update code

ppcls/arch/backbone/legendary_models/resnet.py

@@ -281,7 +281,6 @@ class ResNet(TheseusLayer):
                  lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
                  data_format="NCHW",
                  input_image_channel=3,
-                 stem_act="relu",
                  return_patterns=None,
                  return_stages=None):
         super().__init__()

ppcls/configs/strong_baseline_baseline.yaml → ppcls/configs/Pedestrian/strong_baseline_baseline.yaml

@@ -6,14 +6,14 @@ Global:
   # pretrained_model: "./pd_model_trace/ISE/ISE_MS_model" # pretrained ISE model for MSMT17
   output_dir: "./output/"
   device: "gpu"
-  save_interval: 10
+  save_interval: 40
   eval_during_train: True
   eval_interval: 10
   epochs: 120
-  print_batch_step: 10
+  print_batch_step: 20
   use_visualdl: False
   # used for static mode and model export
-  image_shape: [3, 128, 256]
+  image_shape: [3, 256, 128]
   save_inference_dir: "./inference"
   eval_mode: "retrieval"
 
@@ -22,14 +22,14 @@ Arch:
   name: "RecModel"
   infer_output_key: "features"
   infer_add_softmax: False
-  Backbone: 
+  Backbone:
     name: "ResNet50_last_stage_stride1"
     pretrained: True
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
   Head:
-    name: "FC"  
+    name: "FC"
     embedding_size: 2048
     class_num: 751
 # loss function config for traing/eval process
@@ -138,4 +138,3 @@ Metric:
     - Recallk:
         topk: [1, 5]
     - mAP: {}
-

ppcls/configs/strong_baseline_m1.yaml → ppcls/configs/Pedestrian/strong_baseline_m1.yaml

@@ -4,17 +4,19 @@ Global:
   pretrained_model: null
   output_dir: "./output/"
   device: "gpu"
-  save_interval: 10
+  save_interval: 40
   eval_during_train: True
   eval_interval: 10
   epochs: 120
   print_batch_step: 20
   use_visualdl: False
+  warmup_by_epoch: True
+  eval_mode: "retrieval"
+  re_ranking: False
+  feat_from: "neck" # 'backbone' or 'neck'
   # used for static mode and model export
   image_shape: [3, 256, 128]
   save_inference_dir: "./inference"
-  eval_mode: "retrieval"
-  feat_from: "neck" # 'backbone' or 'neck'
 
 # model architecture
 Arch:
@@ -29,13 +31,17 @@ Arch:
     name: "flatten"
   Neck:
     name: BNNeck
-    num_filters: 2048
-    # trainable: False # TODO: free bn.bias
+    num_features: &feat_dim 2048
+    # trainable: False # TODO: freeze bn.bias
   Head:
     name: "FC"
-    embedding_size: 2048
-    class_num: 751
-    bias_attr: false
+    embedding_size: *feat_dim
+    class_num: &class_num 751
+    weight_attr:
+      initializer:
+        name: Normal
+        std: 0.001
+    bias_attr: False
 
 # loss function config for traing/eval process
 Loss:
@@ -160,4 +166,3 @@ Metric:
     - Recallk:
         topk: [1, 5]
     - mAP: {}
-

ppcls/configs/PersonReID/ResNet50_strong_baseline_market1501.yaml → ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml

@@ -12,7 +12,8 @@ Global:
   use_visualdl: False
   warmup_by_epoch: True
   eval_mode: "retrieval"
-  re_ranking: True
+  re_ranking: False
+  feat_from: "neck" # 'backbone' or 'neck'
   # used for static mode and model export
   image_shape: [3, 256, 128]
   save_inference_dir: "./inference"
@@ -31,6 +32,7 @@ Arch:
   Neck:
     name: BNNeck
     num_features: &feat_dim 2048
+    # trainable: False # TODO: freeze bn.bias
   Head:
     name: "FC"
     embedding_size: *feat_dim
@@ -76,15 +78,15 @@ Optimizer:
       scope: CenterLoss
       lr:
         name: Constant
-        learning_rate: 1000.0
+        learning_rate: 1000.0 # set to ori_lr*(1/centerloss_weight) to void manually scaling centers' gradidents.
 
 # data loader for train and eval
 DataLoader:
   Train:
     dataset:
-        name: "Market1501"
-        image_root: "./dataset/Market-1501-v15.09.15"
-        cls_label_path: "bounding_box_train"
+        name: "VeriWild"
+        image_root: "./dataset/market1501"
+        cls_label_path: "./dataset/market1501/bounding_box_train.txt"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -117,14 +119,14 @@ DataLoader:
         drop_last: True
         shuffle: True
     loader:
-        num_workers: 4
+        num_workers: 6
         use_shared_memory: True
   Eval:
     Query:
       dataset:
-        name: "Market1501"
-        image_root: "./dataset/Market-1501-v15.09.15"
-        cls_label_path: "query"
+        name: "VeriWild"
+        image_root: "./dataset/market1501"
+        cls_label_path: "./dataset/market1501/query.txt"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -142,14 +144,14 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 4
+        num_workers: 6
         use_shared_memory: True
 
