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| from random import shuffle | |
| import torch | |
| import csv, os | |
| from torch.utils.data import TensorDataset, DataLoader, RandomSampler, Dataset, SequentialSampler | |
| from sklearn.model_selection import train_test_split | |
| from torchvision.io import read_image | |
| import torch.nn as nn | |
| from torchvision import transforms | |
| import pandas as pd | |
| import numpy as np | |
| from PIL import Image | |
| import math | |
| from transformers import AutoImageProcessor | |
| class imgDataset(Dataset): | |
| def __init__(self, path, mode='train', use_processor=True): | |
| self.path = path | |
| self.mode = mode | |
| self.use_processor = use_processor | |
| self.image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") | |
| self.transform = { | |
| 'train': transforms.Compose([ | |
| transforms.RandomResizedCrop(224), | |
| transforms.RandomHorizontalFlip(), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]), | |
| 'val': transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| } | |
| self.trans = self.transform[mode] | |
| self.data = self.get_data() | |
| def convert_body_to_int(self, pos, file_name_list): | |
| body_str = file_name_list[1].split('-')[pos] | |
| if not body_str: body_str = '62' | |
| body = int(body_str[1:3]) if not body_str.isdigit() else int(body_str) | |
| body = 100+body if body <= 25 else body | |
| return body | |
| def get_data(self): | |
| data = [] | |
| with open(self.path, 'r', encoding='utf-8') as f: | |
| for line in f.readlines(): | |
| file_name_list = line.split(' ') | |
| if not self.mode in file_name_list:continue | |
| label, h = 0 if file_name_list[2]=="big" else 1, float(file_name_list[3]) | |
| b = self.convert_body_to_int(0, file_name_list) | |
| w = self.convert_body_to_int(1, file_name_list) | |
| hh = self.convert_body_to_int(2, file_name_list) | |
| data.append([os.path.join('images', file_name_list[0], file_name_list[2], file_name_list[1]), label, h, b, w, hh]) | |
| return data | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| img_path, label, h, b, w, hh = self.data[idx] | |
| inp_img = Image.open(img_path).convert("RGB") | |
| if not self.use_processor: image_tensor = self.trans(inp_img) | |
| else:image_tensor = self.image_processor(images=inp_img, return_tensors="pt") | |
| return image_tensor, label, torch.tensor(h, dtype=torch.float), torch.tensor(b, dtype=torch.float), torch.tensor(w, dtype=torch.float), torch.tensor(hh, dtype=torch.float) | |
| if __name__ == "__main__": | |
| train_dataset = imgDataset('labels.txt', mode='train') | |
| test_dataset = imgDataset('labels.txt', mode='val') | |
| train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True) | |
| print(len(train_dataset), len(test_dataset)) | |
| print(next(iter(train_dataloader))) |