用于DataLoader的pytorch数据集

暂时介绍 image-mask型数据集, 以人手分割数据集 EGTEA Gaze+ 为例.

准备数据文件夹

需要将Image和Mask分开存放, 对应文件的文件名必须保持一致. 提醒: Mask 图像一般为 png 单通道

EGTEA Gaze+ 数据集下载解压后即得到如下的目录, 无需处理

hand14k

┣━ Images

┃ ┣━ OP01-R01-PastaSalad_000014.jpg

┃ ┣━ OP01-R01-PastaSalad_000015.jpg

┃ ┣━ OP01-R01-PastaSalad_000016.jpg

┃ ┗━ ···

┗━ Masks

┣━ OP01-R01-PastaSalad_000014.png

┣━ OP01-R01-PastaSalad_000015.png

┣━ OP01-R01-PastaSalad_000016.png

┗━ ···

生成路径文件, 划分数据集

脚本如下:import cv2 as cv

import numpy as np

import PIL.Image as Image

import os

np.random.seed(42)

def split_dataset():

# 读取图像文件

images_path = "./Images/"

images_list = os.listdir(images_path)  # 每次返回文件列表顺序不一致

images_list.sort()  # 需要排序处理

# 读取标签/Mask图像

labels_path = "./Masks/"

labels_list = os.listdir(labels_path)

labels_list.sort()

# 创建路径文件 (使用二进制编码, 避免操作系统不匹配)

train_file = "./train.data"

test_file = "./test.data"

if os.path.isfile(train_file) and os.path.isfile(test_file):

return

train_file = open(train_file, "wb")

test_file = open(test_file, "wb")

# 外汇返佣

split_ratio = 0.8

for image, label in zip(images_list, labels_list):

image = os.path.join(images_path, image)

label = os.path.join(labels_path, label)

if os.path.basename(image).split('.')[0] != os.path.basename(label).split('.')[0]:

continue

file = train_file if np.random.rand() < split_ratio else test_file

file.write((image + "\t" + label + "\n").encode("utf-8"))

train_file.close()

test_file.close()

print("成功划分数据集!")

def read_image(path):

img = np.array(Image.open(path))

if img.ndim == 2:

img = cv.merge([img, img, img])

return img

def test_read():

train_file = "./test.data"

with open(train_file, 'rb') as f:

datalist = f.readlines()

datalist = [(k, v) for k, v in map(lambda x: x.decode('utf-8').strip('\n').split('\t'), datalist)]

item = datalist[np.random.randint(42)]

image = read_image(item[0])

mask = read_image(item[1])

cv.imshow("image", image)

cv.imshow("mask", mask)

cv.waitKey(0)

cv.destroyAllWindows()

if __name__ == '__main__':

split_dataset()

test_read()

派生 Dataset 类

class MyDataset(Dataset):

def __init__(

self, data_file, data_dir, transform_trn=None, transform_val=None

):

"""

Args:

data_file (string): Path to the data file with annotations.

data_dir (string): Directory with all the images.

transform_{trn, val} (callable, optional): Optional transform to be applied

on a sample.

"""

with open(data_file, 'rb') as f:

datalist = f.readlines()

self.datalist = [(k, v) for k, v in map(lambda x: x.decode('utf-8').strip('\n').split('\t'), datalist)]

self.root_dir = data_dir

self.transform_trn = transform_trn

self.transform_val = transform_val

self.stage = 'train'

def set_stage(self, stage):

self.stage = stage

def __len__(self):

return len(self.datalist)

def __getitem__(self, idx):

img_name = os.path.join(self.root_dir, self.datalist[idx][0])

msk_name = os.path.join(self.root_dir, self.datalist[idx][1])

def read_image(x):

img_arr = np.array(Image.open(x))

if len(img_arr.shape) == 2: # grayscale

img_arr = np.tile(img_arr, [3, 1, 1]).transpose(1, 2, 0)

return img_arr

image = read_image(img_name)

mask = np.array(Image.open(msk_name))

if img_name != msk_name:

assert len(mask.shape) == 2, 'Masks must be encoded without colourmap'

sample = {'image': image, 'mask': mask}

if self.stage == 'train':

if self.transform_trn:

sample = self.transform_trn(sample)

elif self.stage == 'val':

if self.transform_val:

sample = self.transform_val(sample)

return sample

构造DataLoader

# 定义Transform

composed_trn = transforms.Compose([ResizeShorterScale(shorter_side, low_scale, high_scale),

Pad(crop_size, [123.675, 116.28, 103.53], ignore_label),

RandomMirror(),

RandomCrop(crop_size),

Normalise(*normalise_params),

ToTensor()])

composed_val = transforms.Compose([Normalise(*normalise_params),

ToTensor()])

# 导入数据集

trainset = MyDataset(data_file=train_list,

data_dir=train_dir,

transform_trn=composed_trn,

transform_val=composed_val)

valset = MyDataset(data_file=val_list,

data_dir=val_dir,

transform_trn=None,

transform_val=composed_val)

# 构建生成器

train_loader = DataLoader(trainset,

batch_size=batch_size,

shuffle=True,

num_workers=num_workers,

pin_memory=True,

drop_last=True)

val_loader = DataLoader(valset,

batch_size=1,

shuffle=False,

num_workers=num_workers,

pin_memory=True)

训练

for i, sample in enumerate(train_loader):

image = sample['image'].cuda()

target = sample['mask'].cuda()

image_var = torch.autograd.Variable(image).float()

target_var = torch.autograd.Variable(target).long()

# Compute output

output = net(image_var)

...

原文链接：https://blog.csdn.net/Augurlee/article/details/103652444