基于ResNet-152模型的172种食物图像分类（教育部产学合作协同育人项目）
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本项目基于paddlepaddle和VireoFood-172数据集，用ResNet模型分三段训练实现食品识别。经不同学习率多轮训练，ResNet152模型训练集正确率92.927%，测试集82.684%，后续将优化以提升精度。
一、项目背景和项目创意
由于计算机视觉技术在监控摄像头、医疗保健等许多领域的应用越来越多。食品识别是其中一个重要的领域，由于其现实意义和科学挑战，值得进一步研究。最近，卷积神经网络(CNN)被用于食品识别。食物识别方法使用CNN模型提取食物图像特征，计算食物图像特征的相似度，并使用分类技术训练分类器来完成食物识别。此项目利用了VireoFood-172 数据集，其中包含了来自172个类别的110241张食品图片，并根据353种配料手工标注，以此来进行训练和测试。二、项目搭建
Step1:准备数据
（1）数据集介绍
本项目基于paddlepaddle，利用VireoFood-172 数据集进行相关开发，此数据集将食品分为172个大类，每大类中有200-1k张从从百度和谷歌图像搜索中抓取的食品图片，基本覆盖日常生活中的绝大多数食品种类。数据集数量庞大，质量较高，能够满足深度学习的训练要求。
（2）对数据集进行解压
In [ ]!unzip -q -o data/data124758/splitedDataset.zip登录后复制
（3）对数据集进行处理
拿到数据集之后首先对数据集进行处理，按8:2的比例切割数据集之后进行随机打乱（4）引入项目必须的模块
In [2]import paddleimport numpy as npimport os登录后复制
（5）利用生成器获得训练集和测试集
引入自己编写的生成器代码FoodDataset训练集：train_dataset = FoodDataset(train_data)测试集：eval_dataset = FoodDataset(validation_data)train大小： 88192eval大小： 22049符合按8:2的比例切割数据集In [3]from dataset import FoodDataset登录后复制 In [4]
train_data = './train_data1.txt'validation_data = './valida_data1.txt'train_dataset = FoodDataset(train_data)eval_dataset = FoodDataset(validation_data)print('train大小：', train_dataset.__len__())print('eval大小：', eval_dataset.__len__())# for data, label in train_dataset:# print(data)# print(np.array(data).shape)# print(label)# break登录后复制 train大小： 88192eval大小： 22049登录后复制 In [ ]
class DenseFoodModel(paddle.nn.Layer): def __init__(self): super(DenseFoodModel, self).__init__() self.num_labels = 172 def dense_block(self, x, blocks, name): for i in range(blocks): x = self.conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def transition_block(self, x, reduction, name): bn_axis = 3 x = paddle.nn.BatchNorm2D(num_features=x.shape[1], epsilon=1.001e-5)(x) x = paddle.nn.ELU(name=name + '_elu')(x) x = paddle.nn.Conv2D(in_channels=x.shape[1], out_channels=int(x.shape[1] * reduction), kernel_size=1, bias_attr=False)(x) x = paddle.nn.MaxPool2D(kernel_size=2, stride=2, name=name + '_pool')(x) return x def conv_block(self, x, growth_rate, name): bn_axis = 3 x1 = paddle.nn.BatchNorm2D(num_features=x.shape[1], epsilon=1.001e-5)(x) x1 = paddle.nn.ELU(name=name + '_0_elu')(x1) x1 = paddle.nn.Conv2D(in_channels=x1.shape[1], out_channels=4 * growth_rate, kernel_size= 1, bias_attr=False)(x1) x1 = paddle.nn.BatchNorm2D(num_features=x1.shape[1], epsilon=1.001e-5)(x1) x1 = paddle.nn.ELU(name=name + '_1_elu')(x1) x1 = paddle.nn.Conv2D(in_channels=x1.shape[1], out_channels=growth_rate, kernel_size=3, padding='SAME', bias_attr=False)(x1) # x = np.concatenate(([x, x1]), axis=1) # x = paddle.to_tensor(x) x = paddle.concat(x=[x, x1], axis=1) return x def forward(self, input): # img_input = paddle.reshape(input, shape=[-1, 3, 224, 224]) # 转换维读 bn_axis = 3 x = paddle.nn.Conv2D(in_channels=3, out_channels=64, kernel_size=7, stride=2, bias_attr=False, padding=3)(input) x = paddle.nn.BatchNorm2D( num_features=64, epsilon=1.001e-5)(x) x = paddle.nn.ELU(name='conv1/elu')(x) x = paddle.nn.MaxPool2D(kernel_size=3, stride=2, name='pool1', padding=1)(x) x = self.dense_block(x, 6, name='conv2') x = self.transition_block(x, 0.5, name='pool2') x = self.dense_block(x, 12, name='conv3') x = self.transition_block(x, 0.5, name='pool3') x = self.dense_block(x, 24, name='conv4') x = self.transition_block(x, 0.5, name='pool4') x = self.dense_block(x, 16, name='conv5') x = paddle.nn.BatchNorm2D( num_features=x.shape[1], epsilon=1.001e-5)(x) x = paddle.nn.ELU(name='elu')(x) x = paddle.nn.AdaptiveAvgPool2D(output_size=1)(x) x = paddle.squeeze(x, axis=[2, 3]) x = paddle.nn.Linear(in_features=1024, out_features=173)(x) x = F.softmax(x) return x登录后复制 In [ ]
import paddle.nn.functional as Fmodel = paddle.Model(DenseFoodModel())model.summary((-1, ) + tuple([3, 224, 224]))登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:653: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance.")登录后复制
---------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # ============================================================================DenseFoodModel-1 [[1, 3, 224, 224]] [1, 173] 0 ============================================================================Total params: 0Trainable params: 0Non-trainable params: 0----------------------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 0.00Params size (MB): 0.00Estimated Total Size (MB): 0.58----------------------------------------------------------------------------登录后复制
{'total_params': 0, 'trainable_params': 0}登录后复制 Step2.网络配置
（1）网络搭建
此项目采用paddlepaddle自带的ResNet残差网络模型，ResNet残差模型快如下图所示：
x为残差块的输入，然后复制成两部分，一部分输入到层（weight layer）之中，进行层间的运算（相当于将x输入到一个函数中做映射）,结果为f(x);另一部分作为分支结构，输出还是原本的x，最后将分别两部分的输出进行叠加:f(x) + x，再通过激活函数。这便是整个残差块的基本结构。
下图是每种ResNet的具体结构：
这里介绍一下ResNet152，152是指152次卷积。
其中block共有3+8+36+3 = 50个，每个block是由3层卷积构成的，共150个卷积，最开始的一个卷积是将3通道的图片提取特征，最后一层是自适应平均池化，输出维度为1。
一开始选用的是ResNet50图像分类模型，但是在进行了100多轮迭代训练后，正确率只能维持在88%左右，因此我们尝试变换模型重新进行训练，选用了ResNet101模型和ResNet152模型分别进行100轮迭代训练，最终，ResNet101模型的训练集正确率88%，测试集正确率80%，ResNet152模型训练集正确率达到了92%，测试集正确率达到了82%。
In [5]network = paddle.vision.models.resnet152(num_classes=173, pretrained=True)model = paddle.Model(network)model.summary((-1, ) + tuple([3, 224, 224]))登录后复制
100%|██████████| 355826/355826 [00:09<00:00, 38920.04it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1441: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [2048, 1000], but the expected shape is [2048, 173]. warnings.warn(("Skip loading for {}. ".format(key) + str(err)))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1441: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [173]. warnings.warn(("Skip loading for {}. ".format(key) + str(err)))登录后复制 ------------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =============================================================================== Conv2D-1 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408 BatchNorm2D-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 256 ReLU-1 [[1, 64, 112, 112]] [1, 64, 112, 112] 0 MaxPool2D-1 [[1, 64, 112, 112]] [1, 64, 56, 56] 0 Conv2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096 BatchNorm2D-3 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-4 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-5 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-5 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 Conv2D-2 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-1 [[1, 64, 56, 56]] [1, 256, 56, 56] 0 Conv2D-6 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-6 