forked from mindspore-Ecosystem/mindspore
166 lines
4.8 KiB
Python
166 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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"""Resnet examples."""
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# pylint: disable=missing-docstring, arguments-differ
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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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 = P.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 ResNet50(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(ResNet50, 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='valid')
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self.layer1 = self.MakeLayer(
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block, 3, in_channels=64, out_channels=256, stride=1)
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self.layer2 = self.MakeLayer(
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block, 4, in_channels=256, out_channels=512, stride=2)
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self.layer3 = self.MakeLayer(
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block, 6, in_channels=512, out_channels=1024, stride=2)
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self.layer4 = self.MakeLayer(
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block, 3, in_channels=1024, out_channels=2048, stride=2)
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self.avgpool = nn.AvgPool2d(7, 1)
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self.flatten = P.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 resnet50():
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return ResNet50(ResidualBlock, 10)
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