refine code and yaml

ppcls/arch/gears/bnneck.py

@@ -17,22 +17,32 @@ from __future__ import absolute_import, division, print_function
 import paddle
 import paddle.nn as nn
 
+from ppcls.arch.utils import get_param_attr_dict
+
 
 class BNNeck(nn.Layer):
-    def __init__(self, num_features):
+    def __init__(self, num_features, **kwargs):
         super().__init__()
         weight_attr = paddle.ParamAttr(
             initializer=paddle.nn.initializer.Constant(value=1.0))
         bias_attr = paddle.ParamAttr(
             initializer=paddle.nn.initializer.Constant(value=0.0),
             trainable=False)
+
+        if 'weight_attr' in kwargs:
+            weight_attr = get_param_attr_dict(kwargs['weight_attr'])
+
+        bias_attr = None
+        if 'bias_attr' in kwargs:
+            bias_attr = get_param_attr_dict(kwargs['bias_attr'])
+
         self.feat_bn = nn.BatchNorm1D(
             num_features,
             momentum=0.9,
             epsilon=1e-05,
             weight_attr=weight_attr,
             bias_attr=bias_attr)
-        # TODO: set bnneck.bias learnable=False
+
         self.flatten = nn.Flatten()
 
     def forward(self, x):

ppcls/configs/Pedestrian/strong_baseline_baseline.yaml

@@ -2,8 +2,6 @@
 Global:
   checkpoints: null
   pretrained_model: null
-  # pretrained_model: "./pd_model_trace/ISE/ISE_M_model" # pretrained ISE model for Market1501
-  # pretrained_model: "./pd_model_trace/ISE/ISE_MS_model" # pretrained ISE model for MSMT17
   output_dir: "./output/"
   device: "gpu"
   save_interval: 40
@@ -12,10 +10,12 @@ Global:
   epochs: 120
   print_batch_step: 20
   use_visualdl: False
+  eval_mode: "retrieval"
+  re_ranking: False
+  feat_from: "backbone" # 'backbone' or 'neck'
   # used for static mode and model export
   image_shape: [3, 256, 128]
   save_inference_dir: "./inference"
-  eval_mode: "retrieval"
 
 # model architecture
 Arch:
@@ -32,6 +32,12 @@ Arch:
     name: "FC"
     embedding_size: 2048
     class_num: 751
+    weight_attr:
+      initializer:
+        name: Normal
+        std: 0.001
+    bias_attr: False
+
 # loss function config for traing/eval process
 Loss:
   Train:
@@ -63,9 +69,9 @@ Optimizer:
 DataLoader:
   Train:
     dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_train.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "bounding_box_train"
         transform_ops:
           - ResizeImage:
               size: [128, 256]
@@ -88,14 +94,14 @@ DataLoader:
         drop_last: True
         shuffle: True
     loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
   Eval:
     Query:
       dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/query.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "query"
         transform_ops:
           - ResizeImage:
               size: [128, 256]
@@ -109,14 +115,14 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
 
     Gallery:
       dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_test.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "bounding_box_test"
         transform_ops:
           - ResizeImage:
               size: [128, 256]
@@ -130,7 +136,7 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
 
 Metric:

ppcls/configs/Pedestrian/strong_baseline_m1.yaml

@@ -32,11 +32,19 @@ Arch:
   Neck:
     name: BNNeck
     num_features: &feat_dim 2048
-    # trainable: False # TODO: freeze bn.bias
+    weight_attr:
+      initializer:
+        name: Constant
+        value: 1.0
+    bias_attr:
+      initializer:
+        name: Constant
+        value: 0.0
+      learning_rate: 1.0e-20 # NOTE: Temporarily set lr small enough to freeze the bias
   Head:
     name: "FC"
     embedding_size: *feat_dim
-    class_num: &class_num 751
+    class_num: 751
     weight_attr:
       initializer:
         name: Normal
@@ -73,9 +81,9 @@ Optimizer:
 DataLoader:
   Train:
     dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_train.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "bounding_box_train"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -108,14 +116,14 @@ DataLoader:
         drop_last: True
         shuffle: True
     loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
   Eval:
     Query:
       dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/query.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "query"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -133,14 +141,14 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
 
