Paddle高层API实现图像分类（CIFAR-100数据集_ResNet101）
2024-06-24 21:48:15
Paddle高层API实现图像分类（CIFAR-100数据集_ResNet101）
『深度学习7日打卡营·大作业』
零基础解锁深度学习神器飞桨框架高层API，七天时间助你掌握CV、NLP领域最火模型及应用。
课程地址
传送门：https://aistudio.baidu.com/aistudio/course/introduce/6771目标
- 掌握深度学习常用模型基础知识
- 熟练掌握一种国产开源深度学习框架
- 具备独立完成相关深度学习任务的能力
- 能用所学为AI加一份年味
数据集：
指定数据集：cifar100，通过高层API调用。
可以自己写数据增强和数据预处理功能。
模型：
随便选，模型参数初始化（如：uniform和normal）可以随意调整。
模型训练
各种超参数（如：epochs、batch_size）可以随意调整。
评判标准
最终以model.evaluate的精度输出值（格式如下），计算方式是将eval_dataset送入evaluate接口即可，需要在model.prepare中配置评估指标Accuracy，所用数据集不能被用于训练过。
{'loss': [6.4980035], 'acc': 0.8485721442885772}
导入相关库
import paddle
import numpy as nppaddle.__version__
'2.0.0'
# 配置GPU
paddle.set_device('gpu')
CUDAPlace(0)
② 数据准备
数据增强
#mean and std of cifar100 datasetCIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)train_transfrom = T.Compose([T.Resize((256, 256)),T.CenterCrop((224, 224)),T.RandomHorizontalFlip(0.5), # 随机水平翻转T.RandomRotation(degrees=15), # （-degrees，+degrees）T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=CIFAR100_MEAN, std=CIFAR100_STD) # 图像归一化])eval_transfrom = T.Compose([T.Resize(224),T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=CIFAR100_MEAN, std=CIFAR100_STD) # 图像归一化])class paddle.vision.transforms.ToTensor
将形状为 （H x W x C）的输入数据 PIL.Image 或 numpy.ndarray 转换为 (C x H x W)。 如果想保持形状不变，可以将参数 data_format 设置为 ‘HWC’。
同时，如果输入的 PIL.Image 的 mode 是 (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) 其中一种，或者输入的 numpy.ndarray 数据类型是'uint8'，那个会将输入数据从（0-255）的范围缩放到 （0-1）的范围。其他的情况，则保持输入不变。
# 训练数据集
train_dataset = paddle.vision.datasets.Cifar100(mode='train', transform=paddle.vision.transforms.ToTensor())# 验证数据集
eval_dataset = paddle.vision.datasets.Cifar100(mode='test', transform=paddle.vision.transforms.ToTensor())# 合并数据集
dataset = paddle.concat([d[0] for d in paddle.io.DataLoader(train_dataset)] + [d[0] for d in paddle.io.DataLoader(eval_dataset)])
# 计算数据均值和方差
print(f'mean:{dataset.mean(axis=[0, 2, 3]).numpy()} \n std:{dataset.std(axis=[0, 2, 3]).numpy()}')
mean:[0.5073715 0.4867007 0.441096 ] std:[0.26750046 0.25658613 0.27630225]
由于要调用resnet101的预训练模型，这里把CIFAR的 32 × 32 32\times 32 32×32的图像resize为 224 × 224 224\times 224 224×224的图像，保持特征尺寸和感受野的一致。
import paddle.vision.transforms as T # CIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
# CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)CIFAR100_MEAN = [0.5073715, 0.4867007, 0.441096]
CIFAR100_STD = [0.26750046, 0.25658613, 0.27630225]# mean=[0.485, 0.456, 0.406]
# std=[0.229, 0.224, 0.225]train_transfrom = T.Compose([T.Resize((256, 256)),T.CenterCrop((224, 224)),T.RandomHorizontalFlip(0.5), # 随机水平翻转T.RandomRotation(degrees=15), # （-degrees，+degrees）T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=CIFAR100_MEAN, std=CIFAR100_STD) # 图像归一化])eval_transfrom = T.Compose([T.Resize(224),T.ToTensor(), # 数据的格式转换和标准化 HWC => CHW T.Normalize(mean=CIFAR100_MEAN, std=CIFAR100_STD) # 图像归一化])# 训练数据集
train_dataset = paddle.vision.datasets.Cifar100(mode='train', transform=train_transfrom)# 验证数据集
eval_dataset = paddle.vision.datasets.Cifar100(mode='test', transform=eval_transfrom)
print(f'训练集大小: {len(train_dataset)}, 测试集大小: {len(eval_dataset)}')print("train data shape:", train_dataset[0][0].shape)print("eval data shape:", eval_dataset[0][0].shape)# train_dataset[3][0]
训练集大小: 50000, 测试集大小: 10000
train data shape: [3, 224, 224]
eval data shape: [3, 224, 224]
import matplotlib.pyplot as pltidx = np.random.randint(0, 50000, size=10)for i in idx:img = train_dataset[i][0].numpy().transpose([1, 2, 0])plt.imshow(img)plt.title(train_dataset[i][1])plt.show()
[WARNING 2021-02-11 13:53:09,315 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,435 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,532 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,644 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,756 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,879 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:09,994 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:10,117 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:10,240 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
[WARNING 2021-02-11 13:53:10,339 image.py:664] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
3.1 模型开发
network = paddle.vision.models.resnet101(num_classes=100, pretrained=True)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1263: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [2048, 1000], but the expected shape is [2048, 100].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:1263: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [100].warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
3.2 模型可视化
model = paddle.Model(network)model.summary((-1, 3, 224, 224))
-------------------------------------------------------------------------------Layer (type) Input Shape Output Shape Param #
===============================================================================Conv2D-105 [[1, 3, 224, 224]] [1, 64, 112, 112] 9,408 BatchNorm2D-105 [[1, 64, 112, 112]] [1, 64, 112, 112] 256 ReLU-35 [[1, 64, 112, 112]] [1, 64, 112, 112] 0 MaxPool2D-2 [[1, 64, 112, 112]] [1, 64, 56, 56] 0 Conv2D-107 [[1, 64, 56, 56]] [1, 64, 56, 56] 4,096 BatchNorm2D-107 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-36 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-108 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-108 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-109 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-109 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024 Conv2D-106 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-106 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
