浙大宁波理工学院数据科学与大数据专业 别玩手机图像分类比赛

1 选手禁止互相抄袭，发现结果雷同者将取消成绩；

2 请在基线模型基础上修改代码，不允许使用第三方封装库、套件或者其他工具，否则做 0 分处理；

(相关资料图)

3 每位同学请独立完成比赛，不允许就比赛技术问题进行相互交流，更不允许索要代码，请自觉遵守规则，保持良好的品格；

4 晚上 12：00 以后不允许递交，否则做 0 分处理；

5 结果文件必须是程序生成，不允许手动修改或者后期处理。

如今，手机已成为大众离不开的生活工具，而且它的迅速发展使得它的功能不再以通讯为主，手机逐渐发展为可移动的大众传播媒体终端设备，甚至可以比作为第五媒体。当今的大学生群体是智能手机使用者中的一支巨大的的队伍，零零后大学生在进入大学以来，学习生活中过度的依赖手机，甚至上课时忘记携带手机便会手足无措，神情恍惚。本比赛要求通过监控摄像头等拍摄到的画面判断画面中的人物是否正在使用手机

本比赛采用的数据集中,训练集共 2180 张使用手机的图片（位于目录 data/data146247/train/0_phone/）、1971 张没有使用手机的图片（位于目录 data/data146247/train/1_no_phone/）测试集共 1849 张图片，无标注信息

本 Baseline 采用 LeNet 模型架构，参赛者可以在此基础上进行修改，也可以使用全新的网络架构冲榜~~LeNet 网络结构图如下LeNet-5 共有 7 层，不包含输入，每层都包含可训练参数；每个层有多个 Feature Map，每个 FeatureMap 通过一种卷积滤波器提取输入的一种特征，然后每个 FeatureMap 有多个神经元。

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!rm aug.csv!rm test.csv!rm train.csv!rm -r augment!rm -r train!rm -r test

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!unzip -oq /home/aistudio/data/data146247/train.zip!unzip -oq /home/aistudio/data/data146247/test.zip  #解压数据集!mkdir augment!mkdir augment/0_phone!mkdir augment/1_no_phone

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import numpy as npimport pandas as pdimport paddle.nn.functional as Fimport paddle.vision.transforms as Tfrom paddle.nn import Conv2D, BatchNorm2D, Linear, Dropoutfrom paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2Dimport paddleimport paddle.nn as nnimport osfrom paddle.io import Dataset, DataLoader, IterableDatasetfrom sklearn.utils import shufflefrom PIL import Imagefrom paddle.vision.transforms import ResizePLACE = paddle.CUDAPlace(0)  # 在gpu上训练

读取解压好的数据集,将图片名全部保存 train.csv(训练集)和 test.csv(测试集)中

处理完后,就可以定义数据集类并读取数据集了

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# 写入训练集csvlist1=[]for path in os.listdir("train/1_no_phone") :    if path[-3:]=="jpg":        k=["train/1_no_phone/"+path,1]        list1.append(k)for path in os.listdir("train/0_phone") :    if path[-3:]=="jpg":        k=["train/0_phone/"+path,0]        list1.append(k)result_df = pd.DataFrame(list1)result_df.columns=["image_id", "label"]data = shuffle(result_df)data.to_csv("train.csv", index=False, header=True)

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# 写入测试集csvlist1=[]for path in os.listdir("test") :    if path[-3:]=="jpg":        k=["test/"+path]        list1.append(k)result_df = pd.DataFrame(list1)result_df.columns=["image_id"]result_df.to_csv("test.csv", index=False, header=True)

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pic_list=pd.read_csv("train.csv")pic_list=np.array(pic_list)train_list=pic_list[:int(len(pic_list)*0.8)]test_list=pic_list[int(len(pic_list)*0.8):]print(len(train_list))train_list

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3320    array([["train/1_no_phone/tWuCoxIkbAedBjParJZQGn9XVYLK16hS.jpg", 1],           ["train/1_no_phone/rkX9Rj7YqENc85bFxdDaw14Is0yCeAnp.jpg", 1],           ["train/0_phone/wfhSF1W5B46doJKEc7VTU2OxbCtRDAI9.jpg", 0],           ...,           ["train/1_no_phone/uvHPyhakBK89o43fjJRXgZ2CIEpGzSAQ.jpg", 1],           ["train/0_phone/rlw854paLXkqc3zhgo9N2idUSeyIPAWf.jpg", 0],           ["train/0_phone/Wy59OfsandAo1lp2S7e3cEutPbi4zqYF.jpg", 0]],          dtype=object)