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-7 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-8 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-8 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-2 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-9 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-9 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-4 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-10 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-11 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-11 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 BottleneckBlock-3 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-13 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768 BatchNorm2D-13 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-14 [[1, 128, 56, 56]] [1, 128, 28, 28] 147,456 BatchNorm2D-14 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-15 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-15 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 Conv2D-12 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072 BatchNorm2D-12 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-4 [[1, 256, 56, 56]] [1, 512, 28, 28] 0 Conv2D-16 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-16 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-17 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-18 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-18 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-5 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-19 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-19 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-20 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-21 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-21 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-6 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-22 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-22 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-8 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-23 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-24 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-24 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-7 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-25 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-25 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-9 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-26 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-26 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-27 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-27 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-8 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-28 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-28 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-10 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-29 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-29 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-30 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-30 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-9 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-31 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-31 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-11 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-32 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-32 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-33 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-33 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-10 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-34 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-34 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-12 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-35 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-35 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-36 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-36 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 BottleneckBlock-11 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-38 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072 BatchNorm2D-38 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-39 [[1, 256, 28, 28]] [1, 256, 14, 14] 589,824 BatchNorm2D-39 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-40 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 Conv2D-37 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288 BatchNorm2D-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-12 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0 Conv2D-41 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-41 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-14 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-42 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-43 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-13 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-44 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-44 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-15 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-45 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-45 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-46 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-46 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-14 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-47 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-47 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-16 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-48 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-48 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-49 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-49 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-15 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-50 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-50 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-17 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-51 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-51 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-52 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-52 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-16 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-53 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-53 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-18 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-54 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-54 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-55 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-55 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-17 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-56 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-56 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-19 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-57 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-57 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-58 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-58 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-18 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-59 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-59 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-20 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-60 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-60 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-61 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-61 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-19 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-62 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-62 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-21 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-63 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-63 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-64 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-64 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-20 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-65 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-65 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-22 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-66 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-66 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-67 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-67 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-21 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-68 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-68 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-23 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-69 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-69 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-70 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-70 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-22 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-71 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-71 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-24 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-72 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-72 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-73 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-73 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-23 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-74 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-74 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-75 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-75 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-76 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-76 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-24 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-77 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-77 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-26 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-78 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-78 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-79 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-79 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-25 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-80 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-80 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-27 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-81 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-81 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-82 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-82 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-26 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-83 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-83 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-84 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-84 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-85 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-85 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-27 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-86 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-86 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-29 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-87 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-87 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-88 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-88 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-28 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-89 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-89 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-30 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-90 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-90 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-91 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-91 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-29 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-92 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-92 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-93 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-93 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-94 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-94 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-30 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-95 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-95 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-32 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-96 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-96 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-97 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-97 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-31 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-98 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-98 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-33 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-99 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-99 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-100 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-100 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-32 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-101 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-101 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-102 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-102 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-103 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-103 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-33 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-104 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-104 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-35 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-105 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-105 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-106 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-106 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-34 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-107 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-107 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-36 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-108 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-108 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-109 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-109 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-35 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-110 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-110 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-111 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-111 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-112 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-112 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-36 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-113 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-113 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-38 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-114 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-114 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-115 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-115 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-37 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-116 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-116 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-39 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-117 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-117 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-118 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-118 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-38 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-119 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-119 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-120 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-120 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-121 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-121 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-39 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-122 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-122 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-41 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-123 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-123 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-124 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-124 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-40 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-125 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-125 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-42 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-126 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-126 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-127 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-127 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-41 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-128 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-128 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-129 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-129 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-130 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-130 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-42 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-131 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-131 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-44 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-132 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-132 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-133 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-133 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-134 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-134 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-45 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-135 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-135 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-136 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-136 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-44 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-137 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-137 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-46 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-138 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-138 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-139 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-139 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-45 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-140 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-140 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-47 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-141 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-141 