fix

deploy/python/ppshitu_v2/configs/test_cls_config.yaml

@@ -27,10 +27,10 @@ Modules:
       - name: PaddlePredictor
         type: predictor
         inference_model_dir: "./MobileNetV2_infer"
-        input_names:
-          inputs: image
-        output_names:
-          save_infer_model/scale_0.tmp_1: logits
+        to_model_names:
+          image: inputs
+        from_model_names:
+          logits: 0
       - name: TopK
         type: postprocessor
         k: 10

deploy/python/ppshitu_v2/configs/test_rec_config.yaml

@@ -26,9 +26,9 @@ Modules:
       - name: PaddlePredictor
         type: predictor
         inference_model_dir: models/product_ResNet50_vd_aliproduct_v1.0_infer
-        input_names:
-          x: image
-        output_names:
-          save_infer_model/scale_0.tmp_1: features
+        to_model_names:
+          image: x
+        from_model_names:
+          features: 0
       - name: FeatureNormalizer
         type: postprocessor
\ No newline at end of file

deploy/python/ppshitu_v2/examples/predict.py

@@ -20,14 +20,20 @@ def main():
     input_data = {"input_image": img}
     data = engine.process(input_data)
 
-    # for det, cls
-    # print(data)
-
+    # for cls
+    if "classification_res" in data:
+        print(data["classification_res"])
+    # for det
+    elif "detection_res" in data:
+        print(data["detection_res"])
     # for rec
-    # features = data["pred"]["features"]
-    # print(features)
-    # print(features.shape)
-    # print(type(features))
+    elif "features" in data["pred"]:
+        features = data["pred"]["features"]
+        print(features)
+        print(features.shape)
+        print(type(features))
+    else:
+        print("ERROR")
 
 
 if __name__ == '__main__':

deploy/python/ppshitu_v2/processor/algo_mod/__init__.py

-# from .postprocessor import build_postprocessor
-# from .preprocessor import build_preprocessor
-# from .predictor import build_predictor
-
-import importlib
-
-from processor.algo_mod import preprocessor
-from processor.algo_mod import predictor
-from processor.algo_mod import postprocessor
-from processor.algo_mod import searcher
+from .postprocessor import build_postprocessor
+from .preprocessor import build_preprocessor
+from .predictor import build_predictor
+from .searcher import build_searcher
 
 from ..base_processor import BaseProcessor
 
@@ -17,20 +11,18 @@ class AlgoMod(BaseProcessor):
         self.processors = []
         for processor_config in config["processors"]:
             processor_type = processor_config.get("type")
-            processor_name = processor_config.get("name")
-            _mod = importlib.import_module(__name__)
-            processor = getattr(
-                getattr(_mod, processor_type),
-                processor_name)(processor_config)
 
-            # if processor_type == "preprocessor":
-            #     processor = build_preprocessor(processor_config)
-            # elif processor_type == "predictor":
-            #     processor = build_predictor(processor_config)
-            # elif processor_type == "postprocessor":
-            #     processor = build_postprocessor(processor_config)
-            # else:
-            #     raise NotImplemented("processor type {} unknown.".format(processor_type))
+            if processor_type == "preprocessor":
+                processor = build_preprocessor(processor_config)
+            elif processor_type == "predictor":
+                processor = build_predictor(processor_config)
+            elif processor_type == "postprocessor":
+                processor = build_postprocessor(processor_config)
+            elif processor_type == "searcher":
+                processor = build_searcher(processor_config)
+            else:
+                raise NotImplemented("processor type {} unknown.".format(
+                    processor_type))
             self.processors.append(processor)
 
     def process(self, input_data):

deploy/python/ppshitu_v2/processor/algo_mod/postprocessor/__init__.py

@@ -4,7 +4,8 @@ from .classification import TopK
 from .det import DetPostPro
 from .rec import FeatureNormalizer
 
-# def build_postprocessor(config):
-#     processor_mod = importlib.import_module(__name__)
-#     processor_name = config.get("name")
-#     return getattr(processor_mod, processor_name)(config)
+
+def build_postprocessor(config):
+    processor_mod = importlib.import_module(__name__)
+    processor_name = config.get("name")
+    return getattr(processor_mod, processor_name)(config)

deploy/python/ppshitu_v2/processor/algo_mod/postprocessor/classification.py

@@ -2,6 +2,7 @@ import os
 
 import numpy as np
 
+from utils import logger
 from ...base_processor import BaseProcessor
 
 
@@ -20,8 +21,8 @@ class TopK(BaseProcessor):
             return None
 
         if not os.path.exists(class_id_map_file):
-            print(
-                "Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
+            logger.warning(
+                "[Classification] If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
             )
             return None
 
