[to #42322933]Merge branch 'master' into ocr/ocr_detection

修复master分支ocr_detection 单元测试bug
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9112290

    * create ocr_detection task

* fix code check error

* fix code check error

* fix code check issue

* fix code check issue

* replace c++ nms with python version

* fix code check issue

* fix code check issue

* rename maas_lib

* merge master to ocr/ocr_detection

* add model_hub sup for ocr_detection

* fix bug

* replace c++ decoder with python version

* fix bug

* Merge branch 'master' into ocr/ocr_detection

* merge master

* fix code check

* update

* add requirements for ocr_detection

* fix model_hub fetch bug

* remove debug code

* Merge branch 'master' into ocr/ocr_detection

* add local test image for ocr_detection

* update requirements for model_hub

* Merge branch 'master' into ocr/ocr_detection

* fix bug for full case test

* remove ema for ocr_detection

* Merge branch 'master' into ocr/ocr_detection

* apply ocr_detection test case

* Merge branch 'master' into ocr/ocr_detection

* update slim dependency for ocr_detection

* add more test case for ocr_detection

* release tf graph before create

* recover ema for ocr_detection model

* fix code

* Merge branch 'master' into ocr/ocr_detection

* fix code

modelscope/pipelines/cv/ocr_detection_pipeline.py

@@ -8,7 +8,6 @@ import cv2
 import numpy as np
 import PIL
 import tensorflow as tf
-import tf_slim as slim
 
 from modelscope.metainfo import Pipelines
 from modelscope.pipelines.base import Input
@@ -19,6 +18,11 @@ from ..base import Pipeline
 from ..builder import PIPELINES
 from .ocr_utils import model_resnet_mutex_v4_linewithchar, ops, utils
 
+if tf.__version__ >= '2.0':
+    import tf_slim as slim
+else:
+    from tensorflow.contrib import slim
+
 if tf.__version__ >= '2.0':
     tf = tf.compat.v1
 tf.compat.v1.disable_eager_execution()
@@ -44,6 +48,7 @@ class OCRDetectionPipeline(Pipeline):
 
     def __init__(self, model: str):
         super().__init__(model=model)
+        tf.reset_default_graph()
         model_path = osp.join(
             osp.join(self.model, ModelFile.TF_CHECKPOINT_FOLDER),
             'checkpoint-80000')
@@ -51,51 +56,56 @@ class OCRDetectionPipeline(Pipeline):
         config = tf.ConfigProto(allow_soft_placement=True)
         config.gpu_options.allow_growth = True
         self._session = tf.Session(config=config)
-        global_step = tf.get_variable(
-            'global_step', [],
-            initializer=tf.constant_initializer(0),
-            dtype=tf.int64,
-            trainable=False)
-        variable_averages = tf.train.ExponentialMovingAverage(
-            0.997, global_step)
         self.input_images = tf.placeholder(
             tf.float32, shape=[1, 1024, 1024, 3], name='input_images')
         self.output = {}
 
