基于MobileNetv2的垃圾分类函数式自动微分-昇思25天打卡

基于MobileNetv2的垃圾分类

本文档主要介绍垃圾分类代码开发的方法。通过读取本地图像数据作为输入,对图像中的垃圾物体进行检测,并且将检测结果图片保存到文件中。

1、实验目的

  • 了解熟悉垃圾分类应用代码的编写(Python语言);
  • 了解Linux操作系统的基本使用;
  • 掌握atc命令进行模型转换的基本操作。

2、MobileNetv2模型原理介绍

MobileNet网络是由Google团队于2017年提出的专注于移动端、嵌入式或IoT设备的轻量级CNN网络,相比于传统的卷积神经网络,MobileNet网络使用深度可分离卷积(Depthwise Separable Convolution)的思想在准确率小幅度降低的前提下,大大减小了模型参数与运算量。并引入宽度系数 α和分辨率系数 β使模型满足不同应用场景的需求。

由于MobileNet网络中Relu激活函数处理低维特征信息时会存在大量的丢失,所以MobileNetV2网络提出使用倒残差结构(Inverted residual block)和Linear Bottlenecks来设计网络,以提高模型的准确率,且优化后的模型更小。

请添加图片描述

图中Inverted residual block结构是先使用1x1卷积进行升维,然后使用3x3的DepthWise卷积,最后使用1x1的卷积进行降维,与Residual block结构相反。Residual block是先使用1x1的卷积进行降维,然后使用3x3的卷积,最后使用1x1的卷积进行升维。

  • 说明:
    详细内容可参见MobileNetV2论文

3、实验环境

本案例支持win_x86和Linux系统,CPU/GPU/Ascend均可运行。

在动手进行实践之前,确保您已经正确安装了MindSpore。不同平台下的环境准备请参考《MindSpore环境搭建实验手册》。

4、数据处理

4.1数据准备

MobileNetV2的代码默认使用ImageFolder格式管理数据集,每一类图片整理成单独的一个文件夹, 数据集结构如下:

└─ImageFolder

├─train
│   class1Folder
│   ......
└─evalclass1Folder......
%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14,如需更换mindspore版本,可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
# 查看当前 mindspore 版本
!pip show mindspore
Name: mindspore
Version: 2.2.14
Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Home-page: https://www.mindspore.cn
Author: The MindSpore Authors
Author-email: contact@mindspore.cn
License: Apache 2.0
Location: /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages
Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy
Required-by: mindnlp
from download import download# 下载data_en数据集
url = "https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MindStudio-pc/data_en.zip" 
path = download(url, "./", kind="zip", replace=True)
Downloading data from https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MindStudio-pc/data_en.zip (21.3 MB)file_sizes: 100%|███████████████████████████| 22.4M/22.4M [00:00<00:00, 106MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
from download import download# 下载预训练权重文件
url = "https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/ComputerVision/mobilenetV2-200_1067.zip" 
path = download(url, "./", kind="zip", replace=True)
Downloading data from https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/ComputerVision/mobilenetV2-200_1067.zip (25.5 MB)file_sizes: 100%|███████████████████████████| 26.7M/26.7M [00:00<00:00, 111MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./

