Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleClas into slim

deploy/configs/build_cartoon.yaml

@@ -28,11 +28,11 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_cartoon/index/"
-  image_root: "./recognition_demo_data_v1.0/gallery_cartoon/"
-  data_file:  "./recognition_demo_data_v1.0/gallery_cartoon/data_file.txt"
-  append_index: False
+  index_method: "HNSW32" # supported: HNSW32, IVF, Flat
+  index_dir: "./recognition_demo_data_v1.1/gallery_cartoon/index/"
+  image_root: "./recognition_demo_data_v1.1/gallery_cartoon/"
+  data_file:  "./recognition_demo_data_v1.1/gallery_cartoon/data_file.txt"
+  index_operation: "new" # suported: "append", "remove", "new"
   delimiter: "\t"
   dist_type: "IP"
-  pq_size: 100
   embedding_size: 2048

deploy/configs/build_logo.yaml

@@ -26,11 +26,11 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_logo/index/"
-  image_root: "./recognition_demo_data_v1.0/gallery_logo/"
-  data_file:  "./recognition_demo_data_v1.0/gallery_logo/data_file.txt"
-  append_index: False
+  index_method: "HNSW32" # supported: HNSW32, IVF, Flat
+  index_dir: "./recognition_demo_data_v1.1/gallery_logo/index/"
+  image_root: "./recognition_demo_data_v1.1/gallery_logo/"
+  data_file:  "./recognition_demo_data_v1.1/gallery_logo/data_file.txt"
+  index_operation: "new" # suported: "append", "remove", "new"
   delimiter: "\t"
   dist_type: "IP"
-  pq_size: 100
   embedding_size: 512

deploy/configs/build_product.yaml

@@ -26,11 +26,11 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_product/index"
-  image_root: "./recognition_demo_data_v1.0/gallery_product/"
-  data_file:  "./recognition_demo_data_v1.0/gallery_product/data_file.txt"
-  append_index: False
+  index_method: "HNSW32" # supported: HNSW32, IVF, Flat
+  index_dir: "./recognition_demo_data_v1.1/gallery_product/index"
+  image_root: "./recognition_demo_data_v1.1/gallery_product/"
+  data_file:  "./recognition_demo_data_v1.1/gallery_product/data_file.txt"
+  index_operation: "new" # suported: "append", "remove", "new"
   delimiter: "\t"
   dist_type: "IP"
-  pq_size: 100
   embedding_size: 512

deploy/configs/build_vehicle.yaml

@@ -26,11 +26,11 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_vehicle/index/"
-  image_root: "./recognition_demo_data_v1.0/gallery_vehicle/"
-  data_file:  "./recognition_demo_data_v1.0/gallery_vehicle/data_file.txt"
-  append_index: False
+  index_method: "HNSW32" # supported: HNSW32, IVF, Flat
+  index_dir: "./recognition_demo_data_v1.1/gallery_vehicle/index/"
+  image_root: "./recognition_demo_data_v1.1/gallery_vehicle/"
+  data_file:  "./recognition_demo_data_v1.1/gallery_vehicle/data_file.txt"
+  index_operation: "new" # suported: "append", "remove", "new"
   delimiter: "\t"
   dist_type: "IP"
-  pq_size: 100
   embedding_size: 512

deploy/configs/inference_cartoon.yaml

 Global:
-  infer_imgs: "./recognition_demo_data_v1.0/test_cartoon"
+  infer_imgs: "./recognition_demo_data_v1.1/test_cartoon"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/cartoon_rec_ResNet50_iCartoon_v1.0_infer/"
   rec_nms_thresold: 0.05
@@ -51,8 +51,6 @@ RecPreProcess:
 RecPostProcess: null
 
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_cartoon/index/"
-  search_budget: 100
+  index_dir: "./recognition_demo_data_v1.1/gallery_cartoon/index/"
   return_k: 5
-  dist_type: "IP"
   score_thres: 0.5

deploy/configs/inference_logo.yaml

 Global:
-  infer_imgs: "./recognition_demo_data_v1.0/test_logo"
+  infer_imgs: "./recognition_demo_data_v1.1/test_logo"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/logo_rec_ResNet50_Logo3K_v1.0_infer/"
   rec_nms_thresold: 0.05
@@ -50,8 +50,6 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_logo/index/"
-  search_budget: 100
+  index_dir: "./recognition_demo_data_v1.1/gallery_logo/index/"
   return_k: 5
-  dist_type: "IP"
   score_thres: 0.5

deploy/configs/inference_product.yaml

 Global:
-  infer_imgs: "./recognition_demo_data_v1.0/test_product/daoxiangcunjinzhubing_6.jpg"
+  infer_imgs: "./recognition_demo_data_v1.1/test_product/daoxiangcunjinzhubing_6.jpg"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer"
   rec_inference_model_dir: "./models/product_ResNet50_vd_aliproduct_v1.0_infer"
   rec_nms_thresold: 0.05
@@ -50,8 +50,6 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-  index_path: "./recognition_demo_data_v1.0/gallery_product/index"
-  search_budget: 100
+  index_dir: "./recognition_demo_data_v1.1/gallery_product/index"
   return_k: 5
-  dist_type: "IP"
   score_thres: 0.5

