你是谁家的小猫咪--基于深度学习框架MegEngine 的猫咪识别

一起用代码吸猫！本文正在参与【喵星人征文活动】。

你是谁家的小猫咪？

小茗同学：二狗子家的小猫又来偷吃了，小花猫已经饿坏了，咋办呀？？？

怎么办？？？小宠物们识别要大学科研团队才能做，作为幼儿园的我怎么办？？？

小茗同学苦恼的挠了挠头，发出痛苦的啊啊啊声音。。。。。。

小茗同学查到了新闻报道《“猴脸识别技术”来了！》 new.qq.com/omn/2021022…

别着急，幼儿园小茗同学我教你用旷视深度学习框架MegEngine来实现小猫识别，不认识的小猫不准进。

1.数据采集

所有猫的视频均采集公开视频,通过视频截图来获得小猫的正脸照片，不用单独拍照了。

!unzip -q data/data71411/cat.zip 
复制代码

replace 1.mp4? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C
复制代码

1.1 python调用openCV每隔1秒从视频中截取一张图片，编号后保存。

import cv2
import os

for i in range(1,5):
    # 创建图片目录
    print(i)
    mp4_file=str(i)+'.mp4'
    dir_path=os.path.join('dataset',str(i))
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)
    # 每秒存一次图片
    vidcap = cv2.VideoCapture(mp4_file)
    success,image = vidcap.read()
    fps = int(vidcap.get(cv2.CAP_PROP_FPS))
    count = 0
    while success:
        if count % fps == 0:
            cv2.imwrite("{}/{}.jpg".format(dir_path, int(count / fps)), image)
        print('Process %dth seconds: ' % int(count / fps), success)
        success,image = vidcap.read()
        count += 1
复制代码

1.2 生成图片进行处理

删除片尾等不正常图片

手动中...................................

import matplotlib.pyplot as plt
%matplotlib inline
import cv2 as cv
import numpy as np


# jupyter notebook显示
def visualize_images():
    img = cv.imread('dataset/1/1.jpg')
    plt.imshow(img)
    plt.show()

visualize_images()

复制代码

1.3 数据集查看

4只不同的小猫

1.4 list生成

自定义的数据集，首先要生成图像列表，把自定的图像分为测试集和训练集，并带有标签。下面的程序可以单独运行，只要把一个大类的文件夹路径传进去就可以了,该程序会把里面的每个小类别都迭代,生成固定格式的列表.比如我们把人脸类别的根目录传进去./dataset。最后会在指定目录下面生成三个文件，readme.json、train.list和test.list.

import os
import json

# 设置要生成文件的路径
data_root_path = 'cat'
# 所有类别的信息
class_detail = []
# 获取所有类别保存的文件夹名称，这里是['1', '2', '3','4']
class_dirs = os.listdir(data_root_path)
# 类别标签
class_label = 0
# 获取总类别的名称
father_paths = data_root_path.split('/')   
while True:
    if father_paths[father_paths.__len__() - 1] == '':
        del father_paths[father_paths.__len__() - 1]
    else:
        break
father_path = father_paths[father_paths.__len__() - 1]
data_list_path='./'

# 清空原来的数据
with open( "test.txt", 'w') as f:
    pass
with open( "train.txt", 'w') as f:
    pass
# 总的图像数量
all_class_images = 0
# 读取每个类别
for class_dir in class_dirs:
    # 每个类别的信息
    class_detail_list = {}
    test_sum = 0
    trainer_sum = 0
    # 统计每个类别有多少张图片
    class_sum = 0
    # 获取类别路径
    path = data_root_path + "/" + class_dir
    # 获取所有图片
    img_paths = os.listdir(path)

    for img_path in img_paths:                                  # 遍历文件夹下的每个图片
        name_path = path + '/' + img_path                       # 每张图片的路径
        if class_sum % 10 == 0:                                 # 每10张图片取一个做测试数据
            test_sum += 1                                       #test_sum测试数据的数目
            with open(data_list_path + "test.txt", 'a') as f:
                f.write(name_path + "\t%d" % class_label + "\n") #class_label 标签：0,1,2
        else:
            trainer_sum += 1                                    #trainer_sum测试数据的数目
            with open(data_list_path + "train.txt", 'a') as f:
                f.write(name_path + "\t%d" % class_label + "\n")#class_label 标签：0,1,2
        class_sum += 1                                          #每类图片的数目
        all_class_images += 1                                   #所有类图片的数目