@@ -33,36 +34,31 @@ class TopK(BaseProcessor):
                     partition = line.split("\n")[0].partition(" ")
                     class_id_map[int(partition[0])] = str(partition[-1])
         except Exception as ex:
-            print(ex)
+            logger.warning(f"[Classification] {ex}")
             class_id_map = None
         return class_id_map
 
     def process(self, data):
-        x = data["pred"]["logits"]
-        # TODO(gaotingquan): support file_name
-        # if file_names is not None:
-        #     assert x.shape[0] == len(file_names)
-        y = []
-        for idx, probs in enumerate(x):
-            index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
-                "int32") if not self.multilabel else np.where(
-                    probs >= 0.5)[0].astype("int32")
-            clas_id_list = []
-            score_list = []
-            label_name_list = []
-            for i in index:
-                clas_id_list.append(i.item())
-                score_list.append(probs[i].item())
-                if self.class_id_map is not None:
-                    label_name_list.append(self.class_id_map[i.item()])
-            result = {
-                "class_ids": clas_id_list,
-                "scores": np.around(
-                    score_list, decimals=5).tolist(),
-            }
-            # if file_names is not None:
-            #     result["file_name"] = file_names[idx]
-            if label_name_list is not None:
-                result["label_names"] = label_name_list
-            y.append(result)
-        return y
+        # TODO(gaotingquan): only support bs==1 when 'connector' is not implemented.
+        probs = data["pred"]["logits"][0]
+        index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
+            "int32") if not self.multilabel else np.where(
+                probs >= 0.5)[0].astype("int32")
+        clas_id_list = []
+        score_list = []
+        label_name_list = []
+        for i in index:
+            clas_id_list.append(i.item())
+            score_list.append(probs[i].item())
+            if self.class_id_map is not None:
+                label_name_list.append(self.class_id_map[i.item()])
+        result = {
+            "class_ids": clas_id_list,
+            "scores": np.around(
+                score_list, decimals=5).tolist(),
+        }
+        if label_name_list is not None:
+            result["label_names"] = label_name_list
+
+        data["classification_res"] = result
+        return data

deploy/python/ppshitu_v2/processor/algo_mod/postprocessor/det.py

@@ -11,27 +11,34 @@ class DetPostPro(BaseProcessor):
         self.label_list = config["label_list"]
         self.max_det_results = config["max_det_results"]
 
-    def process(self, input_data):
-        pred = input_data["pred"]
+    def process(self, data):
+        pred = data["pred"]
         np_boxes = pred[list(pred.keys())[0]]
-        if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
-            logger.warning('[Detector] No object detected.')
-            np_boxes = np.array([])
-
-        keep_indexes = np_boxes[:, 1].argsort()[::-1][:self.max_det_results]
-        results = []
-        for idx in keep_indexes:
-            single_res = np_boxes[idx]
+        if reduce(lambda x, y: x * y, np_boxes.shape) >= 6:
+            keep_indexes = np_boxes[:, 1].argsort()[::-1][:
+                                                          self.max_det_results]
+            # TODO(gaotingquan): only support bs==1
+            single_res = np_boxes[0]
             class_id = int(single_res[0])
             score = single_res[1]
             bbox = single_res[2:]
-            if score < self.threshold:
-                continue
-            label_name = self.label_list[class_id]
-            results.append({
-                "class_id": class_id,
-                "score": score,
-                "bbox": bbox,
-                "label_name": label_name,
-            })
-        return results
+            if score > self.threshold:
+                label_name = self.label_list[class_id]
+                results = {
+                    "class_id": class_id,
+                    "score": score,
+                    "bbox": bbox,
+                    "label_name": label_name,
+                }
+                data["detection_res"] = results
+                return data
+
+        logger.warning('[Detector] No object detected.')
+        results = {
+            "class_id": None,
+            "score": None,
+            "bbox": None,
+            "label_name": None,
+        }
+        data["detection_res"] = results
+        return data

deploy/python/ppshitu_v2/processor/algo_mod/predictor/__init__.py

@@ -3,7 +3,8 @@ import importlib
 from processor.algo_mod.predictor.paddle_predictor import PaddlePredictor
 from processor.algo_mod.predictor.onnx_predictor import ONNXPredictor
 