deploy/configs/inference_vehicle.yaml

 Global:
-  infer_imgs: "./recognition_demo_data_v1.0/test_vehicle/"
+  infer_imgs: "./recognition_demo_data_v1.1/test_vehicle/"
   det_inference_model_dir: "./models/ppyolov2_r50vd_dcn_mainbody_v1.0_infer/"
   rec_inference_model_dir: "./models/vehicle_cls_ResNet50_CompCars_v1.0_infer/"
   rec_nms_thresold: 0.05
@@ -52,8 +52,6 @@ RecPostProcess: null
 
 # indexing engine config
 IndexProcess:
-    index_path: "./recognition_demo_data_v1.0/gallery_vehicle/index/"
-    search_budget: 100
+    index_dir: "./recognition_demo_data_v1.1/gallery_vehicle/index/"
     return_k: 5
-    dist_type: "IP"
     score_thres: 0.5

deploy/python/build_gallery.py

@@ -17,13 +17,13 @@ import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
 
-import copy
 import cv2
+import faiss
 import numpy as np
 from tqdm import tqdm
+import pickle
 
 from python.predict_rec import RecPredictor
-from vector_search import Graph_Index
 
 from utils import logger
 from utils import config
@@ -31,9 +31,9 @@ from utils import config
 
 def split_datafile(data_file, image_root, delimiter="\t"):
     '''
-        data_file: image path and info, which can be splitted by spacer 
+        data_file: image path and info, which can be splitted by spacer
         image_root: image path root
-        delimiter: delimiter 
+        delimiter: delimiter
     '''
     gallery_images = []
     gallery_docs = []
@@ -45,9 +45,8 @@ def split_datafile(data_file, image_root, delimiter="\t"):
             assert text_num >= 2, f"line({ori_line}) must be splitted into at least 2 parts, but got {text_num}"
             image_file = os.path.join(image_root, line[0])
 
-            image_doc = line[1]
             gallery_images.append(image_file)
-            gallery_docs.append(image_doc)
+            gallery_docs.append(ori_line.strip())
 
     return gallery_images, gallery_docs
 
@@ -64,9 +63,91 @@ class GalleryBuilder(object):
         '''
             build index from scratch
         '''
+        operation_method = config.get("index_operation", "new").lower()
+
         gallery_images, gallery_docs = split_datafile(
             config['data_file'], config['image_root'], config['delimiter'])
 
+        # when remove data in index, do not need extract fatures
+        if operation_method != "remove":
+            gallery_features = self._extract_features(gallery_images, config)
+
+        assert operation_method in [
+            "new", "remove", "append"
+        ], "Only append, remove and new operation are supported"
+
+        # vector.index: faiss index file
+        # id_map.pkl: use this file to map id to image_doc
+        if operation_method in ["remove", "append"]:
+            # if remove or append, vector.index and id_map.pkl must exist
+            assert os.path.join(
+                config["index_dir"], "vector.index"
+            ), "The vector.index dose not exist in {} when 'index_operation' is not None".format(
+                config["index_dir"])
+            assert os.path.join(
+                config["index_dir"], "id_map.pkl"
+            ), "The id_map.pkl dose not exist in {} when 'index_operation' is not None".format(
+                config["index_dir"])
+            index = 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:
+                ids = pickle.load(fd)
+            assert index.ntotal == len(ids.keys(
+            )), "data number in index is not equal in in id_map"
+        else:
+            if not os.path.exists(config["index_dir"]):
+                os.makedirs(config["index_dir"], exist_ok=True)
+            index_method = config.get("index_method", "HNSW32")
+
+            # if IVF method, cal ivf number automaticlly
+            if index_method == "IVF":
+                index_method = index_method + str(
+                    min(int(len(gallery_images) // 8), 65536)) + ",Flat"
+            dist_type = faiss.METRIC_INNER_PRODUCT if config[
+                "dist_type"] == "IP" else faiss.METRIC_L2
+            index = faiss.index_factory(config["embedding_size"], index_method,
+                                        dist_type)
+            index = faiss.IndexIDMap2(index)
+            ids = {}
+
+        if config["index_method"] == "HNSW32":
+            logger.warning(
+                "The HNSW32 method dose not support 'remove' operation")
+
+        if operation_method != "remove":
+            # calculate id for new data
+            start_id = max(ids.keys()) + 1 if ids else 0
+            ids_now = (
+                np.arange(0, len(gallery_images)) + start_id).astype(np.int64)
+
+            # only train when new index file
+            if operation_method == "new":
+                index.train(gallery_features)
+            index.add_with_ids(gallery_features, ids_now)
+
+            for i, d in zip(list(ids_now), gallery_docs):
+                ids[i] = d
+        else:
+            if config["index_method"] == "HNSW32":
+                raise RuntimeError(
+                    "The index_method: HNSW32 dose not support 'remove' operation"
+                )
+            # remove ids in id_map, remove index data in faiss index
+            remove_ids = list(
+                filter(lambda k: ids.get(k) in gallery_docs, ids.keys()))
+            remove_ids = np.asarray(remove_ids)
+            index.remove_ids(remove_ids)
+            for k in remove_ids:
+                del ids[k]
+
+        # store faiss index file and id_map file
+        faiss.write_index(index,
+                          os.path.join(config["index_dir"], "vector.index"))
+        with open(os.path.join(config["index_dir"], "id_map.pkl"), 'wb') as fd:
+            pickle.dump(ids, fd)
+
+    def _extract_features(self, gallery_images, config):
         # extract gallery features
         gallery_features = np.zeros(
             [len(gallery_images), config['embedding_size']], dtype=np.float32)
@@ -91,19 +172,11 @@ class GalleryBuilder(object):
             rec_feat = self.rec_predictor.predict(batch_img)
             gallery_features[-len(batch_img):, :] = rec_feat
             batch_img = []
-
-        # train index 
-        self.Searcher = Graph_Index(dist_type=config['dist_type'])
-        self.Searcher.build(
-            gallery_vectors=gallery_features,
-            gallery_docs=gallery_docs,
-            pq_size=config['pq_size'],
-            index_path=config['index_path'],
-            append_index=config["append_index"])
+        return gallery_features
 