对图像进行 70%概率的随机水平翻转，并对翻转后的图像亮度、对比度和饱和度进行变化，将增广后的图像保存到 augment 文件夹，将图像数据追加到 train_list 后面，

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import paddle.vision.transforms as Tdirectory_name = "augment/"for i in range(len(train_list)):    img_path = "/".join(train_list[i][0].split("/")[:4])    img_path[6:-4]    img = Image.open(img_path)    img = T.RandomHorizontalFlip(0.7)(img)    # img.save(directory_name + "/" + img_path[6:-4] + "-tramsforms1.jpg")    img = T.ColorJitter(0.5, 0.5, 0.5, 0.0)(img)    img.save(directory_name + "/" + img_path[6:-4] + "-tramsforms.jpg")

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# 写入训练集csvlist4=[]for path in os.listdir("augment/1_no_phone") :    if path[-3:]=="jpg":        k=["augment/1_no_phone/"+path,1]        list4.append(k)for path in os.listdir("augment/0_phone") :    if path[-3:]=="jpg":        k=["augment/0_phone/"+path,0]        list4.append(k)result_df = pd.DataFrame(list4)result_df.columns=["image_id", "label"]data = shuffle(result_df)data.to_csv("aug.csv", index=False, header=True)

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aug_list=pd.read_csv("aug.csv")aug_list=np.array(aug_list)train_list = np.append(train_list, aug_list, axis=0)print(len(train_list))train_list

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6640    array([["train/1_no_phone/tWuCoxIkbAedBjParJZQGn9XVYLK16hS.jpg", 1],           ["train/1_no_phone/rkX9Rj7YqENc85bFxdDaw14Is0yCeAnp.jpg", 1],           ["train/0_phone/wfhSF1W5B46doJKEc7VTU2OxbCtRDAI9.jpg", 0],           ...,           ["augment/1_no_phone/WPNLg72lV8Jhn5TpokKMf34QR1Fd6IcH-tramsforms.jpg",            1],           ["augment/0_phone/qJcVtZH2yiAL6w34rOQopT9IemzEDjWK-tramsforms.jpg",            0],           ["augment/0_phone/qwoDX3ENaVuCk9IYjAnBHbv7TLUPR5Kr-tramsforms.jpg",            0]], dtype=object)

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class H2ZDateset(Dataset):    def __init__(self, data_dir):        super(H2ZDateset, self).__init__()        self.pic_list=data_dir    def __getitem__(self, idx):        image_file,label=self.pic_list[idx]        img = Image.open(image_file)  # 读取图片        img = img.resize((256, 256), Image.ANTIALIAS)  # 图片大小样式归一化        img = np.array(img).astype("float32")  # 转换成数组类型浮点型32位        img = img.transpose((2, 0, 1))        img = img/255.0  # 数据缩放到0-1的范围        return img, np.array(label, dtype="int64").reshape(-1)    def __len__(self):        return len(self.pic_list)

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h2zdateset = H2ZDateset(train_list)BATCH_SIZE = 32loader = DataLoader(h2zdateset, places=PLACE, shuffle=True, batch_size=BATCH_SIZE, drop_last=False, num_workers=0, use_shared_memory=False)data,label = next(loader())print("读取的数据形状：", data.shape,label.shape)

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读取的数据形状： [32, 3, 256, 256] [32, 1]

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train_data = H2ZDateset(train_list)test_data = H2ZDateset(test_list)train_data_reader = DataLoader(train_data, places=PLACE, shuffle=True, batch_size=BATCH_SIZE, drop_last=False, num_workers=2, use_shared_memory=True)test_data_reader = DataLoader(test_data, places=PLACE, shuffle=True, batch_size=BATCH_SIZE, drop_last=False, num_workers=2, use_shared_memory=True)

首先进行 LeNet 模型组网

这里用到了 paddle.Model 的 summary()方法来将模型可视化,通过 summary()可以快速打印模型的网络结构，并且，执行该语句的时候会执行一次网络。在动态图中，我们需要手算网络的输入和输出层，如果出现一点问题就会报错非常麻烦，而 summary()能大大缩短 debug 时间

自定义 ResNeXt 类，导入 resnext101_64x4d 网络，并开启预训练模型的选项，增一个线性层，将原本模型的 1000 分类问题变成 2 分类问题