     Gallery:
       dataset:
-        name: "VeriWild"
-        image_root: "./dataset/market1501"
-        cls_label_path: "./dataset/market1501/bounding_box_test.txt"
+        name: "Market1501"
+        image_root: "./dataset/"
+        cls_label_path: "bounding_box_test"
         transform_ops:
           - DecodeImage:
               to_rgb: True
@@ -158,7 +166,7 @@ DataLoader:
         drop_last: False
         shuffle: False
       loader:
-        num_workers: 6
+        num_workers: 4
         use_shared_memory: True
 
 Metric:

ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml

@@ -25,14 +25,22 @@ Arch:
   infer_add_softmax: False
   Backbone:
     name: "ResNet50_last_stage_stride1"
-    pretrained: True
+    pretrained: "./dataset/resnet50-19c8e357_for_strong_baseline"
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
   Neck:
     name: BNNeck
     num_features: &feat_dim 2048
-    # trainable: False # TODO: freeze bn.bias
+    weight_attr:
+      initializer:
+        name: Constant
+        value: 1.0
+    bias_attr:
+      initializer:
+        name: Constant
+        value: 0.0
+      learning_rate: 1.0e-20 # TODO: Temporarily set lr small enough to freeze the bias
   Head:
     name: "FC"
     embedding_size: *feat_dim
@@ -78,7 +86,7 @@ Optimizer:
       scope: CenterLoss
       lr:
         name: Constant
-        learning_rate: 1000.0 # set to ori_lr*(1/centerloss_weight) to void manually scaling centers' gradidents.
+        learning_rate: 1000.0 # NOTE: set to ori_lr*(1/centerloss_weight) to avoid manually scaling centers' gradidents.
 
 # data loader for train and eval
 DataLoader:

ppcls/configs/ResNet50_UReID_infer.yaml

@@ -6,9 +6,9 @@ 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: 1
   eval_during_train: True
-  eval_interval: 10
+  eval_interval: 1
   epochs: 120
   print_batch_step: 10
   use_visualdl: False
@@ -25,7 +25,6 @@ Arch:
   Backbone: 
     name: "ResNet50_last_stage_stride1"
     pretrained: True
-    stem_act: null
   BackboneStopLayer:
     name: "avg_pool"
   Neck:
@@ -115,7 +114,7 @@ DataLoader:
               order: ''
       sampler:
         name: DistributedBatchSampler
-        batch_size: 128
+        batch_size: 64
         drop_last: False
         shuffle: False
       loader:
@@ -138,7 +137,7 @@ DataLoader:
               order: ''
       sampler:
         name: DistributedBatchSampler
-        batch_size: 128
+        batch_size: 64
         drop_last: False
         shuffle: False
       loader:

ppcls/engine/engine.py

@@ -299,11 +299,12 @@ class Engine(object):
         self.max_iter = len(self.train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(self.train_dataloader)
 
+        # step lr once before first epoch when when Global.warmup_by_epoch=True
         if self.config["Global"].get("warmup_by_epoch", False):
             for i in range(len(self.lr_sch)):
                 self.lr_sch[i].step()
             logger.info(
-                "lr_sch step once before first epoch, when Global.warmup_by_epoch=True"
+                "lr_sch step once before the first epoch, when Global.warmup_by_epoch=True"
             )
 
         for epoch_id in range(best_metric["epoch"] + 1,
@@ -312,6 +313,7 @@ class Engine(object):
             # for one epoch train
             self.train_epoch_func(self, epoch_id, print_batch_step)
 
+            # lr step when Global.warmup_by_epoch=True
             if self.config["Global"].get("warmup_by_epoch", False):
                 for i in range(len(self.lr_sch)):
                     self.lr_sch[i].step()

ppcls/engine/train/train.py

@@ -53,20 +53,14 @@ def train_epoch(engine, epoch_id, print_batch_step):
             out = forward(engine, batch)
             loss_dict = engine.train_loss_func(out, batch[1])
 
-        # step opt
+        # backward & step opt
         if engine.amp:
             scaled = engine.scaler.scale(loss_dict["loss"])
             scaled.backward()
-            # set BNneck.bias grad to zero
-            engine.model.neck.feat_bn.bias.grad.set_value(
-                paddle.zeros_like(engine.model.neck.feat_bn.bias.grad))
             for i in range(len(engine.optimizer)):
                 engine.scaler.minimize(engine.optimizer[i], scaled)
         else:
             loss_dict["loss"].backward()
-            # set BNneck.bias grad to zero
-            engine.model.neck.feat_bn.bias.grad.set_value(
-                paddle.zeros_like(engine.model.neck.feat_bn.bias.grad))
             for i in range(len(engine.optimizer)):
                 engine.optimizer[i].step()