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bdce880 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import os
from dataset.dataset import get_datalist
def get_samples(root):
folds = [f'param{i}' for i in range(9)]
samples = []
for fold in folds:
fold_samples = []
files = os.listdir(os.path.join(root, fold))
for file in files:
path = os.path.join(root, os.path.join(fold, file))
if os.path.isdir(path):
fold_samples.append(os.path.join(fold, file))
samples.append(fold_samples)
return samples # 100 + 99 + 97 + 100 + 100 + 96 + 100 + 98 + 99 = 889 samples
def load_train_val_fold(args, preprocessed):
samples = get_samples(args.data_dir)
trainlst = []
for i in range(len(samples)):
if i == args.fold_id:
continue
trainlst += samples[i]
vallst = samples[args.fold_id] if 0 <= args.fold_id < len(samples) else None
if preprocessed:
print("use preprocessed data")
print("loading data")
train_dataset, coef_norm = get_datalist(args.data_dir, trainlst, norm=True, savedir=args.save_dir,
preprocessed=preprocessed)
val_dataset = get_datalist(args.data_dir, vallst, coef_norm=coef_norm, savedir=args.save_dir,
preprocessed=preprocessed)
print("load data finish")
return train_dataset, val_dataset, coef_norm
def load_train_val_fold_file(args, preprocessed):
samples = get_samples(args.data_dir)
trainlst = []
for i in range(len(samples)):
if i == args.fold_id:
continue
trainlst += samples[i]
vallst = samples[args.fold_id] if 0 <= args.fold_id < len(samples) else None
if preprocessed:
print("use preprocessed data")
print("loading data")
train_dataset, coef_norm = get_datalist(args.data_dir, trainlst, norm=True, savedir=args.save_dir,
preprocessed=preprocessed)
val_dataset = get_datalist(args.data_dir, vallst, coef_norm=coef_norm, savedir=args.save_dir,
preprocessed=preprocessed)
print("load data finish")
return train_dataset, val_dataset, coef_norm, vallst
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