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class ResNeXt(nn.Layer):    def __init__(self):        super(ResNeXt, self).__init__()        self.layer = paddle.vision.models.resnext101_64x4d(pretrained=True)        self.fc = nn.Sequential(            nn.Dropout(0.5),            nn.Linear(1000, 2)        )    def forward(self, inputs):        outputs = self.layer(inputs)        outputs = self.fc(outputs)        return outputsmodel = ResNeXt()paddle.Model(model).summary((-1, 3, 256, 256))

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W0510 00:32:22.100802  8743 gpu_context.cc:244] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1    W0510 00:32:22.104816  8743 gpu_context.cc:272] device: 0, cuDNN Version: 7.6.    -------------------------------------------------------------------------------      Layer (type)         Input Shape          Output Shape         Param #    ===============================================================================        Conv2D-1        [[1, 3, 256, 256]]   [1, 64, 128, 128]        9,408        BatchNorm-1     [[1, 64, 128, 128]]   [1, 64, 128, 128]         256      ConvBNLayer-1     [[1, 3, 256, 256]]   [1, 64, 128, 128]          0        MaxPool2D-1     [[1, 64, 128, 128]]    [1, 64, 64, 64]           0        Conv2D-2        [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384        BatchNorm-2      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-2     [[1, 64, 64, 64]]     [1, 256, 64, 64]          0        Conv2D-3        [[1, 256, 64, 64]]    [1, 256, 64, 64]        9,216        BatchNorm-3      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-3     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-4        [[1, 256, 64, 64]]    [1, 256, 64, 64]       65,536        BatchNorm-4      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-4     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-5        [[1, 64, 64, 64]]     [1, 256, 64, 64]       16,384        BatchNorm-5      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-5     [[1, 64, 64, 64]]     [1, 256, 64, 64]          0    BottleneckBlock-1   [[1, 64, 64, 64]]     [1, 256, 64, 64]          0        Conv2D-6        [[1, 256, 64, 64]]    [1, 256, 64, 64]       65,536        BatchNorm-6      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-6     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-7        [[1, 256, 64, 64]]    [1, 256, 64, 64]        9,216        BatchNorm-7      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-7     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-8        [[1, 256, 64, 64]]    [1, 256, 64, 64]       65,536        BatchNorm-8      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-8     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0    BottleneckBlock-2   [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-9        [[1, 256, 64, 64]]    [1, 256, 64, 64]       65,536        BatchNorm-9      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-9     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-10       [[1, 256, 64, 64]]    [1, 256, 64, 64]        9,216      BatchNorm-10      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-10     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-11       [[1, 256, 64, 64]]    [1, 256, 64, 64]       65,536      BatchNorm-11      [[1, 256, 64, 64]]    [1, 256, 64, 64]        1,024      ConvBNLayer-11     [[1, 256, 64, 64]]    [1, 256, 64, 64]          0    BottleneckBlock-3   [[1, 256, 64, 64]]    [1, 256, 64, 64]          0        Conv2D-12       [[1, 256, 64, 64]]    [1, 512, 64, 64]       131,072      BatchNorm-12      [[1, 512, 64, 64]]    [1, 512, 64, 64]        2,048      ConvBNLayer-12     [[1, 256, 64, 64]]    [1, 512, 64, 64]          0        Conv2D-13       [[1, 512, 64, 64]]    [1, 512, 32, 32]       36,864      BatchNorm-13      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-13     [[1, 512, 64, 64]]    [1, 512, 32, 32]          0        Conv2D-14       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-14      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-14     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-15       [[1, 256, 64, 64]]    [1, 512, 32, 32]       131,072      BatchNorm-15      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-15     [[1, 256, 64, 64]]    [1, 512, 32, 32]          0    BottleneckBlock-4   [[1, 256, 64, 64]]    [1, 512, 32, 32]          0        Conv2D-16       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-16      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-16     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-17       [[1, 512, 32, 32]]    [1, 512, 32, 32]       36,864      BatchNorm-17      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-17     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-18       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-18      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-18     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0    BottleneckBlock-5   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-19       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-19      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-19     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-20       [[1, 512, 32, 32]]    [1, 512, 32, 32]       36,864      BatchNorm-20      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-20     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-21       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-21      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-21     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0    BottleneckBlock-6   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-22       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-22      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-22     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-23       [[1, 512, 32, 32]]    [1, 512, 32, 32]       36,864      BatchNorm-23      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-23     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-24       [[1, 512, 32, 32]]    [1, 512, 32, 32]       262,144      BatchNorm-24      [[1, 512, 32, 32]]    [1, 512, 32, 32]        2,048      ConvBNLayer-24     [[1, 512, 32, 32]]    [1, 512, 32, 32]          0    BottleneckBlock-7   [[1, 512, 32, 32]]    [1, 512, 32, 32]          0        Conv2D-25       [[1, 512, 32, 32]]   [1, 1024, 32, 32]       524,288      BatchNorm-25     [[1, 1024, 32, 32]]   [1, 1024, 32, 32]        4,096      ConvBNLayer-25     [[1, 512, 32, 32]]   [1, 1024, 32, 32]          0        Conv2D-26      [[1, 1024, 32, 32]]   [1, 1024, 16, 16]       147,456      BatchNorm-26     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-26    [[1, 1024, 32, 32]]   [1, 1024, 16, 16]          0        Conv2D-27      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-27     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-27    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-28       [[1, 512, 32, 32]]   [1, 1024, 16, 16]       524,288      BatchNorm-28     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-28     [[1, 512, 32, 32]]   [1, 1024, 16, 16]          0    BottleneckBlock-8   [[1, 512, 32, 32]]   [1, 1024, 16, 16]          0        Conv2D-29      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-29     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-29    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-30      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-30     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-30    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-31      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-31     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-31    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-9  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-32      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-32     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-32    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-33      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-33     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-33    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-34      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-34     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-34    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-10  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-35      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-35     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-35    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-36      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-36     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-36    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-37      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-37     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-37    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-11  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-38      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-38     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-38    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-39      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-39     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-39    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-40      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-40     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-40    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-12  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-41      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-41     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-41    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-42      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-42     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-42    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-43      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-43     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-43    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-13  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-44      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-44     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-44    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-45      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-45     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-45    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-46      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-46     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-46    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-14  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-47      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-47     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-47    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-48      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-48     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-48    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-49      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-49     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-49    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-15  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-50      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-50     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-50    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-51      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-51     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-51    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-52      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-52     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-52    