 
 def main(config):
-    system_builder = GalleryBuilder(config)
+    GalleryBuilder(config)
     return
 
 

deploy/python/predict_system.py

 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
+# 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
 #
@@ -20,10 +20,11 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
 import copy
 import cv2
 import numpy as np
+import faiss
+import pickle
 
 from python.predict_rec import RecPredictor
 from python.predict_det import DetPredictor
-from vector_search import Graph_Index
 
 from utils import logger
 from utils import config
@@ -40,11 +41,16 @@ class SystemPredictor(object):
 
         assert 'IndexProcess' in config.keys(), "Index config not found ... "
         self.return_k = self.config['IndexProcess']['return_k']
-        self.search_budget = self.config['IndexProcess']['search_budget']
 
-        self.Searcher = Graph_Index(
-            dist_type=config['IndexProcess']['dist_type'])
-        self.Searcher.load(config['IndexProcess']['index_path'])
+        index_dir = self.config["IndexProcess"]["index_dir"]
+        assert os.path.exists(os.path.join(
+            index_dir, "vector.index")), "vector.index not found ..."
+        assert os.path.exists(os.path.join(
+            index_dir, "id_map.pkl")), "id_map.pkl not found ... "
+        self.Searcher = faiss.read_index(
+            os.path.join(index_dir, "vector.index"))
+        with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
+            self.id_map = pickle.load(fd)
 
     def append_self(self, results, shape):
         results.append({
@@ -98,14 +104,11 @@ class SystemPredictor(object):
             crop_img = img[ymin:ymax, xmin:xmax, :].copy()
             rec_results = self.rec_predictor.predict(crop_img)
             preds["bbox"] = [xmin, ymin, xmax, ymax]
-            scores, docs = self.Searcher.search(
-                query=rec_results,
-                return_k=self.return_k,
-                search_budget=self.search_budget)
+            scores, docs = self.Searcher.search(rec_results, self.return_k)
             # just top-1 result will be returned for the final
-            if scores[0] >= self.config["IndexProcess"]["score_thres"]:
-                preds["rec_docs"] = docs[0]
-                preds["rec_scores"] = scores[0]
+            if scores[0][0] >= self.config["IndexProcess"]["score_thres"]:
+                preds["rec_docs"] = self.id_map[docs[0][0]].split()[1]
+                preds["rec_scores"] = scores[0][0]
                 output.append(preds)
 
         # st5: nms to the final results to avoid fetching duplicate results

deploy/vector_search/README.md

 # 向量检索
 
-
+**注意**：由于系统适配性问题，在新版本中，此检索算法将被废弃。新版本中将使用[faiss](https://github.com/facebookresearch/faiss)，整体检索的过程保持不变，但建立索引及检索时的yaml文件有所修改。
 ## 1. 简介
 
 一些垂域识别任务（如车辆、商品等）需要识别的类别数较大，往往采用基于检索的方式，通过查询向量与底库向量进行快速的最近邻搜索，获得匹配的预测类别。向量检索模块提供基础的近似最近邻搜索算法，基于百度自研的Möbius算法，一种基于图的近似最近邻搜索算法，用于最大内积搜索 (MIPS)。 该模块提供python接口，支持numpy和 tensor类型向量,支持L2和Inner Product距离计算。

deploy/vector_search/README_en.md

 # Vector search
 
+**Attention**: Due to the system adaptability problem, this retrieval algorithm will be abandoned in the new version. [faiss](https://github.com/facebookresearch/faiss) will be used in the new version. The use process of the overall retrieval system base will remain unchanged, but the yaml files for build indexes and retrieval will be modified.
+
 ## 1. Introduction
 
