Clean the code.

Author: huangzh13

README.md

@@ -2,6 +2,14 @@
 
 ## Results
 
+| | Rank@1 | mAP| 
+| -------- | ----- | ---- | 
+| Market1501 | 88.84% | 71.59% |
+| DukeMTMC | 90.17% | 74.02% | 
+| CUHK03-labeled | 92.64% | 77.47% | 
+| CUHK03-detected | 88.03% | 71.40% | 
+| CUHK01 | 91.83% | 78.32% | 
+
 ## Test
 
 ## Train

config/default.py

@@ -25,7 +25,7 @@
 # Size of the image during test
 _C.INPUT.SIZE_TEST = [384, 128]
 # Random probability for image horizontal flip
-_C.INPUT.HF_PROB = 0.5
+# _C.INPUT.HF_PROB = 0.5
 
 # Values to be used for image normalization
 # _C.INPUT.PIXEL_MEAN = [0.5, 0.5, 0.5]
@@ -67,7 +67,6 @@
 
 _C.SOLVER.MAX_EPOCHS = 120
 _C.SOLVER.OPTIMIZER_NAME = "Adam"
-
 _C.SOLVER.BASE_LR = 3e-4
 
 # SGD
@@ -78,16 +77,16 @@
 # Adam
 _C.SOLVER.WEIGHT_DECAY = 0.0005
 
-_C.SOLVER.CHECK_PERIOD = 50
 _C.SOLVER.EVAL_PERIOD = 50
 _C.SOLVER.PRINT_FREQ = 10
 
+# LR Scheduler
 _C.SCHEDULER = CN()
 _C.SCHEDULER.NAME = 'StepLR'
 _C.SCHEDULER.STEP = 5
 _C.SCHEDULER.GAMMA = 0.1
 
-# warm up factor
+# Warm up factor
 _C.SCHEDULER.WARMUP_FACTOR = 100
 # iterations of warm up
 _C.SCHEDULER.WARMUP_ITERS = 20
@@ -98,6 +97,4 @@
 # This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will
 # see 2 images per batch
 _C.TEST = CN()
-
 _C.TEST.BATCH_SIZE = 128
-_C.TEST.WEIGHTS_NAME = ''

configs/sample.yaml

@@ -1,4 +1,4 @@
-OUTPUT_DIR: "./checkpoint/CUHK03L/Exp-13"
+OUTPUT_DIR: "./checkpoint/CUHK03D/Exp-0922-1"
 DEVICE: "cuda"
 DEVICE_ID: ('3')
 
@@ -7,10 +7,10 @@ MODEL:
   ARCH: 'resnet50'
 
 INPUT:
-  HF_PROB: 0.5 # random horizontal flip
+  SIZE_TRAIN: [384, 128]
 
 DATASETS:
-  NAME: 'CUHK03/labeled'
+  NAME: 'CUHK03/detected'
   ROOT: '/home/hzh/data'
 
 DATALOADER:
@@ -21,24 +21,23 @@ DATALOADER:
 SOLVER:
   LOSS: 'softmax_triplet'
   OPTIMIZER_NAME: 'Adam'
-  MAX_EPOCHS: 20
+  MAX_EPOCHS: 360
   BASE_LR: 0.0008
   WEIGHT_DECAY: 0.0005
 
-  CHECK_PERIOD: 5
-  EVAL_PERIOD: 4
+  EVAL_PERIOD: 5
   PRINT_FREQ: 10
 
 SCHEDULER:
   NAME: 'WarmupStepLR'
-  STEP: 5
+  STEP: 40
   GAMMA: 0.5
 
   WARMUP_FACTOR: 100
-  WARMUP_ITERS: 5
+  WARMUP_ITERS: 20
 
 
 TEST:
-  BATCH_SIZE: 128
+  BATCH_SIZE: 64
 
 

configs/sampleAdam.yaml

@@ -1,4 +1,4 @@
-OUTPUT_DIR: "./checkpoint/CUHK03L/Exp-12"
+OUTPUT_DIR: "./checkpoint/CUHK03L/Exp-0922-1"
 DEVICE: "cuda"
 DEVICE_ID: ('3')
 
@@ -7,7 +7,7 @@ MODEL:
   ARCH: 'resnet50'
 
 INPUT:
-  HF_PROB: 0.5 # random horizontal flip
+  SIZE_TRAIN: [384, 128]
 
 DATASETS:
   NAME: 'CUHK03/labeled'
@@ -21,12 +21,11 @@ DATALOADER:
 SOLVER:
   LOSS: 'softmax_triplet'
   OPTIMIZER_NAME: 'Adam'
-  MAX_EPOCHS: 320
+  MAX_EPOCHS: 360
   BASE_LR: 0.0008
   WEIGHT_DECAY: 0.0005
 
