Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

tools/infer/predict_rec.py

@@ -41,6 +41,7 @@ class TextRecognizer(object):
         self.character_type = args.rec_char_type
         self.rec_batch_num = args.rec_batch_num
         self.rec_algorithm = args.rec_algorithm
+        self.max_text_length = args.max_text_length
         postprocess_params = {
             'name': 'CTCLabelDecode',
             "character_type": args.rec_char_type,
@@ -186,8 +187,9 @@ class TextRecognizer(object):
                     norm_img = norm_img[np.newaxis, :]
                     norm_img_batch.append(norm_img)
                 else:
-                    norm_img = self.process_image_srn(
-                        img_list[indices[ino]], self.rec_image_shape, 8, 25)
+                    norm_img = self.process_image_srn(img_list[indices[ino]],
+                                                      self.rec_image_shape, 8,
+                                                      self.max_text_length)
                     encoder_word_pos_list = []
                     gsrm_word_pos_list = []
                     gsrm_slf_attn_bias1_list = []

tools/infer/predict_system.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
@@ -141,6 +142,7 @@ def sorted_boxes(dt_boxes):
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list[args.process_id::args.total_process_num]
     text_sys = TextSystem(args)
     is_visualize = True
     font_path = args.vis_font_path
@@ -184,4 +186,18 @@ def main(args):
 
 
 if __name__ == "__main__":
-    main(utility.parse_args())
+    args = utility.parse_args()
+    if args.use_mp:
+        p_list = []
+        total_process_num = args.total_process_num
+        for process_id in range(total_process_num):
+            cmd = [sys.executable, "-u"] + sys.argv + [
+                "--process_id={}".format(process_id),
+                "--use_mp={}".format(False)
+            ]
+            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+            p_list.append(p)
+        for p in p_list:
+            p.wait()
+    else:
+        main(args)

tools/infer/utility.py

@@ -48,6 +48,7 @@ def parse_args():
     parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
     parser.add_argument("--max_batch_size", type=int, default=10)
     parser.add_argument("--use_dilation", type=bool, default=False)
+    parser.add_argument("--det_db_score_mode", type=str, default="fast")
     # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
@@ -74,6 +75,20 @@ def parse_args():
         "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
     parser.add_argument("--drop_score", type=float, default=0.5)
 
+    # params for e2e
+    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
+    parser.add_argument("--e2e_model_dir", type=str)
+    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
+    parser.add_argument("--e2e_limit_type", type=str, default='max')
+
+    # PGNet parmas
+    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
+    parser.add_argument(
+        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
+    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
+    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
+    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
+
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
     parser.add_argument("--cls_model_dir", type=str)
@@ -85,6 +100,10 @@ def parse_args():
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
 
+    parser.add_argument("--use_mp", type=str2bool, default=False)
+    parser.add_argument("--total_process_num", type=int, default=1)
+    parser.add_argument("--process_id", type=int, default=0)
+
     return parser.parse_args()
 
 
@@ -93,8 +112,10 @@ def create_predictor(args, mode, logger):
         model_dir = args.det_model_dir
     elif mode == 'cls':
         model_dir = args.cls_model_dir
-    else:
+    elif mode == 'rec':
         model_dir = args.rec_model_dir
+    else:
+        model_dir = args.e2e_model_dir
 
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
@@ -148,6 +169,22 @@ def create_predictor(args, mode, logger):
     return predictor, input_tensor, output_tensors
 
 
+def draw_e2e_res(dt_boxes, strs, img_path):
+    src_im = cv2.imread(img_path)
+    for box, str in zip(dt_boxes, strs):
+        box = box.astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+        cv2.putText(
+            src_im,
+            str,
+            org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+            fontFace=cv2.FONT_HERSHEY_COMPLEX,
+            fontScale=0.7,
+            color=(0, 255, 0),
+            thickness=1)
+    return src_im
+
+
 def draw_text_det_res(dt_boxes, img_path):
     src_im = cv2.imread(img_path)
     for box in dt_boxes:

tools/infer_e2e.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def draw_e2e_res(dt_boxes, strs, config, img, img_name):
+    if len(dt_boxes) > 0:
+        src_im = img
+        for box, str in zip(dt_boxes, strs):
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+            cv2.putText(
+                src_im,
+                str,
+                org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+                fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                fontScale=0.7,
+                color=(0, 255, 0),
+                thickness=1)
+        save_det_path = os.path.dirname(config['Global'][
+            'save_res_path']) + "/e2e_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The e2e Image saved in {}".format(save_path))
+
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+
+    init_model(config, model, logger)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image', 'shape']
+        transforms.append(op)
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, shape_list)
+            points, strs = post_result['points'], post_result['texts']
+            # write resule
+            dt_boxes_json = []
+            for poly, str in zip(points, strs):
+                tmp_json = {"transcription": str}
+                tmp_json['points'] = poly.tolist()
+                dt_boxes_json.append(tmp_json)
+            otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
+            fout.write(otstr.encode())
+            src_img = cv2.imread(file)
+            draw_e2e_res(points, strs, config, src_img, file)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/infer_rec.py