 Some vertical domain recognition tasks (e.g., vehicles, commodities, etc.) require a large number of recognized categories, and often use a retrieval-based approach to obtain matching predicted categories by performing a fast nearest neighbor search with query vectors and underlying library vectors. The vector search module provides the basic approximate nearest neighbor search algorithm based on Baidu's self-developed Möbius algorithm, a graph-based approximate nearest neighbor search algorithm for maximum inner product search (MIPS). This module provides python interface, supports numpy and tensor type vectors, and supports L2 and Inner Product distance calculation.
@@ -57,7 +59,7 @@ brew install gcc
 1. If prompted with `Error: Running Homebrew as root is extremely dangerous and no longer supported... `, refer to this [link](https://jingyan.baidu.com/article/e52e3615057a2840c60c519c.html)
 2.  If prompted with `Error: Failure while executing; tar --extract --no-same-owner --file... `, refer to this [link](https://blog.csdn.net/Dawn510/article/details/117787358).
 
-After installation the compiled executable is copied under /usr/local/bin, look at the gcc in this folder: 
+After installation the compiled executable is copied under /usr/local/bin, look at the gcc in this folder:
 
 ```
 ls /usr/local/bin/gcc*

docs/en/tutorials/quick_start_recognition_en.md

@@ -40,11 +40,11 @@ The detection model with the recognition inference model for the 4 directions (L
 | Logo Recognition Model | Logo Scenario  |  [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/logo_rec_ResNet50_Logo3K_v1.0_infer.tar) | [inference_logo.yaml](../../../deploy/configs/inference_logo.yaml) | [build_logo.yaml](../../../deploy/configs/build_logo.yaml) |
 | Cartoon Face Recognition Model| Cartoon Face Scenario  | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/cartoon_rec_ResNet50_iCartoon_v1.0_infer.tar) | [inference_cartoon.yaml](../../../deploy/configs/inference_cartoon.yaml) | [build_cartoon.yaml](../../../deploy/configs/build_cartoon.yaml) |
 | Vehicle Fine-Grained Classfication Model | Vehicle Scenario  |   [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_cls_ResNet50_CompCars_v1.0_infer.tar) | [inference_vehicle.yaml](../../../deploy/configs/inference_vehicle.yaml) | [build_vehicle.yaml](../../../deploy/configs/build_vehicle.yaml) |
-| Product Recignition Model | Product Scenario  |  [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_Inshop_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
+| Product Recignition Model | Product Scenario  |  [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/product_ResNet50_vd_aliproduct_v1.0_infer.tar) | [inference_product.yaml](../../../deploy/configs/inference_product.yaml) | [build_product.yaml](../../../deploy/configs/build_product.yaml) |
 | Vehicle ReID Model | Vehicle ReID Scenario | [Model Download Link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
 
 
-Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.0.tar).
+Demo data in this tutorial can be downloaded here: [download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_en_v1.1.tar).
 
 
 **Attention**

docs/zh_CN/tutorials/quick_start_recognition.md

@@ -44,7 +44,7 @@
 | 车辆ReID模型 | 车辆ReID场景 | [模型下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/vehicle_reid_ResNet50_VERIWild_v1.0_infer.tar) | - | - |
 
 
-本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.0.tar)。
+本章节demo数据下载地址如下: [数据下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/recognition_demo_data_v1.1.tar)。
 
 
 **注意**

hubconf.py

@@ -41,10 +41,15 @@ class _SysPathG(object):
             self.path)
 
 
-with _SysPathG(
-        os.path.join(
-            os.path.dirname(os.path.abspath(__file__)), 'ppcls', 'arch')):
-    import backbone
+with _SysPathG(os.path.dirname(os.path.abspath(__file__)), ):
+    import ppcls
+    import ppcls.arch.backbone as backbone
+
+    def ppclas_init():
+        if ppcls.utils.logger._logger is None:
+            ppcls.utils.logger.init_logger()
+
+    ppclas_init()
 
     def _load_pretrained_parameters(model, name):
         url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format(
@@ -63,9 +68,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `AlexNet` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.AlexNet(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'AlexNet')
 
         return model
 
@@ -80,9 +84,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `VGG11` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.VGG11(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'VGG11')
 
         return model
 
@@ -97,9 +100,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `VGG13` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.VGG13(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'VGG13')
 
         return model
 
@@ -114,9 +116,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `VGG16` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.VGG16(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'VGG16')
 
         return model
 
@@ -131,9 +132,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `VGG19` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.VGG19(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'VGG19')
 
         return model
 
@@ -149,9 +149,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNet18` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNet18(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNet18')
 
         return model
 
@@ -167,9 +166,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNet34` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNet34(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNet34')
 
         return model
 
@@ -185,9 +183,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNet50` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNet50(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNet50')
 
         return model
 
@@ -203,9 +200,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNet101` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNet101(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNet101')
 
         return model
 
@@ -221,9 +217,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNet152` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNet152(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNet152')
 
         return model
 
@@ -237,9 +232,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `SqueezeNet1_0` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.SqueezeNet1_0(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'SqueezeNet1_0')
 
         return model
 
@@ -253,9 +247,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `SqueezeNet1_1` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.SqueezeNet1_1(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'SqueezeNet1_1')
 
         return model
 
@@ -271,9 +264,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `DenseNet121` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DenseNet121(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DenseNet121')
 
         return model
 
@@ -289,9 +281,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `DenseNet161` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DenseNet161(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DenseNet161')
 
         return model
 
@@ -307,9 +298,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `DenseNet169` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DenseNet169(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DenseNet169')
 
         return model
 
@@ -325,9 +315,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `DenseNet201` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DenseNet201(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DenseNet201')
 
         return model
 
@@ -343,9 +332,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `DenseNet264` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DenseNet264(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DenseNet264')
 
         return model
 
@@ -359,9 +347,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `InceptionV3` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.InceptionV3(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'InceptionV3')
 
         return model
 
@@ -375,9 +362,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `InceptionV4` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.InceptionV4(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'InceptionV4')
 
         return model
 
@@ -391,9 +377,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `GoogLeNet` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.GoogLeNet(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'GoogLeNet')
 
         return model
 
@@ -407,9 +392,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ShuffleNetV2_x0_25` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ShuffleNetV2_x0_25(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ShuffleNetV2_x0_25')
 
         return model
 
@@ -423,9 +407,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV1` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV1(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV1')
 
         return model
 
@@ -439,9 +422,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV1_x0_25(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV1_x0_25')
 
         return model
 
@@ -455,9 +437,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV1_x0_5(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV1_x0_5')
 
         return model
 
@@ -471,9 +452,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV1_x0_75(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV1_x0_75')
 
         return model
 
@@ -487,9 +467,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV2_x0_25` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV2_x0_25(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV2_x0_25')
 
         return model
 
@@ -503,9 +482,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV2_x0_5` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV2_x0_5(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV2_x0_5')
 
         return model
 
@@ -519,9 +497,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV2_x0_75` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV2_x0_75(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV2_x0_75')
 
         return model
 
@@ -535,9 +512,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV2_x1_5` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV2_x1_5(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV2_x1_5')
 
         return model
 
@@ -551,9 +527,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV2_x2_0` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV2_x2_0(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'MobileNetV2_x2_0')
 
         return model
 
@@ -567,10 +542,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_large_x0_35` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_large_x0_35(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_large_x0_35')
 
         return model
 
@@ -584,10 +557,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_large_x0_5` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_large_x0_5(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_large_x0_5')
 
         return model
 
@@ -601,10 +572,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_large_x0_75` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_large_x0_75(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_large_x0_75')
 
         return model
 
@@ -618,10 +587,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_large_x1_0` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_large_x1_0(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_large_x1_0')
 
         return model
 
@@ -635,10 +602,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_large_x1_25` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_large_x1_25(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_large_x1_25')
 
         return model
 
@@ -652,10 +617,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_small_x0_35` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_small_x0_35(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_small_x0_35')
 
         return model
 
@@ -669,10 +632,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_small_x0_5` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_small_x0_5(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_small_x0_5')
 
         return model
 
@@ -686,10 +647,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_small_x0_75` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_small_x0_75(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_small_x0_75')
 
         return model
 
@@ -703,10 +662,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_small_x1_0` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_small_x1_0(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_small_x1_0')
 
         return model
 
@@ -720,10 +677,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `MobileNetV3_small_x1_25` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.MobileNetV3_small_x1_25(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model,
-                                                'MobileNetV3_small_x1_25')
 
         return model
 
@@ -737,9 +692,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt101_32x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt101_32x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt101_32x4d')
 
         return model
 
@@ -753,9 +707,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt101_64x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt101_64x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt101_64x4d')
 
         return model
 
@@ -769,9 +722,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt152_32x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt152_32x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt152_32x4d')
 
         return model
 
@@ -785,9 +737,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt152_64x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt152_64x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt152_64x4d')
 
         return model
 
@@ -801,9 +752,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt50_32x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt50_32x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt50_32x4d')
 
         return model
 
@@ -817,9 +767,8 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt50_64x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.ResNeXt50_64x4d(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'ResNeXt50_64x4d')
 
         return model
 
@@ -833,8 +782,7 @@ with _SysPathG(
         Returns:
             model: nn.Layer. Specific `ResNeXt50_64x4d` model depends on args.
         """
+        kwargs.update({'pretrained': pretrained})
         model = backbone.DarkNet53(**kwargs)
-        if pretrained:
-            model = _load_pretrained_parameters(model, 'DarkNet53')
 
         return model

ppcls/arch/backbone/__init__.py

@@ -58,6 +58,7 @@ from ppcls.arch.backbone.model_zoo.rednet import RedNet26, RedNet38, RedNet50, R
 from ppcls.arch.backbone.model_zoo.tnt import TNT_small
 from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
 from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
+from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
 
 
 def get_apis():

ppcls/arch/backbone/variant_models/__init__.py

 from .resnet_variant import ResNet50_last_stage_stride1
+from .vgg_variant import VGG19Sigmoid

ppcls/arch/backbone/variant_models/vgg_variant.py

+import paddle
+from paddle.nn import Sigmoid
+from ppcls.arch.backbone.legendary_models.vgg import VGG19
+ 
+__all__ = ["VGG19Sigmoid"]
+ 
+ 
+class SigmoidSuffix(paddle.nn.Layer):
+    def __init__(self, origin_layer):
+        super(SigmoidSuffix, self).__init__()
+        self.origin_layer = origin_layer
+        self.sigmoid = Sigmoid()
+ 
+    def forward(self, input, res_dict=None, **kwargs):
+        x = self.origin_layer(input)
+        x = self.sigmoid(x)
+        return x
+ 
+ 
+def VGG19Sigmoid(pretrained=False, use_ssld=False, **kwargs):
+    def replace_function(origin_layer):
+        new_layer = SigmoidSuffix(origin_layer)
+        return new_layer
+ 
+    match_re = "linear_2"
+    model = VGG19(pretrained=pretrained, use_ssld=use_ssld, **kwargs)
+    model.replace_sub(match_re, replace_function, True)
+    return model

ppcls/arch/gears/circlemargin.py

@@ -28,7 +28,7 @@ class CircleMargin(nn.Layer):
 
         weight_attr = paddle.ParamAttr(
             initializer=paddle.nn.initializer.XavierNormal())
-        self.fc0 = paddle.nn.Linear(
+        self.fc = paddle.nn.Linear(
             self.embedding_size, self.class_num, weight_attr=weight_attr)
 
     def forward(self, input, label):
@@ -36,19 +36,22 @@ class CircleMargin(nn.Layer):
             paddle.sum(paddle.square(input), axis=1, keepdim=True))
         input = paddle.divide(input, feat_norm)
 
-        weight = self.fc0.weight
+        weight = self.fc.weight
         weight_norm = paddle.sqrt(
             paddle.sum(paddle.square(weight), axis=0, keepdim=True))
         weight = paddle.divide(weight, weight_norm)
 
         logits = paddle.matmul(input, weight)
+        if not self.training or label is None:
+            return logits
 
         alpha_p = paddle.clip(-logits.detach() + 1 + self.margin, min=0.)
         alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
         delta_p = 1 - self.margin
         delta_n = self.margin
-        index = paddle.fluid.layers.where(label != -1).reshape([-1])
