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import io
import math
import random
import cv2
import lmdb
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms as T
from torchvision.transforms import functional as F
from openrec.preprocess import create_operators, transform
class RatioDataSetTVResize(Dataset):
def __init__(self, config, mode, logger, seed=None, epoch=1):
super(RatioDataSetTVResize, self).__init__()
self.ds_width = config[mode]['dataset'].get('ds_width', True)
global_config = config['Global']
dataset_config = config[mode]['dataset']
loader_config = config[mode]['loader']
max_ratio = loader_config.get('max_ratio', 10)
min_ratio = loader_config.get('min_ratio', 1)
data_dir_list = dataset_config['data_dir_list']
self.padding = dataset_config.get('padding', True)
self.padding_rand = dataset_config.get('padding_rand', False)
self.padding_doub = dataset_config.get('padding_doub', False)
self.do_shuffle = loader_config['shuffle']
self.seed = epoch
data_source_num = len(data_dir_list)
ratio_list = dataset_config.get('ratio_list', 1.0)
if isinstance(ratio_list, (float, int)):
ratio_list = [float(ratio_list)] * int(data_source_num)
assert (
len(ratio_list) == data_source_num
), 'The length of ratio_list should be the same as the file_list.'
self.lmdb_sets = self.load_hierarchical_lmdb_dataset(
data_dir_list, ratio_list)
for data_dir in data_dir_list:
logger.info('Initialize indexs of datasets:%s' % data_dir)
self.logger = logger
self.data_idx_order_list = self.dataset_traversal()
wh_ratio = np.around(np.array(self.get_wh_ratio()))
self.wh_ratio = np.clip(wh_ratio, a_min=min_ratio, a_max=max_ratio)
for i in range(max_ratio + 1):
logger.info((1 * (self.wh_ratio == i)).sum())
self.wh_ratio_sort = np.argsort(self.wh_ratio)
self.ops = create_operators(dataset_config['transforms'],
global_config)
self.need_reset = True in [x < 1 for x in ratio_list]
self.error = 0
self.base_shape = dataset_config.get(
'base_shape', [[64, 64], [96, 48], [112, 40], [128, 32]])
self.base_h = dataset_config.get('base_h', 32)
self.interpolation = T.InterpolationMode.BICUBIC
transforms = []
transforms.extend([
T.ToTensor(),
T.Normalize(0.5, 0.5),
])
self.transforms = T.Compose(transforms)
def get_wh_ratio(self):
wh_ratio = []
for idx in range(self.data_idx_order_list.shape[0]):
lmdb_idx, file_idx = self.data_idx_order_list[idx]
lmdb_idx = int(lmdb_idx)
file_idx = int(file_idx)
wh_key = 'wh-%09d'.encode() % file_idx
wh = self.lmdb_sets[lmdb_idx]['txn'].get(wh_key)
if wh is None:
img_key = f'image-{file_idx:09d}'.encode()
img = self.lmdb_sets[lmdb_idx]['txn'].get(img_key)
buf = io.BytesIO(img)
w, h = Image.open(buf).size
else:
wh = wh.decode('utf-8')
w, h = wh.split('_')
wh_ratio.append(float(w) / float(h))
return wh_ratio
def load_hierarchical_lmdb_dataset(self, data_dir_list, ratio_list):
lmdb_sets = {}
dataset_idx = 0
for dirpath, ratio in zip(data_dir_list, ratio_list):
env = lmdb.open(dirpath,
max_readers=32,
readonly=True,
lock=False,
readahead=False,
meminit=False)
txn = env.begin(write=False)
num_samples = int(txn.get('num-samples'.encode()))
lmdb_sets[dataset_idx] = {
'dirpath': dirpath,
'env': env,
'txn': txn,
'num_samples': num_samples,
'ratio_num_samples': int(ratio * num_samples)
}
dataset_idx += 1
return lmdb_sets
def dataset_traversal(self):
lmdb_num = len(self.lmdb_sets)
total_sample_num = 0
for lno in range(lmdb_num):
total_sample_num += self.lmdb_sets[lno]['ratio_num_samples']
data_idx_order_list = np.zeros((total_sample_num, 2))
beg_idx = 0
for lno in range(lmdb_num):
tmp_sample_num = self.lmdb_sets[lno]['ratio_num_samples']
end_idx = beg_idx + tmp_sample_num
data_idx_order_list[beg_idx:end_idx, 0] = lno
data_idx_order_list[beg_idx:end_idx, 1] = list(
random.sample(range(1, self.lmdb_sets[lno]['num_samples'] + 1),
self.lmdb_sets[lno]['ratio_num_samples']))
beg_idx = beg_idx + tmp_sample_num
return data_idx_order_list
def get_img_data(self, value):
"""get_img_data."""
if not value:
return None
imgdata = np.frombuffer(value, dtype='uint8')
if imgdata is None:
return None
imgori = cv2.imdecode(imgdata, 1)
if imgori is None:
return None
return imgori
def resize_norm_img(self, data, gen_ratio, padding=True):
img = data['image']
w, h = img.size
if self.padding_rand and random.random() < 0.5:
padding = not padding
imgW, imgH = self.base_shape[gen_ratio - 1] if gen_ratio <= 4 else [
self.base_h * gen_ratio, self.base_h
]
use_ratio = imgW // imgH
if use_ratio >= (w // h) + 2:
self.error += 1
return None
if not padding:
resized_w = imgW
else:
ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW:
resized_w = imgW
else:
resized_w = int(
math.ceil(imgH * ratio * (random.random() + 0.5)))
resized_w = min(imgW, resized_w)
resized_image = F.resize(img, (imgH, resized_w),
interpolation=self.interpolation)
img = self.transforms(resized_image)
if resized_w < imgW and padding:
# img = F.pad(img, [0, 0, imgW-resized_w, 0], fill=0.)
if self.padding_doub and random.random() < 0.5:
img = F.pad(img, [0, 0, imgW - resized_w, 0], fill=0.)
else:
img = F.pad(img, [imgW - resized_w, 0, 0, 0], fill=0.)
valid_ratio = min(1.0, float(resized_w / imgW))
data['image'] = img
data['valid_ratio'] = valid_ratio
return data
def get_lmdb_sample_info(self, txn, index):
label_key = 'label-%09d'.encode() % index
label = txn.get(label_key)
if label is None:
return None
label = label.decode('utf-8')
img_key = 'image-%09d'.encode() % index
imgbuf = txn.get(img_key)
return imgbuf, label
def __getitem__(self, properties):
img_width = properties[0]
img_height = properties[1]
idx = properties[2]
ratio = properties[3]
lmdb_idx, file_idx = self.data_idx_order_list[idx]
lmdb_idx = int(lmdb_idx)
file_idx = int(file_idx)
sample_info = self.get_lmdb_sample_info(
self.lmdb_sets[lmdb_idx]['txn'], file_idx)
if sample_info is None:
ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist()
ids = random.sample(ratio_ids, 1)
return self.__getitem__([img_width, img_height, ids[0], ratio])
img, label = sample_info
data = {'image': img, 'label': label}
outs = transform(data, self.ops[:-1])
if outs is not None:
outs = self.resize_norm_img(outs, ratio, padding=self.padding)
if outs is None:
ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist()
ids = random.sample(ratio_ids, 1)
return self.__getitem__([img_width, img_height, ids[0], ratio])
outs = transform(outs, self.ops[-1:])
if outs is None:
ratio_ids = np.where(self.wh_ratio == ratio)[0].tolist()
ids = random.sample(ratio_ids, 1)
return self.__getitem__([img_width, img_height, ids[0], ratio])
return outs
def __len__(self):
return self.data_idx_order_list.shape[0]
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