forked from mindspore-Ecosystem/mindspore
308 lines
9.2 KiB
Python
308 lines
9.2 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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resnet50 example
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"""
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import numpy as np
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from mindspore import Tensor, Model, ParallelMode
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from mindspore.ops.operations import TensorAdd
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.nn.optim import Momentum
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from ....dataset_mock import MindData
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def conv3x3(in_channels, out_channels, stride=1, padding=1, pad_mode='pad'):
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"""3x3 convolution """
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=3, stride=stride, padding=padding, pad_mode=pad_mode)
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def conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='pad'):
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"""1x1 convolution"""
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=1, stride=stride, padding=padding, pad_mode=pad_mode)
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class ResidualBlock(nn.Cell):
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"""
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residual Block
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"""
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expansion = 4
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def __init__(self,
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in_channels,
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out_channels,
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stride=1,
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down_sample=False):
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super(ResidualBlock, self).__init__()
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out_chls = out_channels // self.expansion
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self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
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self.bn1 = nn.BatchNorm2d(out_chls)
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self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
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self.bn2 = nn.BatchNorm2d(out_chls)
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self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
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self.bn3 = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU()
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self.downsample = down_sample
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self.conv_down_sample = conv1x1(in_channels, out_channels,
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stride=stride, padding=0)
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self.bn_down_sample = nn.BatchNorm2d(out_channels)
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self.add = TensorAdd()
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def construct(self, x):
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"""
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:param x:
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:return:
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"""
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample:
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identity = self.conv_down_sample(identity)
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identity = self.bn_down_sample(identity)
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out = self.add(out, identity)
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out = self.relu(out)
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return out
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class ResNet18(nn.Cell):
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"""
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resnet nn.Cell
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"""
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def __init__(self, block, num_classes=100):
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super(ResNet18, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
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self.layer1 = self.MakeLayer(
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block, 2, in_channels=64, out_channels=256, stride=1)
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self.layer2 = self.MakeLayer(
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block, 2, in_channels=256, out_channels=512, stride=2)
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self.layer3 = self.MakeLayer(
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block, 2, in_channels=512, out_channels=1024, stride=2)
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self.layer4 = self.MakeLayer(
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block, 2, in_channels=1024, out_channels=2048, stride=2)
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self.avgpool = nn.AvgPool2d(7, 1)
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(512 * block.expansion, num_classes)
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def MakeLayer(self, block, layer_num, in_channels, out_channels, stride):
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"""
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make block layer
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:param block:
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:param layer_num:
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:param in_channels:
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:param out_channels:
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:param stride:
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:return:
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"""
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layers = []
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resblk = block(in_channels, out_channels,
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stride=stride, down_sample=True)
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layers.append(resblk)
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for _ in range(1, layer_num):
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resblk = block(out_channels, out_channels, stride=1)
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layers.append(resblk)
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return nn.SequentialCell(layers)
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def construct(self, x):
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"""
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:param x:
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:return:
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"""
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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class ResNet9(nn.Cell):
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"""
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resnet nn.Cell
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"""
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def __init__(self, block, num_classes=100):
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super(ResNet9, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
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self.layer1 = self.MakeLayer(
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block, 1, in_channels=64, out_channels=256, stride=1)
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self.layer2 = self.MakeLayer(
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block, 1, in_channels=256, out_channels=512, stride=2)
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self.layer3 = self.MakeLayer(
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block, 1, in_channels=512, out_channels=1024, stride=2)
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self.layer4 = self.MakeLayer(
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block, 1, in_channels=1024, out_channels=2048, stride=2)
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self.avgpool = nn.AvgPool2d(7, 1)
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(512 * block.expansion, num_classes)
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def MakeLayer(self, block, layer_num, in_channels, out_channels, stride):
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"""
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make block layer
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:param block:
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:param layer_num:
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:param in_channels:
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:param out_channels:
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:param stride:
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:return:
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"""
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layers = []
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resblk = block(in_channels, out_channels,
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stride=stride, down_sample=True)
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layers.append(resblk)
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for _ in range(1, layer_num):
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resblk = block(out_channels, out_channels, stride=1)
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layers.append(resblk)
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return nn.SequentialCell(layers)
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def construct(self, x):
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"""
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:param x:
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:return:
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"""
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def resnet9(classnum):
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return ResNet9(ResidualBlock, classnum)
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class DatasetLenet(MindData):
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"""DatasetLenet definition"""
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def __init__(self, predict, label, length=3, size=None, batch_size=None,
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np_types=None, output_shapes=None, input_indexs=()):
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super(DatasetLenet, self).__init__(size=size, batch_size=batch_size,
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np_types=np_types, output_shapes=output_shapes,
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input_indexs=input_indexs)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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def test_resnet_train_tensor():
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"""test_resnet_train_tensor"""
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batch_size = 1
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size = 2
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context.set_context(mode=context.GRAPH_MODE)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, device_num=size,
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parameter_broadcast=True)
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one_hot_len = 10
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dataset_types = (np.float32, np.float32)
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dataset_shapes = [[batch_size, 3, 224, 224], [batch_size, one_hot_len]]
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predict = Tensor(np.ones([batch_size, 3, 224, 224]).astype(np.float32) * 0.01)
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label = Tensor(np.zeros([batch_size, one_hot_len]).astype(np.float32))
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dataset = DatasetLenet(predict, label, 2,
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size=2, batch_size=2,
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np_types=dataset_types,
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output_shapes=dataset_shapes,
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input_indexs=(0, 1))
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dataset.reset()
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network = resnet9(one_hot_len)
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network.set_train()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), learning_rate=0.1, momentum=0.9)
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model = Model(network=network, loss_fn=loss_fn, optimizer=optimizer)
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model.train(epoch=2, train_dataset=dataset, dataset_sink_mode=False)
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context.set_context(mode=context.GRAPH_MODE)
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context.reset_auto_parallel_context()
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class_num = 10
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def get_dataset():
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dataset_types = (np.float32, np.float32)
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dataset_shapes = ((32, 3, 224, 224), (32, class_num))
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dataset = MindData(size=2, batch_size=1,
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np_types=dataset_types,
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output_shapes=dataset_shapes,
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input_indexs=(0, 1))
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return dataset
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