Merge branch 'develop_reg' of https://github.com/weisy11/PaddleClas into develop_reg

ppcls/configs/Vehicle/ResNet50.yaml

@@ -148,9 +148,9 @@ Infer:
 
 Metric:
     Train:
-    - Topk:
-        k: [1, 5]
+    - TopkAcc:
+        topk: [1, 5]
     Eval:
-    - Topk:
-        k: [1, 5]
+    - TopkAcc:
+        topk: [1, 5]
 

ppcls/configs/Vehicle/ResNet50_ReID.yaml

 # global configs
-Trainer:
-  name: TrainerReID
 Global:
   checkpoints: null
   pretrained_model: null
@@ -16,8 +14,7 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: "./inference"
-  num_split: 1
-  feature_normalize: True
+  eval_mode: "retrieval"
 
 # model architecture
 Arch:
@@ -99,10 +96,10 @@ DataLoader:
     loader:
         num_workers: 6
         use_shared_memory: False
-
-  Query:
+  Eval:
+    Query:
     # TOTO: modify to the latest trainer
-    dataset: 
+      dataset: 
         name: "VeriWild"
         image_root: "/work/dataset/VeRI-Wild/images"
         cls_label_path: "/work/dataset/VeRI-Wild/train_test_split/test_3000_id_query.txt"
@@ -114,18 +111,18 @@ DataLoader:
               mean: [0.485, 0.456, 0.406]
               std: [0.229, 0.224, 0.225]
               order: ''
-    sampler:
+      sampler:
         name: DistributedBatchSampler
         batch_size: 64
         drop_last: False
         shuffle: False
-    loader:
+      loader:
         num_workers: 6
         use_shared_memory: False
 
-  Gallery:
+    Gallery:
     # TOTO: modify to the latest trainer
-    dataset: 
+      dataset: 
         name: "VeriWild"
         image_root: "/work/dataset/VeRI-Wild/images"
         cls_label_path: "/work/dataset/VeRI-Wild/train_test_split/test_3000_id.txt"
@@ -137,15 +134,21 @@ DataLoader:
               mean: [0.485, 0.456, 0.406]
               std: [0.229, 0.224, 0.225]
               order: ''
-    sampler:
+      sampler:
         name: DistributedBatchSampler
         batch_size: 64
         drop_last: False
         shuffle: False
-    loader:
+      loader:
         num_workers: 6
         use_shared_memory: False
 
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]
+    - mAP: {}
+
 Infer:
   infer_imgs: "docs/images/whl/demo.jpg"
   batch_size: 10

ppcls/metric/metrics.py

@@ -15,6 +15,7 @@
 import numpy as np
 import paddle
 import paddle.nn as nn
+from functools import lru_cache
 
 
 # TODO: fix the format
@@ -38,23 +39,13 @@ class TopkAcc(nn.Layer):
 
 
 class mAP(nn.Layer):
-    def __init__(self, max_rank=50):
+    def __init__(self):
         super().__init__()
-        self.max_rank = max_rank
 
     def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
-        num_q, num_g = similarities_matrix.shape
-        q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
-        g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
-        if num_g < self.max_rank:
-            self.max_rank = num_g
-            print('Note: number of gallery samples is quite small, got {}'.
-                  format(num_g))
-        indices = paddle.argsort(
-            similarities_matrix, axis=1, descending=True).numpy()
-        _, all_AP, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
-                                   self.max_rank)
+        _, all_AP, _ = get_metrics(similarities_matrix, query_img_id,
+                                   gallery_img_id)
 
         mAP = np.mean(all_AP)
         metric_dict["mAP"] = mAP
@@ -62,23 +53,13 @@ class mAP(nn.Layer):
 
 
 class mINP(nn.Layer):
-    def __init__(self, max_rank=50):
+    def __init__(self):
         super().__init__()
-        self.max_rank = max_rank
 
     def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
-        num_q, num_g = similarities_matrix.shape
-        q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
-        g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
-        if num_g < self.max_rank:
-            max_rank = num_g
-            print('Note: number of gallery samples is quite small, got {}'.
-                  format(num_g))
-        indices = paddle.argsort(
-            similarities_matrix, axis=1, descending=True).numpy()
-        _, _, all_INP = get_metrics(indices, num_q, num_g, q_pids, g_pids,
-                                    self.max_rank)
+        _, _, all_INP = get_metrics(similarities_matrix, query_img_id,
+                                    gallery_img_id)
 
         mINP = np.mean(all_INP)
         metric_dict["mINP"] = mINP
@@ -86,34 +67,37 @@ class mINP(nn.Layer):
 
 
 class Recallk(nn.Layer):
-    def __init__(self, max_rank=50, topk=(1, 5)):
+    def __init__(self, topk=(1, 5)):
         super().__init__()
-        self.max_rank = max_rank
         assert isinstance(topk, (int, list))
         if isinstance(topk, int):
             topk = [topk]
         self.topk = topk
+        self.max_rank = max(self.topk) if max(self.topk) > 50 else 50
 
     def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
-        num_q, num_g = similarities_matrix.shape
-        q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
-        g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
-        if num_g < self.max_rank:
-            max_rank = num_g
-            print('Note: number of gallery samples is quite small, got {}'.
-                  format(num_g))
-        indices = paddle.argsort(
-            similarities_matrix, axis=1, descending=True).numpy()
-        all_cmc, _, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
-                                    self.max_rank)
+        all_cmc, _, _ = get_metrics(similarities_matrix, query_img_id,
+                                    gallery_img_id, self.max_rank)
 
         for k in self.topk:
             metric_dict["recall{}".format(k)] = all_cmc[k - 1]
         return metric_dict
 
 
-def get_metrics(indices, num_q, num_g, q_pids, g_pids, max_rank=50):
+@lru_cache()
+def get_metrics(similarities_matrix, query_img_id, gallery_img_id,
+                max_rank=50):
+    num_q, num_g = similarities_matrix.shape
+    q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
+    g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
+    if num_g < max_rank:
+        max_rank = num_g
+        print('Note: number of gallery samples is quite small, got {}'.format(
+            num_g))
+    indices = paddle.argsort(
+        similarities_matrix, axis=1, descending=True).numpy()
+
     all_cmc = []
     all_AP = []
     all_INP = []