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-16  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-53      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-53     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-53    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-54      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-54     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-54    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-55      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-55     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-55    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-17  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-56      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-56     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-56    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-57      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-57     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-57    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-58      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-58     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-58    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-18  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-59      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-59     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-59    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-60      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-60     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-60    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-61      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-61     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-61    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-19  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-62      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-62     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-62    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-63      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-63     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-63    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-64      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-64     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-64    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-20  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-65      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-65     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-65    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-66      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-66     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-66    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-67      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-67     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-67    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-21  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-68      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-68     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-68    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-69      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-69     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-69    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-70      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-70     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-70    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-22  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-71      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-71     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-71    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-72      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-72     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-72    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-73      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-73     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-73    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-23  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-74      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-74     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-74    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-75      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-75     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-75    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-76      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-76     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-76    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-24  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-77      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-77     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-77    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-78      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-78     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-78    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-79      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-79     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-79    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-25  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-80      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-80     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-80    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-81      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-81     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-81    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-82      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-82     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-82    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-26  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-83      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-83     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-83    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-84      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-84     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-84    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-85      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-85     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-85    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-27  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-86      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-86     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-86    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-87      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-87     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-87    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-88      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-88     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-88    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-28  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-89      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-89     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-89    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-90      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-90     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-90    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-91      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-91     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-91    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-29  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-92      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-92     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-92    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-93      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]       147,456      BatchNorm-93     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-93    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-94      [[1, 1024, 16, 16]]   [1, 1024, 16, 16]      1,048,576      BatchNorm-94     [[1, 1024, 16, 16]]   [1, 1024, 16, 16]        4,096      ConvBNLayer-94    [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0    BottleneckBlock-30  [[1, 1024, 16, 16]]   [1, 1024, 16, 16]          0        Conv2D-95      [[1, 1024, 16, 16]]   [1, 2048, 16, 16]      2,097,152      BatchNorm-95     [[1, 2048, 16, 16]]   [1, 2048, 16, 16]        8,192      ConvBNLayer-95    [[1, 1024, 16, 16]]   [1, 2048, 16, 16]          0        Conv2D-96      [[1, 2048, 16, 16]]    [1, 2048, 8, 8]        589,824      BatchNorm-96      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-96    [[1, 2048, 16, 16]]    [1, 2048, 8, 8]           0        Conv2D-97       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]       4,194,304      BatchNorm-97      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-97     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-98      [[1, 1024, 16, 16]]    [1, 2048, 8, 8]       2,097,152      BatchNorm-98      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-98    [[1, 1024, 16, 16]]    [1, 2048, 8, 8]           0    BottleneckBlock-31  [[1, 1024, 16, 16]]    [1, 2048, 8, 8]           0        Conv2D-99       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]       4,194,304      BatchNorm-99      [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-99     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-100       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]        589,824      BatchNorm-100     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-100    [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-101       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]       4,194,304      BatchNorm-101     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-101    [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0    BottleneckBlock-32   [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-102       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]       4,194,304      BatchNorm-102     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-102    [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-103       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]        589,824      BatchNorm-103     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-103    [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0        Conv2D-104       [[1, 2048, 8, 8]]     [1, 2048, 8, 8]       4,194,304      BatchNorm-104     [[1, 2048, 8, 8]]     [1, 2048, 8, 8]         8,192      ConvBNLayer-104    [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0    BottleneckBlock-33   [[1, 2048, 8, 8]]     [1, 2048, 8, 8]           0    AdaptiveAvgPool2D-1  [[1, 2048, 8, 8]]     [1, 2048, 1, 1]           0        Linear-1           [[1, 2048]]           [1, 1000]          2,049,000        ResNeXt-2       [[1, 3, 256, 256]]       [1, 1000]              0        Dropout-1          [[1, 1000]]           [1, 1000]              0        Linear-2           [[1, 1000]]             [1, 2]             2,002    ===============================================================================    Total params: 83,660,154    Trainable params: 83,254,394    Non-trainable params: 405,760    -------------------------------------------------------------------------------    Input size (MB): 0.75    Forward/backward pass size (MB): 1024.04    Params size (MB): 319.14    Estimated Total Size (MB): 1343.93    -------------------------------------------------------------------------------    {"total_params": 83660154, "trainable_params": 83254394}

