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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- '''resnet
- The sample can be run on Ascend 910 AI processor.
- '''
- import numpy as np
- import mindspore.nn as nn
- from mindspore import Tensor
- import mindspore.ops as ops
-
-
- def weight_variable_0(shape):
- """weight_variable_0"""
- zeros = np.zeros(shape).astype(np.float32)
- return Tensor(zeros)
-
-
- def weight_variable_1(shape):
- """weight_variable_1"""
- ones = np.ones(shape).astype(np.float32)
- return Tensor(ones)
-
-
- def conv3x3(in_channels, out_channels, stride=1, padding=0):
- """3x3 convolution """
- return nn.Conv2d(in_channels, out_channels,
- kernel_size=3, stride=stride, padding=padding, weight_init='XavierUniform',
- has_bias=False, pad_mode="same")
-
-
- def conv1x1(in_channels, out_channels, stride=1, padding=0):
- """1x1 convolution"""
- return nn.Conv2d(in_channels, out_channels,
- kernel_size=1, stride=stride, padding=padding, weight_init='XavierUniform',
- has_bias=False, pad_mode="same")
-
-
- def conv7x7(in_channels, out_channels, stride=1, padding=0):
- """1x1 convolution"""
- return nn.Conv2d(in_channels, out_channels,
- kernel_size=7, stride=stride, padding=padding, weight_init='XavierUniform',
- has_bias=False, pad_mode="same")
-
-
- def bn_with_initialize(out_channels):
- """bn_with_initialize"""
- shape = (out_channels)
- mean = weight_variable_0(shape)
- var = weight_variable_1(shape)
- beta = weight_variable_0(shape)
- bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init='Uniform',
- beta_init=beta, moving_mean_init=mean, moving_var_init=var)
- return bn
-
-
- def bn_with_initialize_last(out_channels):
- """bn_with_initialize_last"""
- shape = (out_channels)
- mean = weight_variable_0(shape)
- var = weight_variable_1(shape)
- beta = weight_variable_0(shape)
- bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init='Uniform',
- beta_init=beta, moving_mean_init=mean, moving_var_init=var)
- return bn
-
-
- def fc_with_initialize(input_channels, out_channels):
- """fc_with_initialize"""
- return nn.Dense(input_channels, out_channels, weight_init='XavierUniform', bias_init='Uniform')
-
-
- class ResidualBlock(nn.Cell):
- """ResidualBlock"""
- expansion = 4
-
- def __init__(self,
- in_channels,
- out_channels,
- stride=1):
- """init block"""
- super(ResidualBlock, self).__init__()
-
- out_chls = out_channels // self.expansion
- self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
- self.bn1 = bn_with_initialize(out_chls)
-
- self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
- self.bn2 = bn_with_initialize(out_chls)
-
- self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
- self.bn3 = bn_with_initialize_last(out_channels)
-
- self.relu = ops.ReLU()
- self.add = ops.Add()
-
- def construct(self, x):
- """construct"""
- identity = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- out = self.add(out, identity)
- out = self.relu(out)
-
- return out
-
-
- class ResidualBlockWithDown(nn.Cell):
- """ResidualBlockWithDown"""
- expansion = 4
-
- def __init__(self,
- in_channels,
- out_channels,
- stride=1,
- down_sample=False):
- """init block with down"""
- super(ResidualBlockWithDown, self).__init__()
-
- out_chls = out_channels // self.expansion
- self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
- self.bn1 = bn_with_initialize(out_chls)
-
- self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
- self.bn2 = bn_with_initialize(out_chls)
-
- self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
- self.bn3 = bn_with_initialize_last(out_channels)
-
- self.relu = ops.ReLU()
- self.down_sample = down_sample
-
- self.conv_down_sample = conv1x1(in_channels, out_channels, stride=stride, padding=0)
