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Datasets_all1.py
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Datasets_all1.py
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# -*- codeing = utf-8 -*-
# @Time : 2022/7/15 10:49
# @Author : wzh
# @File : dataset.py
# @Softwafe : PyCharm
import os
import torch
import torchvision
from PIL import Image
from torch.utils.data import Dataset, DataLoader
label_to_label = {'bacterial_leaf_blight': 0, 'bacterial_leaf_streak': 1,
'bacterial_panicle_blight': 2, 'blast': 3,
'brown_spot': 4, 'dead_heart': 5,
'downy_mildew': 6, 'hispa': 7,
'normal': 8, 'tungro': 9
}
id_to_label = {0:'bacterial_leaf_blight', 1:'bacterial_leaf_streak',
2:'bacterial_panicle_blight', 3:'blast',
4:'brown_spot', 5:'dead_heart',
6:'downy_mildew', 7:'hispa',
8:'normal', 9:'tungro'
}
# 构建训练集
class TrainData(Dataset):
# 根据图像数据集路径（由根目录名‘root_dir’和数据集名‘label_dir’组成）生成Dataset
def __init__(self, root_dir, label_dir, img_size=(320,240)):
self.root_dir = root_dir
self.label_dir = label_dir
# 获取数据集路径
self.path = os.path.join(self.root_dir, self.label_dir)
# 将数据集路径下单个图像的名称存储在列表中
self.img_path = os.listdir(self.path)
# 图像转化成tensor型,并调整成统一格式
trans_resize = torchvision.transforms.Resize(img_size)
# 注意，如果是PIL图像，.ToTensor()基本上都会缩放到[0.0,1.0]之间，详细看官方教程
trans_tensor = torchvision.transforms.ToTensor() # 转化为tensor类型
trans_normalize = torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) # 按照预训练模型参数进行归一化
self.trans_compose = torchvision.transforms.Compose([trans_resize, trans_tensor, trans_normalize])
# 获取单个样本图像相关信息
def __getitem__(self, idx):
# 获取单个样本图像名
img_name = self.img_path[idx]
# 获取单个样本图像路径
img_item_path = os.path.join(self.root_dir, self.label_dir, img_name)
# 根据图像路径获取图像
img = Image.open(img_item_path)
# img [channel, H, W]
img = self.trans_compose(img)
# 获取图像标签（此处图像label为其文件夹名称）
label = self.label_dir
label = label_to_label[label]
label = torch.tensor(label)
# 返回图像与标签
return img, label
# 获取样本长度
def __len__(self):
return len(self.img_path)
# 构建训练集
class TestData(Dataset):
# 根据图像数据集路径（由根目录名‘root_dir’和数据集名‘label_dir’组成）生成Dataset
def __init__(self, root_dir, img_size=(320,240)):
self.root_dir = root_dir
# 将数据集路径下单个图像的名称存储在列表中
self.img_path = os.listdir(self.root_dir)
# 图像转化成tensor型,并调整成统一格式
trans_resize = torchvision.transforms.Resize(img_size)
# 注意，如果是PIL图像，.ToTensor()基本上都会缩放到[0.0,1.0]之间，详细看官方教程
trans_tensor = torchvision.transforms.ToTensor() # 转化为tensor类型
trans_normalize = torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) # 按照预训练模型参数进行归一化
self.trans_compose = torchvision.transforms.Compose([trans_resize, trans_tensor, trans_normalize])
# 获取单个样本图像相关信息
def __getitem__(self, idx):
# 获取单个样本图像名
img_name = self.img_path[idx]
# 获取单个样本图像路径
img_item_path = os.path.join(self.root_dir, img_name)
# 根据图像路径获取图像
img = Image.open(img_item_path)
img = self.trans_compose(img)
# 返回图像与标签
return img, img_name
# 获取样本长度
def __len__(self):
return len(self.img_path)
def dataset_fn(root_dir, img_size, mode='train'):
bacterial_leaf_blight_label_dir = 'bacterial_leaf_blight'
bacterial_leaf_streak_label_dir = 'bacterial_leaf_streak'
bacterial_panicle_blight_label_dir = 'bacterial_panicle_blight'
blast_label_dir = 'blast'
brown_spot_label_dir = 'brown_spot'
dead_heart_label_dir = 'dead_heart'
downy_mildew_label_dir = 'downy_mildew'
hispa_label_dir = 'hispa'
normal_label_dir = 'normal'
tungro_label_dir = 'tungro'
bacterial_leaf_blight_dataset = TrainData(root_dir, bacterial_leaf_blight_label_dir, img_size=img_size)
bacterial_leaf_streak_dataset = TrainData(root_dir, bacterial_leaf_streak_label_dir, img_size=img_size)
bacterial_panicle_blight_dataset = TrainData(root_dir, bacterial_panicle_blight_label_dir, img_size=img_size)
blast_dataset = TrainData(root_dir, blast_label_dir, img_size=img_size)
