diff --git a/README.md b/README.md index 6627dedb..6fb6c4ac 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 #### 2.inference模型下载 +*windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下* + + #### (1)超轻量级中文OCR模型下载 ``` mkdir inference && cd inference @@ -63,6 +66,9 @@ cd .. # 设置PYTHONPATH环境变量 export PYTHONPATH=. +# windows下设置环境变量 +SET PYTHONPATH=. + # 预测image_dir指定的单张图像 python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_det_mv3_db/" --rec_model_dir="./inference/ch_rec_mv3_crnn/" diff --git a/configs/rec/rec_benchmark_reader.yml b/configs/rec/rec_benchmark_reader.yml index 3d1e3e0b..524f2f68 100755 --- a/configs/rec/rec_benchmark_reader.yml +++ b/configs/rec/rec_benchmark_reader.yml @@ -10,4 +10,3 @@ EvalReader: TestReader: reader_function: ppocr.data.rec.dataset_traversal,LMDBReader lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/ - infer_img: ./infer_img diff --git a/configs/rec/rec_chinese_lite_train.yml b/configs/rec/rec_chinese_lite_train.yml index ec1b7a69..b64313a1 100755 --- a/configs/rec/rec_chinese_lite_train.yml +++ b/configs/rec/rec_chinese_lite_train.yml @@ -15,9 +15,11 @@ Global: character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt loss_type: ctc reader_yml: ./configs/rec/rec_chinese_reader.yml - pretrain_weights: + pretrain_weights: checkpoints: save_inference_dir: + infer_img: + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_chinese_reader.yml b/configs/rec/rec_chinese_reader.yml index f09a1ea7..a44efd99 100755 --- a/configs/rec/rec_chinese_reader.yml +++ b/configs/rec/rec_chinese_reader.yml @@ -11,4 +11,3 @@ EvalReader: TestReader: reader_function: ppocr.data.rec.dataset_traversal,SimpleReader - infer_img: ./infer_img diff --git a/configs/rec/rec_icdar15_reader.yml b/configs/rec/rec_icdar15_reader.yml index 12facda1..322d5f25 100755 --- a/configs/rec/rec_icdar15_reader.yml +++ b/configs/rec/rec_icdar15_reader.yml @@ -11,4 +11,3 @@ EvalReader: TestReader: reader_function: ppocr.data.rec.dataset_traversal,SimpleReader - infer_img: ./infer_img diff --git a/configs/rec/rec_icdar15_train.yml b/configs/rec/rec_icdar15_train.yml index 6596fc33..934a9410 100755 --- a/configs/rec/rec_icdar15_train.yml +++ b/configs/rec/rec_icdar15_train.yml @@ -14,9 +14,11 @@ Global: character_type: en loss_type: ctc reader_yml: ./configs/rec/rec_icdar15_reader.yml - pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy + pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy checkpoints: save_inference_dir: + infer_img: + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml index 11a09ee9..d2e096fb 100755 --- a/configs/rec/rec_mv3_none_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml @@ -17,6 +17,7 @@ Global: pretrain_weights: checkpoints: save_inference_dir: + infer_img: Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_mv3_none_none_ctc.yml b/configs/rec/rec_mv3_none_none_ctc.yml index bbbb6d1f..ceec09ce 100755 --- a/configs/rec/rec_mv3_none_none_ctc.yml +++ b/configs/rec/rec_mv3_none_none_ctc.yml @@ -17,6 +17,7 @@ Global: pretrain_weights: checkpoints: save_inference_dir: + infer_img: Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_mv3_tps_bilstm_attn.yml b/configs/rec/rec_mv3_tps_bilstm_attn.yml index 03a2e901..7fc4f679 100755 --- a/configs/rec/rec_mv3_tps_bilstm_attn.yml +++ b/configs/rec/rec_mv3_tps_bilstm_attn.yml @@ -13,11 +13,14 @@ Global: max_text_length: 25 character_type: en loss_type: attention + tps: true reader_yml: ./configs/rec/rec_benchmark_reader.yml pretrain_weights: checkpoints: save_inference_dir: - + infer_img: + + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml index 47247b72..4b9660bc 100755 --- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml @@ -13,10 +13,12 @@ Global: max_text_length: 25 character_type: en loss_type: ctc + tps: true reader_yml: ./configs/rec/rec_benchmark_reader.yml pretrain_weights: checkpoints: save_inference_dir: + infer_img: Architecture: diff --git a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml index 10181936..b71e8fea 100755 --- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml @@ -17,7 +17,9 @@ Global: pretrain_weights: checkpoints: save_inference_dir: - + infer_img: + + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_r34_vd_none_none_ctc.yml b/configs/rec/rec_r34_vd_none_none_ctc.yml index ff4c5763..d9c9458d 100755 --- a/configs/rec/rec_r34_vd_none_none_ctc.yml +++ b/configs/rec/rec_r34_vd_none_none_ctc.yml @@ -17,6 +17,7 @@ Global: pretrain_weights: checkpoints: save_inference_dir: + infer_img: Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_r34_vd_tps_bilstm_attn.yml b/configs/rec/rec_r34_vd_tps_bilstm_attn.yml index 4d96e9e7..405082bd 100755 --- a/configs/rec/rec_r34_vd_tps_bilstm_attn.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_attn.yml @@ -17,6 +17,8 @@ Global: pretrain_weights: checkpoints: save_inference_dir: + infer_img: + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml index 844721a2..517322c3 100755 --- a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml @@ -17,6 +17,8 @@ Global: pretrain_weights: checkpoints: save_inference_dir: + infer_img: + Architecture: function: ppocr.modeling.architectures.rec_model,RecModel diff --git a/doc/detection.md b/doc/detection.md index 0fa32116..ab12dfe9 100644 --- a/doc/detection.md +++ b/doc/detection.md @@ -46,6 +46,9 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/Res ``` **启动训练** + +*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* + ``` python3 tools/train.py -c configs/det/det_mv3_db.yml ``` diff --git a/doc/inference.md b/doc/inference.md index a15043b5..0d5f45fd 100644 --- a/doc/inference.md +++ b/doc/inference.md @@ -165,6 +165,16 @@ STAR-Net文本识别模型推理，可以执行如下命令： ``` python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en" ``` + +### 3.基于Attention损失的识别模型推理 + +基于Attention损失的识别模型与ctc不同，需要额外设置识别算法参数 --rec_algorithm="RARE" + +RARE 文本识别模型推理，可以执行如下命令： +``` +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/sare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE" +``` + ![](imgs_words_en/word_336.png) 执行命令后，上面图像的识别结果如下： diff --git a/doc/installation.md b/doc/installation.md index 9bdd5379..f1edbf4a 100644 --- a/doc/installation.md +++ b/doc/installation.md @@ -8,6 +8,8 @@ PaddleOCR 工作环境 建议使用我们提供的docker运行PaddleOCR，有关docker使用请参考[链接](https://docs.docker.com/get-started/)。 +*如您希望使用 mac 或 windows直接运行预测代码，可以从第2步开始执行。* + 1. （建议）准备docker环境。第一次使用这个镜像，会自动下载该镜像，请耐心等待。 ``` # 切换到工作目录下 @@ -54,6 +56,10 @@ python3 -m pip install paddlepaddle-gpu==1.7.2.post97 -i https://pypi.tuna.tsing 如果您的机器安装的是CUDA10，请运行以下命令安装 python3 -m pip install paddlepaddle-gpu==1.7.2.post107 -i https://pypi.tuna.tsinghua.edu.cn/simple +如果您的机器是CPU，请运行以下命令安装 + +python3 -m pip install paddlepaddle==1.7.2 -i https://pypi.tuna.tsinghua.edu.cn/simple + 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 ``` diff --git a/doc/recognition.md b/doc/recognition.md index a5a8119c..004487f9 100644 --- a/doc/recognition.md +++ b/doc/recognition.md @@ -41,6 +41,8 @@ PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通 wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt # 测试集标签 wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt + + ``` 最终训练集应有如下文件结构： @@ -111,6 +113,8 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar 开始训练: +*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* + ``` # 设置PYTHONPATH路径 export PYTHONPATH=$PYTHONPATH:. @@ -168,10 +172,11 @@ Global: 评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml` 修改EvalReader中的 `label_file_path` 设置。 +*注意* 评估时必须确保配置文件中 infer_img 字段为空 ``` export CUDA_VISIBLE_DEVICES=0 # GPU 评估， Global.checkpoints 为待测权重 -python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy +python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` ### 预测 @@ -184,7 +189,7 @@ python3 tools/eval.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkp ``` # 预测英文结果 -python3 tools/infer_rec.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg +python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png ``` 预测图片： diff --git a/ppocr/data/det/dataset_traversal.py b/ppocr/data/det/dataset_traversal.py index 272d7317..ab635de3 100644 --- a/ppocr/data/det/dataset_traversal.py +++ b/ppocr/data/det/dataset_traversal.py @@ -61,8 +61,6 @@ class TrainReader(object): if len(batch_outs) == self.batch_size: yield batch_outs batch_outs = [] - if len(batch_outs) != 0: - yield batch_outs return batch_iter_reader diff --git a/ppocr/data/det/db_process.py b/ppocr/data/det/db_process.py index d347ed44..0993324a 100644 --- a/ppocr/data/det/db_process.py +++ b/ppocr/data/det/db_process.py @@ -17,6 +17,8 @@ import cv2 import numpy as np import json import sys +from ppocr.utils.utility import initial_logger +logger = initial_logger() from .data_augment import AugmentData from .random_crop_data import RandomCropData @@ -100,6 +102,7 @@ class DBProcessTrain(object): img_path, gt_label = self.convert_label_infor(label_infor) imgvalue = cv2.imread(img_path) if imgvalue is None: + logger.info("{} does not exist!".format(img_path)) return None data = self.make_data_dict(imgvalue, gt_label) data = AugmentData(data) diff --git a/ppocr/data/rec/dataset_traversal.py b/ppocr/data/rec/dataset_traversal.py index f60b9fe3..71b488f4 100755 --- a/ppocr/data/rec/dataset_traversal.py +++ b/ppocr/data/rec/dataset_traversal.py @@ -41,13 +41,18 @@ class LMDBReader(object): self.loss_type = params['loss_type'] self.max_text_length = params['max_text_length'] self.mode = params['mode'] + self.drop_last = False + self.use_tps = False + if "tps" in params: + self.ues_tps = True if params['mode'] == 'train': self.batch_size = params['train_batch_size_per_card'] - elif params['mode'] == "eval": + self.drop_last = True + else: self.batch_size = params['test_batch_size_per_card'] - elif params['mode'] == "test": - self.batch_size = 1 - self.infer_img = params["infer_img"] + self.drop_last = False + self.infer_img = params['infer_img'] + def load_hierarchical_lmdb_dataset(self): lmdb_sets = {} dataset_idx = 0 @@ -100,13 +105,18 @@ class LMDBReader(object): process_id = 0 def sample_iter_reader(): - if self.mode == 'test': + if self.mode != 'train' and self.infer_img is not None: image_file_list = get_image_file_list(self.infer_img) for single_img in image_file_list: img = cv2.imread(single_img) - if img.shape[-1]==1 or len(list(img.shape))==2: + if img.shape[-1] == 1 or len(list(img.shape)) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - norm_img = process_image(img, self.image_shape) + norm_img = process_image( + img=img, + image_shape=self.image_shape, + char_ops=self.char_ops, + tps=self.use_tps, + infer_mode=True) yield norm_img else: lmdb_sets = self.load_hierarchical_lmdb_dataset() @@ -126,9 +136,13 @@ class LMDBReader(object): if sample_info is None: continue img, label = sample_info - outs = process_image(img, self.image_shape, label, - self.char_ops, self.loss_type, - self.max_text_length) + outs = process_image( + img=img, + image_shape=self.image_shape, + label=label, + char_ops=self.char_ops, + loss_type=self.loss_type, + max_text_length=self.max_text_length) if outs is None: continue yield outs @@ -136,6 +150,7 @@ class LMDBReader(object): if finish_read_num == len(lmdb_sets): break