Add fine-tune scripts for CUHK01.

Author: huangzh13

README.md

@@ -8,7 +8,7 @@
 | DukeMTMC | 86.4% | 74.5% | 
 | CUHK03-labeled | 75.9% | 72.5% | 
 | CUHK03-detected | 71.2% | 68.0% | 
-| CUHK01 | [] | [] | 
+| CUHK01(finetune on CUHK03) | 90.5% | 89.4% | 
 
 
 ## Test

configs/sample_CUHK01.yaml

@@ -0,0 +1,44 @@
+OUTPUT_DIR: "./checkpoint/CUHK01/Exp-1011-2-finetune-2"
+DEVICE: "cuda"
+DEVICE_ID: ('2')
+
+MODEL:
+  NAME: 'baseline'
+  ARCH: 'resnet50'
+  LABEL_SMOOTH: True
+
+INPUT:
+  SIZE_TRAIN: [384, 128]
+
+DATASETS:
+  NAME: 'CUHK01'
+  ROOT: '/home/hzh/data'
+
+DATALOADER:
+  NUM_WORKERS: 4
+  BATCH_SIZE: 64
+  NUM_INSTANCES: 4
+
+SOLVER:
+  LOSS: 'softmax_triplet'
+  OPTIMIZER_NAME: 'Adam'
+  MAX_EPOCHS: 60
+  BASE_LR: 0.0004
+  WEIGHT_DECAY: 0.0005
+
+  EVAL_PERIOD: 5
+  PRINT_FREQ: 10
+
+SCHEDULER:
+  NAME: 'WarmupStepLR'
+  STEP: 20
+  GAMMA: 0.1
+
+  WARMUP_FACTOR: 100
+  WARMUP_ITERS: 20
+
+
+TEST:
+  BATCH_SIZE: 64
+
+

data/__init__.py

@@ -9,6 +9,7 @@
 
 from torch.utils.data import DataLoader
 
+from data.cuhk01 import CUHK01
 from data.dataset import ReIDDataset, ImageData
 from data.transforms import TrainTransform, TestTransform
 from data.samplers import RandomIdentitySampler
@@ -46,3 +47,23 @@ def make_loader(cfg):
                                 pin_memory=True)
 
     return train_loader, query_loader, gallery_loader, num_train_pids
+
+
+def make_loader_cuhk01(cfg):
+    _data = CUHK01(root=cfg.DATASETS.ROOT)
+    num_train_pids = _data.num_train_pids
+
+    train_loader = DataLoader(ImageData(_data.train, TrainTransform(p=0.5)),
+                              sampler=RandomIdentitySampler(_data.train, cfg.DATALOADER.NUM_INSTANCES),
+                              batch_size=cfg.DATALOADER.BATCH_SIZE, num_workers=cfg.DATALOADER.NUM_WORKERS,
+                              pin_memory=True, drop_last=True)
+
+    query_loader = DataLoader(ImageData(_data.query, TestTransform(flip=False)),
+                              batch_size=cfg.DATALOADER.BATCH_SIZE, num_workers=cfg.DATALOADER.NUM_WORKERS,
+                              pin_memory=True)
+
+    gallery_loader = DataLoader(ImageData(_data.gallery, TestTransform(flip=False)),
+                                batch_size=cfg.DATALOADER.BATCH_SIZE, num_workers=cfg.DATALOADER.NUM_WORKERS,
+                                pin_memory=True)
+
+    return train_loader, query_loader, gallery_loader, num_train_pids