    # 说明的json文件的class_detail数据
    class_detail_list['class_name'] = class_dir             #类别名称，如jiangwen
    class_detail_list['class_label'] = class_label          #类别标签，0,1,2
    class_detail_list['class_test_images'] = test_sum       #该类数据的测试集数目
    class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目
    class_detail.append(class_detail_list)         
    class_label += 1                                            #class_label 标签：0,1,2
# 获取类别数量
all_class_sum = class_dirs.__len__()
# 说明的json文件信息
readjson = {}
readjson['all_class_name'] = father_path                  #文件父目录
readjson['all_class_sum'] = all_class_sum                #
readjson['all_class_images'] = all_class_images
readjson['class_detail'] = class_detail
jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': '))
with open(data_list_path + "readme.json",'w') as f:
    f.write(jsons)
print ('生成数据列表完成！')
复制代码

生成数据列表完成！
复制代码

1.5 DataSet构造

import megengine.hub
import urllib
import cv2
import numpy as np
import megengine.data.transform as T
import megengine.functional as F
import numpy as np
from PIL import Image

class MiaoMiaoDataset(megengine.data.dataset.Dataset):
    """
    2类Bee数据集类的定义
    """
    def __init__(self,mode='train'):
        """
        初始化函数
        """
        self.data = []
        with open('{}.txt'.format(mode)) as f:
            for line in f.readlines():
                info = line.strip().split('\t')
                if len(info) > 0:
                    self.data.append([info[0].strip(), info[1].strip()])

        if mode == 'train':
            self.transforms = T.Compose([
                T.Resize((224,224)), 
                T.RandomHorizontalFlip(0.5),        # 随机水平翻转
                T.ToMode("CHW"),                      # 数据的格式转换和标准化 HWC => CHW  
                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 图像归一化
            ])
        else:
            self.transforms = T.Compose([
                T.Resize((224,224)),           # 图像大小修改
                # T.RandomCrop(IMAGE_SIZE),    # 随机裁剪
                T.ToMode("CHW"),                  # 数据的格式转换和标准化 HWC => CHW
                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])   # 图像归一化
            ])

    def get_origin_data(self):
        return self.data

    def __getitem__(self, index):
        """
        根据索引获取单个样本
        """
        image_file, label = self.data[index]
        image = Image.open(image_file)
        if image.mode != 'RGB':
            image = image.convert('RGB')
        image = self.transforms(image)
        return image, np.array(label, dtype='int64')

    def __len__(self):
        """
        获取样本总数
        """
        return len(self.data)
复制代码

train_dataset=MiaoMiaoDataset(mode='train')
test_dataset=MiaoMiaoDataset(mode='test')
print('train_data len: {}, test_data len:{}'.format(train_dataset.__len__(), test_dataset.__len__()))
复制代码

train_data len: 45, test_data len:7
复制代码

2.Model 定义

目前，数据已经划分了train和test数据集，以及分类数量等。

下一步我们将定义model，再复习一下resnet网络了。

import math

import megengine.functional as F
import megengine.hub as hub
import megengine.module as M


class BasicBlock(M.Module):
    expansion = 1

    def __init__(
        self,
        in_channels,
        channels,
        stride=1,
        groups=1,
        base_width=64,
        dilation=1,
        norm=M.BatchNorm2d,
    ):
        super().__init__()
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        self.conv1 = M.Conv2d(
            in_channels, channels, 3, stride, padding=dilation, bias=False
        )
        self.bn1 = norm(channels)
        self.conv2 = M.Conv2d(channels, channels, 3, 1, padding=1, bias=False)
        self.bn2 = norm(channels)
        self.downsample = (
            M.Identity()
            if in_channels == channels and stride == 1
            else M.Sequential(
                M.Conv2d(in_channels, channels, 1, stride, bias=False), norm(channels),
            )
        )

