179 lines
6.2 KiB
Python
179 lines
6.2 KiB
Python
import os
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import random
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import json
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import torch
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import math
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import numpy as np
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import os.path as osp
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from PIL import Image
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from tqdm import tqdm
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from sklearn.model_selection import train_test_split
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from federatedscope.core.data.utils import save_local_data, download_url
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from federatedscope.cv.dataset.leaf import LEAF
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IMAGE_SIZE = {'femnist': (28, 28), 'celeba': (84, 84, 3)}
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MODE = {'femnist': 'L', 'celeba': 'RGB'}
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class LEAF_CV(LEAF):
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"""
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LEAF CV dataset from "LEAF: A Benchmark for Federated Settings"
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leaf.cmu.edu
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Arguments:
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root (str): root path.
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name (str): name of dataset, ‘femnist’ or ‘celeba’.
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s_frac (float): fraction of the dataset to be used; default=0.3.
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tr_frac (float): train set proportion for each task; default=0.8.
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val_frac (float): valid set proportion for each task; default=0.0.
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train_tasks_frac (float): fraction of test tasks; default=1.0.
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transform: transform for x.
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target_transform: transform for y.
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"""
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def __init__(self,
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root,
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name,
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s_frac=0.3,
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tr_frac=0.8,
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val_frac=0.0,
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train_tasks_frac=1.0,
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seed=123,
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transform=None,
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target_transform=None):
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self.s_frac = s_frac
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self.tr_frac = tr_frac
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self.val_frac = val_frac
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self.seed = seed
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self.train_tasks_frac = train_tasks_frac
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super(LEAF_CV, self).__init__(root, name, transform, target_transform)
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files = os.listdir(self.processed_dir)
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files = [f for f in files if f.startswith('task_')]
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if len(files):
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# Sort by idx
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files.sort(key=lambda k: int(k[5:]))
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for file in files:
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train_data, train_targets = torch.load(
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osp.join(self.processed_dir, file, 'train.pt'))
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test_data, test_targets = torch.load(
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osp.join(self.processed_dir, file, 'test.pt'))
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self.data_dict[int(file[5:])] = {
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'train': (train_data, train_targets),
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'test': (test_data, test_targets)
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}
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if osp.exists(osp.join(self.processed_dir, file, 'val.pt')):
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val_data, val_targets = torch.load(
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osp.join(self.processed_dir, file, 'val.pt'))
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self.data_dict[int(file[5:])]['val'] = (val_data,
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val_targets)
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else:
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raise RuntimeError(
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'Please delete ‘processed’ folder and try again!')
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@property
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def raw_file_names(self):
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names = [f'{self.name}_all_data.zip']
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return names
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def download(self):
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# Download to `self.raw_dir`.
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url = 'https://federatedscope.oss-cn-beijing.aliyuncs.com'
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os.makedirs(self.raw_dir, exist_ok=True)
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for name in self.raw_file_names:
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download_url(f'{url}/{name}', self.raw_dir)
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def __getitem__(self, index):
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"""
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Arguments:
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index (int): Index
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:returns:
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dict: {'train':[(image, target)],
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'test':[(image, target)],
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'val':[(image, target)]}
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where target is the target class.
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"""
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img_dict = {}
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data = self.data_dict[index]
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for key in data:
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img_dict[key] = []
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imgs, targets = data[key]
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for idx in range(targets.shape[0]):
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img = np.resize(imgs[idx].numpy().astype(np.uint8),
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IMAGE_SIZE[self.name])
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img = Image.fromarray(img, mode=MODE[self.name])
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if self.transform is not None:
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img = self.transform(img)
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if self.target_transform is not None:
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targets[idx] = self.target_transform(targets[idx])
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img_dict[key].append((img, targets[idx]))
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return img_dict
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def process(self):
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raw_path = osp.join(self.raw_dir, "all_data")
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files = os.listdir(raw_path)
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files = [f for f in files if f.endswith('.json')]
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n_tasks = math.ceil(len(files) * self.s_frac)
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random.shuffle(files)
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files = files[:n_tasks]
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print("Preprocess data (Please leave enough space)...")
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idx = 0
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for num, file in enumerate(tqdm(files)):
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with open(osp.join(raw_path, file), 'r') as f:
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raw_data = json.load(f)
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# Numpy to Tensor
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for writer, v in raw_data['user_data'].items():
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data, targets = v['x'], v['y']
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if len(v['x']) > 2:
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data = torch.tensor(np.stack(data))
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targets = torch.LongTensor(np.stack(targets))
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else:
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data = torch.tensor(data)
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targets = torch.LongTensor(targets)
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train_data, test_data, train_targets, test_targets =\
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train_test_split(
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data,
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targets,
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train_size=self.tr_frac,
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random_state=self.seed
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)
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if self.val_frac > 0:
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val_data, test_data, val_targets, test_targets = \
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train_test_split(
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test_data,
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test_targets,
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train_size=self.val_frac / (1.-self.tr_frac),
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random_state=self.seed
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)
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else:
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val_data, val_targets = None, None
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save_path = osp.join(self.processed_dir, f"task_{idx}")
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os.makedirs(save_path, exist_ok=True)
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save_local_data(dir_path=save_path,
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train_data=train_data,
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train_targets=train_targets,
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test_data=test_data,
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test_targets=test_targets,
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val_data=val_data,
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val_targets=val_targets)
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idx += 1
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