bugfix for attribute project (#450)

Summary: refactor sample weight in attribute recognition;
change all options to False in defaults.py and modify yaml files

configs/Base-bagtricks.yml

@@ -34,9 +34,14 @@ MODEL:
 INPUT:
   SIZE_TRAIN: [ 256, 128 ]
   SIZE_TEST: [ 256, 128 ]
+
   REA:
     ENABLED: True
     PROB: 0.5
+
+  FLIP:
+    ENABLED: True
+
   PADDING:
     ENABLED: True
 

configs/VERIWild/bagtricks_R50-ibn.yml

@@ -9,7 +9,7 @@ MODEL:
     WITH_IBN: True
 
   HEADS:
-    POOL_LAYER: gempool
+    POOL_LAYER: GeneralizedMeanPooling
 
   LOSSES:
     TRI:

configs/VehicleID/bagtricks_R50-ibn.yml

@@ -8,7 +8,8 @@ MODEL:
   BACKBONE:
     WITH_IBN: True
   HEADS:
-    POOL_LAYER: gempool
+    POOL_LAYER: GeneralizedMeanPooling
+
   LOSSES:
     TRI:
       HARD_MINING: False

fastreid/config/defaults.py

@@ -47,7 +47,7 @@ _C.MODEL.BACKBONE.WITH_SE = False
 # If use Non-local block in backbone
 _C.MODEL.BACKBONE.WITH_NL = False
 # If use ImageNet pretrain model
-_C.MODEL.BACKBONE.PRETRAIN = True
+_C.MODEL.BACKBONE.PRETRAIN = False
 # Pretrain model path
 _C.MODEL.BACKBONE.PRETRAIN_PATH = ''
 
@@ -63,14 +63,14 @@ _C.MODEL.HEADS.NUM_CLASSES = 0
 # Embedding dimension in head
 _C.MODEL.HEADS.EMBEDDING_DIM = 0
 # If use BNneck in embedding
-_C.MODEL.HEADS.WITH_BNNECK = True
+_C.MODEL.HEADS.WITH_BNNECK = False
 # Triplet feature using feature before(after) bnneck
 _C.MODEL.HEADS.NECK_FEAT = "before"  # options: before, after
 # Pooling layer type
-_C.MODEL.HEADS.POOL_LAYER = "avgpool"
+_C.MODEL.HEADS.POOL_LAYER = "GlobalAvgPool"
 
 # Classification layer type
-_C.MODEL.HEADS.CLS_LAYER = "linear"  # "arcSoftmax" or "circleSoftmax"
+_C.MODEL.HEADS.CLS_LAYER = "Linear"  # ArcSoftmax" or "CircleSoftmax"
 
 # Margin and Scale for margin-based classification layer
 _C.MODEL.HEADS.MARGIN = 0.15
@@ -100,7 +100,7 @@ _C.MODEL.LOSSES.FL.SCALE = 1.0
 _C.MODEL.LOSSES.TRI = CN()
 _C.MODEL.LOSSES.TRI.MARGIN = 0.3
 _C.MODEL.LOSSES.TRI.NORM_FEAT = False
-_C.MODEL.LOSSES.TRI.HARD_MINING = True
+_C.MODEL.LOSSES.TRI.HARD_MINING = False
 _C.MODEL.LOSSES.TRI.SCALE = 1.0
 
 # Circle Loss options
@@ -150,11 +150,11 @@ _C.INPUT.CROP.SCALE = [0.16, 1]
 _C.INPUT.CROP.RATIO = [3./4., 4./3.]
 
 # Random probability for image horizontal flip
-_C.INPUT.FLIP = CN({"ENABLED": True})
+_C.INPUT.FLIP = CN({"ENABLED": False})
 _C.INPUT.FLIP.PROB = 0.5
 
 # Value of padding size
-_C.INPUT.PADDING = CN({"ENABLED": True})
+_C.INPUT.PADDING = CN({"ENABLED": False})
 _C.INPUT.PADDING.MODE = 'constant'
 _C.INPUT.PADDING.SIZE = 10
 
