def reorg_dog_data(data_dir, label_file, train_dir, test_dir, input_dir,
valid_ratio):
with open(os.path.join(data_dir, label_file), 'r') as f:
# Skipp first line
lines = f.readlines()[1:]
tokens = [l.rstrip().split(',') for l in lines]
idx_label = dict(((idx, label) for idx, label in tokens))
reorg_train_valid(data_dir, train_dir, input_dir, valid_ratio, idx_label)
# Manage test set
d2l.mkdir_if_not_exist([data_dir, input_dir, 'test', 'unknown'])
for test_file in os.listdir(os.path.join(data_dir, test_dir)):
shutil.copy(os.path.join(data_dir, test_dir, test_file),
os.path.join(data_dir, input_dir, 'test', 'unknown'))
def reorg_train_valid(data_dir, train_dir, input_dir, valid_ratio, idx_label):
# 训练集中数量最少一类的狗的样本数
min_n_train_per_label = (collections.Counter(
idx_label.values()).most_common()[:-2:-1][0][1])
# 验证集中每类狗的样本数
n_valid_per_label = math.floor(min_n_train_per_label * valid_ratio)
label_count = {}
for train_file in os.listdir(os.path.join(data_dir, train_dir)):
idx = train_file.split('.')[0]
label = idx_label[idx]
d2l.mkdir_if_not_exist([data_dir, input_dir, 'train_valid', label])
shutil.copy(os.path.join(data_dir, train_dir, train_file),
os.path.join(data_dir, input_dir, 'train_valid', label))
if label not in label_count or label_count[label] < n_valid_per_label:
d2l.mkdir_if_not_exist([data_dir, input_dir, 'valid', label])
shutil.copy(os.path.join(data_dir, train_dir, train_file),
os.path.join(data_dir, input_dir, 'valid', label))
label_count[label] = label_count.get(label, 0) + 1
else:
d2l.mkdir_if_not_exist([data_dir, input_dir, 'train', label])
shutil.copy(os.path.join(data_dir, train_dir, train_file),
os.path.join(data_dir, input_dir, 'train', label))