+        
         m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
+        
         logits_p = alpha_p * (logits - delta_p)
         logits_n = alpha_n * (logits - delta_n)
         pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)

ppcls/arch/gears/cosmargin.py

@@ -46,6 +46,9 @@ class CosMargin(paddle.nn.Layer):
         weight = paddle.divide(weight, weight_norm)
 
         cos = paddle.matmul(input, weight)
+        if not self.training or label is None:
+            return cos
+        
         cos_m = cos - self.margin
 
         one_hot = paddle.nn.functional.one_hot(label, self.class_num)

ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output_dlbhc/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 100
+  #eval_mode: "retrieval"
+  print_batch_step: 10
+  use_visualdl: False
+
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+  #feature postprocess
+  feature_normalize: False
+  feature_binarize: "round"
+
+# model architecture
+Arch:  
+  name: "RecModel"
+  Backbone:
+    name: "MobileNetV3_large_x1_0"
+    pretrained: True
+    class_num: 512
+  Head:
+    name: "FC"
+    class_num: 50030
+    embedding_size: 512
+    
+  infer_output_key:  "features"
+  infer_add_softmax: "false"
+ 
+# loss function config for train/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [50, 150]
+    values: [0.1, 0.01, 0.001]
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/Aliproduct/
+      cls_label_path: ./dataset/Aliproduct/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 256
+        - RandCropImage:
+            size: 227
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/Aliproduct/
+      cls_label_path: ./dataset/Aliproduct/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 227
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/whl/demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 227
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.4914, 0.4822, 0.4465]
+        std: [0.2023, 0.1994, 0.2010]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]
+        
+# switch to metric below when eval by retrieval
+#     - Recallk:
+#         topk: [1]
+#     - mAP:
+#     - Precisionk:
+#         topk: [1]
+

ppcls/configs/quick_start/professional/VGG19_CIFAR10_DeepHash.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  eval_mode: "retrieval"
+  epochs: 128
+  print_batch_step: 10
+  use_visualdl: False
+
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+  #feature postprocess
+  feature_normalize: False
+  feature_binarize: "round"
+
+# model architecture
+Arch:
+  name: "RecModel"
+  Backbone:
+    name: "VGG19Sigmoid"
+    pretrained: True
+    class_num: 48
+  Head:
+    name: "FC"
+    class_num: 10
+    embedding_size: 48
+    
+  infer_output_key:  "features"
+  infer_add_softmax: "false"
+
+# loss function config for train/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.01
+    decay_epochs: [200]
+    values: [0.01, 0.001]
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/cifar10/
+      cls_label_path: ./dataset/cifar10/cifar10-2/train.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 256
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    Query:
+      dataset: 
+        name: ImageNetDataset
+        image_root: ./dataset/cifar10/
+        cls_label_path: ./dataset/cifar10/cifar10-2/test.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.4914, 0.4822, 0.4465]
+              std: [0.2023, 0.1994, 0.2010]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 512
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+    Gallery:
+      dataset: 
+        name: ImageNetDataset
+        image_root: ./dataset/cifar10/
+        cls_label_path: ./dataset/cifar10/cifar10-2/database.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.4914, 0.4822, 0.4465]
+              std: [0.2023, 0.1994, 0.2010]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 512
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - mAP:
+    - Precisionk:
+        topk: [1, 5]
+        

ppcls/engine/engine.py

@@ -52,6 +52,11 @@ class Engine(object):
         self.config = config
         self.eval_mode = self.config["Global"].get("eval_mode",
                                                    "classification")
+        if "Head" in self.config["Arch"]:
+            self.is_rec = True
+        else:
+            self.is_rec = False
+
         # init logger
         self.output_dir = self.config['Global']['output_dir']
         log_file = os.path.join(self.output_dir, self.config["Arch"]["name"],

ppcls/engine/evaluation/retrieval.py

@@ -124,6 +124,13 @@ def cal_feature(evaler, name='gallery'):
             feas_norm = paddle.sqrt(
                 paddle.sum(paddle.square(batch_feas), axis=1, keepdim=True))
             batch_feas = paddle.divide(batch_feas, feas_norm)
+            
+        # do binarize
+        if evaler.config["Global"].get("feature_binarize") == "round":
+            batch_feas = paddle.round(batch_feas).astype("float32") * 2.0 - 1.0
+
+        if evaler.config["Global"].get("feature_binarize") == "sign":
+            batch_feas = paddle.sign(batch_feas).astype("float32")
 
         if all_feas is None:
             all_feas = batch_feas
@@ -135,8 +142,10 @@ def cal_feature(evaler, name='gallery'):
             all_image_id = paddle.concat([all_image_id, batch[1]])
             if has_unique_id:
                 all_unique_id = paddle.concat([all_unique_id, batch[2]])
+                
     if evaler.use_dali:
         dataloader_tmp.reset()
+        
     if paddle.distributed.get_world_size() > 1:
         feat_list = []
         img_id_list = []

ppcls/engine/train/train.py

@@ -79,7 +79,7 @@ def train_epoch(trainer, epoch_id, print_batch_step):
 
 
 def forward(trainer, batch):
-    if trainer.eval_mode == "classification":
+    if not trainer.is_rec:
         return trainer.model(batch[0])
     else:
