add bnneck, last stride=1

ppcls/arch/gears/__init__.py

@@ -18,13 +18,14 @@ from .circlemargin import CircleMargin
 from .fc import FC
 from .vehicle_neck import VehicleNeck
 from paddle.nn import Tanh
+from .bnneck import BNNeck
 
 __all__ = ['build_gear']
 
 
 def build_gear(config):
     support_dict = [
-        'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh'
+        'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh', "BNNeck"
     ]
     module_name = config.pop('name')
     assert module_name in support_dict, Exception(

ppcls/arch/gears/bnneck.py

@@ -0,0 +1,14 @@
+class BNNeck(nn.Layer):
+    def __init__(self, num_filters, trainable=False):
+        super(BNNeck, self).__init__()
+        self.num_filters = num_filters
+
+        self.bn = paddle.nn.BatchNorm(
+            self.num_filters)
+        if not trainable:
+            self.bn.weight.trainable = False
+            self.bn.bias.trainable = False
+
+    def forward(self, input, label=None):
+        out = self.bn(input)
+        return out

ppcls/configs/Pedestrian/strong_baseline_baseline.yaml

@@ -36,6 +36,7 @@ Loss:
   Train:
     - CELoss:
         weight: 1.0
+        epsilon: 0.1
     - TripletLossV2:
         weight: 1.0
         margin: 0.3
@@ -48,8 +49,10 @@ Optimizer:
   name: Adam
   lr:
     name: Piecewise
-    decay_epochs: [40, 70]
+    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
@@ -73,17 +76,13 @@ DataLoader:
               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

ppcls/configs/Pedestrian/strong_baseline_m1.yaml

@@ -21,11 +21,15 @@ Arch:
   infer_output_key: "features"
   infer_add_softmax: False
   Backbone:
-    name: "ResNet50"
+    name: "ResNet50_last_stage_stride1"
     pretrained: True
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
+  Neck:
+    name: BNNeck
+    num_filters: 2048
+    trainale: false
   Head:
     name: "FC"
     embedding_size: 2048
@@ -76,6 +80,8 @@ DataLoader:
               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]
@@ -88,9 +94,9 @@ DataLoader:
               r1: 0.3
               mean: [0.4914, 0.4822, 0.4465]
     sampler:
-        name: PKSampler
+        name: DistributedRandomIdentitySampler
         batch_size: 64
-        sample_per_id: 4
+        num_instances: 4
         drop_last: True
         shuffle: True
     loader: