pytorch（08）数据模型的读取（2）

import numpy as np

import torch

import os

import random

from PIL import Image

from torch.utils.data import Dataset

from torch.utils.data import DataLoader

import torchvision.transforms as transforms

BASE_PATH = os.path.abspath(__file__)

# print(BASE_PATH)

base_path = os.path.abspath(os.path.join(BASE_PATH, '..', 'TestDir'))

# print(base_path)

data_dir = os.path.abspath(os.path.join(BASE_PATH, '..', 'RMB_data'))

random.seed(1)

# print(data_dir)

test_label = {"1": 0, "100": 1}

data_info = list()

for path, dirs, files in os.walk(base_path):

    for sub_dir in dirs:

        # print(sub_dir)

        sub_dirlist = os.listdir(os.path.join(base_path, sub_dir))

        pynames = list(filter(lambda y: y.endswith('.jpg'), sub_dirlist))

        # print(pynames)

        # print(test_label[sub_dir])

        for pyname in pynames:

            datainfo_dir = os.path.join(base_path, sub_dir, pyname)

            t_label=test_label[sub_dir]

            t_label = int(t_label)

            data_info.append((datainfo_dir, t_label))

# print(data_info)

new_data_info = list()

for data_info_e in data_info:

    x_dir, x_label = data_info_e

    x_img = Image.open(x_dir).convert('RGB')

    ok_transform = transforms.Compose([

        transforms.Resize((32, 32)),

        transforms.RandomCrop(32, padding=4),

        transforms.ToTensor(),

    ])

    x_img = ok_transform(x_img)

    new_data_info.append((x_img,x_label))

# print(len(new_data_info[0][0]))

print(len(new_data_info))

newdataLoader = DataLoader(new_data_info,batch_size=14, shuffle=True)

for ids, data in enumerate(newdataLoader):

    print(ids)
巴特西

pytorch（08）数据模型的读取（2）

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