BottleneckBlock-34 [[1, 64, 56, 56]] [1, 256, 56, 56] 0 Conv2D-110 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-110 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-37 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-111 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-111 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-112 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-112 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
BottleneckBlock-35 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-113 [[1, 256, 56, 56]] [1, 64, 56, 56] 16,384 BatchNorm2D-113 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-38 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-114 [[1, 64, 56, 56]] [1, 64, 56, 56] 36,864 BatchNorm2D-114 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 Conv2D-115 [[1, 64, 56, 56]] [1, 256, 56, 56] 16,384 BatchNorm2D-115 [[1, 256, 56, 56]] [1, 256, 56, 56] 1,024
BottleneckBlock-36 [[1, 256, 56, 56]] [1, 256, 56, 56] 0 Conv2D-117 [[1, 256, 56, 56]] [1, 128, 56, 56] 32,768 BatchNorm2D-117 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-39 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-118 [[1, 128, 56, 56]] [1, 128, 28, 28] 147,456 BatchNorm2D-118 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-119 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-119 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048 Conv2D-116 [[1, 256, 56, 56]] [1, 512, 28, 28] 131,072 BatchNorm2D-116 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
BottleneckBlock-37 [[1, 256, 56, 56]] [1, 512, 28, 28] 0 Conv2D-120 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-120 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-40 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-121 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-121 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-122 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-122 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
BottleneckBlock-38 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-123 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-123 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-41 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-124 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-124 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-125 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-125 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
BottleneckBlock-39 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-126 [[1, 512, 28, 28]] [1, 128, 28, 28] 65,536 BatchNorm2D-126 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-42 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-127 [[1, 128, 28, 28]] [1, 128, 28, 28] 147,456 BatchNorm2D-127 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 Conv2D-128 [[1, 128, 28, 28]] [1, 512, 28, 28] 65,536 BatchNorm2D-128 [[1, 512, 28, 28]] [1, 512, 28, 28] 2,048
BottleneckBlock-40 [[1, 512, 28, 28]] [1, 512, 28, 28] 0 Conv2D-130 [[1, 512, 28, 28]] [1, 256, 28, 28] 131,072 BatchNorm2D-130 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-131 [[1, 256, 28, 28]] [1, 256, 14, 14] 589,824 BatchNorm2D-131 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-132 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-132 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096 Conv2D-129 [[1, 512, 28, 28]] [1, 1024, 14, 14] 524,288 BatchNorm2D-129 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-41 [[1, 512, 28, 28]] [1, 1024, 14, 14] 0 Conv2D-133 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-133 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-44 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-134 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-134 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-135 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-135 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-42 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-136 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-136 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-45 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-137 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-137 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-138 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-138 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-43 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-139 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-139 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-46 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-140 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-140 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-141 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-141 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-44 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-142 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-142 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-47 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-143 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-143 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-144 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-144 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-45 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-145 