         return trainer.model(batch[0], batch[1])

ppcls/loss/celoss.py

@@ -29,7 +29,7 @@ class CELoss(nn.Layer):
         self.epsilon = epsilon
 
     def _labelsmoothing(self, target, class_num):
-        if target.shape[-1] != class_num:
+        if target.ndim == 1 or target.shape[-1] != class_num:
             one_hot_target = F.one_hot(target, class_num)
         else:
             one_hot_target = target

ppcls/loss/deephashloss.py

+#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 paddle
+import paddle.nn as nn
+
+class DSHSDLoss(nn.Layer):
+    """
+    # DSHSD(IEEE ACCESS 2019)
+    # paper [Deep Supervised Hashing Based on Stable Distribution](https://ieeexplore.ieee.org/document/8648432/)
+    # [DSHSD] epoch:70,  bit:48,  dataset:cifar10-1,  MAP:0.809, Best MAP: 0.809
+    # [DSHSD] epoch:250, bit:48,  dataset:nuswide_21, MAP:0.809, Best MAP: 0.815
+    # [DSHSD] epoch:135, bit:48,  dataset:imagenet,   MAP:0.647, Best MAP: 0.647
+    """
+    def __init__(self, n_class, bit, alpha, multi_label=False):
+        super(DSHSDLoss, self).__init__()
+        self.m = 2 * bit     
+        self.alpha = alpha
+        self.multi_label = multi_label
+        self.n_class = n_class
+        self.fc = paddle.nn.Linear(bit, n_class, bias_attr=False)
+
+    def forward(self, input, label):        
+        feature = input["features"]
+        feature = feature.tanh().astype("float32")
+
+        dist = paddle.sum(
+                    paddle.square((paddle.unsqueeze(feature, 1) - paddle.unsqueeze(feature, 0))), 
+                    axis=2)
+        
+        # label to ont-hot
+        label = paddle.flatten(label)
+        label = paddle.nn.functional.one_hot(label,  self.n_class).astype("float32")
+
+        s = (paddle.matmul(label, label, transpose_y=True) == 0).astype("float32")
+        Ld = (1 - s) / 2 * dist + s / 2 * (self.m - dist).clip(min=0)
+        Ld = Ld.mean()
+        
+        logits = self.fc(feature)
+        if self.multi_label:
+            # multiple labels classification loss
+            Lc = (logits - label * logits + ((1 + (-logits).exp()).log())).sum(axis=1).mean()
+        else:
+            # single labels classification loss
+            Lc = (-paddle.nn.functional.softmax(logits).log() * label).sum(axis=1).mean()
+
+        return {"dshsdloss": Lc + Ld * self.alpha}
+
+class LCDSHLoss(nn.Layer):
+    """
+    # paper [Locality-Constrained Deep Supervised Hashing for Image Retrieval](https://www.ijcai.org/Proceedings/2017/0499.pdf)
+    # [LCDSH] epoch:145, bit:48, dataset:cifar10-1,  MAP:0.798, Best MAP: 0.798
+    # [LCDSH] epoch:183, bit:48, dataset:nuswide_21, MAP:0.833, Best MAP: 0.834
+    """
+    def __init__(self, n_class, _lambda):
+        super(LCDSHLoss, self).__init__()
+        self._lambda = _lambda
+        self.n_class = n_class
+
+    def forward(self, input, label):
+        feature = input["features"]
+
+        # label to ont-hot
+        label = paddle.flatten(label)
+        label = paddle.nn.functional.one_hot(label,  self.n_class).astype("float32")
+        
+        s = 2 * (paddle.matmul(label, label, transpose_y=True) > 0).astype("float32") - 1
+        inner_product = paddle.matmul(feature, feature, transpose_y=True) * 0.5
+
+        inner_product = inner_product.clip(min=-50, max=50)
+        L1 = paddle.log(1 + paddle.exp(-s * inner_product)).mean()
+
+        b = feature.sign()
+        inner_product_ = paddle.matmul(b, b, transpose_y=True) * 0.5
+        sigmoid = paddle.nn.Sigmoid()
+        L2 = (sigmoid(inner_product) - sigmoid(inner_product_)).pow(2).mean()
+
+        return {"lcdshloss": L1 + self._lambda * L2}
+

ppcls/metric/__init__.py

@@ -16,7 +16,7 @@ from paddle import nn
 import copy
 from collections import OrderedDict
 
-from .metrics import TopkAcc, mAP, mINP, Recallk
+from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
 from .metrics import DistillationTopkAcc
 from .metrics import GoogLeNetTopkAcc
 

ppcls/metric/metrics.py

@@ -168,6 +168,47 @@ class Recallk(nn.Layer):
         return metric_dict
 
 
+class Precisionk(nn.Layer):
+    def __init__(self, topk=(1, 5)):
+        super().__init__()
+        assert isinstance(topk, (int, list, tuple))
+        if isinstance(topk, int):
+            topk = [topk]
+        self.topk = topk
+
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
+                keep_mask):
+        metric_dict = dict()
+
+        #get cmc
+        choosen_indices = paddle.argsort(
+            similarities_matrix, axis=1, descending=True)
+        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
+        gallery_labels_transpose = paddle.broadcast_to(
+            gallery_labels_transpose,
+            shape=[
+                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
+            ])
+        choosen_label = paddle.index_sample(gallery_labels_transpose,
+                                            choosen_indices)
+        equal_flag = paddle.equal(choosen_label, query_img_id)
+        if keep_mask is not None:
+            keep_mask = paddle.index_sample(
+                keep_mask.astype('float32'), choosen_indices)
+            equal_flag = paddle.logical_and(equal_flag,
+                                            keep_mask.astype('bool'))
+        equal_flag = paddle.cast(equal_flag, 'float32')
+        
+        Ns = paddle.arange(gallery_img_id.shape[0]) + 1
+        equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
+        Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()
+
+        for k in self.topk:
+            metric_dict["precision@{}".format(k)] = Precision_at_k[k - 1]
+
+        return metric_dict
+
+
 class DistillationTopkAcc(TopkAcc):
     def __init__(self, model_key, feature_key=None, topk=(1, 5)):
         super().__init__(topk=topk)

requirements.txt

@@ -8,3 +8,4 @@ visualdl >= 2.0.0b
 scipy
 scikit-learn==0.23.2
 gast==0.3.3
+faiss-cpu==1.7.1