Support mini-batch in Pipeline mode

python/examples/pipeline/ocr/web_service.py

@@ -79,6 +79,9 @@ class RecOp(Op):
         feed_list = []
         img_list = []
         max_wh_ratio = 0
+
+        ## One batch, the type of feed_data is dict.
+        """ 
         for i, dtbox in enumerate(dt_boxes):
             boximg = self.get_rotate_crop_image(im, dt_boxes[i])
             img_list.append(boximg)
@@ -92,14 +95,73 @@ class RecOp(Op):
             norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
             imgs[id] = norm_img
         feed = {"image": imgs.copy()}
-        return feed, False, None, ""
 
-    def postprocess(self, input_dicts, fetch_dict, log_id):
-        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": str(res_lst)}
+        """
+
+        ## Many mini-batchs, the type of feed_data is list.
+        max_batch_size = 6  # len(dt_boxes)
+
+        # If max_batch_size is 0, skipping predict stage
+        if max_batch_size == 0:
+            return {}, True, None, ""
+        boxes_size = len(dt_boxes)
+        batch_size = boxes_size // max_batch_size
+        rem = boxes_size % max_batch_size
+        #_LOGGER.info("max_batch_len:{}, batch_size:{}, rem:{}, boxes_size:{}".format(max_batch_size, batch_size, rem, boxes_size))
+        for bt_idx in range(0, batch_size + 1):
+            imgs = None
+            boxes_num_in_one_batch = 0
+            if bt_idx == batch_size:
+                if rem == 0:
+                    continue
+                else:
+                    boxes_num_in_one_batch = rem
+            elif bt_idx < batch_size:
+                boxes_num_in_one_batch = max_batch_size
+            else:
+                _LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
+                              format(bt_idx, batch_size))
+                break
+
+            start = bt_idx * max_batch_size
+            end = start + boxes_num_in_one_batch
+            img_list = []
+            for box_idx in range(start, end):
+                boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
+                img_list.append(boximg)
+                h, w = boximg.shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
+                                                      max_wh_ratio).shape
+            #_LOGGER.info("---- idx:{}, w:{}, h:{}".format(bt_idx, w, h))
+
+            imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
+            for id, img in enumerate(img_list):
+                norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+                imgs[id] = norm_img
+            feed = {"image": imgs.copy()}
+            feed_list.append(feed)
+        #_LOGGER.info("feed_list : {}".format(feed_list))
+
+        return feed_list, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_data, log_id):
+        res_list = []
+        if isinstance(fetch_data, dict):
+            if len(fetch_data) > 0:
+                rec_batch_res = self.ocr_reader.postprocess(
+                    fetch_data, with_score=True)
+                for res in rec_batch_res:
+                    res_list.append(res[0])
+        elif isinstance(fetch_data, list):
+            for one_batch in fetch_data:
+                one_batch_res = self.ocr_reader.postprocess(
+                    one_batch, with_score=True)
+                for res in one_batch_res:
+                    res_list.append(res[0])
+
+        res = {"res": str(res_list)}
         return res, None, ""