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-145 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-48 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-146 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-146 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-147 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-147 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-46 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-148 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-148 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-49 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-149 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-149 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-150 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-150 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-47 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-151 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-151 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-50 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-152 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-152 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-153 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-153 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-48 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-154 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-154 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-51 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-155 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-155 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-156 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-156 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-49 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-157 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-157 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-52 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-158 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-158 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-159 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-159 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-50 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-160 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-160 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-53 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-161 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-161 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-162 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-162 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-51 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-163 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-163 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-54 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-164 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-164 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-165 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-165 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-52 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-166 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-166 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-55 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-167 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-167 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-168 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-168 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-53 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-169 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-169 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-56 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-170 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-170 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-171 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-171 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-54 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-172 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-172 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-57 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-173 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-173 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-174 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-174 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-55 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-175 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-175 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-58 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-176 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-176 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-177 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-177 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-56 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-178 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-178 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-59 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-179 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-179 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-180 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-180 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-57 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-181 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-181 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-60 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-182 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-182 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-183 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-183 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-58 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-184 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-184 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-61 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-185 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-185 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-186 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-186 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-59 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-187 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-187 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-62 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-188 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-188 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-189 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-189 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-60 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-190 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-190 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-63 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-191 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-191 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-192 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-192 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-61 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-193 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-193 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-64 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-194 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-194 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-195 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-195 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-62 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-196 [[1, 1024, 14, 14]] [1, 256, 14, 14] 262,144 BatchNorm2D-196 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-65 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-197 [[1, 256, 14, 14]] [1, 256, 14, 14] 589,824 BatchNorm2D-197 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 Conv2D-198 [[1, 256, 14, 14]] [1, 1024, 14, 14] 262,144 BatchNorm2D-198 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 4,096
BottleneckBlock-63 [[1, 1024, 14, 14]] [1, 1024, 14, 14] 0 Conv2D-200 [[1, 1024, 14, 14]] [1, 512, 14, 14] 524,288 BatchNorm2D-200 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-66 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-201 [[1, 512, 14, 14]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-201 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-202 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-202 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192 Conv2D-199 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 2,097,152 BatchNorm2D-199 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
BottleneckBlock-64 [[1, 1024, 14, 14]] [1, 2048, 7, 7] 0 Conv2D-203 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-203 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-67 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-204 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-204 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-205 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-205 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
BottleneckBlock-65 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-206 [[1, 2048, 7, 7]] [1, 512, 7, 7] 1,048,576 BatchNorm2D-206 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-68 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0 Conv2D-207 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,359,296 BatchNorm2D-207 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 Conv2D-208 [[1, 512, 7, 7]] [1, 2048, 7, 7] 1,048,576 BatchNorm2D-208 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 8,192
BottleneckBlock-66 [[1, 2048, 7, 7]] [1, 2048, 7, 7] 0
AdaptiveAvgPool2D-2 [[1, 2048, 7, 7]] [1, 2048, 1, 1] 0 Linear-2 [[1, 2048]] [1, 100] 204,900
===============================================================================
Total params: 42,810,404
Trainable params: 42,599,716
Non-trainable params: 210,688
-------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 391.63
Params size (MB): 163.31
Estimated Total Size (MB): 555.52
-------------------------------------------------------------------------------{'total_params': 42810404, 'trainable_params': 42599716}
④ 模型训练和调优
class paddle.optimizer.lr.PiecewiseDecay(boundaries, values, last_epoch=- 1, verbose=False)
该接口提供分段设置学习率的策略。
class paddle.optimizer.lr.LinearWarmup(learing_rate, warmup_steps, start_lr, end_lr, last_epoch=- 1, verbose=False)
该接口提供一种学习率优化策略-线性学习率热身(warm up)对学习率进行初步调整。在正常调整学习率之前，先逐步增大学习率。
class paddle.callbacks.EarlyStopping(monitor=‘loss’, mode=‘auto’, patience=0, verbose=1, min_delta=0, baseline=None, save_best_model=True)
在模型评估阶段，模型效果如果没有提升，EarlyStopping 会让模型提前停止训练。
monitor (str，可选) - 监控量。该量作为模型是否停止学习的监控指标。默认值：‘loss’。