brown_spot_dataset = TrainData(root_dir, brown_spot_label_dir, img_size=img_size)
dead_heart_dataset = TrainData(root_dir, dead_heart_label_dir, img_size=img_size)
downy_mildew_dataset = TrainData(root_dir, downy_mildew_label_dir, img_size=img_size)
hispa_dataset = TrainData(root_dir, hispa_label_dir, img_size=img_size)
normal_dataset = TrainData(root_dir, normal_label_dir, img_size=img_size)
tungro_dataset = TrainData(root_dir, tungro_label_dir, img_size=img_size)
# 数据集的拼接
train_dataset = bacterial_leaf_blight_dataset + bacterial_leaf_streak_dataset + \
bacterial_panicle_blight_dataset + blast_dataset + \
brown_spot_dataset + dead_heart_dataset + \
downy_mildew_dataset + hispa_dataset + \
normal_dataset + tungro_dataset
if mode == 'train':
root_dir = '../dataset/val_images'
bacterial_leaf_blight_dataset = TrainData(root_dir, bacterial_leaf_blight_label_dir, img_size=img_size)
bacterial_leaf_streak_dataset = TrainData(root_dir, bacterial_leaf_streak_label_dir, img_size=img_size)
bacterial_panicle_blight_dataset = TrainData(root_dir, bacterial_panicle_blight_label_dir, img_size=img_size)
blast_dataset = TrainData(root_dir, blast_label_dir, img_size=img_size)
brown_spot_dataset = TrainData(root_dir, brown_spot_label_dir, img_size=img_size)
dead_heart_dataset = TrainData(root_dir, dead_heart_label_dir, img_size=img_size)
downy_mildew_dataset = TrainData(root_dir, downy_mildew_label_dir, img_size=img_size)
hispa_dataset = TrainData(root_dir, hispa_label_dir, img_size=img_size)
normal_dataset = TrainData(root_dir, normal_label_dir, img_size=img_size)
tungro_dataset = TrainData(root_dir, tungro_label_dir, img_size=img_size)
train_dataset += bacterial_leaf_blight_dataset + bacterial_leaf_streak_dataset + \
bacterial_panicle_blight_dataset + blast_dataset + \
brown_spot_dataset + dead_heart_dataset + \
downy_mildew_dataset + hispa_dataset + \
normal_dataset + tungro_dataset
return train_dataset
def create_dataloader(data_path, img_size, shuffle=False, batch_size=1, num_workers=8, mode='train'):
dataset = dataset_fn(data_path, img_size, mode=mode)
dataloader = DataLoader(dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers)
return dataloader, dataset
'''
# 测试
import torchvision
from torch import nn
resnet50_true = torchvision.models.resnet50(pretrained=True)
resnet50_true.fc = nn.Linear(2048, 10)
verification_root_dir = 'verification_images'
verification_dataset = dataset_fn(verification_root_dir)
verification_dataloader = DataLoader(verification_dataset, batch_size=64)
for data in verification_dataloader:
imgs, targets = data
# imgs = imgs.to(device)
# targets = targets.to(device)
outputs = resnet50_true(imgs)
# print(outputs)
# test_loss = loss_fn(outputs, targets)
outputs = outputs.argmax(1)
print('真实值：{}'.format(targets))
print('输出值：{}'.format(outputs))
'''
'''
# 验证数据集的构建
train_root_dir = 'train_images'
verification_root_dir = 'verification_images'
train_dataset = dataset_fn(train_root_dir)
verification_dataset = dataset_fn(verification_root_dir)
# print('训练集大小:{},验证集大小:{}.'.format(len(train_dataset), len(verification_dataset)))
imgs, targets = verification_dataset[100]
print(targets)
print(targets.type)
'''
'''
# 构建测试集
class TestData(Dataset):
# 根据图像数据集路径（由根目录名‘root_dir’）生成Dataset
def __init__(self, root_dir):
self.root_dir = root_dir
# 获取数据集路径
self.path = self.root_dir
# 将数据集路径下单个图像的名称存储在列表中
self.img_path = os.listdir(self.path)
# 获取单个样本图像相关信息
def __getitem__(self, idx):
# 获取单个样本图像名
img_name = self.img_path[idx]
# 获取单个样本图像路径
img_item_path = os.path.join(self.root_dir, img_name)
# 根据图像路径获取图像
img = Image.open(img_item_path)
# 返回图像
return img
# 获取样本长度
def __len__(self):
return len(self.img_path)
test_root_dir = 'verification_images'
test_dataset = TestData(test_root_dir)
print(len(test_dataset))
img = test_dataset[0]
img.show()
'''