昇思MindSpore学习笔记6-04计算机视觉--Shufflenet图像分类

发布于:2024-07-11 ⋅ 阅读:(8) ⋅ 点赞:(0)

摘要:

     记录MindSpore AI框架使用ShuffleNet网络对CIFAR-10数据集进行分类的过程、步骤和方法。包括环境准备、下载数据集、数据集加载和预处理、构建模型、模型训练、模型评估、模型测试等。

一、

1.ShuffleNet网络

旷视科技提出的CNN模型

应用在移动端

通过设计更高效的网络结构来实现模型的压缩和加速。

目标

        利用有限资源达到最好的模型精度。

核心引入了两种操作

        Pointwise Group Convolution

        Channel Shuffle

优点

        保持准确率不低,降低参数量

2.模型架构

ShuffleNet最显著的特点

重排不同通道解决Group Convolution弊端

改进ResNet Bottleneck单元

较小计算量达到较高准确率

Pointwise Group Convolution

Group Convolution(分组卷积)原理图

分组卷积

每组卷积核大小为in_channels/g*k*k

共有g组

所有组共有(in_channels/g*k*k)*out_channels个参数

是正常卷积参数的1/g

每个卷积核只处理输入特征图部分通道

优点

        降低参数量,输出通道数仍等于卷积核的数量

Depthwise Convolution(深度可分离卷积)

将组数g分为和输入通道相等的in_channels

卷积操作每个in_channels

        每个卷积核只处理一个通道

        卷积核大小为1*k*k

        卷积核参数量:in_channels*k*k

        feature maps通道数与输入通道数相等

Pointwise Group Convolution(逐点分组卷积)

分组卷积基础

每组卷积核大小 1×11×1

卷积核参数量为(in_channels/g*1*1)*out_channels

3.Channel Shuffle

通道重排

Group Convolution的弊端

        不同组别的通道无法进行信息交流

        降低网络的特征提取能力

不同分组通道均匀分散重组

下一层网络能处理不同组别通道的信息

对于g组

每组有n个通道的特征图

        reshape成g行n列的矩阵

        矩阵转置成n行g列

        flatten操作得到新排列

        轻操作

二、环境准备

%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14,如需更换mindspore版本,可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
# 查看当前 mindspore 版本
!pip show mindspore

输出:

Name: mindspore
Version: 2.2.14
Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Home-page: https://www.mindspore.cn
Author: The MindSpore Authors
Author-email: contact@mindspore.cn
License: Apache 2.0
Location: /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages
Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy
Required-by: 

分组卷积类

from mindspore import nn
import mindspore.ops as ops
from mindspore import Tensor
​
class GroupConv(nn.Cell):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
        super(GroupConv, self).__init__()
        self.groups = groups
        self.convs = nn.CellList()
        for _ in range(groups):
            self.convs.append(nn.Conv2d(in_channels // groups, out_channels // groups,
                                        kernel_size=kernel_size, stride=stride, has_bias=has_bias,
                                        padding=pad, pad_mode=pad_mode, group=1, weight_init='xavier_uniform'))
​
    def construct(self, x):
        features = ops.split(x, split_size_or_sections=int(len(x[0]) // self.groups), axis=1)
        outputs = ()
        for i in range(self.groups):
            outputs = outputs + (self.convs[i](features[i].astype("float32")),)
        out = ops.cat(outputs, axis=1)
        return out

三、ShuffleNet模块

ShuffleNet的改进,从(a)->(b)->(c)

对ResNet中的Bottleneck结构进行由(a)到(b), (c)的更改:

1.(a)中的一、三层1×1Conv卷积模块(降维、升维)改成1×1GConv逐点分组卷积;

2.(a)中一层降维后进行通道重排,让不同通道的信息交流;

3.(a)中的二层3×3 DWConv降采样模块中步长设置为2

        长宽降为原来的一半((c)中三层)