-# def build_predictor(config):
-#     processor_mod = importlib.import_module(__name__)
-#     processor_name = config.get("name")
-#     return getattr(processor_mod, processor_name)(config)
+
+def build_predictor(config):
+    processor_mod = importlib.import_module(__name__)
+    processor_name = config.get("name")
+    return getattr(processor_mod, processor_name)(config)

deploy/python/ppshitu_v2/processor/algo_mod/predictor/paddle_predictor.py

@@ -48,30 +48,40 @@ class PaddlePredictor(BaseProcessor):
         paddle_config.switch_use_feed_fetch_ops(False)
         self.predictor = create_predictor(paddle_config)
 
-        if "input_names" in config and config["input_names"]:
-            self.input_name_mapping = config["input_names"]
+        if "to_model_names" in config and config["to_model_names"]:
+            self.input_name_map = {
+                v: k
+                for k, v in config["to_model_names"].items()
+            }
         else:
-            self.input_name_mapping = []
-        if "output_names" in config and config["output_names"]:
-            self.output_name_mapping = config["output_names"]
+            self.input_name_map = {}
+        if "from_model_names" in config and config["from_model_names"]:
+            self.output_name_map = config["from_model_names"]
         else:
-            self.output_name_mapping = []
+            self.output_name_map = {}
 
     def process(self, data):
         input_names = self.predictor.get_input_names()
         for input_name in input_names:
             input_tensor = self.predictor.get_input_handle(input_name)
-            name = self.input_name_mapping[
-                input_name] if input_name in self.input_name_mapping else input_name
+            name = self.input_name_map[
+                input_name] if input_name in self.input_name_map else input_name
             input_tensor.copy_from_cpu(data[name])
         self.predictor.run()
 
-        output_data = {}
+        model_output = []
         output_names = self.predictor.get_output_names()
         for output_name in output_names:
             output = self.predictor.get_output_handle(output_name)
-            name = self.output_name_mapping[
-                output_name] if output_name in self.output_name_mapping else output_name
-            output_data[name] = output.copy_to_cpu()
+            model_output.append((output_name, output.copy_to_cpu()))
+
+        if self.output_name_map:
+            output_data = {}
+            for name in self.output_name_map:
+                idx = self.output_name_map[name]
+                output_data[name] = model_output[idx][1]
+        else:
+            output_data = dict(model_output)
+
         data["pred"] = output_data
         return data

deploy/python/ppshitu_v2/processor/algo_mod/preprocessor/__init__.py

@@ -2,7 +2,8 @@ import importlib
 
 from processor.algo_mod.preprocessor.image_processor import ImageProcessor
 
-# def build_preprocessor(config):
-#     processor_mod = importlib.import_module(__name__)
-#     processor_name = config.get("name")
-#     return getattr(processor_mod, processor_name)(config)
+
+def build_preprocessor(config):
+    processor_mod = importlib.import_module(__name__)
+    processor_name = config.get("name")
+    return getattr(processor_mod, processor_name)(config)

deploy/python/ppshitu_v2/processor/algo_mod/searcher/__init__.py

@@ -4,11 +4,15 @@ import pickle
 import faiss
 
 
+def build_searcher(config):
+    return Searcher(config)
+
+
 class Searcher:
     def __init__(self, config):
         super().__init__()
 
-        self.Searcher = faiss.read_index(
+        self.faiss_searcher = faiss.read_index(
             os.path.join(config["index_dir"], "vector.index"))
 
         with open(os.path.join(config["index_dir"], "id_map.pkl"), "rb") as fd:
@@ -18,6 +22,11 @@ class Searcher:
 
     def process(self, data):
         features = data["features"]
-        scores, docs = self.Searcher.search(features, self.return_k)
-        data["search_res"] = (scores, docs)
+        scores, docs = self.faiss_searcher.search(features, self.return_k)
+
+        preds = {}
+        preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
+        preds["rec_scores"] = scores[0][0]
+
+        data["search_res"] = preds
         return data