forked from mindspore-Ecosystem/mindspore
301 lines
9.3 KiB
Python
301 lines
9.3 KiB
Python
# Copyright 2019 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.
|
|
# ============================================================================
|
|
import numpy as np
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
def weight_variable(shape):
|
|
return initializer('XavierUniform', shape=shape, dtype=mstype.float32)
|
|
|
|
|
|
def weight_variable_uniform(shape):
|
|
return initializer('Uniform', shape=shape, dtype=mstype.float32)
|
|
|
|
|
|
def weight_variable_0(shape):
|
|
zeros = np.zeros(shape).astype(np.float32)
|
|
return Tensor(zeros)
|
|
|
|
|
|
def weight_variable_1(shape):
|
|
ones = np.ones(shape).astype(np.float32)
|
|
return Tensor(ones)
|
|
|
|
|
|
def conv3x3(in_channels, out_channels, stride=1, padding=0):
|
|
"""3x3 convolution """
|
|
weight_shape = (out_channels, in_channels, 3, 3)
|
|
weight = weight_variable(weight_shape)
|
|
return nn.Conv2d(in_channels, out_channels,
|
|
kernel_size=3, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
|
|
|
|
|
|
def conv1x1(in_channels, out_channels, stride=1, padding=0):
|
|
"""1x1 convolution"""
|
|
weight_shape = (out_channels, in_channels, 1, 1)
|
|
weight = weight_variable(weight_shape)
|
|
return nn.Conv2d(in_channels, out_channels,
|
|
kernel_size=1, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
|
|
|
|
|
|
def conv7x7(in_channels, out_channels, stride=1, padding=0):
|
|
"""1x1 convolution"""
|
|
weight_shape = (out_channels, in_channels, 7, 7)
|
|
weight = weight_variable(weight_shape)
|
|
return nn.Conv2d(in_channels, out_channels,
|
|
kernel_size=7, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
|
|
|
|
|
|
def bn_with_initialize(out_channels):
|
|
shape = (out_channels)
|
|
mean = weight_variable_0(shape)
|
|
var = weight_variable_1(shape)
|
|
beta = weight_variable_0(shape)
|
|
gamma = weight_variable_uniform(shape)
|
|
bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
|
|
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
|
|
return bn
|
|
|
|
|
|
def bn_with_initialize_last(out_channels):
|
|
shape = (out_channels)
|
|
mean = weight_variable_0(shape)
|
|
var = weight_variable_1(shape)
|
|
beta = weight_variable_0(shape)
|
|
gamma = weight_variable_uniform(shape)
|
|
bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
|
|
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
|
|
return bn
|
|
|
|
|
|
def fc_with_initialize(input_channels, out_channels):
|
|
weight_shape = (out_channels, input_channels)
|
|
weight = weight_variable(weight_shape)
|
|
bias_shape = (out_channels)
|
|
bias = weight_variable_uniform(bias_shape)
|
|
return nn.Dense(input_channels, out_channels, weight, bias)
|
|
|
|
|
|
class ResidualBlock(nn.Cell):
|
|
expansion = 4
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride=1):
|
|
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 = P.ReLU()
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, x):
|
|
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):
|
|
expansion = 4
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride=1,
|
|
down_sample=False):
|
|
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 = P.ReLU()
|
|
self.downSample = 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 = P.TensorAdd()
|
|
|
|
def construct(self, x):
|
|
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):
|
|
|
|
def __init__(self, block, in_channels, out_channels, stride):
|
|
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):
|
|
x = self.a(x)
|
|
x = self.b(x)
|
|
x = self.c(x)
|
|
|
|
return x
|
|
|
|
|
|
class MakeLayer1(nn.Cell):
|
|
|
|
def __init__(self, block, in_channels, out_channels, stride):
|
|
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):
|
|
x = self.a(x)
|
|
x = self.b(x)
|
|
x = self.c(x)
|
|
x = self.d(x)
|
|
|
|
return x
|
|
|
|
|
|
class MakeLayer2(nn.Cell):
|
|
|
|
def __init__(self, block, in_channels, out_channels, stride):
|
|
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):
|
|
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):
|
|
|
|
def __init__(self, block, in_channels, out_channels, stride):
|
|
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):
|
|
x = self.a(x)
|
|
x = self.b(x)
|
|
x = self.c(x)
|
|
|
|
return x
|
|
|
|
|
|
class ResNet(nn.Cell):
|
|
|
|
def __init__(self, block, num_classes=100, batch_size=32):
|
|
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 = P.ReLU()
|
|
self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="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 = P.ReduceMean(keep_dims=True)
|
|
self.squeeze = P.Squeeze(axis=(2, 3))
|
|
self.fc = fc_with_initialize(512 * block.expansion, num_classes)
|
|
|
|
def construct(self, x):
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)[0]
|
|
|
|
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):
|
|
return ResNet(ResidualBlock, num_classes, batch_size)
|