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-142 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-142 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-46 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-143 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-143 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-48 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-144 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-144 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-145 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-145 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 BottleneckBlock-47 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-147 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288 BatchNorm2D-147 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-49 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-148 [[1, 512, 14, 14]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-148 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-149 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-149 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 Conv2D-146 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152 BatchNorm2D-146 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-48 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0 Conv2D-150 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-150 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-151 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-151 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-152 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-152 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-49 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-153 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-153 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-51 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-154 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-154 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-155 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-155 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 BottleneckBlock-50 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 AdaptiveAvgPool2D-1 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0 Linear-1 [[1, 2048]] [1, 173] 354,477 ===============================================================================Total params: 58,649,709Trainable params: 58,346,861Non-trainable params: 302,848-------------------------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 552.42Params size (MB): 223.73Estimated Total Size (MB): 776.72-------------------------------------------------------------------------------登录后复制
{'total_params': 58649709, 'trainable_params': 58346861}登录后复制 （2）定义损失函数和准确率
这次使用的是交叉熵损失函数，该函数在分类任务上比较常用。定义了一个损失函数之后，还有对它求平均值，因为定义的是一个Batch的损失值。同时我们还可以定义一个准确率函数，这个可以在我们训练的时候输出分类的准确率。（3）定义优化方法
这次我们使用的是Adam优化方法，同时指定学习率为0.001Step3.训练模型与训练评估
分为三段式来训练样本：
0-19epoch 学习率0.01训练集：loss迅速下降，正确率经过二十轮迭代上升至0.80929
测试集：loss迅速下降，正确率迅速上升至0.7384
训练集：loss震荡下降，正确率从0.80929上升至0.85714
loss震荡，正确率从0.7384上升至0.7592
训练集：loss震荡下降,正确率从0.85714上升至0.92927
测试集：正确率趋于平稳，正确率取最高值0.82684，即第7个epoch
考虑到训练集持续上升而测试集接近收敛，继续训练会导致过拟合，故训练到此结束。
In [ ]model.prepare(optimizer=paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()), loss=paddle.nn.CrossEntropyLoss(), metrics=paddle.metric.Accuracy())# 训练可视化VisualDL工具的回调函数visualdl = paddle.callbacks.VisualDL(log_dir='visualdl_log')# 启动模型全流程训练model.fit(train_dataset, # 训练数据集 eval_dataset, # 评估数据集 epochs=20, # 总的训练轮次 batch_size=64, # 批次计算的样本量大小 shuffle=True, # 是否打乱样本集 verbose=1, # 日志展示格式 save_dir='0_20_resnet152_0.001', # 分阶段的训练模型存储路径 callbacks=[visualdl]) # 回调函数使用登录后复制
The loss value printed in the log is the current step, and the metric is the average value of previous steps.Epoch 1/20step 1378/1378 [==============================] - loss: 2.0446 - acc: 0.2861 - 538ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/0Eval begin...step 345/345 [==============================] - loss: 1.2795 - acc: 0.3776 - 256ms/step Eval samples: 22049Epoch 2/20step 1378/1378 [==============================] - loss: 1.5286 - acc: 0.5133 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/1Eval begin...step 345/345 [==============================] - loss: 0.7893 - acc: 0.5104 - 239ms/step Eval samples: 22049Epoch 3/20step 1378/1378 [==============================] - loss: 1.1590 - acc: 0.5851 - 524ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/2Eval begin...step 345/345 [==============================] - loss: 0.6739 - acc: 0.5879 - 240ms/step Eval samples: 22049Epoch 4/20step 1378/1378 [==============================] - loss: 1.1931 - acc: 0.6275 - 522ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/3Eval begin...step 345/345 [==============================] - loss: 0.2853 - acc: 0.6071 - 237ms/step Eval samples: 22049Epoch 5/20step 1378/1378 [==============================] - loss: 1.3321 - acc: 0.6558 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/4Eval begin...step 345/345 [==============================] - loss: 0.5777 - acc: 0.6388 - 242ms/step Eval samples: 22049Epoch 6/20step 1378/1378 [==============================] - loss: 1.4460 - acc: 0.6820 - 524ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/5Eval begin...step 345/345 [==============================] - loss: 0.5820 - acc: 0.6541 - 244ms/step Eval samples: 22049Epoch 7/20step 1378/1378 [==============================] - loss: 1.0251 - acc: 0.6960 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/6Eval begin...step 345/345 [==============================] - loss: 0.3825 - acc: 0.6865 - 239ms/step Eval samples: 22049Epoch 8/20step 1378/1378 [==============================] - loss: 0.8302 - acc: 0.7145 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/7Eval begin...step 345/345 [==============================] - loss: 0.0702 - acc: 0.6644 - 240ms/step Eval samples: 22049Epoch 9/20step 1378/1378 [==============================] - loss: 1.0031 - acc: 0.7274 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/8Eval begin...step 345/345 [==============================] - loss: 0.3258 - acc: 0.6950 - 240ms/step Eval samples: 22049Epoch 10/20step 1378/1378 [==============================] - loss: 0.9562 - acc: 0.7366 - 522ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/9Eval begin...step 345/345 [==============================] - loss: 0.2758 - acc: 0.7111 - 240ms/step Eval samples: 22049Epoch 11/20step 