    def forward(self, x):
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        identity = self.downsample(identity)
        x += identity
        x = F.relu(x)
        return x


class Bottleneck(M.Module):
    expansion = 4

    def __init__(
        self,
        in_channels,
        channels,
        stride=1,
        groups=1,
        base_width=64,
        dilation=1,
        norm=M.BatchNorm2d,
    ):
        super().__init__()
        width = int(channels * (base_width / 64.0)) * groups
        self.conv1 = M.Conv2d(in_channels, width, 1, 1, bias=False)
        self.bn1 = norm(width)
        self.conv2 = M.Conv2d(
            width,
            width,
            3,
            stride,
            padding=dilation,
            groups=groups,
            dilation=dilation,
            bias=False,
        )
        self.bn2 = norm(width)
        self.conv3 = M.Conv2d(width, channels * self.expansion, 1, 1, bias=False)
        self.bn3 = norm(channels * self.expansion)
        self.downsample = (
            M.Identity()
            if in_channels == channels * self.expansion and stride == 1
            else M.Sequential(
                M.Conv2d(in_channels, channels * self.expansion, 1, stride, bias=False),
                norm(channels * self.expansion),
            )
        )

    def forward(self, x):
        identity = x

        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)

        identity = self.downsample(identity)

        x += identity
        x = F.relu(x)

        return x


class ResNet(M.Module):
    def __init__(
        self,
        block,
        layers,
        num_classes=1000,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm=M.BatchNorm2d,
    ):
        super().__init__()
        self.in_channels = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = M.Conv2d(
            3, self.in_channels, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm(self.in_channels)
        self.maxpool = M.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], norm=norm)
        self.layer2 = self._make_layer(
            block,
            128,
            layers[1],
            stride=2,
            dilate=replace_stride_with_dilation[0],
            norm=norm,
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1],
            norm=norm,
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=2,
            dilate=replace_stride_with_dilation[2],
            norm=norm,
        )
        self.fc = M.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, M.Conv2d):
                M.init.msra_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    fan_in, _ = M.init.calculate_fan_in_and_fan_out(m.weight)
                    bound = 1 / math.sqrt(fan_in)
                    M.init.uniform_(m.bias, -bound, bound)
            elif isinstance(m, M.BatchNorm2d):
                M.init.ones_(m.weight)
                M.init.zeros_(m.bias)
            elif isinstance(m, M.Linear):
                M.init.msra_uniform_(m.weight, a=math.sqrt(5))
                if m.bias is not None:
                    fan_in, _ = M.init.calculate_fan_in_and_fan_out(m.weight)
                    bound = 1 / math.sqrt(fan_in)
                    M.init.uniform_(m.bias, -bound, bound)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block
        # behaves like an identity. According to https://arxiv.org/abs/1706.02677
        # This improves the model by 0.2~0.3%.
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    M.init.zeros_(m.bn3.weight)
                elif isinstance(m, BasicBlock):
                    M.init.zeros_(m.bn2.weight)

    def _make_layer(
        self, block, channels, blocks, stride=1, dilate=False, norm=M.BatchNorm2d
    ):
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1

        layers = []
        layers.append(
            block(
                self.in_channels,
                channels,
                stride,
                groups=self.groups,
                base_width=self.base_width,
                dilation=previous_dilation,
                norm=norm,
            )
        )
        self.in_channels = channels * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.in_channels,
                    channels,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm=norm,
                )
            )

        return M.Sequential(*layers)

    def extract_features(self, x):
        outputs = {}
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.maxpool(x)
        outputs["stem"] = x

        x = self.layer1(x)
        outputs["res2"] = x
        x = self.layer2(x)
        outputs["res3"] = x
        x = self.layer3(x)
        outputs["res4"] = x
        x = self.layer4(x)
        outputs["res5"] = x
        return outputs

    def forward(self, x):
        x = self.extract_features(x)["res5"]

        x = F.avg_pool2d(x, 7)
        x = F.flatten(x, 1)
        x = self.fc(x)