4.2数据加载

将模块导入,具体如下:
import math
import numpy as np
import os
import randomfrom matplotlib import pyplot as plt
from easydict import EasyDict
from PIL import Image
import numpy as np
import mindspore.nn as nn
from mindspore import ops as P
from mindspore.ops import add
from mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.dataset as de
import mindspore.dataset.vision as C
import mindspore.dataset.transforms as C2
import mindspore as ms
from mindspore import set_context, nn, Tensor, load_checkpoint, save_checkpoint, export
from mindspore.train import Model
from mindspore.train import Callback, LossMonitor, ModelCheckpoint, CheckpointConfigos.environ['GLOG_v'] = '3' # Log level includes 3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
os.environ['GLOG_logtostderr'] = '0' # 0:输出到文件,1:输出到屏幕
os.environ['GLOG_log_dir'] = '../../log' # 日志目录
os.environ['GLOG_stderrthreshold'] = '2' # 输出到目录也输出到屏幕:3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
set_context(mode=ms.GRAPH_MODE, device_target="CPU", device_id=0) # 设置采用图模式执行,设备为Ascend#
配置后续训练、验证、推理用到的参数:
# 垃圾分类数据集标签,以及用于标签映射的字典。
garbage_classes = {'干垃圾': ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服'],'可回收物': ['报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张'],'湿垃圾': ['菜叶', '橙皮', '蛋壳', '香蕉皮'],'有害垃圾': ['电池', '药片胶囊', '荧光灯', '油漆桶']
}class_cn = ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服','报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张','菜叶', '橙皮', '蛋壳', '香蕉皮','电池', '药片胶囊', '荧光灯', '油漆桶']
class_en = ['Seashell', 'Lighter','Old Mirror', 'Broom','Ceramic Bowl', 'Toothbrush','Disposable Chopsticks','Dirty Cloth','Newspaper', 'Glassware', 'Basketball', 'Plastic Bottle', 'Cardboard','Glass Bottle', 'Metalware', 'Hats', 'Cans', 'Paper','Vegetable Leaf','Orange Peel', 'Eggshell','Banana Peel','Battery', 'Tablet capsules','Fluorescent lamp', 'Paint bucket']index_en = {'Seashell': 0, 'Lighter': 1, 'Old Mirror': 2, 'Broom': 3, 'Ceramic Bowl': 4, 'Toothbrush': 5, 'Disposable Chopsticks': 6, 'Dirty Cloth': 7,'Newspaper': 8, 'Glassware': 9, 'Basketball': 10, 'Plastic Bottle': 11, 'Cardboard': 12, 'Glass Bottle': 13, 'Metalware': 14, 'Hats': 15, 'Cans': 16, 'Paper': 17,'Vegetable Leaf': 18, 'Orange Peel': 19, 'Eggshell': 20, 'Banana Peel': 21,'Battery': 22, 'Tablet capsules': 23, 'Fluorescent lamp': 24, 'Paint bucket': 25}# 训练超参
config = EasyDict({"num_classes": 26,"image_height": 224,"image_width": 224,#"data_split": [0.9, 0.1],"backbone_out_channels":1280,"batch_size": 16,"eval_batch_size": 8,"epochs": 10,"lr_max": 0.05,"momentum": 0.9,"weight_decay": 1e-4,"save_ckpt_epochs": 1,"dataset_path": "./data_en","class_index": index_en,"pretrained_ckpt": "./mobilenetV2-200_1067.ckpt" # mobilenetV2-200_1067.ckpt 
})
数据预处理操作

利用ImageFolderDataset方法读取垃圾分类数据集,并整体对数据集进行处理。

读取数据集时指定训练集和测试集,首先对整个数据集进行归一化,修改图像频道等预处理操作。然后对训练集的数据依次进行RandomCropDecodeResize、RandomHorizontalFlip、RandomColorAdjust、shuffle操作,以增加训练数据的丰富度;对测试集进行Decode、Resize、CenterCrop等预处理操作;最后返回处理后的数据集。

def create_dataset(dataset_path, config, training=True, buffer_size=1000):"""create a train or eval datasetArgs:dataset_path(string): the path of dataset.config(struct): the config of train and eval in diffirent platform.Returns:train_dataset, val_dataset"""data_path = os.path.join(dataset_path, 'train' if training else 'test')ds = de.ImageFolderDataset(data_path, num_parallel_workers=4, class_indexing=config.class_index)resize_height = config.image_heightresize_width = config.image_widthnormalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])change_swap_op = C.HWC2CHW()type_cast_op = C2.TypeCast(mstype.int32)if training:crop_decode_resize = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)color_adjust = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)train_trans = [crop_decode_resize, horizontal_flip_op, color_adjust, normalize_op, change_swap_op]train_ds = ds.map(input_columns="image", operations=train_trans, num_parallel_workers=4)train_ds = train_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=4)train_ds = train_ds.shuffle(buffer_size=buffer_size)ds = train_ds.batch(config.batch_size, drop_remainder=True)else:decode_op = C.Decode()resize_op = C.Resize((int(resize_width/0.875), int(resize_width/0.875)))center_crop = C.CenterCrop(resize_width)eval_trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]eval_ds = ds.map(input_columns="image", operations=eval_trans, num_parallel_workers=4)eval_ds = eval_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=4)ds = eval_ds.batch(config.eval_batch_size, drop_remainder=True)return ds
展示部分处理后的数据:
ds = create_dataset(dataset_path=config.dataset_path, config=config, training=False)
print(ds.get_dataset_size())
data = ds.create_dict_iterator(output_numpy=True)._get_next()
images = data['image']
labels = data['label']for i in range(1, 5):plt.subplot(2, 2, i)plt.imshow(np.transpose(images[i], (1,2,0)))plt.title('label: %s' % class_en[labels[i]])plt.xticks([])
plt.show()
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.7240347..2.64].
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.64].
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.64].
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0357144..2.64].32

