Merge branch 'dygraph' into android_demo

ppocr/metrics/eval_det_iou.py

@@ -200,7 +200,8 @@ class DetectionIoUEvaluator(object):
         methodPrecision = 0 if numGlobalCareDet == 0 else float(
             matchedSum) / numGlobalCareDet
         methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
-            methodRecall * methodPrecision / (methodRecall + methodPrecision)
+                                                                    methodRecall * methodPrecision / (
+                                                                            methodRecall + methodPrecision)
         # print(methodRecall, methodPrecision, methodHmean)
         # sys.exit(-1)
         methodMetrics = {

ppocr/modeling/backbones/__init__.py

@@ -26,6 +26,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_vd import ResNet
         from .rec_resnet_fpn import ResNetFPN
         support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
+    elif model_type == 'e2e':
+        from .e2e_resnet_vd_pg import ResNet
+        support_dict = ['ResNet']
     else:
         raise NotImplementedError
 

ppocr/modeling/heads/__init__.py

@@ -20,6 +20,7 @@ def build_head(config):
     from .det_db_head import DBHead
     from .det_east_head import EASTHead
     from .det_sast_head import SASTHead
+    from .e2e_pg_head import PGHead
 
     # rec head
     from .rec_ctc_head import CTCHead
@@ -30,8 +31,8 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead'
-    ]
+        'SRNHead', 'PGHead']
+
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/rec_att_head.py

@@ -38,7 +38,7 @@ class AttentionHead(nn.Layer):
         return input_ont_hot
 
     def forward(self, inputs, targets=None, batch_max_length=25):
-        batch_size = inputs.shape[0]
+        batch_size = paddle.shape(inputs)[0]
         num_steps = batch_max_length
 
         hidden = paddle.zeros((batch_size, self.hidden_size))
@@ -57,6 +57,9 @@ class AttentionHead(nn.Layer):
         else:
             targets = paddle.zeros(shape=[batch_size], dtype="int32")
             probs = None
+            char_onehots = None
+            outputs = None
+            alpha = None
 
             for i in range(num_steps):
                 char_onehots = self._char_to_onehot(

ppocr/modeling/necks/__init__.py

@@ -14,12 +14,14 @@
 
 __all__ = ['build_neck']
 
+
 def build_neck(config):
     from .db_fpn import DBFPN
     from .east_fpn import EASTFPN
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder']
+    from .pg_fpn import PGFPN
+    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/postprocess/__init__.py

@@ -28,10 +28,11 @@ def build_post_process(config, global_config=None):
     from .sast_postprocess import SASTPostProcess
     from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
     from .cls_postprocess import ClsPostProcess
+    from .pg_postprocess import PGPostProcess
 
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/utils/e2e_metric/polygon_fast.py

+# Copyright (c) 2021 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.
+import numpy as np
+from shapely.geometry import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

requirements.txt

 shapely
-imgaug
+scikit-image==0.17.2
+imgaug==0.4.0
 pyclipper
 lmdb
 opencv-python==4.2.0.32
@@ -7,3 +8,4 @@ tqdm
 numpy
 visualdl
 python-Levenshtein
+opencv-contrib-python
\ No newline at end of file

setup.py

@@ -32,7 +32,7 @@ setup(
     package_dir={'paddleocr': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='2.0.2',
+    version='2.0.3',
     install_requires=requirements,
     license='Apache License 2.0',
     description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',

tools/eval.py

@@ -47,6 +47,7 @@ def main():
         config['Architecture']["Head"]['out_channels'] = len(
             getattr(post_process_class, 'character'))
     model = build_model(config['Architecture'])
+    use_srn = config['Architecture']['algorithm'] == "SRN"
 
     best_model_dict = init_model(config, model, logger)
     if len(best_model_dict):
@@ -59,7 +60,7 @@ def main():
 
     # start eval
     metirc = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class)
+                          eval_class, use_srn)
     logger.info('metric eval ***************')
     for k, v in metirc.items():
         logger.info('{}:{}'.format(k, v))

tools/infer/predict_cls.py

@@ -98,10 +98,10 @@ class TextClassifier(object):
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
             starttime = time.time()
-
             self.input_tensor.copy_from_cpu(norm_img_batch)
             self.predictor.run()
             prob_out = self.output_tensors[0].copy_to_cpu()
+            self.predictor.try_shrink_memory()
             cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
             for rno in range(len(cls_result)):