verification_step:
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fw_output = net(input_data)
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loss_output = loss(fw_output, label)
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print("The loss of %s-th iteration is %s" % (i, loss_output.asnumpy()))
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