请添加图片描述

5、MobileNetV2模型搭建

使用MindSpore定义MobileNetV2网络的各模块时需要继承mindspore.nn.Cell。Cell是所有神经网络(Conv2d等)的基类。

神经网络的各层需要预先在__init__方法中定义,然后通过定义construct方法来完成神经网络的前向构造。原始模型激活函数为ReLU6,池化模块采用是全局平均池化层。

__all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']def _make_divisible(v, divisor, min_value=None):if min_value is None:min_value = divisornew_v = max(min_value, int(v + divisor / 2) // divisor * divisor)if new_v < 0.9 * v:new_v += divisorreturn new_vclass GlobalAvgPooling(nn.Cell):"""Global avg pooling definition.Args:Returns:Tensor, output tensor.Examples:>>> GlobalAvgPooling()"""def __init__(self):super(GlobalAvgPooling, self).__init__()def construct(self, x):x = P.mean(x, (2, 3))return xclass ConvBNReLU(nn.Cell):"""Convolution/Depthwise fused with Batchnorm and ReLU block definition.Args:in_planes (int): Input channel.out_planes (int): Output channel.kernel_size (int): Input kernel size.stride (int): Stride size for the first convolutional layer. Default: 1.groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.Returns:Tensor, output tensor.Examples:>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)"""def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):super(ConvBNReLU, self).__init__()padding = (kernel_size - 1) // 2in_channels = in_planesout_channels = out_planesif groups == 1:conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding)else:out_channels = in_planesconv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad',padding=padding, group=in_channels)layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]self.features = nn.SequentialCell(layers)def construct(self, x):output = self.features(x)return outputclass InvertedResidual(nn.Cell):"""Mobilenetv2 residual block definition.Args:inp (int): Input channel.oup (int): Output channel.stride (int): Stride size for the first convolutional layer. Default: 1.expand_ratio (int): expand ration of input channelReturns:Tensor, output tensor.Examples:>>> ResidualBlock(3, 256, 1, 1)"""def __init__(self, inp, oup, stride, expand_ratio):super(InvertedResidual, self).__init__()assert stride in [1, 2]hidden_dim = int(round(inp * expand_ratio))self.use_res_connect = stride == 1 and inp == ouplayers = []if expand_ratio != 1:layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))layers.extend([ConvBNReLU(hidden_dim, hidden_dim,stride=stride, groups=hidden_dim),nn.Conv2d(hidden_dim, oup, kernel_size=1,stride=1, has_bias=False),nn.BatchNorm2d(oup),])self.conv = nn.SequentialCell(layers)self.cast = P.Cast()def construct(self, x):identity = xx = self.conv(x)if self.use_res_connect:return P.add(identity, x)return xclass MobileNetV2Backbone(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): number of classes.width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.has_dropout (bool): Is dropout used. Default is falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, width_mult=1., inverted_residual_setting=None, round_nearest=8,input_channel=32, last_channel=1280):super(MobileNetV2Backbone, self).__init__()block = InvertedResidual# setting of inverted residual blocksself.cfgs = inverted_residual_settingif inverted_residual_setting is None:self.cfgs = [# t, c, n, s[1, 16, 1, 1],[6, 24, 2, 2],[6, 32, 3, 2],[6, 64, 4, 2],[6, 96, 3, 1],[6, 160, 3, 2],[6, 320, 1, 1],]# building first layerinput_channel = _make_divisible(input_channel * width_mult, round_nearest)self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)features = [ConvBNReLU(3, input_channel, stride=2)]# building inverted residual blocksfor t, c, n, s in self.cfgs:output_channel = _make_divisible(c * width_mult, round_nearest)for i in range(n):stride = s if i == 0 else 1features.append(block(input_channel, output_channel, stride, expand_ratio=t))input_channel = output_channelfeatures.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))self.features = nn.SequentialCell(features)self._initialize_weights()def construct(self, x):x = self.features(x)return xdef _initialize_weights(self):"""Initialize weights.Args:Returns:None.Examples:>>> _initialize_weights()"""self.init_parameters_data()for _, m in self.cells_and_names():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),m.weight.data.shape).astype("float32")))if m.bias is not None:m.bias.set_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))elif isinstance(m, nn.BatchNorm2d):m.gamma.set_data(Tensor(np.ones(m.gamma.data.shape, dtype="float32")))m.beta.set_data(Tensor(np.zeros(m.beta.data.shape, dtype="float32")))@propertydef get_features(self):return self.featuresclass MobileNetV2Head(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): Number of classes. Default is 1000.has_dropout (bool): Is dropout used. Default is falseReturns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False, activation="None"):super(MobileNetV2Head, self).__init__()# mobilenet headhead = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_bias=True)] if not has_dropout else[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_bias=True)])self.head = nn.SequentialCell(head)self.need_activation = Trueif activation == "Sigmoid":self.activation = nn.Sigmoid()elif activation == "Softmax":self.activation = nn.Softmax()else:self.need_activation = Falseself._initialize_weights()def construct(self, x):x = self.head(x)if self.need_activation:x = self.activation(x)return xdef _initialize_weights(self):"""Initialize weights.Args:Returns:None.Examples:>>> _initialize_weights()"""self.init_parameters_data()for _, m in self.cells_and_names():if isinstance(m, nn.Dense):m.weight.set_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))if m.bias is not None:m.bias.set_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))@propertydef get_head(self):return self.headclass MobileNetV2(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): number of classes.width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.has_dropout (bool): Is dropout used. Default is falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples:>>> MobileNetV2(backbone, head)"""def __init__(self, num_classes=1000, width_mult=1., has_dropout=False, inverted_residual_setting=None, \round_nearest=8, input_channel=32, last_channel=1280):super(MobileNetV2, self).__init__()self.backbone = MobileNetV2Backbone(width_mult=width_mult, \inverted_residual_setting=inverted_residual_setting, \round_nearest=round_nearest, input_channel=input_channel, last_channel=last_channel).get_featuresself.head = MobileNetV2Head(input_channel=self.backbone.out_channel, num_classes=num_classes, \has_dropout=has_dropout).get_headdef construct(self, x):x = self.backbone(x)x = self.head(x)return xclass MobileNetV2Combine(nn.Cell):"""MobileNetV2Combine architecture.Args:backbone (Cell): the features extract layers.head (Cell):  the fully connected layers.Returns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, backbone, head):super(MobileNetV2Combine, self).__init__(auto_prefix=False)self.backbone = backboneself.head = headdef construct(self, x):x = self.backbone(x)x = self.head(x)return xdef mobilenet_v2(backbone, head):return MobileNetV2Combine(backbone, head)