@@ -201,7 +201,7 @@ _C.DATASETS.COMBINEALL = False
 # -----------------------------------------------------------------------------
 _C.DATALOADER = CN()
 # Options: TrainingSampler, NaiveIdentitySampler, BalancedIdentitySampler
-_C.DATALOADER.SAMPLER_TRAIN = "NaiveIdentitySampler"
+_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
 # Number of instance for each person
 _C.DATALOADER.NUM_INSTANCE = 4
 _C.DATALOADER.NUM_WORKERS = 8
@@ -224,7 +224,7 @@ _C.SOLVER.BIAS_LR_FACTOR = 1.
 _C.SOLVER.HEADS_LR_FACTOR = 1.
 
 _C.SOLVER.MOMENTUM = 0.9
-_C.SOLVER.NESTEROV = True
+_C.SOLVER.NESTEROV = False
 
 _C.SOLVER.WEIGHT_DECAY = 0.0005
 _C.SOLVER.WEIGHT_DECAY_BIAS = 0.

projects/FastAttr/configs/Base-attribute.yml

@@ -14,8 +14,8 @@ MODEL:
   HEADS:
     NAME: AttrHead
     WITH_BNNECK: True
-    POOL_LAYER: fastavgpool
+    POOL_LAYER: FastGlobalAvgPool
-    CLS_LAYER: linear
+    CLS_LAYER: Linear
     NUM_CLASSES: 26
 
   LOSSES:
@@ -28,11 +28,15 @@ MODEL:
 INPUT:
   SIZE_TRAIN: [ 256, 192 ]
   SIZE_TEST: [ 256, 192 ]
-  REA:
+
-    ENABLED: False
+  FLIP:
-  DO_PAD: True
+    ENABLED: True
+
+  PADDING:
+    ENABLED: True
 
 DATALOADER:
+  SAMPLER_TRAIN: TrainingSampler
   NUM_WORKERS: 8
 
 SOLVER:

projects/FastAttr/fastattr/attr_baseline.py

@@ -11,13 +11,16 @@ from .bce_loss import cross_entropy_sigmoid_loss
 
 @META_ARCH_REGISTRY.register()
 class AttrBaseline(Baseline):
-    def __init__(self, cfg, sample_weights):
+
-        super(AttrBaseline, self).__init__(cfg)
+    @classmethod
-        bce_weight_enabled = cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED
+    def from_config(cls, cfg):
-        if bce_weight_enabled:
+        base_res = Baseline.from_config(cfg)
-            self.register_buffer("sample_weight", sample_weights)
+        base_res["loss_kwargs"].update({
-        else:
+            'bce': {
-            self.sample_weights = None
+                'scale': cfg.MODEL.LOSSES.BCE.SCALE
+            }
+        })
+        return base_res
 
     def losses(self, outputs, gt_labels):
         r"""
@@ -25,16 +28,17 @@ class AttrBaseline(Baseline):
         must be the same as the outputs of the model forwarding.
         """
         # model predictions
-        cls_outputs = outputs['cls_outputs']
+        cls_outputs = outputs["cls_outputs"]
 
         loss_dict = {}
-        loss_names = self._cfg.MODEL.LOSSES.NAME
+        loss_names = self.loss_kwargs["loss_names"]
 
         if "BinaryCrossEntropyLoss" in loss_names:
+            bce_kwargs = self.loss_kwargs.get('bce')
             loss_dict["loss_bce"] = cross_entropy_sigmoid_loss(
                 cls_outputs,
                 gt_labels,
-                self.sample_weight,
+                self.sample_weights,
-            ) * self._cfg.MODEL.LOSSES.BCE.SCALE
+            ) * bce_kwargs.get('scale')
 
         return loss_dict

projects/FastAttr/fastattr/attr_dataset.py

@@ -13,10 +13,10 @@ from fastreid.data.data_utils import read_image
 class AttrDataset(Dataset):
     """Image Person Attribute Dataset"""
 