-  CHECK_PERIOD: 4
-  EVAL_PERIOD: 4
+  EVAL_PERIOD: 5
   PRINT_FREQ: 10
 
 SCHEDULER:
@@ -40,6 +39,5 @@ SCHEDULER:
 
 TEST:
   BATCH_SIZE: 64
-  WEIGHTS_NAME: Baseline_epoch_7.pth
 
 

configs/sample_Adam_CUHK03L_Warmup.yaml

@@ -1,40 +1,42 @@
-OUTPUT_DIR: "./checkpoint/CUHK03L/Exp-1"
+OUTPUT_DIR: "./checkpoint/DukeMTMC/Exp-0922-1"
 DEVICE: "cuda"
-DEVICE_ID: ('1')
+DEVICE_ID: ('3')
 
 MODEL:
   NAME: 'baseline'
   ARCH: 'resnet50'
 
 INPUT:
-  HF_PROB: 0.5 # random horizontal flip
+  SIZE_TRAIN: [384, 128]
 
 DATASETS:
-  NAME: 'CUHK03/labeled'
+  NAME: 'DukeMTMC'
   ROOT: '/home/hzh/data'
 
 DATALOADER:
-  NUM_WORKERS: 2
+  NUM_WORKERS: 4
   BATCH_SIZE: 64
   NUM_INSTANCES: 4
 
 SOLVER:
   LOSS: 'softmax_triplet'
   OPTIMIZER_NAME: 'Adam'
-  MAX_EPOCHS: 320
+  MAX_EPOCHS: 360
   BASE_LR: 0.0008
   WEIGHT_DECAY: 0.0005
 
-  CHECK_PERIOD: 5
   EVAL_PERIOD: 5
   PRINT_FREQ: 10
 
 SCHEDULER:
-  NAME: 'StepLR'
+  NAME: 'WarmupStepLR'
   STEP: 40
   GAMMA: 0.5
 
+  WARMUP_FACTOR: 100
+  WARMUP_ITERS: 20
+
 TEST:
-  BATCH_SIZE: 128
+  BATCH_SIZE: 64
 
 

configs/sample_Adam_CUHK03L_Warmup_Exp-13.yaml renamed to configs/sample_CUHK03D.yaml

@@ -1,32 +1,31 @@
-OUTPUT_DIR: "./checkpoint/Market1501/Exp-4"
+OUTPUT_DIR: "./checkpoint/Market1501/Exp-0922-1"
 DEVICE: "cuda"
-DEVICE_ID: ('1')
+DEVICE_ID: ('3')
 
 MODEL:
-  NAME: 'PCB'
+  NAME: 'baseline'
   ARCH: 'resnet50'
 
 INPUT:
-  HF_PROB: 0.5 # random horizontal flip
+  SIZE_TRAIN: [384, 128]
 
 DATASETS:
   NAME: 'Market1501'
   ROOT: '/home/hzh/data'
 
 DATALOADER:
-  NUM_WORKERS: 2
+  NUM_WORKERS: 4
   BATCH_SIZE: 64
   NUM_INSTANCES: 4
 
 SOLVER:
   LOSS: 'softmax_triplet'
   OPTIMIZER_NAME: 'Adam'
-  MAX_EPOCHS: 320
+  MAX_EPOCHS: 360
   BASE_LR: 0.0008
   WEIGHT_DECAY: 0.0005
 
-  CHECK_PERIOD: 5
-  EVAL_PERIOD: 1
+  EVAL_PERIOD: 5
   PRINT_FREQ: 10
 
 SCHEDULER:
@@ -39,6 +38,6 @@ SCHEDULER:
 
 
 TEST:
-  BATCH_SIZE: 128
+  BATCH_SIZE: 64
 
 

configs/sample_Adam_Market1501_Warmup.yaml renamed to configs/sample_CUHK03L.yaml

@@ -45,7 +45,7 @@ def __call__(self, x=None):
         return x
 
 
-class RandomErasing(object):
+class RandomErasing:
     """ Randomly selects a rectangle region in an image and erases its pixels.
         'Random Erasing Data Augmentation' by Zhong et al.
         See https://arxiv.org/pdf/1708.04896.pdf

configs/sample_Adam_CUHK03L.yaml renamed to configs/sample_DukeMTMC.yaml

@@ -81,7 +81,7 @@ def train(cfg):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description="Person Re-identification Project.")
-    parser.add_argument('--config', default='./configs/sample_Adam_Market1501_Warmup.yaml')
+    parser.add_argument('--config', default='./configs/sample.yaml')
     args = parser.parse_args()
 
     from config import cfg as opt

configs/PCB_Adam_Market1501_Warmup.yaml renamed to configs/sample_Market1501.yaml

@@ -0,0 +1,4 @@
+python train.py --config ./configs/sample_CUHK03L.yaml
+python train.py --config ./configs/sample_CUHK03D.yaml
+python train.py --config ./configs/sample_DukeMTMC.yaml
+python train.py --config ./configs/sample_Market1501.yaml