from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from os import path import random import traceback import copy import math import tarfile from PIL import Image import logging logger = logging.getLogger(__name__) import paddle from paddle import fluid from paddle.fluid.dygraph.parallel import ParallelEnv DATA_MD5 = "7256b1d5420d8c3e74815196e58cdad5" DATA_URL = "http://paddle-ocr-data.bj.bcebos.com/data.tar.gz" CACHE_DIR_NAME = "attention_data" SAVED_FILE_NAME = "data.tar.gz" DATA_DIR_NAME = "data" TRAIN_DATA_DIR_NAME = "train_images" TEST_DATA_DIR_NAME = "test_images" TRAIN_LIST_FILE_NAME = "train.list" TEST_LIST_FILE_NAME = "test.list" class Resize(object): def __init__(self, height=48): self.interp = Image.NEAREST # Image.ANTIALIAS self.height = height def __call__(self, samples): shape = samples[0][0].size for i in range(len(samples)): im = samples[i][0] im = im.resize((shape[0], self.height), self.interp) samples[i][0] = im return samples class Normalize(object): def __init__(self, mean=[127.5], std=[1.0], scale=False, channel_first=True): self.mean = mean self.std = std self.scale = scale self.channel_first = channel_first if not (isinstance(self.mean, list) and isinstance(self.std, list) and isinstance(self.scale, bool)): raise TypeError("{}: input type is invalid.".format(self)) def __call__(self, samples): for i in range(len(samples)): im = samples[i][0] im = np.array(im).astype(np.float32, copy=False) im = im[np.newaxis, ...] mean = np.array(self.mean)[np.newaxis, np.newaxis, :] std = np.array(self.std)[np.newaxis, np.newaxis, :] if self.scale: im = im / 255.0 #im -= mean im -= 127.5 #im /= std samples[i][0] = im return samples class PadTarget(object): def __init__(self, SOS=0, EOS=1): self.SOS = SOS self.EOS = EOS def __call__(self, samples): lens = np.array([len(s[1]) for s in samples], dtype="int64") max_len = np.max(lens) for i in range(len(samples)): label = samples[i][1] if max_len > len(label): pad_label = label + [self.EOS] * (max_len - len(label)) else: pad_label = label samples[i][1] = np.array([self.SOS] + pad_label, dtype='int64') # label_out samples[i].append(np.array(pad_label + [self.EOS], dtype='int64')) mask = np.zeros((max_len + 1)).astype('float32') mask[:len(label) + 1] = 1.0 # mask samples[i].append(np.array(mask, dtype='float32')) return samples class MyBatchSampler(fluid.io.BatchSampler): def __init__(self, dataset, batch_size, shuffle=False, drop_last=True, seed=None): self._dataset = dataset self._batch_size = batch_size self._shuffle = shuffle self._drop_last = drop_last self._random = np.random self._random.seed(seed) self._nranks = ParallelEnv().nranks self._local_rank = ParallelEnv().local_rank self._device_id = ParallelEnv().dev_id self._num_samples = int( math.ceil(len(self._dataset) * 1.0 / self._nranks)) self._total_size = self._num_samples * self._nranks self._epoch = 0 def __iter__(self): infos = copy.copy(self._dataset._sample_infos) skip_num = 0 if self._shuffle: if self._batch_size == 1: self._random.RandomState(self._epoch).shuffle(infos) else: # partial shuffle infos = sorted(infos, key=lambda x: x.w) skip_num = random.randint(1, 100) infos = infos[skip_num:] + infos[:skip_num] infos += infos[:(self._total_size - len(infos))] last_size = self._total_size % (self._batch_size * self._nranks) batches = [] for i in range(self._local_rank * self._batch_size, len(infos) - last_size, self._batch_size * self._nranks): batches.append(infos[i:i + self._batch_size]) if (not self._drop_last) and last_size != 0: last_local_size = last_size // self._nranks last_infos = infos[len(infos) - last_size:] start = self._local_rank * last_local_size batches.append(last_infos[start:start + last_local_size]) if self._shuffle: self._random.RandomState(self._epoch).shuffle(batches) self._epoch += 1 for batch in batches: batch_indices = [info.idx for info in batch] yield batch_indices def __len__(self): if self._drop_last: return self._total_size // self._batch_size else: return math.ceil(self._total_size / float(self._batch_size)) class SampleInfo(object): def __init__(self, idx, h, w, im_name, labels): self.idx = idx self.h = h self.w = w self.im_name = im_name self.labels = labels class OCRDataset(paddle.io.Dataset): def __init__(self, image_dir, anno_file): self.image_dir = image_dir self.anno_file = anno_file self._sample_infos = [] with open(anno_file, 'r') as f: for i, line in enumerate(f): w, h, im_name, labels = line.strip().split(' ') h, w = int(h), int(w) labels = [int(c) for c in labels.split(',')] self._sample_infos.append(SampleInfo(i, h, w, im_name, labels)) #self._sample_infos = sorted(self._sample_infos, # key=lambda x: x.w) def __getitem__(self, idx): info = self._sample_infos[idx] im_name, labels = info.im_name, info.labels image = Image.open(path.join(self.image_dir, im_name)).convert('L') return [image, labels] def __len__(self): return len(self._sample_infos) def train( root_dir=None, images_dir=None, anno_file=None, shuffle=True, ): if root_dir is None: root_dir = download_data() if images_dir is None: images_dir = TRAIN_DATA_DIR_NAME images_dir = path.join(root_dir, TRAIN_DATA_DIR_NAME) if anno_file is None: anno_file = TRAIN_LIST_FILE_NAME anno_file = path.join(root_dir, TRAIN_LIST_FILE_NAME) return OCRDataset(images_dir, anno_file) def test( root_dir=None, images_dir=None, anno_file=None, shuffle=True, ): if root_dir is None: root_dir = download_data() if images_dir is None: images_dir = TEST_DATA_DIR_NAME images_dir = path.join(root_dir, TEST_DATA_DIR_NAME) if anno_file is None: anno_file = TEST_LIST_FILE_NAME anno_file = path.join(root_dir, TEST_LIST_FILE_NAME) return OCRDataset(images_dir, anno_file) def download_data(): '''Download train and test data. ''' tar_file = paddle.dataset.common.download( DATA_URL, CACHE_DIR_NAME, DATA_MD5, save_name=SAVED_FILE_NAME) data_dir = path.join(path.dirname(tar_file), DATA_DIR_NAME) if not path.isdir(data_dir): t = tarfile.open(tar_file, "r:gz") t.extractall(path=path.dirname(tar_file)) t.close() return data_dir