我们收集并标记了一个小型数据集 下载数据集
%matplotlib inline
import os
import pandas as pd
import torch
import torchvision
from d2l import torch as d2l
d2l.DATA_HUB['banana-detection'] = (
d2l.DATA_URL + 'banana-detection.zip',
'5de26c8fce5ccdea9f91267273464dc968d20d72')
读取香蕉检测数据集
def read_data_bananas(is_train=True):
"""读取香蕉检测数据集中的图像和标签。"""
data_dir = d2l.download_extract('banana-detection')
csv_fname = os.path.join(data_dir,
'bananas_train' if is_train else 'bananas_val',
'label.csv')
csv_data = pd.read_csv(csv_fname)
csv_data = csv_data.set_index('img_name')
images, targets = [], []
for img_name, target in csv_data.iterrows():
images.append(
torchvision.io.read_image(
os.path.join(data_dir,
'bananas_train' if is_train else 'bananas_val',
'images', f'{img_name}')))
targets.append(list(target))
return images, torch.tensor(targets).unsqueeze(1) / 256
创建一个自定义 Dataset
实例
class BananasDataset(torch.utils.data.Dataset):
"""一个用于加载香蕉检测数据集的自定义数据集。"""
def __init__(self, is_train):
self.features, self.labels = read_data_bananas(is_train)
print('read ' + str(len(self.features)) + (
f' training examples' if is_train else f' validation examples'))
def __getitem__(self, idx):
return (self.features[idx].float(), self.labels[idx])
def __len__(self):
return len(self.features)
为训练集和测试集返回两个数据加载器实例
def load_data_bananas(batch_size):
"""加载香蕉检测数据集。"""
train_iter = torch.utils.data.DataLoader(BananasDataset(is_train=True),
batch_size, shuffle=True)
val_iter = torch.utils.data.DataLoader(BananasDataset(is_train=False),
batch_size)
return train_iter, val_iter
读取一个小批量,并打印其中的图像和标签的形状
batch_size, edge_size = 32, 256
train_iter, _ = load_data_bananas(batch_size)
batch = next(iter(train_iter))
batch[0].shape, batch[1].shape
read 1000 training examples read 100 validation examples
(torch.Size([32, 3, 256, 256]), torch.Size([32, 1, 5]))
示范
imgs = (batch[0][0:10].permute(0, 2, 3, 1)) / 255
axes = d2l.show_images(imgs, 2, 5, scale=2)
for ax, label in zip(axes, batch[1][0:10]):
d2l.show_bboxes(ax, [label[0][1:5] * edge_size], colors=['w'])