self.close_lmdb_dataset(lmdb_sets) + def batch_iter_reader(): batch_outs = [] for outs in sample_iter_reader(): @@ -143,10 +158,11 @@ class LMDBReader(object): if len(batch_outs) == self.batch_size: yield batch_outs batch_outs = [] - if len(batch_outs) != 0: - yield batch_outs + if not self.drop_last: + if len(batch_outs) != 0: + yield batch_outs - if self.mode != 'test': + if self.infer_img is None: return batch_iter_reader return sample_iter_reader @@ -165,26 +181,34 @@ class SimpleReader(object): self.loss_type = params['loss_type'] self.max_text_length = params['max_text_length'] self.mode = params['mode'] + self.infer_img = params['infer_img'] + self.use_tps = False + if "tps" in params: + self.ues_tps = True if params['mode'] == 'train': self.batch_size = params['train_batch_size_per_card'] - elif params['mode'] == 'eval': - self.batch_size = params['test_batch_size_per_card'] + self.drop_last = True else: - self.batch_size = 1 - self.infer_img = params['infer_img'] + self.batch_size = params['test_batch_size_per_card'] + self.drop_last = False def __call__(self, process_id): if self.mode != 'train': process_id = 0 def sample_iter_reader(): - if self.mode == 'test': + if self.mode != 'train' and self.infer_img is not None: image_file_list = get_image_file_list(self.infer_img) for single_img in image_file_list: img = cv2.imread(single_img) - if img.shape[-1]==1 or len(list(img.shape))==2: + if img.shape[-1] == 1 or len(list(img.shape)) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - norm_img = process_image(img, self.image_shape) + norm_img = process_image( + img=img, + image_shape=self.image_shape, + char_ops=self.char_ops, + tps=self.use_tps, + infer_mode=True) yield norm_img else: with open(self.label_file_path, "rb") as fin: @@ -192,7 +216,7 @@ class SimpleReader(object): img_num = len(label_infor_list) img_id_list = list(range(img_num)) random.shuffle(img_id_list) - if sys.platform=="win32": + if sys.platform == "win32": print("multiprocess is not fully compatible with Windows." "num_workers will be 1.") self.num_workers = 1 @@ -204,7 +228,7 @@ class SimpleReader(object): if img is None: logger.info("{} does not exist!".format(img_path)) continue - if img.shape[-1]==1 or len(list(img.shape))==2: + if img.shape[-1] == 1 or len(list(img.shape)) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) label = substr[1] @@ -222,9 +246,10 @@ class SimpleReader(object): if len(batch_outs) == self.batch_size: yield batch_outs batch_outs = [] - if len(batch_outs) != 0: - yield batch_outs + if not self.drop_last: + if len(batch_outs) != 0: + yield batch_outs - if self.mode != 'test': + if self.infer_img is None: return batch_iter_reader return sample_iter_reader diff --git a/ppocr/data/rec/img_tools.py b/ppocr/data/rec/img_tools.py index a27f108c..57543293 100755 --- a/ppocr/data/rec/img_tools.py +++ b/ppocr/data/rec/img_tools.py @@ -48,6 +48,32 @@ def resize_norm_img(img, image_shape): return padding_im +def resize_norm_img_chinese(img, image_shape): + imgC, imgH, imgW = image_shape + # todo: change to 0 and modified image shape + max_wh_ratio = 0 + h, w = img.shape[0], img.shape[1] + ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, ratio) + imgW = int(32 * max_wh_ratio) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype('float32') + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + def get_img_data(value): """get_img_data""" if not value: @@ -66,8 +92,13 @@ def process_image(img, label=None, char_ops=None, loss_type=None, - max_text_length=None): - norm_img = resize_norm_img(img, image_shape) + max_text_length=None, + tps=None, + infer_mode=False): + if not infer_mode or char_ops.character_type == "en" or tps != None: + norm_img = resize_norm_img(img, image_shape) + else: + norm_img = resize_norm_img_chinese(img, image_shape) norm_img = norm_img[np.newaxis, :] if label is not None: char_num = char_ops.get_char_num() diff --git a/ppocr/modeling/architectures/rec_model.py b/ppocr/modeling/architectures/rec_model.py index d88c620b..e80a50ab 100755 --- a/ppocr/modeling/architectures/rec_model.py +++ b/ppocr/modeling/architectures/rec_model.py @@ -30,6 +30,8 @@ class RecModel(object): global_params = params['Global'] char_num = global_params['char_ops'].get_char_num() global_params['char_num'] = char_num + self.char_type = global_params['character_type'] + self.infer_img = global_params['infer_img'] if "TPS" in params: tps_params = deepcopy(params["TPS"]) tps_params.update(global_params) @@ -60,8 +62,8 @@ class RecModel(object): def create_feed(self, mode): image_shape = deepcopy(self.image_shape) image_shape.insert(0, -1) - image = fluid.data(name='image', shape=image_shape, dtype='float32') if mode == "train": + image = fluid.data(name='image', shape=image_shape, dtype='float32') if self.loss_type == "attention": label_in = fluid.data( name='label_in', @@ -86,6 +88,16 @@ class RecModel(object): use_double_buffer=True, iterable=False) else: + if self.char_type == "ch" and self.infer_img: + image_shape[-1] = -1 + if self.tps != None: + logger.info( + "WARNRNG!!!\n" + "TPS does not support variable shape in chinese!" + "We set img_shape to be the same , it may affect the inference effect" + ) + image_shape = deepcopy(self.image_shape) + image = fluid.data(name='image', shape=image_shape, dtype='float32') labels = None loader = None return image, labels, loader @@ -110,7 +122,11 @@ class RecModel(object): return loader, outputs elif mode == "export": predict = predicts['predict'] - predict = fluid.layers.softmax(predict) + if self.loss_type == "ctc": + predict = fluid.layers.softmax(predict) return [image, {'decoded_out': decoded_out, 'predicts': predict}] else: - return loader, {'decoded_out': decoded_out} + predict = predicts['predict'] + if self.loss_type == "ctc": + predict = fluid.layers.softmax(predict) + return loader, {'decoded_out': decoded_out, 'predicts': predict} diff --git a/ppocr/modeling/heads/rec_attention_head.py b/ppocr/modeling/heads/rec_attention_head.py index 8f5b4cc4..66c8f300 100755 --- a/ppocr/modeling/heads/rec_attention_head.py +++ b/ppocr/modeling/heads/rec_attention_head.py @@ -123,6 +123,8 @@ class AttentionPredict(object): full_ids = fluid.layers.fill_constant_batch_size_like( input=init_state, shape=[-1, 1], dtype='int64', value=1) + full_scores = fluid.layers.fill_constant_batch_size_like( + input=init_state, shape=[-1, 1], dtype='float32', value=1) cond = layers.less_than(x=counter, y=array_len) while_op = layers.While(cond=cond) @@ -171,6 +173,9 @@ class AttentionPredict(object): new_ids = fluid.layers.concat([full_ids, topk_indices], axis=1) fluid.layers.assign(new_ids, full_ids) + new_scores = fluid.layers.concat([full_scores, topk_scores], axis=1) + fluid.layers.assign(new_scores, full_scores) + layers.increment(x=counter, value=1, in_place=True) # update the memories @@ -184,7 +189,7 @@ class AttentionPredict(object): length_cond = layers.less_than(x=counter, y=array_len) finish_cond = layers.logical_not(layers.is_empty(x=topk_indices)) layers.logical_and(x=length_cond, y=finish_cond, out=cond) - return full_ids + return full_ids, full_scores def __call__(self, inputs, labels=None, mode=None): encoder_features = self.encoder(inputs) @@ -223,10 +228,10 @@ class AttentionPredict(object): decoder_size, char_num) _, decoded_out = layers.topk(input=predict, k=1) decoded_out = layers.lod_reset(decoded_out, y=label_out) - predicts = {'predict': predict, 