6、MobileNetV2模型的训练与测试

训练策略

一般情况下,模型训练时采用静态学习率,如0.01。随着训练步数的增加,模型逐渐趋于收敛,对权重参数的更新幅度应该逐渐降低,以减小模型训练后期的抖动。所以,模型训练时可以采用动态下降的学习率,常见的学习率下降策略有:

  • polynomial decay/square decay;
  • cosine decay;
  • exponential decay;
  • stage decay.

这里使用cosine decay下降策略:

def cosine_decay(total_steps, lr_init=0.0, lr_end=0.0, lr_max=0.1, warmup_steps=0):"""Applies cosine decay to generate learning rate array.Args:total_steps(int): all steps in training.lr_init(float): init learning rate.lr_end(float): end learning ratelr_max(float): max learning rate.warmup_steps(int): all steps in warmup epochs.Returns:list, learning rate array."""lr_init, lr_end, lr_max = float(lr_init), float(lr_end), float(lr_max)decay_steps = total_steps - warmup_stepslr_all_steps = []inc_per_step = (lr_max - lr_init) / warmup_steps if warmup_steps else 0for i in range(total_steps):if i < warmup_steps:lr = lr_init + inc_per_step * (i + 1)else:cosine_decay = 0.5 * (1 + math.cos(math.pi * (i - warmup_steps) / decay_steps))lr = (lr_max - lr_end) * cosine_decay + lr_endlr_all_steps.append(lr)return lr_all_steps

在模型训练过程中,可以添加检查点(Checkpoint)用于保存模型的参数,以便进行推理及中断后再训练使用。使用场景如下:

  • 训练后推理场景
  1. 模型训练完毕后保存模型的参数,用于推理或预测操作。
  2. 训练过程中,通过实时验证精度,把精度最高的模型参数保存下来,用于预测操作。
  • 再训练场景
  1. 进行长时间训练任务时,保存训练过程中的Checkpoint文件,防止任务异常退出后从初始状态开始训练。
  2. Fine-tuning(微调)场景,即训练一个模型并保存参数,基于该模型,面向第二个类似任务进行模型训练。