(c)中shortcut中采用步长为2的3×3平均池化

        相加改成拼接

class ShuffleV1Block(nn.Cell):
    def __init__(self, inp, oup, group, first_group, mid_channels, ksize, stride):
        super(ShuffleV1Block, self).__init__()
        self.stride = stride
        pad = ksize // 2
        self.group = group
        if stride == 2:
            outputs = oup - inp
        else:
            outputs = oup
        self.relu = nn.ReLU()
        branch_main_1 = [
            GroupConv(in_channels=inp, out_channels=mid_channels,
                      kernel_size=1, stride=1, pad_mode="pad", pad=0,
                      groups=1 if first_group else group),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(),
        ]
        branch_main_2 = [
            nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride,
                      pad_mode='pad', padding=pad, group=mid_channels,
                      weight_init='xavier_uniform', has_bias=False),
            nn.BatchNorm2d(mid_channels),
            GroupConv(in_channels=mid_channels, out_channels=outputs,
                      kernel_size=1, stride=1, pad_mode="pad", pad=0,
                      groups=group),
            nn.BatchNorm2d(outputs),
        ]
        self.branch_main_1 = nn.SequentialCell(branch_main_1)
        self.branch_main_2 = nn.SequentialCell(branch_main_2)
        if stride == 2:
            self.branch_proj = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')
​
    def construct(self, old_x):
        left = old_x
        right = old_x
        out = old_x
        right = self.branch_main_1(right)
        if self.group > 1:
            right = self.channel_shuffle(right)
        right = self.branch_main_2(right)
        if self.stride == 1:
            out = self.relu(left + right)
        elif self.stride == 2:
            left = self.branch_proj(left)
            out = ops.cat((left, right), 1)
            out = self.relu(out)
        return out
​
    def channel_shuffle(self, x):
        batchsize, num_channels, height, width = ops.shape(x)
        group_channels = num_channels // self.group
        x = ops.reshape(x, (batchsize, group_channels, self.group, height, width))
        x = ops.transpose(x, (0, 2, 1, 3, 4))
        x = ops.reshape(x, (batchsize, num_channels, height, width))
        return x

四、构建ShuffleNet网络

ShuffleNet网络结构图

输入图像224×224,组数3(g = 3)为例

卷积层

        通过数量24

        卷积核大小为3×3

        stride为2

        输出特征图大小为112×112

        channel为24

最大池化层

        stride为2

        输出特征图大小为56×56

        channel数不变

堆叠3个ShuffleNet模块

        Stage2重复4次

                下采样模块

                        特征图长宽减半

                        Channel 240

        Stage3重复8次

                下采样模块

                        特征图长宽减半

                        Channel 480

        Stage4重复4次

                下采样模块

                        特征图长宽减半

                        Channel 960

全局平均池化

        输出大小为1×1×960

全连接层

Softmax

得到分类概率

class ShuffleNetV1(nn.Cell):
    def __init__(self, n_class=1000, model_size='2.0x', group=3):
        super(ShuffleNetV1, self).__init__()
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        if group == 3:
            if model_size == '0.5x':
                self.stage_out_channels = [-1, 12, 120, 240, 480]
            elif model_size == '1.0x':
                self.stage_out_channels = [-1, 24, 240, 480, 960]
            elif model_size == '1.5x':
                self.stage_out_channels = [-1, 24, 360, 720, 1440]
            elif model_size == '2.0x':
                self.stage_out_channels = [-1, 48, 480, 960, 1920]
            else:
                raise NotImplementedError
        elif group == 8:
            if model_size == '0.5x':
                self.stage_out_channels = [-1, 16, 192, 384, 768]
            elif model_size == '1.0x':
                self.stage_out_channels = [-1, 24, 384, 768, 1536]
            elif model_size == '1.5x':
                self.stage_out_channels = [-1, 24, 576, 1152, 2304]
            elif model_size == '2.0x':
                self.stage_out_channels = [-1, 48, 768, 1536, 3072]
            else:
                raise NotImplementedError
        input_channel = self.stage_out_channels[1]
        self.first_conv = nn.SequentialCell(
            nn.Conv2d(3, input_channel, 3, 2, 'pad', 1, weight_init='xavier_uniform', has_bias=False),
            nn.BatchNorm2d(input_channel),
            nn.ReLU(),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
        features = []
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]
            for i in range(numrepeat):
                stride = 2 if i == 0 else 1
                first_group = idxstage == 0 and i == 0
                features.append(ShuffleV1Block(input_channel, output_channel,
                                               group=group, first_group=first_group,
                                               mid_channels=output_channel // 4, ksize=3, stride=stride))
                input_channel = output_channel
        self.features = nn.SequentialCell(features)
        self.globalpool = nn.AvgPool2d(7)
        self.classifier = nn.Dense(self.stage_out_channels[-1], n_class)
​
    def construct(self, x):
        x = self.first_conv(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.globalpool(x)
        x = ops.reshape(x, (-1, self.stage_out_channels[-1]))
        x = self.classifier(x)
        return x

