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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
imagefolder loader
inspired from https://github.com/adambielski/siamese-triplet/blob/master/datasets.py
@author: Tu Bui @surrey.ac.uk
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import io
import time
import pandas as pd
import numpy as np
import random
from PIL import Image
from typing import Any, Callable, List, Optional, Tuple
import torch
from torchvision import transforms
from .base_lmdb import PILlmdb, ArrayDatabase
# from . import debug
def worker_init_fn(worker_id):
# to be passed to torch.utils.data.DataLoader to fix the
# random seed issue with numpy in multi-worker settings
torch_seed = torch.initial_seed()
random.seed(torch_seed + worker_id)
if torch_seed >= 2**30: # make sure torch_seed + workder_id < 2**32
torch_seed = torch_seed % 2**30
np.random.seed(torch_seed + worker_id)
def pil_loader(path: str) -> Image.Image:
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def dataset_wrapper(data_dir, data_list, **kwargs):
if os.path.exists(os.path.join(data_dir, 'data.mdb')):
return ImageDataset(data_dir, data_list, **kwargs)
else:
return ImageFolder(data_dir, data_list, **kwargs)
class ImageFolder(torch.utils.data.Dataset):
_repr_indent = 4
def __init__(self, data_dir, data_list, secret_len=100, resize=256, transform=None, **kwargs):
super().__init__()
self.transform = transforms.RandomResizedCrop((resize, resize), scale=(0.8, 1.0), ratio=(0.75, 1.3333333333333333)) if transform is None else transform
self.build_data(data_dir, data_list, **kwargs)
self.kwargs = kwargs
self.secret_len = secret_len
def build_data(self, data_dir, data_list, **kwargs):
self.data_dir = data_dir
if isinstance(data_list, list):
self.data_list = data_list
elif isinstance(data_list, str):
self.data_list = pd.read_csv(data_list)['path'].tolist()
elif isinstance(data_list, pd.DataFrame):
self.data_list = data_list['path'].tolist()
else:
raise ValueError('data_list must be a list, str or pd.DataFrame')
self.N = len(self.data_list)
def __getitem__(self, index):
path = self.data_list[index]
img = pil_loader(os.path.join(self.data_dir, path))
img = self.transform(img)
img = np.array(img, dtype=np.float32)/127.5-1. # [-1, 1]
secret = torch.zeros(self.secret_len, dtype=torch.float).random_(0, 2)
return {'image': img, 'secret': secret} # {'img': x, 'index': index}
def __len__(self) -> int:
# raise NotImplementedError
return self.N
class ImageDataset(torch.utils.data.Dataset):
r"""
Customised Image Folder class for pytorch.
Accept lmdb and a csv list as the input.
Usage:
dataset = ImageDataset(img_dir, img_list)
dataset.set_transform(some_pytorch_transforms)
loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True,
num_workers=4, worker_init_fn=worker_init_fn)
for x,y in loader:
# x and y is input and target (dict), the keys can be customised.
"""
_repr_indent = 4
def __init__(self, data_dir, data_list, secret_len=100, resize=None, transform=None, target_transform=None, **kwargs):
super().__init__()
if resize is not None:
self.resize = transforms.Resize((resize, resize))
self.set_transform(transform, target_transform)
self.build_data(data_dir, data_list, **kwargs)
self.secret_len = secret_len
self.kwargs = kwargs
def set_transform(self, transform, target_transform=None):
self.transform, self.target_transform = transform, target_transform
def build_data(self, data_dir, data_list, **kwargs):
"""
Args:
data_list (text file) must have at least 3 fields: id, path and label
This method must create an attribute self.samples containing ID, input and target samples; and another attribute N storing the dataset size
Optional attributes: classes (list of unique classes), group (useful for
metric learning)
"""
self.data_dir, self.list = data_dir, data_list
if ('dtype' in kwargs) and (kwargs['dtype'].lower() == 'array'):
data = ArrayDatabase(data_dir, data_list)
else:
data = PILlmdb(data_dir, data_list, **kwargs)
self.N = len(data)
self.classes = np.unique(data.labels)
self.samples = {'x': data, 'y': data.labels}
# assert isinstance(data_list, str) or isinstance(data_list, pd.DataFrame)
# df = pd.read_csv(data_list) if isinstance(data_list, str) else data_list
# assert 'id' in df and 'label' in df, f'[DATA] Error! {data_list} must contains "id" and "label".'
# ids = df['id'].tolist()
# labels = np.array(df['label'].tolist())
# data = PILlmdb(data_dir)
# assert set(ids).issubset(set(data.keys)) # ids should exist in lmdb
# self.N = len(ids)
# self.classes, inds = np.unique(labels, return_index=True)
# self.samples = {'id': ids, 'x': data, 'y': labels}
def set_ids(self, ids):
self.samples['x'].set_ids(ids)
self.samples['y'] = [self.samples['y'][i] for i in ids]
self.N = len(self.samples['x'])
def __getitem__(self, index: int) -> Any:
"""
Args:
index (int): Index
Returns:
dict: (x: sample, y: target, **kwargs)
"""
x, y = self.samples['x'][index], self.samples['y'][index]
if hasattr(self, 'resize'):
x = self.resize(x)
if self.transform is not None:
x = self.transform(x)
if self.target_transform is not None:
y = self.target_transform(y)
x = np.array(x, dtype=np.float32)/127.5-1.
secret = torch.zeros(self.secret_len, dtype=torch.float).random_(0, 2)
return {'image': x, 'secret': secret} # {'img': x, 'index': index}
def __len__(self) -> int:
# raise NotImplementedError
return self.N
def __repr__(self) -> str:
head = "\nDataset " + self.__class__.__name__
body = ["Number of datapoints: {}".format(self.__len__())]
if hasattr(self, 'data_dir') and self.data_dir is not None:
body.append("data_dir location: {}".format(self.data_dir))
if hasattr(self, 'kwargs'):
body.append(f'kwargs: {self.kwargs}')
body += self.extra_repr().splitlines()
if hasattr(self, "transform") and self.transform is not None:
body += [repr(self.transform)]
lines = [head] + [" " * self._repr_indent + line for line in body]
return '\n'.join(lines)
def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:
lines = transform.__repr__().splitlines()
return (["{}{}".format(head, lines[0])] +
["{}{}".format(" " * len(head), line) for line in lines[1:]])
def extra_repr(self) -> str:
return ""
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