        return x


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnet18_naiveaug_70312_78a63ca6.pkl"
)
def resnet18(**kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    """
    return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnet34_naiveaug_73960_fd9d869d.pkl"
)
def resnet34(**kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    """
    return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnet50_fbaug_76254_4e14b7d1.pkl"
)
def resnet50(**kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    """
    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnet101_fbaug_77944_b7932921.pkl"
)
def resnet101(**kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    """
    return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnet152_fbaug_78582_7551aff3.pkl"
)
def resnet152(**kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    """
    return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnext50_32x4d_fbaug_77592_c4b04e5e.pkl"
)
def resnext50_32x4d(**kwargs):
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks"
    <https://arxiv.org/pdf/1611.05431.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs["groups"] = 32
    kwargs["width_per_group"] = 4
    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/resnext101_32x8d_fbaug_79520_80efb344.pkl"
)
def resnext101_32x8d(**kwargs):
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks"
    <https://arxiv.org/pdf/1611.05431.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs["groups"] = 32
    kwargs["width_per_group"] = 8
    return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)

复制代码

class AverageMeter:
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=":.3f"):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
        return fmtstr.format(**self.__dict__)
复制代码

def valid(func, data_queue, args):
    objs = AverageMeter("Loss")
    top1 = AverageMeter("Acc@1")
    top5 = AverageMeter("Acc@5")
    clck = AverageMeter("Time")

    t = time.time()
    for step, (image, label) in enumerate(data_queue):
        image = megengine.tensor(image, dtype="float32")
        label = megengine.tensor(label, dtype="int32")

        n = image.shape[0]

        loss, acc1, acc5 = func(image, label)

        objs.update(loss.item(), n)
        top1.update(100 * acc1.item(), n)
        top5.update(100 * acc5.item(), n)
        clck.update(time.time() - t, n)
        t = time.time()

        if step % args.print_freq == 0 and dist.get_rank() == 0:
            logging.info("Test step %d, %s %s %s %s", step, objs, top1, top5, clck)

    return objs.avg, top1.avg, top5.avg
复制代码

3、Model 训练

def worker(args):
    # pylint: disable=too-many-statements
    if dist.get_rank() == 0:
        os.makedirs(os.path.join(args.save, args.arch), exist_ok=True)
        megengine.logger.set_log_file(os.path.join(args.save, args.arch, "log.txt"))

    # build dataset
    train_dataloader, valid_dataloader = build_dataset(args)
    train_queue = iter(train_dataloader)  # infinite
    steps_per_epoch = 1280000 // (dist.get_world_size() * args.batch_size)

    # build model
    model = snet_model.__dict__[18]()

    # Sync parameters and buffers
    if dist.get_world_size() > 1:
        dist.bcast_list_(model.parameters())
        dist.bcast_list_(model.buffers())

    # Autodiff gradient manager
    gm = autodiff.GradManager().attach(
        model.parameters(),
        callbacks=dist.make_allreduce_cb("mean") if dist.get_world_size() > 1 else None,
    )

    # Optimizer
    params_wd = []
    params_nwd = []
    for n, p in model.named_parameters():
        if n.find("weight") >= 0 and len(p.shape) > 1:
            print("include ", n, p.shape)
            params_wd.append(p)
        else:
            print("NOT include ", n, p.shape)
            params_nwd.append(p)
    opt = optim.SGD(
        [
            {"params": params_wd},
            {"params": params_nwd, "weight_decay": 0},
        ],
        lr=args.lr * dist.get_world_size(),
        momentum=args.momentum,
        weight_decay=args.weight_decay,
    )

    # train and valid func
    def train_step(image, label):
        with gm:
            logits = model(image)
            loss = F.nn.cross_entropy(logits, label, label_smooth=0.1)
            acc1, acc5 = F.topk_accuracy(logits, label, topk=(1, 5))
            gm.backward(loss)
            opt.step().clear_grad()
        return loss, acc1, acc5

    def valid_step(image, label):
        logits = model(image)
        loss = F.nn.cross_entropy(logits, label, label_smooth=0.1)
        acc1, acc5 = F.topk_accuracy(logits, label, topk=(1, 5))
        # calculate mean values
        if dist.get_world_size() > 1:
            loss = F.distributed.all_reduce_sum(loss) / dist.get_world_size()
            acc1 = F.distributed.all_reduce_sum(acc1) / dist.get_world_size()
            acc5 = F.distributed.all_reduce_sum(acc5) / dist.get_world_size()
        return loss, acc1, acc5

