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resnet.py 9.30 KB
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jinyaohui authored 2021-01-29 07:39 . modify TesorAdd Gelu and GatherV2
# 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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