模型训练好之后就可以开始预测

在使用带有动量的 SGD 学习率优化器后训练 5 个 epoch，在 valid_list 中已经可以达到 95.3%的准确率

in[14]

epochs_num = 5 #迭代次数# opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())opt = paddle.optimizer.Momentum(learning_rate=0.002, parameters=model.parameters(), weight_decay=0.005, momentum=0.9)train_acc, train_loss, valid_acc = [], [], []for pass_num in range(epochs_num):    model.train() #训练模式    accs=[]    for batch_id,data in enumerate(train_data_reader):        images, labels = data        predict = model(images)#预测        loss=F.cross_entropy(predict,labels)        avg_loss=paddle.mean(loss)        acc=paddle.metric.accuracy(predict,labels)#计算精度        accs.append(acc.numpy()[0])        if batch_id % 20 == 0:            print("epoch:{}, iter:{}, loss:{}, acc:{}".format(pass_num, batch_id, avg_loss.numpy(), acc.numpy()[0]))        opt.clear_grad()        avg_loss.backward()        opt.step()    print("train_pass:{}, train_loss:{}, train_acc:{}".format(pass_num, avg_loss.numpy(), np.mean(accs)))    train_acc.append(np.mean(accs)), train_loss.append(avg_loss.numpy())    ##得到验证集的性能    model.eval()    val_accs=[]    for batch_id,data in enumerate(test_data_reader):            images, labels = data            predict=model(images)#预测            acc=paddle.metric.accuracy(predict,labels)#计算精度            val_accs.append(acc.numpy()[0])    print("val_acc={}".format(np.mean(val_accs)))    valid_acc.append(np.mean(val_accs))paddle.save(model.state_dict(),"resnext101_64x4d")#保存模型

out[14]