    # linear learning rate scheduler
    def adjust_learning_rate(step):
        lr = args.lr * dist.get_world_size() * (1 - step / (args.epochs * steps_per_epoch))
        for param_group in opt.param_groups:
            param_group["lr"] = lr
        return lr

    # start training
    objs = AverageMeter("Loss")
    top1 = AverageMeter("Acc@1")
    top5 = AverageMeter("Acc@5")
    clck = AverageMeter("Time")

    for step in range(0, args.epochs * steps_per_epoch):
        lr = adjust_learning_rate(step)

        t = time.time()

        image, label = next(train_queue)
        image = megengine.tensor(image, dtype="float32")
        label = megengine.tensor(label, dtype="int32")

        loss, acc1, acc5 = train_step(image, label)

        objs.update(loss.item())
        top1.update(100 * acc1.item())
        top5.update(100 * acc5.item())
        clck.update(time.time() - t)

        if step % args.print_freq == 0 and dist.get_rank() == 0:
            logging.info(
                "Epoch %d Step %d, LR %.4f, %s %s %s %s",
                step // steps_per_epoch,
                step,
                lr,
                objs,
                top1,
                top5,
                clck,
            )
            objs.reset()
            top1.reset()
            top5.reset()
            clck.reset()

        if (step + 1) % steps_per_epoch == 0:
            model.eval()
            _, valid_acc1, valid_acc5 = valid(valid_step, valid_dataloader, args)
            model.train()
            logging.info(
                "Epoch %d Test Acc@1 %.3f, Acc@5 %.3f",
                (step + 1) // steps_per_epoch,
                valid_acc1,
                valid_acc5,
            )
            if dist.get_rank() == 0:
                megengine.save(
                    {
                        "epoch": (step + 1) // steps_per_epoch,
                        "state_dict": model.state_dict(),
                    },
                    os.path.join(args.save, args.arch, "checkpoint.pkl"),
                )
复制代码

复制代码

预测

对test_dataset数据进行预测

print('测试数据集样本量：{}'.format(len(test_dataset)))
复制代码

测试数据集样本量：7
复制代码

# 执行预测
result = model.predict(test_dataset)
复制代码

Predict begin...
step 7/7 [==============================] - 32ms/step         
Predict samples: 7
复制代码

# 打印前10条看看结果
for idx in range(7):
    predict_label = str(np.argmax(result[0][idx]))
    real_label = str(test_dataset.__getitem__(idx)[1])
    print('样本ID：{}, 真实标签：{}, 预测值：{}'.format(idx, real_label, predict_label))
复制代码

样本ID：0, 真实标签：0, 预测值：0
样本ID：1, 真实标签：0, 预测值：0
样本ID：2, 真实标签：2, 预测值：2
样本ID：3, 真实标签：3, 预测值：3
样本ID：4, 真实标签：3, 预测值：3
样本ID：5, 真实标签：4, 预测值：0
样本ID：6, 真实标签：4, 预测值：1
复制代码

# 定义画图方法
from PIL import Image
import matplotlib.font_manager as font_manager
import matplotlib.pyplot as plt
%matplotlib inline

fontpath = 'MINGHEI_R.TTF'
font = font_manager.FontProperties(fname=fontpath, size=10)

def show_img(img, predict):
    plt.figure()
    plt.title(predict, FontProperties=font)
    plt.imshow(img, cmap=plt.cm.binary)
    plt.show()


# 抽样展示
origin_data=test_dataset.get_origin_data()
for i in range(7):
    img_path=origin_data[i][0]
    real_label=str(origin_data[i][1])
    predict_label= str(np.argmax(result[0][i]))
    img=Image.open(img_path)
    title='样本ID：{}, 真实标签：{}, 预测值：{}'.format(idx, real_label, predict_label)
    show_img(img, title)
复制代码