五、模型训练和评估

采用CIFAR-10数据集对ShuffleNet进行预训练。

1.训练集准备与加载

CIFAR-10

有60000张32*32的彩色图像

均匀地分为10个类别

50000张图片作为训练集

10000张图片作为测试集

mindspore.dataset.Cifar10Dataset接口

下载CIFAR-10的训练集

加载

from download import download
​
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"
​
download(url, "./dataset", kind="tar.gz", replace=True)

输出:

Creating data folder...
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz (162.2 MB)

file_sizes: 100%|█████████████████████████████| 170M/170M [00:00<00:00, 177MB/s]
Extracting tar.gz file...
Successfully downloaded / unzipped to ./dataset
[6]:
'./dataset'
import mindspore as ms
from mindspore.dataset import Cifar10Dataset
from mindspore.dataset import vision, transforms
​
def get_dataset(train_dataset_path, batch_size, usage):
    image_trans = []
    if usage == "train":
        image_trans = [
            vision.RandomCrop((32, 32), (4, 4, 4, 4)),
            vision.RandomHorizontalFlip(prob=0.5),
            vision.Resize((224, 224)),
            vision.Rescale(1.0 / 255.0, 0.0),
            vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
            vision.HWC2CHW()
        ]
    elif usage == "test":
        image_trans = [
            vision.Resize((224, 224)),
            vision.Rescale(1.0 / 255.0, 0.0),
            vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
            vision.HWC2CHW()
        ]
    label_trans = transforms.TypeCast(ms.int32)
    dataset = Cifar10Dataset(train_dataset_path, usage=usage, shuffle=True)
    dataset = dataset.map(image_trans, 'image')
    dataset = dataset.map(label_trans, 'label')
    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset
​
dataset = get_dataset("./dataset/cifar-10-batches-bin", 128, "train")
batches_per_epoch = dataset.get_dataset_size()

2.模型训练

随机初始化参数做预训练。

调用ShuffleNetV1定义网络

参数量选择"2.0x"

定义损失函数为交叉熵损失

学习率4轮的warmup后

余弦退火

优化器Momentum

train.model.Model接口封装

model.train()训练

传入回调函数

ModelCheckpoint

CheckpointConfig

TimeMonitor

LossMonitor

打印

训练轮数

损失

时间

保存ckpt文件在当前目录下

import time
import mindspore
import numpy as np
from mindspore import Tensor, nn
from mindspore.train import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor, Model, Top1CategoricalAccuracy, Top5CategoricalAccuracy
​
def train():
    mindspore.set_context(mode=mindspore.PYNATIVE_MODE, device_target="Ascend")
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)
    min_lr = 0.0005
    base_lr = 0.05
    lr_scheduler = mindspore.nn.cosine_decay_lr(min_lr,
                                                base_lr,
                                                batches_per_epoch*250,
                                                batches_per_epoch,
                                                decay_epoch=250)
    lr = Tensor(lr_scheduler[-1])
    optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.00004, loss_scale=1024)
    loss_scale_manager = ms.amp.FixedLossScaleManager(1024, drop_overflow_update=False)
    model = Model(net, loss_fn=loss, optimizer=optimizer, amp_level="O3", loss_scale_manager=loss_scale_manager)
    callback = [TimeMonitor(), LossMonitor()]
    save_ckpt_path = "./"
    config_ckpt = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=5)
    ckpt_callback = ModelCheckpoint("shufflenetv1", directory=save_ckpt_path, config=config_ckpt)
    callback += [ckpt_callback]
​
    print("============== Starting Training ==============")
    start_time = time.time()
    # 由于时间原因,epoch = 5,可根据需求进行调整
    model.train(5, dataset, callbacks=callback)
    use_time = time.time() - start_time
    hour = str(int(use_time // 60 // 60))
    minute = str(int(use_time // 60 % 60))
    second = str(int(use_time % 60))
    print("total time:" + hour + "h " + minute + "m " + second + "s")
    print("============== Train Success ==============")
​
if __name__ == '__main__':
    train()

