我有一個包含圖像路徑的文件，我想加載到 Pytorch 中，同時利用內置的數據加載器功能（多進程加載管道、數據擴充等）。def create_links(): data_dir = "/myfolder" full_path_list = [] assert os.path.isdir(data_dir) for _, _, filenames in os.walk(data_dir): for filename in filenames: full_path_list.append(os.path.join(data_dir, filename)) with open(config.data.links_file, 'w+') as links_file: for full_path in full_path_list: links_file.write(f"{full_path}\n")def read_links_file_to_list(): config = ConfigProvider.config() links_file_path = config.data.links_file if not os.path.isfile(links_file_path): raise RuntimeError("did you forget to create a file with links to images? Try using 'create_links()'") with open(links_file_path, 'r') as links_file: return links_file.readlines()所以我有一個文件列表（或一個生成器，或任何有效的東西）file_list = read_links_file_to_list()，.我如何圍繞它構建一個 Pytorch 數據加載器，我將如何使用它？
1 回答
MYYA
TA貢獻1868條經驗 獲得超4個贊
你想要的是一個Custom Dataset。該__getitem__方法是您應用數據增強等轉換的地方。為了讓您了解它在實踐中的樣子，您可以看看我前幾天寫的這個自定義數據集：
class GTSR43Dataset(Dataset):
"""German Traffic Sign Recognition dataset."""
def __init__(self, root_dir, train_file, transform=None):
self.root_dir = root_dir
self.train_file_path = train_file
self.label_df = pd.read_csv(os.path.join(self.root_dir, self.train_file_path))
self.transform = transform
self.classes = list(self.label_df['ClassId'].unique())
def __getitem__(self, idx):
"""Return (image, target) after resize and preprocessing."""
img = os.path.join(self.root_dir, self.label_df.iloc[idx, 7])
X = Image.open(img)
y = self.class_to_index(self.label_df.iloc[idx, 6])
if self.transform:
X = self.transform(X)
return X, y
def class_to_index(self, class_name):
"""Returns the index of a given class."""
return self.classes.index(class_name)
def index_to_class(self, class_index):
"""Returns the class of a given index."""
return self.classes[class_index]
def get_class_count(self):
"""Return a list of label occurences"""
cls_count = dict(self.label_df.ClassId.value_counts())
# cls_percent = list(map(lambda x: (1 - x / sum(cls_count)), cls_count))
return cls_count
def __len__(self):
"""Returns the length of the dataset."""
return len(self.label_df)
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