- self.bn_down_sample = bn_with_initialize(out_channels)
- self.add = ops.Add()
-
- def construct(self, x):
- """construct"""
- identity = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- identity = self.conv_down_sample(identity)
- identity = self.bn_down_sample(identity)
-
- out = self.add(out, identity)
- out = self.relu(out)
-
- return out
-
-
- class MakeLayer0(nn.Cell):
- """MakeLayer0"""
-
- def __init__(self, block, in_channels, out_channels, stride):
- """init"""
- super(MakeLayer0, self).__init__()
- self.a = ResidualBlockWithDown(in_channels, out_channels, stride=1, down_sample=True)
- self.b = block(out_channels, out_channels, stride=stride)
- self.c = block(out_channels, out_channels, stride=1)
-
- def construct(self, x):
- """construct"""
- x = self.a(x)
- x = self.b(x)
- x = self.c(x)
-
- return x
-
-
- class MakeLayer1(nn.Cell):
- """MakeLayer1"""
-
- def __init__(self, block, in_channels, out_channels, stride):
- """init"""
- super(MakeLayer1, self).__init__()
- self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
- self.b = block(out_channels, out_channels, stride=1)
- self.c = block(out_channels, out_channels, stride=1)
- self.d = block(out_channels, out_channels, stride=1)
-
- def construct(self, x):
- """construct"""
- x = self.a(x)
- x = self.b(x)
- x = self.c(x)
- x = self.d(x)
-
- return x
-
-
- class MakeLayer2(nn.Cell):
- """MakeLayer2"""
-
- def __init__(self, block, in_channels, out_channels, stride):
- """init"""
- super(MakeLayer2, self).__init__()
- self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
- self.b = block(out_channels, out_channels, stride=1)
- self.c = block(out_channels, out_channels, stride=1)
- self.d = block(out_channels, out_channels, stride=1)
- self.e = block(out_channels, out_channels, stride=1)
- self.f = block(out_channels, out_channels, stride=1)
-
- def construct(self, x):
- """construct"""
- x = self.a(x)
- x = self.b(x)
- x = self.c(x)
- x = self.d(x)
- x = self.e(x)
- x = self.f(x)
-
- return x
-
-
- class MakeLayer3(nn.Cell):
- """MakeLayer3"""
-
- def __init__(self, block, in_channels, out_channels, stride):
- """init"""
- super(MakeLayer3, self).__init__()
- self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
- self.b = block(out_channels, out_channels, stride=1)
- self.c = block(out_channels, out_channels, stride=1)
-
- def construct(self, x):
- """construct"""
- x = self.a(x)
- x = self.b(x)
- x = self.c(x)
-
- return x
-
-
- class ResNet(nn.Cell):
- """ResNet"""
-
- def __init__(self, block, num_classes=100, batch_size=32):
- """init"""
- super(ResNet, self).__init__()
- self.batch_size = batch_size
- self.num_classes = num_classes
-
- self.conv1 = conv7x7(3, 64, stride=2, padding=0)
-
- self.bn1 = bn_with_initialize(64)
- self.relu = ops.ReLU()
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
-
- self.layer1 = MakeLayer0(block, in_channels=64, out_channels=256, stride=1)
- self.layer2 = MakeLayer1(block, in_channels=256, out_channels=512, stride=2)
- self.layer3 = MakeLayer2(block, in_channels=512, out_channels=1024, stride=2)
- self.layer4 = MakeLayer3(block, in_channels=1024, out_channels=2048, stride=2)
-
- self.pool = ops.ReduceMean(keep_dims=True)
- self.squeeze = ops.Squeeze(axis=(2, 3))
- self.fc = fc_with_initialize(512 * block.expansion, num_classes)
-
- def construct(self, x):
- """construct"""
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.pool(x, (2, 3))
- x = self.squeeze(x)
- x = self.fc(x)
- return x
-
-
- def resnet50(batch_size, num_classes):
- """create resnet50"""
- return ResNet(ResidualBlock, num_classes, batch_size)
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