mode (str，可选) - 可以是’auto’、‘min’或者’max’。在min模式下，模型会在监控量的值不再减少时停止训练；max模式下，模型会在监控量的值不再增加时停止训练；auto模式下，实际的模式会从
monitor推断出来。如果monitor中有’acc’，将会认为是max模式，其它情况下，都会被推断为min模式。默认值：‘auto’。patience (int，可选) - 多少个epoch模型效果未提升会使模型提前停止训练。默认值：0。
verbose (int，可选) - 可以是0或者1。1代表不打印模型提前停止训练的日志，1代表打印日志。默认值：1。
min_delta (int|float，可选) - 监控量最小改变值。当evaluation的监控变量改变值小于
min_delta，就认为模型没有变化。默认值：0。baseline (int|float，可选) - 监控量的基线。如果模型在训练
patience个epoch后效果对比基线没有提升，将会停止训练。如果是None，代表没有基线。默认值：None。save_best_model (bool，可选) - 是否保存效果最好的模型（监控量的值最优）。文件会保存在
fit中传入的参数save_dir下，前缀名为best_model，默认值: True。
def make_optimizer(parameters=None, momentum=0.9, weight_decay=5e-4, boundaries=None, values=None):learning_rate = paddle.optimizer.lr.PiecewiseDecay(boundaries=boundaries, values=values,verbose=False)# learning_rate = paddle.optimizer.lr.LinearWarmup(# learning_rate=learning_rate,# warmup_steps=wamup_steps,# start_lr=base_lr / 5.,# end_lr=base_lr,# verbose=False)optimizer = paddle.optimizer.Momentum(learning_rate=learning_rate,weight_decay=weight_decay,momentum=momentum,parameters=parameters)# optimizer = paddle.optimizer.AdamW(# learning_rate=learning_rate,# weight_decay=weight_decay,# parameters=parameters)return optimizerbase_lr = 5e-4
boundaries = [5, 10]optimizer = make_optimizer(boundaries=boundaries, values=[base_lr, base_lr*0.2, base_lr*0.1], parameters=model.parameters())model.prepare(# optimizer=paddle.optimizer.Adam(learning_rate=5e-4, weight_decay=paddle.regularizer.L2Decay(5e-4), parameters=model.parameters()),optimizer=optimizer,loss=paddle.nn.CrossEntropyLoss(),metrics=paddle.metric.Accuracy(topk=(1, 5))
)# callbacks
visualdl = paddle.callbacks.VisualDL('./visualdl/resnet101')
earlystop = paddle.callbacks.EarlyStopping( # acc不在上升时停止'acc',mode='max',patience=5,verbose=1,min_delta=0,baseline=None,save_best_model=True)model.fit(train_dataset,eval_dataset,epochs=20,batch_size=128,save_freq=2,save_dir='checkpoint/resnet101',callbacks=[visualdl, earlystop],verbose=1
)
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/20
step 391/391 [==============================] - loss: 1.6645 - acc_top1: 0.5995 - acc_top5: 0.8853 - 889ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/0
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.5161 - acc_top1: 0.6217 - acc_top5: 0.8958 - 421ms/step
Eval samples: 10000
Epoch 2/20/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/hapi/callbacks.py:808: UserWarning: Monitor of EarlyStopping should be loss or metric name.'Monitor of EarlyStopping should be loss or metric name.')step 391/391 [==============================] - loss: 1.6678 - acc_top1: 0.6264 - acc_top5: 0.8994 - 891ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.3930 - acc_top1: 0.6398 - acc_top5: 0.9048 - 421ms/step
Eval samples: 10000
Epoch 3/20
step 391/391 [==============================] - loss: 1.4620 - acc_top1: 0.6483 - acc_top5: 0.9111 - 890ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/2
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.2873 - acc_top1: 0.6574 - acc_top5: 0.9170 - 419ms/step
Eval samples: 10000
Epoch 4/20
step 391/391 [==============================] - loss: 1.2918 - acc_top1: 0.6655 - acc_top5: 0.9203 - 888ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.2550 - acc_top1: 0.6713 - acc_top5: 0.9235 - 422ms/step
Eval samples: 10000
Epoch 5/20
step 391/391 [==============================] - loss: 1.2747 - acc_top1: 0.6815 - acc_top5: 0.9260 - 890ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/4
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.1627 - acc_top1: 0.6817 - acc_top5: 0.9289 - 432ms/step
Eval samples: 10000
Epoch 6/20
step 391/391 [==============================] - loss: 1.2274 - acc_top1: 0.6934 - acc_top5: 0.9329 - 896ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.1079 - acc_top1: 0.6979 - acc_top5: 0.9341 - 429ms/step
Eval samples: 10000
Epoch 7/20
step 391/391 [==============================] - loss: 1.0835 - acc_top1: 0.7030 - acc_top5: 0.9362 - 895ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/6
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.0728 - acc_top1: 0.7092 - acc_top5: 0.9389 - 427ms/step
Eval samples: 10000
Epoch 8/20
step 391/391 [==============================] - loss: 1.0596 - acc_top1: 0.7117 - acc_top5: 0.9420 - 895ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.0304 - acc_top1: 0.7185 - acc_top5: 0.9434 - 428ms/step
Eval samples: 10000
Epoch 9/20
step 391/391 [==============================] - loss: 1.0755 - acc_top1: 0.7246 - acc_top5: 0.9442 - 895ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/8
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.0199 - acc_top1: 0.7282 - acc_top5: 0.9452 - 424ms/step
Eval samples: 10000
Epoch 10/20
step 391/391 [==============================] - loss: 1.1637 - acc_top1: 0.7312 - acc_top5: 0.9478 - 893ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 1.0062 - acc_top1: 0.7315 - acc_top5: 0.9464 - 428ms/step
Eval samples: 10000
Epoch 11/20
step 391/391 [==============================] - loss: 1.0794 - acc_top1: 0.7399 - acc_top5: 0.9518 - 894ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/10
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9966 - acc_top1: 0.7390 - acc_top5: 0.9493 - 426ms/step
Eval samples: 10000
Epoch 12/20
step 391/391 [==============================] - loss: 0.8478 - acc_top1: 0.7469 - acc_top5: 0.9539 - 894ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9767 - acc_top1: 0.7424 - acc_top5: 0.9512 - 425ms/step
Eval samples: 10000
Epoch 13/20
