fix pre-train model bugs

fix bugs locks when downloading pre-train model

README.md

@@ -15,7 +15,7 @@ We write a [chinese blog](https://l1aoxingyu.github.io/blogpages/reid/2020/05/29
 
 ## Installation
 
-See [INSTALL.md](https://github.com/JDAI-CV/fast-reid/blob/master/INSTALL.md).
+See [INSTALL.md](https://github.com/JDAI-CV/fast-reid/docs/blob/master/INSTALL.md).
 
 ## Quick Start
 

docs/INSTALL.md

@@ -8,3 +8,7 @@
 - [yacs](https://github.com/rbgirshick/yacs)
 - Cython (optional to compile evaluation code)
 - tensorboard (needed for visualization): `pip install tensorboard`
+- gdown (for automatically downloading pre-train model)
+- sklearn
+- termcolor
+- tabulate

docs/MODEL_ZOO.md

@@ -60,7 +60,7 @@ Bag of Specials(BoS):
 | :---: | :---: | :---: |:---: | :---: |:---: |
 | [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50.yml) | ImageNet | 95.3% | 88.2% | 66.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50.pth) |
 | [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50-ibn.yml) | ImageNet | 95.1% | 88.7% | 67.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50-ibn.pth) |
-| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml) | ImageNet | 94.7% | 87.1% | 62.2% | - |
+| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml) | ImageNet | 95.3% | 89.3% | 68.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_S50.pth) |
 | [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R101-ibn.yml) | ImageNet | 95.5% | 89.5% | 69.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R101-ibn.pth) |
 
 **SBS**:
@@ -76,7 +76,7 @@ Bag of Specials(BoS):
 
 | Method | Pretrained | Rank@1 | mAP | mINP | download |
 | :---: | :---: | :---: |:---: | :---: | :---:|
-| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/mgn_R50-ibn.yml) | ImageNet | 95.8% | 89.7% | 67.0% | - |
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/mgn_R50-ibn.yml) | ImageNet | 95.8% | 89.8% | 67.7% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_mgn_R50-ibn.pth) |
 
 ### DukeMTMC Baseline
 

fastreid/config/defaults.py

@@ -186,7 +186,7 @@ _C.SOLVER = CN()
 
 _C.SOLVER.OPT = "Adam"
 
-_C.SOLVER.MAX_ITER = 40000
+_C.SOLVER.MAX_ITER = 120
 
 _C.SOLVER.BASE_LR = 3e-4
 _C.SOLVER.BIAS_LR_FACTOR = 1.
@@ -222,7 +222,7 @@ _C.SOLVER.SWA.LR_FACTOR = 10.
 _C.SOLVER.SWA.ETA_MIN_LR = 3.5e-6
 _C.SOLVER.SWA.LR_SCHED = False
 
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
+_C.SOLVER.CHECKPOINT_PERIOD = 20
 
 # Number of images per batch across all machines.
 # This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
@@ -233,7 +233,7 @@ _C.SOLVER.IMS_PER_BATCH = 64
 # see 2 images per batch
 _C.TEST = CN()
 
-_C.TEST.EVAL_PERIOD = 50
+_C.TEST.EVAL_PERIOD = 20
 
 # Number of images per batch in one process.
 _C.TEST.IMS_PER_BATCH = 64

fastreid/data/build.py

@@ -70,7 +70,7 @@ def build_reid_test_loader(cfg, dataset_name):
     test_loader = DataLoader(
         test_set,
         batch_sampler=batch_sampler,
-        num_workers=4,  # save some memory
+        num_workers=0,  # save some memory
         collate_fn=fast_batch_collator)
     return test_loader, len(dataset.query)
 

fastreid/data/datasets/cuhk03.py

@@ -31,8 +31,7 @@ class CUHK03(ImageDataset):
     dataset_url = None
     dataset_name = "cuhk03"
 
-    def __init__(self, root='datasets', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, **kwargs):
-        # self.root = osp.abspath(osp.expanduser(root))
+    def __init__(self, root='datasets', split_id=0, cuhk03_labeled=True, cuhk03_classic_split=False, **kwargs):
         self.root = root
         self.dataset_dir = osp.join(self.root, self.dataset_dir)
 
@@ -82,7 +81,6 @@ class CUHK03(ImageDataset):
         del tmp_train
         query = split['query']
         gallery = split['gallery']
-        from ipdb import set_trace; set_trace()
 
         super(CUHK03, self).__init__(train, query, gallery, **kwargs)
 
@@ -271,5 +269,5 @@ class CUHK03(ImageDataset):
             'num_gallery_pids': gallery_info[1],
             'num_gallery_imgs': gallery_info[2]
         }]
-        with PathManager.open(self.split_new_lab_json_pat, 'w') as f:
+        with PathManager.open(self.split_new_lab_json_path, 'w') as f:
             json.dump(split, f, indent=4, separators=(',', ': '))

fastreid/modeling/backbones/osnet.py

@@ -448,7 +448,7 @@ def init_pretrained_weights(model, key=''):
     if not os.path.exists(cached_file):
         if comm.is_main_process():
             gdown.download(model_urls[key], cached_file, quiet=False)
-        else:
+
     comm.synchronize()
 
     state_dict = torch.load(cached_file, map_location=torch.device('cpu'))

fastreid/modeling/backbones/regnet/regnet.py

@@ -518,7 +518,7 @@ def init_pretrained_weights(key):
     if not os.path.exists(cached_file):
         if comm.is_main_process():
             gdown.download(model_urls[key], cached_file, quiet=False)
-        else:
+
     comm.synchronize()
 
     logger.info(f"Loading pretrained model from {cached_file}")

fastreid/modeling/backbones/resnest.py

@@ -17,7 +17,6 @@ from fastreid.layers import (
 )
 
 from fastreid.utils.checkpoint import get_unexpected_parameters_message, get_missing_parameters_message
-from fastreid.utils import comm
 
 from .build import BACKBONE_REGISTRY
 

fastreid/modeling/backbones/resnet.py

@@ -275,7 +275,7 @@ def init_pretrained_weights(key):
     if not os.path.exists(cached_file):
         if comm.is_main_process():
             gdown.download(model_urls[key], cached_file, quiet=False)
-        else:
+
     comm.synchronize()
 
     logger.info(f"Loading pretrained model from {cached_file}")

fastreid/modeling/backbones/resnext.py

@@ -209,7 +209,7 @@ def init_pretrained_weights(key):
     if not os.path.exists(cached_file):
         if comm.is_main_process():
             gdown.download(model_urls[key], cached_file, quiet=False)
-        else:
+
     comm.synchronize()
 
     logger.info(f"Loading pretrained model from {cached_file}")
@@ -217,6 +217,7 @@ def init_pretrained_weights(key):
 
     return state_dict
 
+
 @BACKBONE_REGISTRY.register()
 def build_resnext_backbone(cfg):
     """