这里加载ImageNet数据上预训练的MobileNetv2进行Fine-tuning,只训练最后修改的FC层,并在训练过程中保存Checkpoint。

def switch_precision(net, data_type):if ms.get_context('device_target') == "Ascend":net.to_float(data_type)for _, cell in net.cells_and_names():if isinstance(cell, nn.Dense):cell.to_float(ms.float32)
模型训练与测试

在进行正式的训练之前,定义训练函数,读取数据并对模型进行实例化,定义优化器和损失函数。

首先简单介绍损失函数及优化器的概念:

  • 损失函数:又叫目标函数,用于衡量预测值与实际值差异的程度。深度学习通过不停地迭代来缩小损失函数的值。定义一个好的损失函数,可以有效提高模型的性能。

  • 优化器:用于最小化损失函数,从而在训练过程中改进模型。

定义了损失函数后,可以得到损失函数关于权重的梯度。梯度用于指示优化器优化权重的方向,以提高模型性能。

在训练MobileNetV2之前对MobileNetV2Backbone层的参数进行了固定,使其在训练过程中对该模块的权重参数不进行更新;只对MobileNetV2Head模块的参数进行更新。

MindSpore支持的损失函数有SoftmaxCrossEntropyWithLogits、L1Loss、MSELoss等。这里使用SoftmaxCrossEntropyWithLogits损失函数。

训练测试过程中会打印loss值,loss值会波动,但总体来说loss值会逐步减小,精度逐步提高。每个人运行的loss值有一定随机性,不一定完全相同。

每打印一个epoch后模型都会在测试集上的计算测试精度,从打印的精度值分析MobileNetV2模型的预测能力在不断提升。

from mindspore.amp import FixedLossScaleManager
import time
LOSS_SCALE = 1024train_dataset = create_dataset(dataset_path=config.dataset_path, config=config)
eval_dataset = create_dataset(dataset_path=config.dataset_path, config=config)
step_size = train_dataset.get_dataset_size()backbone = MobileNetV2Backbone() #last_channel=config.backbone_out_channels
# Freeze parameters of backbone. You can comment these two lines.
for param in backbone.get_parameters():param.requires_grad = False
# load parameters from pretrained model
load_checkpoint(config.pretrained_ckpt, backbone)head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)
network = mobilenet_v2(backbone, head)# define loss, optimizer, and model
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
loss_scale = FixedLossScaleManager(LOSS_SCALE, drop_overflow_update=False)
lrs = cosine_decay(config.epochs * step_size, lr_max=config.lr_max)
opt = nn.Momentum(network.trainable_params(), lrs, config.momentum, config.weight_decay, loss_scale=LOSS_SCALE)# 定义用于训练的train_loop函数。
def train_loop(model, dataset, loss_fn, optimizer):# 定义正向计算函数def forward_fn(data, label):logits = model(data)loss = loss_fn(logits, label)return loss# 定义微分函数,使用mindspore.value_and_grad获得微分函数grad_fn,输出loss和梯度。# 由于是对模型参数求导,grad_position 配置为None,传入可训练参数。grad_fn = ms.value_and_grad(forward_fn, None, optimizer.parameters)# 定义 one-step training函数def train_step(data, label):loss, grads = grad_fn(data, label)optimizer(grads)return losssize = dataset.get_dataset_size()model.set_train()for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):loss = train_step(data, label)if batch % 10 == 0:loss, current = loss.asnumpy(), batchprint(f"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]")# 定义用于测试的test_loop函数。
def test_loop(model, dataset, loss_fn):num_batches = dataset.get_dataset_size()model.set_train(False)total, test_loss, correct = 0, 0, 0for data, label in dataset.create_tuple_iterator():pred = model(data)total += len(data)test_loss += loss_fn(pred, label).asnumpy()correct += (pred.argmax(1) == label).asnumpy().sum()test_loss /= num_batchescorrect /= totalprint(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")print("============== Starting Training ==============")
# 由于时间问题,训练过程只进行了2个epoch ,可以根据需求调整。
epoch_begin_time = time.time()
epochs = 2
for t in range(epochs):begin_time = time.time()print(f"Epoch {t+1}\n-------------------------------")train_loop(network, train_dataset, loss, opt)ms.save_checkpoint(network, "save_mobilenetV2_model.ckpt")end_time = time.time()times = end_time - begin_timeprint(f"per epoch time: {times}s")test_loop(network, eval_dataset, loss)
epoch_end_time = time.time()
times = epoch_end_time - epoch_begin_time
print(f"total time:  {times}s")
print("============== Training Success ==============")
============== Starting Training ==============
Epoch 1
-------------------------------loss: 3.254856  [  0/162]
loss: 3.217916  [ 10/162]
loss: 3.186504  [ 20/162]
loss: 3.355182  [ 30/162]
loss: 3.336528  [ 40/162]
loss: 3.220652  [ 50/162]
loss: 3.253688  [ 60/162]
loss: 3.208350  [ 70/162]
loss: 3.215430  [ 80/162]
loss: 3.244096  [ 90/162]
loss: 3.188400  [100/162]
loss: 3.155391  [110/162]
loss: 3.304114  [120/162]
loss: 3.239458  [130/162]
loss: 3.225820  [140/162]
loss: 3.191090  [150/162]
loss: 3.188383  [160/162]
per epoch time: 70.34860825538635sTest: Accuracy: 11.4%, Avg loss: 3.189114 Epoch 2
-------------------------------loss: 3.156094  [  0/162]
loss: 3.262588  [ 10/162]
loss: 3.141063  [ 20/162]
loss: 3.195691  [ 30/162]
loss: 3.253536  [ 40/162]
loss: 3.236728  [ 50/162]
loss: 3.122650  [ 60/162]
loss: 3.165659  [ 70/162]
loss: 3.144341  [ 80/162]
loss: 3.140723  [ 90/162]
loss: 3.190994  [100/162]
loss: 3.177974  [110/162]
loss: 3.111208  [120/162]
loss: 3.126922  [130/162]
loss: 3.111721  [140/162]
loss: 3.135597  [150/162]
loss: 3.113985  [160/162]
per epoch time: 78.56036615371704s
Test: Accuracy: 20.6%, Avg loss: 3.109391 total time:  287.22266936302185s
============== Training Success ==============