step 391/391 [==============================] - loss: 0.9958 - acc_top1: 0.7526 - acc_top5: 0.9555 - 890ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/12
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9697 - acc_top1: 0.7490 - acc_top5: 0.9539 - 426ms/step
Eval samples: 10000
Epoch 14/20
step 391/391 [==============================] - loss: 0.7780 - acc_top1: 0.7584 - acc_top5: 0.9579 - 894ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9391 - acc_top1: 0.7539 - acc_top5: 0.9564 - 423ms/step
Eval samples: 10000
Epoch 15/20
step 391/391 [==============================] - loss: 1.0491 - acc_top1: 0.7658 - acc_top5: 0.9614 - 891ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9187 - acc_top1: 0.7583 - acc_top5: 0.9571 - 420ms/step
Eval samples: 10000
Epoch 16/20
step 391/391 [==============================] - loss: 0.8458 - acc_top1: 0.7696 - acc_top5: 0.9617 - 890ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9076 - acc_top1: 0.7623 - acc_top5: 0.9589 - 422ms/step
Eval samples: 10000
Epoch 17/20
step 391/391 [==============================] - loss: 1.0365 - acc_top1: 0.7758 - acc_top5: 0.9628 - 892ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/16
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8588 - acc_top1: 0.7638 - acc_top5: 0.9598 - 424ms/step
Eval samples: 10000
Epoch 18/20
step 391/391 [==============================] - loss: 0.9865 - acc_top1: 0.7814 - acc_top5: 0.9650 - 890ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8834 - acc_top1: 0.7701 - acc_top5: 0.9618 - 429ms/step
Eval samples: 10000
Epoch 19/20
step 391/391 [==============================] - loss: 0.6649 - acc_top1: 0.7834 - acc_top5: 0.9670 - 893ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/18
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9119 - acc_top1: 0.7721 - acc_top5: 0.9620 - 427ms/step
Eval samples: 10000
Epoch 20/20
step 391/391 [==============================] - loss: 0.6904 - acc_top1: 0.7891 - acc_top5: 0.9680 - 898ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8655 - acc_top1: 0.7740 - acc_top5: 0.9628 - 430ms/step
Eval samples: 10000
save checkpoint at /home/aistudio/checkpoint/resnet101/final
VisualDL
loss还在下降，继续训练
# 加载finetuning模型训练
model.load('./checkpoint/resnet101/14')def make_optimizer(parameters=None, momentum=0.9, weight_decay=5e-4, boundaries=None, values=None):learning_rate = paddle.optimizer.lr.PiecewiseDecay(boundaries=boundaries, values=values,verbose=False)# learning_rate = paddle.optimizer.lr.LinearWarmup(# learning_rate=learning_rate,# warmup_steps=wamup_steps,# start_lr=base_lr / 5.,# end_lr=base_lr,# verbose=False)# optimizer = paddle.optimizer.Momentum(# learning_rate=learning_rate,# weight_decay=weight_decay,# momentum=momentum,# parameters=parameters)optimizer = paddle.optimizer.Adam(learning_rate=learning_rate,weight_decay=weight_decay,parameters=parameters)return optimizerbase_lr = 5e-5
boundaries = [5]optimizer = make_optimizer(boundaries=boundaries, values=[base_lr, base_lr*0.2], parameters=model.parameters())model.prepare(optimizer=optimizer,loss=paddle.nn.CrossEntropyLoss(),metrics=paddle.metric.Accuracy(topk=(1, 5))
)# callbacks
visualdl = paddle.callbacks.VisualDL('./visualdl/resnet101/14')
earlystop = paddle.callbacks.EarlyStopping( # acc不在上升时停止'acc',mode='max',patience=4,verbose=1,min_delta=0,baseline=None,save_best_model=True)model.fit(train_dataset,eval_dataset,epochs=10,batch_size=128, save_freq=2,save_dir='checkpoint/resnet101/14',callbacks=[visualdl],verbose=1
_dataset,eval_dataset,epochs=10,batch_size=128, save_freq=2,save_dir='checkpoint/resnet101/14',callbacks=[visualdl],verbose=1
)
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/10
step 391/391 [==============================] - loss: 0.7212 - acc_top1: 0.7760 - acc_top5: 0.9648 - 892ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14/0
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.9302 - acc_top1: 0.7996 - acc_top5: 0.9680 - 421ms/step
Eval samples: 10000
Epoch 2/10
step 391/391 [==============================] - loss: 0.5295 - acc_top1: 0.8301 - acc_top5: 0.9776 - 893ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8164 - acc_top1: 0.8103 - acc_top5: 0.9732 - 416ms/step
Eval samples: 10000
Epoch 3/10
step 391/391 [==============================] - loss: 0.4092 - acc_top1: 0.8622 - acc_top5: 0.9853 - 895ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14/2
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8366 - acc_top1: 0.8297 - acc_top5: 0.9747 - 416ms/step
Eval samples: 10000
Epoch 4/10
step 391/391 [==============================] - loss: 0.3479 - acc_top1: 0.8860 - acc_top5: 0.9896 - 899ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.7397 - acc_top1: 0.8325 - acc_top5: 0.9757 - 428ms/step
Eval samples: 10000
Epoch 5/10
step 391/391 [==============================] - loss: 0.3405 - acc_top1: 0.9086 - acc_top5: 0.9925 - 906ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14/4
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8492 - acc_top1: 0.8373 - acc_top5: 0.9780 - 430ms/step
Eval samples: 10000
Epoch 6/10
step 391/391 [==============================] - loss: 0.1852 - acc_top1: 0.9242 - acc_top5: 0.9948 - 902ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.7244 - acc_top1: 0.8436 - acc_top5: 0.9761 - 422ms/step
Eval samples: 10000
Epoch 7/10
step 391/391 [==============================] - loss: 0.3084 - acc_top1: 0.9387 - acc_top5: 0.9969 - 895ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14/6
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8724 - acc_top1: 0.8458 - acc_top5: 0.9767 - 419ms/step