epoch:0, iter:0, loss:[1.2695745], acc:0.5625    epoch:0, iter:20, loss:[0.72218126], acc:0.78125    epoch:0, iter:40, loss:[0.8521573], acc:0.625    epoch:0, iter:60, loss:[0.9325488], acc:0.78125    epoch:0, iter:80, loss:[0.43677044], acc:0.90625    epoch:0, iter:100, loss:[0.37998652], acc:0.84375    epoch:0, iter:120, loss:[0.17167747], acc:0.9375    epoch:0, iter:140, loss:[0.13679577], acc:0.9375    epoch:0, iter:160, loss:[0.27065688], acc:0.9375    epoch:0, iter:180, loss:[0.27734363], acc:0.875    epoch:0, iter:200, loss:[0.27193183], acc:0.90625    train_pass:0, train_loss:[0.37479421], train_acc:0.8610276579856873    val_acc=0.9120657444000244    epoch:1, iter:0, loss:[0.29650056], acc:0.875    epoch:1, iter:20, loss:[0.03968127], acc:0.96875    epoch:1, iter:40, loss:[0.0579604], acc:0.96875    epoch:1, iter:60, loss:[0.08308301], acc:0.96875    epoch:1, iter:80, loss:[0.02734788], acc:1.0    epoch:1, iter:100, loss:[0.04930278], acc:0.96875    epoch:1, iter:120, loss:[0.08352702], acc:0.96875    epoch:1, iter:140, loss:[0.180908], acc:0.96875    epoch:1, iter:160, loss:[0.00271969], acc:1.0    epoch:1, iter:180, loss:[0.23547903], acc:0.90625    epoch:1, iter:200, loss:[0.02033656], acc:1.0    train_pass:1, train_loss:[0.00160835], train_acc:0.9695011973381042    val_acc=0.9362205266952515    epoch:2, iter:0, loss:[0.06544636], acc:0.96875    epoch:2, iter:20, loss:[0.06581119], acc:0.96875    epoch:2, iter:40, loss:[0.00562927], acc:1.0    epoch:2, iter:60, loss:[0.01040748], acc:1.0    epoch:2, iter:80, loss:[0.03810382], acc:0.96875    epoch:2, iter:100, loss:[0.01447718], acc:1.0    epoch:2, iter:120, loss:[0.12421186], acc:0.96875    epoch:2, iter:140, loss:[0.00112416], acc:1.0    epoch:2, iter:160, loss:[0.003324], acc:1.0    epoch:2, iter:180, loss:[0.01755645], acc:1.0    epoch:2, iter:200, loss:[0.06159591], acc:0.96875    train_pass:2, train_loss:[0.00045859], train_acc:0.990234375    val_acc=0.9543269276618958    epoch:3, iter:0, loss:[0.00377628], acc:1.0    epoch:3, iter:20, loss:[0.0032537], acc:1.0    epoch:3, iter:40, loss:[0.00095566], acc:1.0    epoch:3, iter:60, loss:[0.1955388], acc:0.9375    epoch:3, iter:80, loss:[0.00345089], acc:1.0    epoch:3, iter:100, loss:[0.00279539], acc:1.0    epoch:3, iter:120, loss:[0.01505984], acc:1.0    epoch:3, iter:140, loss:[0.06922489], acc:0.96875    epoch:3, iter:160, loss:[0.05011526], acc:0.96875    epoch:3, iter:180, loss:[0.13640904], acc:0.96875    epoch:3, iter:200, loss:[0.01501912], acc:1.0    train_pass:3, train_loss:[0.00018014], train_acc:0.991135835647583    val_acc=0.944672703742981    epoch:4, iter:0, loss:[0.00011357], acc:1.0    epoch:4, iter:20, loss:[0.00145513], acc:1.0    epoch:4, iter:40, loss:[0.00594005], acc:1.0    epoch:4, iter:60, loss:[0.00403259], acc:1.0    epoch:4, iter:80, loss:[0.00525207], acc:1.0    epoch:4, iter:100, loss:[0.01096269], acc:1.0    epoch:4, iter:120, loss:[0.0166808], acc:1.0    epoch:4, iter:140, loss:[0.00298717], acc:1.0    epoch:4, iter:160, loss:[0.00306062], acc:1.0    epoch:4, iter:180, loss:[0.0080948], acc:1.0    epoch:4, iter:200, loss:[0.08889838], acc:0.9375    train_pass:4, train_loss:[0.07838897], train_acc:0.9887319803237915    val_acc=0.9530086517333984

in[15]

from tqdm import tqdmtest=pd.read_csv("test.csv")test["label"]=0with tqdm(total=len(test)) as pbar:    for i in range(len(test)):        image_file=test["image_id"][i]        pbar.set_description(f"Processing: {test["image_id"][i]}")        img = Image.open(image_file)  # 读取图片        img = img.resize((256, 256), Image.ANTIALIAS)  # 图片大小样式归一化        img = np.array(img).astype("float32")  # 转换成数组类型浮点型32位        img = img.transpose((2, 0, 1))        img = img/255.0  # 数据缩放到0-1的范围        img=paddle.to_tensor(img.reshape(-1,3,256,256))        predict = model(img)        result = int(np.argmax(predict.numpy()))        test.loc[i, "image_id"]=test.loc[i, "image_id"][5:]        test.loc[i, "label"]=result        pbar.update(1)test.to_csv("predict_result.csv", index=False)print("end")

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Processing: test/yheWlrsgStOjfLdqHbYE7p1P28ViDBQ9.jpg: 100%|██████████| 1849/1849 [01:58<00:00, 15.65it/s]    end