输出:

model size is  2.0x
============== Starting Training ==============
epoch: 1 step: 1, loss is 2.702430248260498
epoch: 1 step: 2, loss is 2.5544934272766113
epoch: 1 step: 3, loss is 2.3527920246124268
epoch: 1 step: 4, loss is 2.432495355606079
epoch: 1 step: 5, loss is 2.442847490310669
......
epoch: 1 step: 386, loss is 1.8315027952194214
epoch: 1 step: 387, loss is 1.9081732034683228
epoch: 1 step: 388, loss is 1.8965389728546143
epoch: 1 step: 389, loss is 1.8942060470581055
epoch: 1 step: 390, loss is 1.8646998405456543
Train epoch time: 439745.086 ms, per step time: 1127.552 ms
epoch: 2 step: 1, loss is 1.9022231101989746
epoch: 2 step: 2, loss is 1.8828961849212646
epoch: 2 step: 3, loss is 1.8220021724700928
epoch: 2 step: 4, loss is 2.003005027770996
epoch: 2 step: 5, loss is 1.8657888174057007
......
epoch: 2 step: 386, loss is 1.754606008529663
epoch: 2 step: 387, loss is 1.73811674118042
epoch: 2 step: 388, loss is 1.5935282707214355
epoch: 2 step: 389, loss is 1.7022861242294312
epoch: 2 step: 390, loss is 1.7202574014663696
Train epoch time: 121300.859 ms, per step time: 311.028 ms
epoch: 3 step: 1, loss is 1.6813828945159912
epoch: 3 step: 2, loss is 1.7341467142105103
epoch: 3 step: 3, loss is 1.8423044681549072
epoch: 3 step: 4, loss is 1.8151057958602905
epoch: 3 step: 5, loss is 1.727158784866333
......
epoch: 3 step: 386, loss is 1.6009197235107422
epoch: 3 step: 387, loss is 1.7389277219772339
epoch: 3 step: 388, loss is 1.6847612857818604
epoch: 3 step: 389, loss is 1.7618985176086426
epoch: 3 step: 390, loss is 1.719774842262268
Train epoch time: 121936.621 ms, per step time: 312.658 ms
epoch: 4 step: 1, loss is 1.6524462699890137
epoch: 4 step: 2, loss is 1.5743780136108398
epoch: 4 step: 3, loss is 1.7330453395843506
epoch: 4 step: 4, loss is 1.6160061359405518
epoch: 4 step: 5, loss is 1.6632086038589478
......
epoch: 4 step: 386, loss is 1.6585990190505981
epoch: 4 step: 387, loss is 1.6520838737487793
epoch: 4 step: 388, loss is 1.4504361152648926
epoch: 4 step: 389, loss is 1.8115458488464355
epoch: 4 step: 390, loss is 1.6291583776474
Train epoch time: 121944.082 ms, per step time: 312.677 ms
epoch: 5 step: 1, loss is 1.737457275390625
epoch: 5 step: 2, loss is 1.6314475536346436
epoch: 5 step: 3, loss is 1.6039154529571533
epoch: 5 step: 4, loss is 1.59605073928833
epoch: 5 step: 5, loss is 1.6140247583389282
......
epoch: 5 step: 386, loss is 1.599562406539917
epoch: 5 step: 387, loss is 1.486626148223877
epoch: 5 step: 388, loss is 1.6146260499954224
epoch: 5 step: 389, loss is 1.6220197677612305
epoch: 5 step: 390, loss is 1.610574722290039
Train epoch time: 121699.011 ms, per step time: 312.049 ms
total time:0h 15m 26s
============== Train Success ==============