-    def __init__(self, img_items, attr_dict, transform=None):
+    def __init__(self, img_items, transform, attr_dict):
         self.img_items = img_items
-        self.attr_dict = attr_dict
         self.transform = transform
+        self.attr_dict = attr_dict
 
     def __len__(self):
         return len(self.img_items)
@@ -24,6 +24,7 @@ class AttrDataset(Dataset):
     def __getitem__(self, index):
         img_path, labels = self.img_items[index]
         img = read_image(img_path)
+
         if self.transform is not None: img = self.transform(img)
 
         labels = torch.as_tensor(labels)

projects/FastAttr/fastattr/attr_evaluation.py

@@ -12,7 +12,7 @@ import torch
 from fastreid.evaluation.evaluator import DatasetEvaluator
 from fastreid.utils import comm
 
-logger = logging.getLogger(__name__)
+logger = logging.getLogger("fastreid.attr_evaluation")
 
 
 class AttrEvaluator(DatasetEvaluator):

projects/FastAttr/fastattr/attr_head.py

@@ -28,22 +28,16 @@ class AttrHead(EmbeddingHead):
         """
         pool_feat = self.pool_layer(features)
         neck_feat = self.bottleneck(pool_feat)
-        neck_feat = neck_feat[..., 0, 0]
+        neck_feat = neck_feat.view(neck_feat.size(0), -1)
 
-        if self.cls_layer.__class__.__name__ == 'Linear':
         logits = F.linear(neck_feat, self.weight)
-        else:
+        logits = self.bnneck(logits)
-            logits = F.linear(F.normalize(neck_feat), F.normalize(self.weight))
 
         # Evaluation
         if not self.training:
-            logits = self.bnneck(logits * self.cls_layer.s)
             cls_outptus = torch.sigmoid(logits)
             return cls_outptus
 
-        cls_outputs = self.cls_layer(logits, targets)
-        cls_outputs = self.bnneck(cls_outputs)
-
         return {
-            'cls_outputs': cls_outputs,
+            "cls_outputs": logits,
         }

projects/FastAttr/fastattr/data_build.py

@@ -20,9 +20,6 @@ _root = os.getenv("FASTREID_DATASETS", "datasets")
 
 
 def build_attr_train_loader(cfg):
-    cfg = cfg.clone()
-    cfg.defrost()
-
     train_items = list()
     attr_dict = None
     for d in cfg.DATASETS.NAMES:
@@ -30,13 +27,13 @@ def build_attr_train_loader(cfg):
         if comm.is_main_process():
             dataset.show_train()
         if attr_dict is not None:
-            assert attr_dict == dataset.attr_dict, "attr_dict in {} does not match with previous ones".format(d)
+            assert attr_dict == dataset.attr_dict, f"attr_dict in {d} does not match with previous ones"
         else:
             attr_dict = dataset.attr_dict
         train_items.extend(dataset.train)
 
     train_transforms = build_transforms(cfg, is_train=True)
-    train_set = AttrDataset(train_items, attr_dict, train_transforms)
+    train_set = AttrDataset(train_items, train_transforms, attr_dict)
 
     num_workers = cfg.DATALOADER.NUM_WORKERS
     mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()
@@ -55,16 +52,14 @@ def build_attr_train_loader(cfg):
 
 
 def build_attr_test_loader(cfg, dataset_name):
-    cfg = cfg.clone()
-    cfg.defrost()
-
     dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, combineall=cfg.DATASETS.COMBINEALL)
+    attr_dict = dataset.attr_dict
     if comm.is_main_process():
         dataset.show_test()
     test_items = dataset.test
 
     test_transforms = build_transforms(cfg, is_train=False)
-    test_set = AttrDataset(test_items, dataset.attr_dict, test_transforms)
+    test_set = AttrDataset(test_items, test_transforms, attr_dict)
 
     mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
     data_sampler = samplers.InferenceSampler(len(test_set))

projects/FastAttr/train_net.py

@@ -3,14 +3,12 @@
 @author:  xingyu liao
 @contact: sherlockliao01@gmail.com
 """
-import logging
 import sys
 
 sys.path.append('.')
 