1378/1378 [==============================] - loss: 0.9920 - acc: 0.7492 - 524ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/10Eval begin...step 345/345 [==============================] - loss: 0.1771 - acc: 0.7077 - 240ms/step Eval samples: 22049Epoch 12/20step 1378/1378 [==============================] - loss: 0.8259 - acc: 0.7558 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/11Eval begin...step 345/345 [==============================] - loss: 0.1928 - acc: 0.7075 - 241ms/step Eval samples: 22049Epoch 13/20step 1378/1378 [==============================] - loss: 0.8234 - acc: 0.7649 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/12Eval begin...step 345/345 [==============================] - loss: 0.4049 - acc: 0.7215 - 243ms/step Eval samples: 22049Epoch 14/20step 1378/1378 [==============================] - loss: 0.7254 - acc: 0.7730 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/13Eval begin...step 345/345 [==============================] - loss: 0.3297 - acc: 0.7173 - 239ms/step Eval samples: 22049Epoch 15/20step 1378/1378 [==============================] - loss: 0.7475 - acc: 0.7817 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/14Eval begin...step 345/345 [==============================] - loss: 0.1445 - acc: 0.7199 - 240ms/step Eval samples: 22049Epoch 16/20step 1378/1378 [==============================] - loss: 0.7856 - acc: 0.7905 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/15Eval begin...step 345/345 [==============================] - loss: 0.3110 - acc: 0.7332 - 238ms/step Eval samples: 22049Epoch 17/20step 1378/1378 [==============================] - loss: 1.0853 - acc: 0.7945 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/16Eval begin...step 345/345 [==============================] - loss: 0.2105 - acc: 0.7461 - 240ms/step Eval samples: 22049Epoch 18/20step 1378/1378 [==============================] - loss: 1.1084 - acc: 0.8027 - 523ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/17Eval begin...step 345/345 [==============================] - loss: 0.1809 - acc: 0.7313 - 241ms/step Eval samples: 22049Epoch 19/20step 1378/1378 [==============================] - loss: 1.1126 - acc: 0.8079 - 525ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/18Eval begin...step 345/345 [==============================] - loss: 0.0287 - acc: 0.7268 - 241ms/step Eval samples: 22049Epoch 20/20step 1378/1378 [==============================] - loss: 0.6276 - acc: 0.8125 - 524ms/step save checkpoint at /home/aistudio/0_20_resnet152_0.001/19Eval begin...step 345/345 [==============================] - loss: 0.2583 - acc: 0.7326 - 240ms/step Eval samples: 22049save checkpoint at /home/aistudio/0_20_resnet152_0.001/final登录后复制 In [ ]
# visualdl --logdir=visualdl_log/ --port=8040# 终端运行此代码登录后复制 File "/tmp/ipykernel_101/1350602942.py", line 1 visualdl --logdir=visualdl_log/ --port=8080 ^ SyntaxError: can't assign to operatorIn [6]
model.save('infer/mnist',training=False) # 保存模型登录后复制 ---------------------------------------------------------------------------RuntimeError Traceback (most recent call last)/tmp/ipykernel_129/3232998588.py in Step5.模型预测
读取模型参数对预测图片进行预处理开始预测In [ ]from PIL import Imageimport paddle.vision.transforms as Tmodel_state_dict = paddle.load('50epoches_chk/final.pdparams') # 读取模型model = paddle.vision.models.resnet50(num_classes=173)model.set_state_dict(model_state_dict)model.eval()image_file = './splitedDataset/train/108/5_41.jpg'# image_file = './splitedDataset/Yu-Shiang Shredded Pork.webp'# braised pork in brown sauce.jfif \ 1.webp \ rice.jfif \ Yu-Shiang Shredded Pork.webptransforms = T.Compose([ T.RandomResizedCrop((224, 224)), # 随机裁剪大小，裁剪地方不同等于间接增加了数据样本 300*300-224*224 T.ToTensor(), # 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])img = Image.open(image_file) # 读取图片if img.mode != 'RGB': img = img.convert('RGB')img = transforms(img)img = paddle.unsqueeze(img, axis=0)foodLabel = './FoodList.txt'foodList = []with open(foodLabel) as f: for line in f.readlines(): info = line.split('\t') if len(info) > 0: foodList.append(info)ceshi = model(img) # 测试print('预测的结果为:', np.argmax(ceshi.numpy()), foodList[np.argmax(ceshi.numpy())-1]) # 获取值Image.open(image_file) # 显示图片登录后复制 预测的结果为: 108 ['Four-Joy Meatballs\n']登录后复制
登录后复制
断点续训：
在训练初期，希望训练速度快，使用0.01的学习率。在此基础上需要更小的学习率继续训练，以获得更准确的正确率。
读取模型参数读取优化器参数对预测图片进行预处理开始预测In [ ]import paddle.vision.transforms as Timport paddlefrom dataset import FoodDatasettrain_data = './train_data1.txt'validation_data = './valida_data1.txt'train_dataset = FoodDataset(train_data)eval_dataset = FoodDataset(validation_data)network = paddle.vision.models.resnet101(num_classes=173)params_dict = paddle.load('15_15_resnet101_0.0001/final.pdparams')network.set_state_dict(params_dict)model = paddle.Model(network)opt = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())opt_dict = paddle.load('15_15_resnet101_0.0001/final.pdopt')opt.set_state_dict(opt_dict)model.prepare(optimizer=opt, loss=paddle.nn.CrossEntropyLoss(), metrics=paddle.metric.Accuracy())# 训练可视化VisualDL工具的回调函数visualdl = paddle.callbacks.VisualDL(log_dir='visualdl_log')# 启动模型全流程训练model.fit(train_dataset, # 训练数据集 eval_dataset, # 评估数据集 epochs=25, # 总的训练轮次 batch_size=64, # 批次计算的样本量大小 shuffle=True, # 是否打乱样本集 verbose=1, # 日志展示格式 save_dir='30_25_resnet101', # 分阶段的训练模型存储路径 callbacks=[visualdl]) # 回调函数使用登录后复制 W0129 23:08:45.468212 6682 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1W0129 23:08:45.472950 6682 device_context.cc:465] device: 0, cuDNN Version: 7.6.登录后复制
The loss value printed in the log is the current step, and the metric is the average value of previous steps.Epoch 1/25登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return (isinstance(seq, collections.Sequence) and/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:653: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance.")登录后复制
step 30/1378 [..............................] - loss: 0.4691 - acc: 0.8672 - ETA: 10:18 - 459ms/st登录后复制
三、项目总结
实验结果 本次实验基于paddlepaddle，利用VireoFood-172 数据集来实现食品识别。网络模型采用的是paddlepaddle自带的ResNet网络模型，可以解决深层网络梯度消失的问题。 项目分为三段式来训练样本，第一段为0-19epoch 学习率为0.01，第二段为从0-19的16开始断点续训14个epoch，学习率为0.001，第三段选取16+14=30的数据，断点续训11个epoch，学习率为0.0001，训练结束时测试集正确率达到0.82684，训练集正确率达到0.92927。实验分析 项目一开始选用的是ResNet50模型，但是效果并不理想，因此我们尝试变换模型增加模型深度重新进行训练，选用了ResNet101模型和ResNet152模型，发现随着模型深度加深，训练集和测试集的正确率确实有明显的提升。后续计划 此项目测试集的最高正确率也只能达到0.82684，因此在后续的工作中，尝试使用其他的网络模型，或者继续调节网络模型的超参数，以此来提高训练集和测试集的正确率，这是后续的工作计划。免责声明
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