训练好的模型保存在当前目录的shufflenetv1-5_390.ckpt中,用作评估。

3.模型评估

在CIFAR-10的测试集上对模型进行评估。

设置评估模型路径

加载数据集

设置Top 1、Top 5的评估标准

model.eval()接口对模型进行评估

from mindspore import load_checkpoint, load_param_into_net
​
def test():
    mindspore.set_context(mode=mindspore.GRAPH_MODE, device_target="Ascend")
    dataset = get_dataset("./dataset/cifar-10-batches-bin", 128, "test")
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    param_dict = load_checkpoint("shufflenetv1-5_390.ckpt")
    load_param_into_net(net, param_dict)
    net.set_train(False)
    loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)
    eval_metrics = {'Loss': nn.Loss(), 'Top_1_Acc': Top1CategoricalAccuracy(),
                    'Top_5_Acc': Top5CategoricalAccuracy()}
    model = Model(net, loss_fn=loss, metrics=eval_metrics)
    start_time = time.time()
    res = model.eval(dataset, dataset_sink_mode=False)
    use_time = time.time() - start_time
    hour = str(int(use_time // 60 // 60))
    minute = str(int(use_time // 60 % 60))
    second = str(int(use_time % 60))
    log = "result:" + str(res) + ", ckpt:'" + "./shufflenetv1-5_390.ckpt" \
        + "', time: " + hour + "h " + minute + "m " + second + "s"
    print(log)
    filename = './eval_log.txt'
    with open(filename, 'a') as file_object:
        file_object.write(log + '\n')
​
if __name__ == '__main__':
    test()

输出:

model size is  2.0x
[ERROR] CORE(263,ffffa833e930,python):2024-07-07-15:29:24.418.000 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_263/3162391481.py]
[ERROR] CORE(263,ffffa833e930,python):2024-07-07-15:29:24.418.539 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_263/3162391481.py]
......
result:{'Loss': 1.6150915485162, 'Top_1_Acc': 0.4930889423076923, 'Top_5_Acc': 0.9283854166666666}, ckpt:'./shufflenetv1-5_390.ckpt', time: 0h 1m 26s

4.模型预测

在CIFAR-10的测试集上对模型进行预测,并将预测结果可视化。

import mindspore
import matplotlib.pyplot as plt
import mindspore.dataset as ds
​
net = ShuffleNetV1(model_size="2.0x", n_class=10)
show_lst = []
param_dict = load_checkpoint("shufflenetv1-5_390.ckpt")
load_param_into_net(net, param_dict)
model = Model(net)
dataset_predict = ds.Cifar10Dataset(dataset_dir="./dataset/cifar-10-batches-bin", shuffle=False, usage="train")
dataset_show = ds.Cifar10Dataset(dataset_dir="./dataset/cifar-10-batches-bin", shuffle=False, usage="train")
dataset_show = dataset_show.batch(16)
show_images_lst = next(dataset_show.create_dict_iterator())["image"].asnumpy()
image_trans = [
    vision.RandomCrop((32, 32), (4, 4, 4, 4)),
    vision.RandomHorizontalFlip(prob=0.5),
    vision.Resize((224, 224)),
    vision.Rescale(1.0 / 255.0, 0.0),
    vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
    vision.HWC2CHW()
        ]
dataset_predict = dataset_predict.map(image_trans, 'image')
dataset_predict = dataset_predict.batch(16)
class_dict = {0:"airplane", 1:"automobile", 2:"bird", 3:"cat", 4:"deer", 5:"dog", 6:"frog", 7:"horse", 8:"ship", 9:"truck"}
# 推理效果展示(上方为预测的结果,下方为推理效果图片)
plt.figure(figsize=(16, 5))
predict_data = next(dataset_predict.create_dict_iterator())
output = model.predict(ms.Tensor(predict_data['image']))
pred = np.argmax(output.asnumpy(), axis=1)
index = 0
for image in show_images_lst:
    plt.subplot(2, 8, index+1)
    plt.title('{}'.format(class_dict[pred[index]]))
    index += 1
    plt.imshow(image)
    plt.axis("off")
plt.show()

输出:

model size is  2.0x
[ERROR] CORE(263,ffffa833e930,python):2024-07-07-15:30:55.337.972 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_263/1681751341.py]
[ERROR] CORE(263,ffffa833e930,python):2024-07-07-15:30:55.338.097 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_263/1681751341.py]
......