File size: 4,557 Bytes
29d330e | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | from torch.utils.data import random_split
from pathlib import Path
import random
from utils.Dataset import augment_sample,augment_data,balance_data
from statistics import mean
import warnings
import torchvision.transforms as transforms
from utils.utils import *
warnings.filterwarnings("ignore")
warnings.filterwarnings("default")
import os
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
])
def create_data_set(
root_dir,
train_test_split_dir,
label_list,
n_way,
n_shot,
n_query,
env_no,
):
train_filenames = []
train_labels = []
valid_data = []
permutations_str = "_".join(map(str, label_list))
env_str = "_".join(map(str, env_no))
with open(train_test_split_dir, 'r') as file:
lines = file.readlines()
for line in lines:
parts = line.strip().split()
if int(parts[-1]) in env_no or parts[-1] == '0':
train_filenames.append(os.path.join(root_dir, parts[0].split("/")[1]))
train_labels.append(int(parts[1]))
# print(train_filenames)
# Randomly shuffle the data and labels
train_data = list(zip(train_filenames, train_labels))
unique_labels = set(train_labels)
for label in unique_labels:
label_indices = [i for i, l in enumerate(train_labels) if l == label]
random.shuffle(label_indices)
num_valid_samples = int(len(label_indices) * 0.2)
valid_indices = label_indices[:num_valid_samples]
valid_data.extend([(train_data[i][0], train_data[i][1]) for i in valid_indices])
filtered_train_data = [(filename, label) for filename, label in train_data if label in label_list]
# print(valid_data)
filtered_valid_data = [(filename, label) for filename, label in valid_data if label in label_list]
# print(filtered_valid_data)
filtered_valid_data_balnced = balance_data(n_shot,n_query,filtered_valid_data)
# train_data = balance_data(n_shot,n_query,train_data)
save_balanced_data_to_csv(filtered_train_data, f'train_data_{permutations_str}_env{env_str}.csv')
print(f"file has stored in train_data_{permutations_str}_env{env_no}.csv")
# valid_data = balance_data(n_shot,n_query,valid_data)
save_balanced_data_to_csv(filtered_valid_data_balnced, f'valid_data_{permutations_str}_env{env_str}.csv')
print(f"file has stored in 'valid_data_{permutations_str}_env{env_no}.csv")
def create_data_set_fs(
root_dir,
train_test_split_dir,
label_list,
n_way,
n_shot,
n_query,
):
train_filenames = []
train_labels = []
valid_data = []
permutations_str = "_".join(map(str, label_list))
with open(train_test_split_dir, 'r') as file:
lines = file.readlines()
for line in lines:
parts = line.strip().split()
train_filenames.append(os.path.join(root_dir, parts[0].split("/")[1]))
train_labels.append(int(parts[1]))
# print(train_filenames)
# Randomly shuffle the data and labels
train_data = list(zip(train_filenames, train_labels))
unique_labels = set(train_labels)
for label in unique_labels:
label_indices = [i for i, l in enumerate(train_labels) if l == label]
random.shuffle(label_indices)
num_valid_samples = int(len(label_indices) * 0.2)
valid_indices = label_indices[:num_valid_samples]
valid_data.extend([(train_data[i][0], train_data[i][1]) for i in valid_indices])
filtered_train_data = [(filename, label) for filename, label in train_data if label in label_list]
# print(valid_data)
filtered_valid_data = [(filename, label) for filename, label in valid_data if label in label_list]
# print(filtered_valid_data)
filtered_valid_data_balnced = balance_data(n_shot,n_query,filtered_valid_data)
# train_data = balance_data(n_shot,n_query,train_data)
save_balanced_data_to_csv(filtered_train_data, f'train_data_{permutations_str}.csv')
print(f"file has stored in train_data_{permutations_str}.csv")
# valid_data = balance_data(n_shot,n_query,valid_data)
save_balanced_data_to_csv(filtered_valid_data_balnced, f'valid_data_{permutations_str}.csv')
print(f"file has stored in 'valid_data_{permutations_str}.csv") |