@@ -73,35 +73,45 @@ def main():
     global_config['infer_mode'] = True
     ops = create_operators(transforms, global_config)
 
+    save_res_path = config['Global'].get('save_res_path',
+                                         "./output/rec/predicts_rec.txt")
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
     model.eval()
-    for file in get_image_file_list(config['Global']['infer_img']):
-        logger.info("infer_img: {}".format(file))
-        with open(file, 'rb') as f:
-            img = f.read()
-            data = {'image': img}
-        batch = transform(data, ops)
-        if config['Architecture']['algorithm'] == "SRN":
-            encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
-            gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
-            gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
-            gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
-
-            others = [
-                paddle.to_tensor(encoder_word_pos_list),
-                paddle.to_tensor(gsrm_word_pos_list),
-                paddle.to_tensor(gsrm_slf_attn_bias1_list),
-                paddle.to_tensor(gsrm_slf_attn_bias2_list)
-            ]
-
-        images = np.expand_dims(batch[0], axis=0)
-        images = paddle.to_tensor(images)
-        if config['Architecture']['algorithm'] == "SRN":
-            preds = model(images, others)
-        else:
-            preds = model(images)
-        post_result = post_process_class(preds)
-        for rec_reuslt in post_result:
-            logger.info('\t result: {}'.format(rec_reuslt))
+
+    with open(save_res_path, "w") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            if config['Architecture']['algorithm'] == "SRN":
+                encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
+                gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
+                gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
+                gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
+
+                others = [
+                    paddle.to_tensor(encoder_word_pos_list),
+                    paddle.to_tensor(gsrm_word_pos_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias1_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias2_list)
+                ]
+
+            images = np.expand_dims(batch[0], axis=0)
+            images = paddle.to_tensor(images)
+            if config['Architecture']['algorithm'] == "SRN":
+                preds = model(images, others)
+            else:
+                preds = model(images)
+            post_result = post_process_class(preds)
+            for rec_reuslt in post_result:
+                logger.info('\t result: {}'.format(rec_reuslt))
+                if len(rec_reuslt) >= 2:
+                    fout.write(file + "\t" + rec_reuslt[0] + "\t" + str(
+                        rec_reuslt[1]) + "\n")
     logger.info("success!")
 
 

tools/program.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+import platform
 import yaml
 import time
 import shutil
@@ -159,6 +160,8 @@ def train(config,
     eval_batch_step = config['Global']['eval_batch_step']
 
     global_step = 0
+    if 'global_step' in pre_best_model_dict:
+        global_step = pre_best_model_dict['global_step']
     start_eval_step = 0
     if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
         start_eval_step = eval_batch_step[0]
@@ -196,9 +199,11 @@ def train(config,
         train_reader_cost = 0.0
         batch_sum = 0
         batch_start = time.time()
+        max_iter = len(train_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(train_dataloader)
         for idx, batch in enumerate(train_dataloader):
             train_reader_cost += time.time() - batch_start
-            if idx >= len(train_dataloader):
+            if idx >= max_iter:
                 break
             lr = optimizer.get_lr()
             images = batch[0]
@@ -287,7 +292,8 @@ def train(config,
                         is_best=True,
                         prefix='best_accuracy',
                         best_model_dict=best_model_dict,
-                        epoch=epoch)
+                        epoch=epoch,
+                        global_step=global_step)
                 best_str = 'best metric, {}'.format(', '.join([
                     '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                 ]))
@@ -309,7 +315,8 @@ def train(config,
                 is_best=False,
                 prefix='latest',
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
         if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
             save_model(
                 model,
@@ -319,7 +326,8 @@ def train(config,
                 is_best=False,
                 prefix='iter_epoch_{}'.format(epoch),
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)
@@ -335,8 +343,10 @@ def eval(model, valid_dataloader, post_process_class, eval_class,
         total_frame = 0.0
         total_time = 0.0
         pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
+        max_iter = len(valid_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(valid_dataloader)
         for idx, batch in enumerate(valid_dataloader):
-            if idx >= len(valid_dataloader):
+            if idx >= max_iter:
                 break
             images = batch[0]
             start = time.time()
@@ -375,7 +385,8 @@ def preprocess(is_train=False):
 
     alg = config['Architecture']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
+        'CLS', 'PGNet'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'