'decoded_out': decoded_out} + predicts = {'predict':predict, 'decoded_out':decoded_out} else: - ids = self.gru_attention_infer( + ids, predict = self.gru_attention_infer( decoder_boot, self.max_length, char_num, word_vector_dim, encoded_vector, encoded_proj, decoder_size) - predicts = {'decoded_out': ids} + predicts = {'predict':predict, 'decoded_out':ids} return predicts diff --git a/tools/eval_utils/eval_rec_utils.py b/tools/eval_utils/eval_rec_utils.py index 3ceaa159..2d7d7e1d 100644 --- a/tools/eval_utils/eval_rec_utils.py +++ b/tools/eval_utils/eval_rec_utils.py @@ -48,7 +48,7 @@ def eval_rec_run(exe, config, eval_info_dict, mode): total_sample_num = 0 total_acc_num = 0 total_batch_num = 0 - if mode == "test": + if mode == "eval": is_remove_duplicate = False else: is_remove_duplicate = True @@ -91,11 +91,11 @@ def test_rec_benchmark(exe, config, eval_info_dict): total_correct_number = 0 eval_data_acc_info = {} for eval_data in eval_data_list: - config['EvalReader']['lmdb_sets_dir'] = \ + config['TestReader']['lmdb_sets_dir'] = \ eval_data_dir + "/" + eval_data - eval_reader = reader_main(config=config, mode="eval") + eval_reader = reader_main(config=config, mode="test") eval_info_dict['reader'] = eval_reader - metrics = eval_rec_run(exe, config, eval_info_dict, "eval") + metrics = eval_rec_run(exe, config, eval_info_dict, "test") total_evaluation_data_number += metrics['total_sample_num'] total_correct_number += metrics['total_acc_num'] eval_data_acc_info[eval_data] = metrics diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index c005bfb7..d47b81af 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -32,10 +32,16 @@ class TextRecognizer(object): self.rec_image_shape = image_shape self.character_type = args.rec_char_type self.rec_batch_num = args.rec_batch_num + self.rec_algorithm = args.rec_algorithm char_ops_params = {} char_ops_params["character_type"] = args.rec_char_type char_ops_params["character_dict_path"] = args.rec_char_dict_path - char_ops_params['loss_type'] = 'ctc' + if self.rec_algorithm != "RARE": + char_ops_params['loss_type'] = 'ctc' + self.loss_type = 'ctc' + else: + char_ops_params['loss_type'] = 'attention' + self.loss_type = 'attention' self.char_ops = CharacterOps(char_ops_params) def resize_norm_img(self, img, max_wh_ratio): @@ -80,26 +86,43 @@ class TextRecognizer(object): starttime = time.time() self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.zero_copy_run() - rec_idx_batch = self.output_tensors[0].copy_to_cpu() - rec_idx_lod = self.output_tensors[0].lod()[0] - predict_batch = self.output_tensors[1].copy_to_cpu() - predict_lod = self.output_tensors[1].lod()[0] - elapse = time.time() - starttime - predict_time += elapse - starttime = time.time() - for rno in range(len(rec_idx_lod) - 1): - beg = rec_idx_lod[rno] - end = rec_idx_lod[rno + 1] - rec_idx_tmp = rec_idx_batch[beg:end, 0] - preds_text = self.char_ops.decode(rec_idx_tmp) - beg = predict_lod[rno] - end = predict_lod[rno + 1] - probs = predict_batch[beg:end, :] - ind = np.argmax(probs, axis=1) - blank = probs.shape[1] - valid_ind = np.where(ind != (blank - 1))[0] - score = np.mean(probs[valid_ind, ind[valid_ind]]) - rec_res.append([preds_text, score]) + + if self.loss_type == "ctc": + rec_idx_batch = self.output_tensors[0].copy_to_cpu() + rec_idx_lod = self.output_tensors[0].lod()[0] + predict_batch = self.output_tensors[1].copy_to_cpu() + predict_lod = self.output_tensors[1].lod()[0] + elapse = time.time() - starttime + predict_time += elapse + for rno in range(len(rec_idx_lod) - 1): + beg = rec_idx_lod[rno] + end = rec_idx_lod[rno + 1] + rec_idx_tmp = rec_idx_batch[beg:end, 0] + preds_text = self.char_ops.decode(rec_idx_tmp) + beg = predict_lod[rno] + end = predict_lod[rno + 1] + probs = predict_batch[beg:end, :] + ind = np.argmax(probs, axis=1) + blank = probs.shape[1] + valid_ind = np.where(ind != (blank - 1))[0] + score = np.mean(probs[valid_ind, ind[valid_ind]]) + rec_res.append([preds_text, score]) + else: + rec_idx_batch = self.output_tensors[0].copy_to_cpu() + predict_batch = self.output_tensors[1].copy_to_cpu() + elapse = time.time() - starttime + predict_time += elapse + for rno in range(len(rec_idx_batch)): + end_pos = np.where(rec_idx_batch[rno, :] == 1)[0] + if len(end_pos) <= 1: + preds = rec_idx_batch[rno, 1:] + score = np.mean(predict_batch[rno, 1:]) + else: + preds = rec_idx_batch[rno, 1:end_pos[1]] + score = np.mean(predict_batch[rno, 1:end_pos[1]]) + preds_text = self.char_ops.decode(preds) + rec_res.append([preds_text, score]) + return rec_res, predict_time @@ -116,7 +139,17 @@ if __name__ == "__main__": continue valid_image_file_list.append(image_file) img_list.append(img) - rec_res, predict_time = text_recognizer(img_list) + try: + rec_res, predict_time = text_recognizer(img_list) + except Exception as e: + print(e) + logger.info( + "ERROR!!!! \n" + "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n" + "If your model has tps module: " + "TPS does not support variable shape.\n" + "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ") + exit() for ino in range(len(img_list)): print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino])) print("Total predict time for %d images:%.3f" % diff --git a/tools/infer_rec.py b/tools/infer_rec.py index 25bae1ca..ec64a38b 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -21,6 +21,7 @@ import time import multiprocessing import numpy as np + def set_paddle_flags(**kwargs): for key, value in kwargs.items(): if os.environ.get(key, None) is None: @@ -54,6 +55,7 @@ def main(): program.merge_config(FLAGS.opt) logger.info(config) char_ops = CharacterOps(config['Global']) + loss_type = config['Global']['loss_type'] config['Global']['char_ops'] = char_ops # check if set use_gpu=True in paddlepaddle cpu version @@ -78,35 +80,44 @@ def main(): init_model(config, eval_prog, exe) blobs = reader_main(config, 'test')() - infer_img = config['TestReader']['infer_img'] + infer_img = config['Global']['infer_img'] infer_list = get_image_file_list(infer_img) max_img_num = len(infer_list) if len(infer_list) == 0: logger.info("Can not find img in infer_img dir.") for i in range(max_img_num): - print("infer_img:",infer_list[i]) + print("infer_img:%s" % infer_list[i]) img = next(blobs) predict = exe.run(program=eval_prog, feed={"image": img}, fetch_list=fetch_varname_list, return_numpy=False) - - preds = np.array(predict[0]) - if preds.shape[1] == 1: + if loss_type == "ctc": + preds = np.array(predict[0]) preds = preds.reshape(-1) preds_lod = predict[0].lod()[0] preds_text = char_ops.decode(preds) - else: + probs = np.array(predict[1]) + ind = np.argmax(probs, axis=1) + blank = probs.shape[1] + valid_ind = np.where(ind != (blank - 1))[0] + score = np.mean(probs[valid_ind, ind[valid_ind]]) + elif loss_type == "attention": + preds = np.array(predict[0]) + probs = np.array(predict[1]) end_pos = np.where(preds[0, :] == 1)[0] if len(end_pos) <= 1: - preds_text = preds[0, 1:] + preds = preds[0, 1:] + score = np.mean(probs[0, 1:]) else: - preds_text = preds[0, 1:end_pos[1]] - preds_text = preds_text.reshape(-1) - preds_text = char_ops.decode(preds_text) + preds = preds[0, 1:end_pos[1]] + score = np.mean(probs[0, 1:end_pos[1]]) + preds = preds.reshape(-1) + preds_text = char_ops.decode(preds) - print("\t index:",preds) - print("\t word :",preds_text) + print("\t index:", preds) + print("\t word :", preds_text) + print("\t score :", score) # save for inference model target_var = []