7、模型推理

加载模型Checkpoint进行推理,使用load_checkpoint接口加载数据时,需要把数据传入给原始网络,而不能传递给带有优化器和损失函数的训练网络。

CKPT="save_mobilenetV2_model.ckpt"
def image_process(image):"""Precess one image per time.Args:image: shape (H, W, C)"""mean=[0.485*255, 0.456*255, 0.406*255]std=[0.229*255, 0.224*255, 0.225*255]image = (np.array(image) - mean) / stdimage = image.transpose((2,0,1))img_tensor = Tensor(np.array([image], np.float32))return img_tensordef infer_one(network, image_path):image = Image.open(image_path).resize((config.image_height, config.image_width))logits = network(image_process(image))pred = np.argmax(logits.asnumpy(), axis=1)[0]print(image_path, class_en[pred])def infer():backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)network = mobilenet_v2(backbone, head)load_checkpoint(CKPT, network)for i in range(91, 100):infer_one(network, f'data_en/test/Cardboard/000{i}.jpg')
infer()
data_en/test/Cardboard/00091.jpg Old Mirror
data_en/test/Cardboard/00092.jpg Cardboard
data_en/test/Cardboard/00093.jpg Lighter
data_en/test/Cardboard/00094.jpg Glass Bottle
data_en/test/Cardboard/00095.jpg Lighter
data_en/test/Cardboard/00096.jpg Tablet capsules
data_en/test/Cardboard/00097.jpg Broom
data_en/test/Cardboard/00098.jpg Cardboard
data_en/test/Cardboard/00099.jpg Plastic Bottle

8、导出AIR/GEIR/ONNX模型文件

导出AIR模型文件,用于后续Atlas 200 DK上的模型转换与推理。当前仅支持MindSpore+Ascend环境。

backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)
head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)
network = mobilenet_v2(backbone, head)
load_checkpoint(CKPT, network)input = np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]).astype(np.float32)
# export(network, Tensor(input), file_name='mobilenetv2.air', file_format='AIR')
# export(network, Tensor(input), file_name='mobilenetv2.pb', file_format='GEIR')
export(network, Tensor(input), file_name='mobilenetv2.onnx', file_format='ONNX')
from datetime import datetime
import pytz
# 设置时区为北京时区
beijing_tz = pytz.timezone('Asia/shanghai')
# 获取当前时间,并转为北京时间
current_beijing_time = datetime.now(beijing_tz)
# 格式化时间输出
formatted_time = current_beijing_time.strftime('%Y-%m-%d %H:%M:%S')
print("当前北京时间:",formatted_time)
print('用户名:matpandas 显似')
当前北京时间: 2024-07-23 23:00:05
用户名:matpandas 显似

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:http://www.mzph.cn/diannao/49248.shtml

如若内容造成侵权/违法违规/事实不符,请联系多彩编程网进行投诉反馈email:809451989@qq.com,一经查实,立即删除!