-        # detector
-        detector = model_resnet_mutex_v4_linewithchar.SegLinkDetector()
-        all_maps = detector.build_model(self.input_images, is_training=False)
-
-        # decode local predictions
-        all_nodes, all_links, all_reg = [], [], []
-        for i, maps in enumerate(all_maps):
-            cls_maps, lnk_maps, reg_maps = maps[0], maps[1], maps[2]
-            reg_maps = tf.multiply(reg_maps, OFFSET_VARIANCE)
-
-            cls_prob = tf.nn.softmax(tf.reshape(cls_maps, [-1, 2]))
-
-            lnk_prob_pos = tf.nn.softmax(tf.reshape(lnk_maps, [-1, 4])[:, :2])
-            lnk_prob_mut = tf.nn.softmax(tf.reshape(lnk_maps, [-1, 4])[:, 2:])
-            lnk_prob = tf.concat([lnk_prob_pos, lnk_prob_mut], axis=1)
-
-            all_nodes.append(cls_prob)
-            all_links.append(lnk_prob)
-            all_reg.append(reg_maps)
-
-        # decode segments and links
-        image_size = tf.shape(self.input_images)[1:3]
-        segments, group_indices, segment_counts, _ = ops.decode_segments_links_python(
-            image_size,
-            all_nodes,
-            all_links,
-            all_reg,
-            anchor_sizes=list(detector.anchor_sizes))
-
-        # combine segments
-        combined_rboxes, combined_counts = ops.combine_segments_python(
-            segments, group_indices, segment_counts)
-        self.output['combined_rboxes'] = combined_rboxes
-        self.output['combined_counts'] = combined_counts
+        with tf.variable_scope('', reuse=tf.AUTO_REUSE):
+            global_step = tf.get_variable(
+                'global_step', [],
+                initializer=tf.constant_initializer(0),
+                dtype=tf.int64,
+                trainable=False)
+            variable_averages = tf.train.ExponentialMovingAverage(
+                0.997, global_step)
+
+            # detector
+            detector = model_resnet_mutex_v4_linewithchar.SegLinkDetector()
+            all_maps = detector.build_model(
+                self.input_images, is_training=False)
+
+            # decode local predictions
+            all_nodes, all_links, all_reg = [], [], []
+            for i, maps in enumerate(all_maps):
+                cls_maps, lnk_maps, reg_maps = maps[0], maps[1], maps[2]
+                reg_maps = tf.multiply(reg_maps, OFFSET_VARIANCE)
+
+                cls_prob = tf.nn.softmax(tf.reshape(cls_maps, [-1, 2]))
+
+                lnk_prob_pos = tf.nn.softmax(
+                    tf.reshape(lnk_maps, [-1, 4])[:, :2])
+                lnk_prob_mut = tf.nn.softmax(
+                    tf.reshape(lnk_maps, [-1, 4])[:, 2:])
+                lnk_prob = tf.concat([lnk_prob_pos, lnk_prob_mut], axis=1)
+
+                all_nodes.append(cls_prob)
+                all_links.append(lnk_prob)
+                all_reg.append(reg_maps)
+
+            # decode segments and links
+            image_size = tf.shape(self.input_images)[1:3]
+            segments, group_indices, segment_counts, _ = ops.decode_segments_links_python(
+                image_size,
+                all_nodes,
+                all_links,
+                all_reg,
+                anchor_sizes=list(detector.anchor_sizes))
+
+            # combine segments
+            combined_rboxes, combined_counts = ops.combine_segments_python(
+                segments, group_indices, segment_counts)
+            self.output['combined_rboxes'] = combined_rboxes
+            self.output['combined_counts'] = combined_counts
 
         with self._session.as_default() as sess:
             logger.info(f'loading model from {model_path}')

modelscope/pipelines/cv/ocr_utils/model_resnet_mutex_v4_linewithchar.py

@@ -1,8 +1,12 @@
 import tensorflow as tf
-import tf_slim as slim
 
 from . import ops, resnet18_v1, resnet_utils
 
+if tf.__version__ >= '2.0':
+    import tf_slim as slim
+else:
+    from tensorflow.contrib import slim
+
 if tf.__version__ >= '2.0':
     tf = tf.compat.v1
 

modelscope/pipelines/cv/ocr_utils/resnet18_v1.py

@@ -30,10 +30,14 @@ ResNet-101 for semantic segmentation into 21 classes:
                                                 output_stride=16)
 """
 import tensorflow as tf
-import tf_slim as slim
 
 from . import resnet_utils
 
+if tf.__version__ >= '2.0':
+    import tf_slim as slim
+else:
+    from tensorflow.contrib import slim
+
 if tf.__version__ >= '2.0':
     tf = tf.compat.v1
 

modelscope/pipelines/cv/ocr_utils/resnet_utils.py

@@ -19,7 +19,11 @@ implementation is more memory efficient.
 import collections
 
 import tensorflow as tf
-import tf_slim as slim
+
+if tf.__version__ >= '2.0':
+    import tf_slim as slim
+else:
+    from tensorflow.contrib import slim
 
 if tf.__version__ >= '2.0':
     tf = tf.compat.v1

tests/pipelines/test_ocr_detection.py

@@ -27,6 +27,11 @@ class OCRDetectionTest(unittest.TestCase):
         print('ocr detection results: ')
         print(result)
 
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_run_with_model_from_modelhub(self):
+        ocr_detection = pipeline(Tasks.ocr_detection, model=self.model_id)
+        self.pipeline_inference(ocr_detection, self.test_image)
+
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_modelhub_default_model(self):
         ocr_detection = pipeline(Tasks.ocr_detection)