     Gallery:
       dataset:
-        name: "Market1501"
-        image_root: "./dataset/Market-1501-v15.09.15"
-        cls_label_path: "bounding_box_test"
+        name: "VeriWild"
+        image_root: "./dataset/market1501"
+        cls_label_path: "./dataset/market1501/bounding_box_test.txt"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -167,7 +169,7 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 4
+        num_workers: 6
         use_shared_memory: True
 
 Metric:

ppcls/configs/strong_baseline_m1_centerloss.yaml

-# global configs
-Global:
-  checkpoints: null
-  pretrained_model: null
-  output_dir: "./output/"
-  device: "gpu"
-  save_interval: 10
-  eval_during_train: True
-  eval_interval: 10
-  epochs: 120
-  print_batch_step: 20
-  use_visualdl: False
-  # used for static mode and model export
-  image_shape: [3, 256, 128]
-  save_inference_dir: "./inference"
-  eval_mode: "retrieval"
-  feat_from: "neck" # 'backbone' or 'neck'
-
-# model architecture
-Arch:
-  name: "RecModel"
-  infer_output_key: "features"
-  infer_add_softmax: False
-  Backbone:
-    name: "ResNet50_last_stage_stride1"
-    pretrained: True
-    stem_act: null
-  BackboneStopLayer:
-    name: "flatten"
-  Neck:
-    name: BNNeck
-    num_filters: 2048
-    # trainable: False # TODO: free bn.bias
-  Head:
-    name: "FC"
-    embedding_size: 2048
-    class_num: 751
-    bias_attr: false
-
-# loss function config for traing/eval process
-Loss:
-  Train:
-    - CELoss:
-        weight: 1.0
-        epsilon: 0.1
-    - TripletLossV2:
-        weight: 1.0
-        margin: 0.3
-        normalize_feature: false
-    - CenterLoss:
-        weight: 0.0005
-        num_classes: 751
-        feat_dim: 2048
-  Eval:
-    - CELoss:
-        weight: 1.0
-
-Optimizer:
-  model:
-    name: Adam
-    lr:
-      name: Piecewise
-      decay_epochs: [30, 60]
-      values: [0.00035, 0.000035, 0.0000035]
-      warmup_epoch: 10
-      warmup_start_lr: 0.0000035
-    regularizer:
-      name: 'L2'
-      coeff: 0.0005
-  loss:
-    name: SGD
-    lr:
-      name: Constant
-      learning_rate: 0.5
-
-# data loader for train and eval
-DataLoader:
-  Train:
-    dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_train.txt"
-        transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
-          - ResizeImage:
-              size: [128, 256]
-          - RandFlipImage:
-              flip_code: 1
-          - Pad:
-              padding: 10
-          - RandCropImage:
-              size: [128, 256]
-              scale: [ 0.8022, 0.8022 ]
-              ratio: [ 0.5, 0.5 ]
-          - NormalizeImage:
-              scale: 0.00392157
-              mean: [0.485, 0.456, 0.406]
-              std: [0.229, 0.224, 0.225]
-              order: ''
-          - RandomErasing:
-              EPSILON: 0.5
-              sl: 0.02
-              sh: 0.4
-              r1: 0.3
-              mean: [0.4914, 0.4822, 0.4465]
-    sampler:
-        name: DistributedRandomIdentitySampler
-        batch_size: 64
-        num_instances: 4
-        drop_last: True
-        shuffle: True
-    loader:
-        num_workers: 6
-        use_shared_memory: True
-  Eval:
-    Query:
-      dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/query.txt"
-        transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
-          - ResizeImage:
-              size: [128, 256]
-          - NormalizeImage:
-              scale: 0.00392157
-              mean: [0.485, 0.456, 0.406]
-              std: [0.229, 0.224, 0.225]
-              order: ''
-      sampler:
-        name: DistributedBatchSampler
-        batch_size: 128
-        drop_last: False
-        shuffle: False
-      loader:
-        num_workers: 6
-        use_shared_memory: True
-
-    Gallery:
-      dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_test.txt"
-        transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
-          - ResizeImage:
-              size: [128, 256]
-          - NormalizeImage:
-              scale: 0.00392157
-              mean: [0.485, 0.456, 0.406]
-              std: [0.229, 0.224, 0.225]
-              order: ''
-      sampler:
-        name: DistributedBatchSampler
-        batch_size: 128
-        drop_last: False
-        shuffle: False
-      loader:
-        num_workers: 6
-        use_shared_memory: True
-
-Metric:
-  Eval:
-    - Recallk:
-        topk: [1, 5]
-    - mAP: {}
-

ppcls/engine/evaluation/retrieval.py

@@ -82,6 +82,7 @@ def retrieval_eval(engine, epoch_id=0):
                         metric_dict[key] += metric_tmp[key] * block_fea.shape[
                             0] / len(query_feas)
         else:
+            metric_dict = dict()
             distmat = re_ranking(
                 query_feas, gallery_feas, k1=20, k2=6, lambda_value=0.3)
             cmc, mAP = eval_func(distmat,
@@ -93,9 +94,6 @@ def retrieval_eval(engine, epoch_id=0):
             metric_dict["recall5(RK)"] = cmc[4]
             metric_dict["mAP(RK)"] = mAP
 
-            for key in metric_tmp:
-                metric_dict[key] = metric_tmp[key] * block_fea.shape[0] / len(
-                    query_feas)
     metric_info_list = []
     for key in metric_dict:
         if metric_key is None: