forked from mindspore-Ecosystem/mindspore
204 lines
6.5 KiB
Python
204 lines
6.5 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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import time
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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 context, Tensor, ParameterTuple
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from mindspore.common import dtype as mstype
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.nn.optim import Momentum
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from mindspore.nn.wrap.cell_wrapper import WithLossCell
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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np.random.seed(1)
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grad_by_list = C.GradOperation(get_by_list=True)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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class LeNet(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Num classes, Default: 10.
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Returns:
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Tensor, output tensor
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Examples:
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>>> LeNet(num_class=10)
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"""
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def __init__(self, num_class=10):
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super(LeNet, self).__init__()
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self.num_class = num_class
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self.batch_size = 32
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self.conv1 = conv(1, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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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.reshape = P.Reshape()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.reshape(x, (self.batch_size, -1))
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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class CrossEntropyLoss(nn.Cell):
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"""
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Define loss for network
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"""
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def __init__(self):
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super(CrossEntropyLoss, self).__init__()
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self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean()
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self.one_hot = P.OneHot()
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self.on_value = Tensor(1.0, mstype.float32)
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self.off_value = Tensor(0.0, mstype.float32)
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self.num = Tensor(32.0, mstype.float32)
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def construct(self, logits, label):
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label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
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loss = self.cross_entropy(logits, label)[0]
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loss = P.RealDiv()(P.ReduceSum()(loss, -1), self.num)
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return loss
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class GradWrap(nn.Cell):
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"""
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GradWrap definition
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"""
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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(filter(lambda x: x.requires_grad, 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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ascend_pynative_lenet():
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context.set_context(mode=context.PYNATIVE_MODE)
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epoch_size = 20
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batch_size = 32
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inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32))
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labels = Tensor(np.ones([batch_size]).astype(np.int32))
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net = LeNet()
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criterion = CrossEntropyLoss()
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
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net_with_criterion = WithLossCell(net, criterion)
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train_network = GradWrap(net_with_criterion)
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train_network.set_train()
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total_time = 0
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for epoch in range(0, epoch_size):
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start_time = time.time()
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fw_output = net(inputs)
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loss_output = criterion(fw_output, labels)
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grads = train_network(inputs, labels)
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optimizer(grads)
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end_time = time.time()
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cost_time = end_time - start_time
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total_time = total_time + cost_time
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print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
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assert loss_output.asnumpy() < 0.004
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assert loss_output.asnumpy() > 0.003
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_lenet_with_new_interface():
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context.set_context(mode=context.PYNATIVE_MODE)
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epoch_size = 20
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batch_size = 32
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inputs = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32))
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labels = Tensor(np.ones([batch_size]).astype(np.int32))
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net = LeNet()
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criterion = CrossEntropyLoss()
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net_with_criterion = WithLossCell(net, criterion)
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net_with_criterion.set_train()
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weights = ParameterTuple(filter(lambda x: x.requires_grad, net.get_parameters()))
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optimizer = Momentum(weights, 0.1, 0.9)
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forward_value_and_grad = nn.ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True)
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total_time = 0
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for epoch in range(0, epoch_size):
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start_time = time.time()
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loss_output, grads = forward_value_and_grad(inputs, labels)
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optimizer(grads)
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end_time = time.time()
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cost_time = end_time - start_time
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total_time = total_time + cost_time
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print("======epoch: ", epoch, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
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assert loss_output.asnumpy() < 0.005
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assert loss_output.asnumpy() > 0.003
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