update

Add cuhk03

config/defaults.py

@@ -43,7 +43,7 @@ _C.INPUT.PADDING = 10
 # -----------------------------------------------------------------------------
 _C.DATASETS = CN()
 # List of the dataset names for training, as present in paths_catalog.py
-_C.DATASETS.NAMES = ()
+_C.DATASETS.NAMES = ("cuhk03",)
 
 # -----------------------------------------------------------------------------
 # DataLoader

data/build.py

@@ -11,6 +11,7 @@ import re
 from fastai.vision import *
 from .transforms import RandomErasing
 from .samplers import RandomIdentitySampler
+from .datasets import CUHK03
 
 
 def get_data_bunch(cfg):
@@ -39,7 +40,6 @@ def get_data_bunch(cfg):
             v_paths.append([img_path,pid,camid])
         return v_paths
 
-
     market_train_path = 'datasets/Market-1501-v15.09.15/bounding_box_train'
     duke_train_path = 'datasets/DukeMTMC-reID/bounding_box_train'
     cuhk03_train_path = 'datasets/cuhk03/'
@@ -58,12 +58,11 @@ def get_data_bunch(cfg):
             train_img_names.extend(_process_dir(duke_train_path))
         elif d == 'beijing':
             train_img_names.extend(_process_dir(bjStation_train_path, True))
+        elif d == 'cuhk03':
+            train_img_names.extend(CUHK03().train)
         else:
             raise NameError("{} is not available".format(d))
         
-    # train_img_names = _process_dir(market_train_path) + _process_dir(duke_train_path) + _process_dir(bjStation_train_path)
-    # train_img_names = _process_dir(market_train_path)
-    # train_img_names = CUHK03().train
     train_names = [i[0] for i in train_img_names]
 
     query_names = _process_dir(bj_query_path)
@@ -77,17 +76,17 @@ def get_data_bunch(cfg):
 
     def get_labels(file_path):
         """ Suitable for muilti-dataset training """
-        # if 'cuhk03' in file_path:
-            # prefix = 'cuhk'
-            # pid = file_path.split('/')[-1].split('_')[1]
-        # else:
+        if 'cuhk03' in file_path:
+            prefix = 'cuhk'
+            pid = '_'.join(file_path.split('/')[-1].split('_')[0:2])
+        else:
             prefix = file_path.split('/')[1]
             pat = re.compile(r'([-\d]+)_c(\d)')
             pid, _ = pat.search(file_path).groups()
         return prefix + '_' + pid
 
     data_bunch = ImageDataBunch.from_name_func('datasets', train_names, label_func=get_labels, valid_pct=0,
-                                               size=(256, 128), ds_tfms=ds_tfms, bs=cfg.SOLVER.IMS_PER_BATCH,
+                                               size=cfg.INPUT.SIZE_TRAIN, ds_tfms=ds_tfms, bs=cfg.SOLVER.IMS_PER_BATCH,
                                                val_bs=cfg.TEST.IMS_PER_BATCH)
 
     if 'triplet' in cfg.DATALOADER.SAMPLER:

data/samplers/triplet_sampler.py

@@ -34,7 +34,11 @@ class RandomIdentitySampler(Sampler):
         self.index_dic = defaultdict(list)
         for index, fname in enumerate(self.data_source):
             prefix = fname.split('/')[1]
+            try:
                 pid, _ = pat.search(fname).groups()
+            except:
+                prefix = fname.split('/')[4]
+                pid = '_'.join(fname.split('/')[-1].split('_')[:2])
             pid = prefix + '_' + pid
             self.index_dic[pid].append(index)
         self.pids = list(self.index_dic.keys())

scripts/test_model.sh

@@ -1,6 +1,7 @@
-gpu=3
+gpu=2
 
 CUDA_VISIBLE_DEVICES=$gpu python tools/test.py -cfg='configs/softmax_triplet.yml' \
+INPUT.SIZE_TRAIN '(384, 128)' \
 DATASETS.NAMES '("market1501","duke","beijing")'  \
 OUTPUT_DIR 'logs/test' \
 TEST.WEIGHT 'logs/beijing/market+duke+bj/models/model_149.pth'

scripts/train_market_bj.sh

@@ -0,0 +1,5 @@
+gpu=3
+
+CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
+DATASETS.NAMES '("market1501","beijing",)'  \
+OUTPUT_DIR 'logs/beijing/market_bj'

scripts/train_market_duke_cuhk_bj.sh

@@ -0,0 +1,5 @@
+gpu=2
+
+CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
+DATASETS.NAMES '("market1501","duke","cuhk03","beijing")'  \
+OUTPUT_DIR 'logs/beijing/market_duke_cuhk03_beijing_256x128_bs512'

tools/test.py

@@ -50,10 +50,11 @@ def main():
     cudnn.benchmark = True
 
     data_bunch, test_labels, num_query = get_data_bunch(cfg)
-    model = build_model(cfg, data_bunch.c)
-    state_dict = torch.load(cfg.TEST.WEIGHT)
-    model.load_state_dict(state_dict['model'])
-    model.cuda()
+    # model = build_model(cfg, data_bunch.c)
+    # state_dict = torch.load(cfg.TEST.WEIGHT)
+    # model.load_state_dict(state_dict['model'])
+    # model.cuda()
+    model = torch.jit.load("/export/home/lxy/reid_baseline/pcb_model_v0.2.pt")
 
     inference(cfg, model, data_bunch, test_labels, num_query)
 

tools/train.py

@@ -27,8 +27,8 @@ def train(cfg):
 
     # prepare model
     model = build_model(cfg, data_bunch.c)
-    state_dict = torch.load("logs/beijing/market_duke_softmax_triplet_256_128_bs512/models/model_149.pth")
-    model.load_params_wo_fc(state_dict['model'])
+    # state_dict = torch.load("logs/beijing/market_duke_softmax_triplet_256_128_bs512/models/model_149.pth")
+    # model.load_params_wo_fc(state_dict['model'])
 
     opt_func = partial(torch.optim.Adam)