Eval samples: 10000
Epoch 8/10
step 391/391 [==============================] - loss: 0.1378 - acc_top1: 0.9529 - acc_top5: 0.9979 - 893ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.8844 - acc_top1: 0.8443 - acc_top5: 0.9765 - 419ms/step
Eval samples: 10000
Epoch 9/10
step 391/391 [==============================] - loss: 0.1667 - acc_top1: 0.9623 - acc_top5: 0.9981 - 894ms/step
save checkpoint at /home/aistudio/checkpoint/resnet101/14/8
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.7474 - acc_top1: 0.8490 - acc_top5: 0.9775 - 416ms/step
Eval samples: 10000
Epoch 10/10
step 391/391 [==============================] - loss: 0.1841 - acc_top1: 0.9697 - acc_top5: 0.9989 - 895ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.7419 - acc_top1: 0.8471 - acc_top5: 0.9758 - 428ms/step
Eval samples: 10000
save checkpoint at /home/aistudio/checkpoint/resnet101/14/final
VisualDL
model.save('./finetuning/resnet101/model', training=True)
评分输出
Eval samples: 10000
{'loss': [1.4640276], 'acc_top1': 0.6361581096849475, 'acc_top5': 0.8786464410735122}
result = model.evaluate(eval_dataset, batch_size=128, verbose=1)print(result)
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 79/79 [==============================] - loss: 0.7419 - acc_top1: 0.8468 - acc_top5: 0.9757 - 417ms/step
Eval samples: 10000
{'loss': [0.74186254], 'acc_top1': 0.8467935528120714, 'acc_top5': 0.9757373113854595}
CIFAR-100网络模型效果参考
https://paperswithcode.com/sota/image-classification-on-cifar-100
自定义模型训练结果参考：
| dataset | network | params | top1 err | top5 err | epoch(lr = 0.1) | epoch(lr = 0.02) | epoch(lr = 0.004) | epoch(lr = 0.0008) | total epoch |
|---|---|---|---|---|---|---|---|---|---|
| cifar100 | mobilenet | 3.3M | 34.02 | 10.56 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | mobilenetv2 | 2.36M | 31.92 | 09.02 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | squeezenet | 0.78M | 30.59 | 8.36 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | shufflenet | 1.0M | 29.94 | 8.35 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | shufflenetv2 | 1.3M | 30.49 | 8.49 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | vgg11_bn | 28.5M | 31.36 | 11.85 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | vgg13_bn | 28.7M | 28.00 | 9.71 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | vgg16_bn | 34.0M | 27.07 | 8.84 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | vgg19_bn | 39.0M | 27.77 | 8.84 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnet18 | 11.2M | 24.39 | 6.95 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnet34 | 21.3M | 23.24 | 6.63 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnet50 | 23.7M | 22.61 | 6.04 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnet101 | 42.7M | 22.22 | 5.61 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnet152 | 58.3M | 22.31 | 5.81 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | preactresnet18 | 11.3M | 27.08 | 8.53 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | preactresnet34 | 21.5M | 24.79 | 7.68 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | preactresnet50 | 23.9M | 25.73 | 8.15 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | preactresnet101 | 42.9M | 24.84 | 7.83 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | preactresnet152 | 58.6M | 22.71 | 6.62 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnext50 | 14.8M | 22.23 | 6.00 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnext101 | 25.3M | 22.22 | 5.99 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | resnext152 | 33.3M | 22.40 | 5.58 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | attention59 | 55.7M | 33.75 | 12.90 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | attention92 | 102.5M | 36.52 | 11.47 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | densenet121 | 7.0M | 22.99 | 6.45 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | densenet161 | 26M | 21.56 | 6.04 | 60 | 60 | 60 | 40 | 200 |
| cifar100 | densenet201 | 18M | 21.46 | 5.9 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | googlenet | 6.2M | 21.97 | 5.94 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | inceptionv3 | 22.3M | 22.81 | 6.39 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | inceptionv4 | 41.3M | 24.14 | 6.90 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | inceptionresnetv2 | 65.4M | 27.51 | 9.11 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | xception | 21.0M | 25.07 | 7.32 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | seresnet18 | 11.4M | 23.56 | 6.68 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | seresnet34 | 21.6M | 22.07 | 6.12 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | seresnet50 | 26.5M | 21.42 | 5.58 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | seresnet101 | 47.7M | 20.98 | 5.41 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | seresnet152 | 66.2M | 20.66 | 5.19 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | nasnet | 5.2M | 22.71 | 5.91 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | wideresnet-40-10 | 55.9M | 21.25 | 5.77 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | stochasticdepth18 | 11.22M | 31.40 | 8.84 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | stochasticdepth34 | 21.36M | 27.72 | 7.32 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | stochasticdepth50 | 23.71M | 23.35 | 5.76 | 60 | 60 | 40 | 40 | 200 |
| cifar100 | stochasticdepth101 | 42.69M | 21.28 | 5.39 | 60 | 60 | 40 | 40 | 200 |
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