相关文章

推荐推荐两款免费的WIN PE工具,很好用两款免费的WIN PE工具,很好用

上次推荐了三款WIN PE工具​&#xff1a;推荐3款装机必不可少的PE工具箱&#xff0c;全是宝藏工具&#xff0c;千万不要错过-CSDN博客 今天继续推荐两款WIN PE工具。 FirPE FirPE是一款系统预安装环境&#xff08;Windows PE&#xff09;&#xff0c;它具有简约、易操作等特点…

《SeTformer Is What You Need for Vision and Language》

会议&#xff1a;AAAI 年份&#xff1a;2024 论文&#xff1a;DDAE: Towards Deep Dynamic Vision BERT Pretraining - AMinerhttps://www.aminer.cn/pub/6602613613fb2c6cf6c387c2/ddae-towards-deep-dynamic-vision-bert-pretraining 摘要 这篇论文介绍了一种新型的变换器…

如何将 M.2 HAT+ 与 Raspberry Pi 5 一起使用?

树莓派 M.2 HAT M Key 可以让您连接 M.2 外围设备,如 NVMe 硬盘和其他 PCIe 配件,到树莓派 5 的 PCIe 接口。 M.2 HAT 转接板可以把树莓派 5 上的 PCIe 连接器转换为单个 M.2 M key 边缘连接器。您可以连接任何使用 2230 或 2242 尺寸的设备。M.2 HAT 最大可提供 3A 的电源输出…

Superset 4.0.1导出csv数据中文乱码问题解决

Apache Superset 是一个开源的数据探索和可视化平台,专门用于创建交互式数据报表和仪表盘。它具有强大的数据集成和可视化能力,广泛用于数据分析和商业智能领域。 Superset详细介绍详见 报表系统之Superset-CSDN博客 Superset 导出CSV 默认编码为utf-8,在导出包含中文的文…

jenkins替换配置文件

1.点击首页的【Manage Jenkins】-【Manage Plugins】&#xff0c;在选项【Available plugins】安装 Config File Provider Plugin &#xff0c;安装后重启jenkins 2.安装完成后会有这个图标&#xff0c;点进去 3.点击新建&#xff0c;选择自定义&#xff0c;填入要替换的文件…

深入浅出理解 C 语言中的 qsort 函数

目录 引言 一、什么是qsort 二、函数原型 1.qsort函数 2.比较函数 三、qsort函数使用示例 1.使用qsort排序整形数据 2.使用qsort排序结构数据 总结 引言 在编程中&#xff0c;排序是一个常见且重要的操作。C 语言标准库提供了一系列排序函数&#xff0c;其中 qsort 函…

华为IoTDA解码插件报告错误:The decoding result is empty.data

前面的博文讲过&#xff0c;在使用Neuron上传数据到华为IoTDA的时候没有使用华为的物模型进行解析&#xff0c;因为两者的数据格式不同。具体的说Neuron上传的格式是 {"node": "RS485", "group": "Data", "timestamp": 172…

CSS画边框线带有渐变线和流光边框实例

流光边框css流光边框动画效果_哔哩哔哩_bilibili流光边框css流光边框动画效果_哔哩哔哩_bilibili纯CSS写一个动态流水灯边框的效果&#xff5e;_哔哩哔哩_bilibili荧光边框CSS 动画发光渐变边框特效_哔哩哔哩_bilibili [data-v-25d37a3a] .flow-dialog-custom {background-col…

xhs全参

声明 本文章中所有内容仅供学习交流&#xff0c;抓包内容、敏感网址、数据接口均已做脱敏处理&#xff0c;严禁用于商业用途和非法用途&#xff0c;否则由此产生的一切后果均与作者无关&#xff0c;若有侵权&#xff0c;请联系我立即删除&#xff01; 目标网站 aHR0cHM6Ly93d…