 from fastreid.config import get_cfg
 from fastreid.engine import DefaultTrainer
-from fastreid.modeling import build_model
 from fastreid.engine import default_argument_parser, default_setup, launch
 from fastreid.utils.checkpoint import Checkpointer
 
@@ -18,22 +16,28 @@ from fastattr import *
 
 
 class Trainer(DefaultTrainer):
+    sample_weights = None
 
-    def build_model(self, cfg):
+    @classmethod
+    def build_model(cls, cfg):
         """
         Returns:
             torch.nn.Module:
         It now calls :func:`fastreid.modeling.build_model`.
         Overwrite it if you'd like a different model.
         """
-        model = build_model(cfg, sample_weights=self.sample_weights)
+        model = DefaultTrainer.build_model(cfg)
-        logger = logging.getLogger("fastreid.attr_model")
+        if cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED and \
-        logger.info("Model:\n{}".format(model))
+                Trainer.sample_weights is not None:
+            setattr(model, "sample_weights", Trainer.sample_weights.to(model.device))
+        else:
+            setattr(model, "sample_weights", None)
         return model
 
-    def build_train_loader(self, cfg):
+    @classmethod
+    def build_train_loader(cls, cfg):
         data_loader = build_attr_train_loader(cfg)
-        self.sample_weights = data_loader.dataset.sample_weights
+        Trainer.sample_weights = data_loader.dataset.sample_weights
         return data_loader
 
     @classmethod

projects/FastClas/configs/base-clas.yaml

@@ -26,25 +26,14 @@ MODEL:
 
 INPUT:
   SIZE_TEST: [256, 256]
+
   CROP:
     ENABLED: True
     SIZE: [224, 224]
+
   FLIP:
     ENABLED: True
 
-  AUGMIX:
-    ENABLED: False
-  AUTOAUG:
-    ENABLED: False
-  PADDING:
-    ENABLED: False
-  CJ:
-    ENABLED: False
-  AFFINE:
-    ENABLED: False
-  REA:
-    ENABLED: False
-
 DATALOADER:
   SAMPLER_TRAIN: TrainingSampler
   NUM_WORKERS: 8

projects/FastClas/train_net.py

@@ -20,7 +20,7 @@ from fastreid.utils.checkpoint import Checkpointer, PathManager
 from fastreid.utils import comm
 from fastreid.engine import DefaultTrainer
 
-from fastcls import *
+from fastclas import *
 
 
 class Trainer(DefaultTrainer):

projects/FastRetri/configs/base-image_retri.yml

@@ -34,8 +34,8 @@ INPUT:
     SCALE: [0.16, 1.]
     RATIO: [0.75, 1.33333]
 
-  PADDING:
+  FLIP:
-    ENABLED: False
+    ENABLED: True
 
   CJ:
     ENABLED: False
@@ -44,6 +44,7 @@ INPUT:
     SATURATION: 0.1
     HUE: 0.1
 
+
 DATALOADER:
   SAMPLER_TRAIN: TrainingSampler
   NUM_WORKERS: 8

projects/NAIC20/configs/Base-naic.yml

@@ -35,6 +35,11 @@ INPUT:
   SIZE_TRAIN: [ 256, 128 ]
   SIZE_TEST: [ 256, 128 ]
 
+  FLIP:
+    ENABLED: True
+
+  PADDING:
+    ENABLED: True
 
   AUGMIX:
     ENABLED: True

projects/NAIC20/configs/submit.yml

@@ -19,6 +19,9 @@ TEST:
     K2: 3
     LAMBDA: 0.8
 
+  FLIP:
+    ENABLED: True
+
   SAVE_DISTMAT: True
 
 OUTPUT_DIR: projects/NAIC20/logs/r34_ibn-128x256-submit