【线性代数】矩阵变换

一些特殊的矩阵 一&#xff0c;对角矩阵 1&#xff0c;什么是对角矩阵 表示将矩阵进行伸缩&#xff08;反射&#xff09;变换&#xff0c;仅沿坐标轴方向伸缩&#xff08;反射&#xff09;变换。 2&#xff0c;对角矩阵可分解为多个F1矩阵&#xff0c;如下&#xff1a; 二&a…

.NET C# 配置 Options

.NET C# 配置 Options 使用 options 模式可以带来许多好处&#xff0c;包括清晰的配置管理、类型安全、易于测试和灵活性。但在使用过程中&#xff0c;也需要注意配置复杂性、性能开销和依赖框架等问题。通过合理设计和使用&#xff0c;可以充分发挥 options 模式的优势&#…

Vue.js 2 项目实战(五):水果购物车

前言 Vue.js 是一个用于构建用户界面的渐进式 JavaScript 框架。它的设计目标是通过采用易于上手的结构和强大的功能&#xff0c;使前端开发变得更加简便和高效。以下是 Vue.js 的一些关键特性和优点&#xff1a; 核心特性 声明式渲染 Vue.js 使用声明式语法来描述用户界面&a…

MybatisPlus的使用与详细讲解

今天我们来讲解一下Mybatis的升级版&#xff0c;就是MybatisPlus. MybatisPlus是如何获取实现CRUD的数据库表信息的&#xff1f; 默认以类名驼峰转下划线作为表名 默认把名为id的字段作为主键 默认把变量名驼峰转下划线作为表的字段名 1.MybatisPlus中比较常见的注解 TableN…

宠物空气净化器哪款除臭效果好?质量好的养狗空气净化器排名

作为一个宠物家电小博主&#xff0c;炎炎夏日&#xff0c;家中的宠物给你带来的不仅仅是温暖的陪伴&#xff0c;还有那挥之不去的宠物异味。普通空气净化器虽然能够应对一般的空气净化需求&#xff0c;但对于养猫家庭特有的挑战&#xff0c;如宠物毛发、皮屑和异味等&#xff0…

mysql中的索引和分区

目录 1.编写目的 2.索引 2.1 创建方法 2.2 最佳适用 2.3 索引相关语句 3.分区 3.1 创建方法 3.2 最佳适用 Welcome to Code Blocks blog 本篇文章主要介绍了 [Mysql中的分区和索引] ❤博主广交技术好友&#xff0c;喜欢文章的可以关注一下❤ 1.编写目的 在MySQL中&…

JAVA中的输入输出流

FileInputStream、FileOutputStream&#xff08;字节流&#xff09; 字节输入流InputStream主要方法&#xff1a; read() &#xff1a;从此输入流中读取一个数据字节。 read(byte[] b) &#xff1a;从此输入流中将最多 b.length 个字节的数据读入一个 byte 数组中。 read(b…

Redis系列命令更新--Redis有序集合命令

Redis有序集合&#xff08;sorted set&#xff09; &#xff08;1&#xff09;说明&#xff1a; A、Redis有序集合和集合一样也是string类型元素的集合&#xff0c;且不允许重复的成员&#xff1b;不同的是每个元素都会关联一个double类型的分数&#xff1b;redis正式通过分数…

MongoDB 文档存储

安装 下载&#xff1a; Download MongoDB Community Server | MongoDB 说明&#xff1a; 现在基本都安装的是4.4以后的版本。安装完成后使用 mongod 来查看是否安装成功 会输出一堆内容 而如果想要操作数据库&#xff0c;则需要安装一个工具&#xff0c;mongosh-2.2.12-x64.m…

【2024最新华为OD-C/D卷试题汇总】[支持在线评测] 堆内存申请(100分) - 三语言AC题解(Python/Java/Cpp)

🍭 大家好这里是清隆学长 ,一枚热爱算法的程序员 ✨ 本系列打算持续跟新华为OD-C/D卷的三语言AC题解 💻 ACM银牌🥈| 多次AK大厂笔试 | 编程一对一辅导 👏 感谢大家的订阅➕ 和 喜欢💗 🍿 最新华为OD机试D卷目录,全、新、准,题目覆盖率达 95% 以上,支持题目在线…

微信小程序canvas 使用案例(一)

一、cavans 对象获取、上线文创建 1.wxml <!-- canvas.wxml --><canvas type"2d" id"myCanvas"></canvas> 2.js /*** 生命周期函数--监听页面加载*/onLoad(options) {const query wx.createSelectorQuery()query.select(#myCanvas).f…