update readme

.gitignore

@@ -4,5 +4,4 @@ __pycache__
 .vscode
 csrc/eval_cylib/build/
 logs/
-.ipynb_checkpoints
-tests/
+.ipynb_checkpoints

README.md

@@ -1,12 +1,21 @@
 # ReID_baseline
-**The results in CUHK03 is wrong becuase I use the same evaluation protocol with market1501 and duke. I will fix this later. You can use the right evaluation protocol to get accurate result.**
 
-Baseline model (with bottleneck) for person ReID (using softmax and triplet loss).
+A strong baseline (state-of-the-art) for person re-identification.
 
 We support
 - [x] easy dataset preparation
 - [x] end-to-end training and evaluation
+- [ ] multi-GPU distributed training
+- [ ] fast training speed with fp16
+- [ ] support both image and video reid
+- [x] multi-dataset training
+- [x] cross-dataset evaluation
 - [x] high modular management
+- [x] state-of-the-art performance with simple model
+- [ ] high efficient backbone
+- [ ] advanced training techniques
+- [ ] various loss functions
+- [ ] visualization tools
 
 ## Get Started
 The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.

datasets

@@ -1 +1 @@
-/export/home/datasets/
+../datasets/

engine/trainer.py

@@ -20,7 +20,7 @@ class TrackValue(Callback):
     total_iter: int
 
     def on_epoch_end(self, epoch, smooth_loss, **kwargs):
-        self.logger.info('Epoch {}[Iter {}], loss: {:.4f}'.format(epoch, self.total_iter, smooth_loss.item()))
+        self.logger.info(f'Epoch {epoch}[Iter {self.total_iter}], loss: {smooth_loss.item():.4f}')
             
 
 @dataclass
@@ -76,10 +76,10 @@ class TestModel(LearnerCallback):
             distmat.addmm_(1, -2, qf, gf.t())
             distmat = to_np(distmat)
             cmc, mAP = evaluate(distmat, self.q_pids, self.g_pids, self.q_camids, self.g_camids)
-            self._logger.info("Test Results - Epoch: {}".format(epoch + 1))
-            self._logger.info("mAP: {:.1%}".format(mAP))
+            self._logger.info(f"Test Results - Epoch: {epoch+1}")
+            self._logger.info(f"mAP: {mAP:.1%}")
             for r in [1, 5, 10]:
-                self._logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1]))
+                self._logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r-1]:.1%}")
             self.learn.save("model_{}".format(epoch))
 
 

scripts/debug.sh

@@ -1,8 +0,0 @@
-gpu=2
-
-CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
-DATASETS.NAMES '("market1501",)'  \
-DATASETS.TEST_NAMES 'market1501' \
-INPUT.DO_LIGHTING 'True' \
-INPUT.MIXUP 'True' \
-OUTPUT_DIR 'logs/test'

scripts/train_bj.sh

@@ -1,21 +0,0 @@
-gpu=1
-
-CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
-DATASETS.NAMES '("market1501","duke","cuhk03","beijing")'  \
-DATASETS.TEST_NAMES 'bj' \
-INPUT.DO_LIGHTING 'False' \
-OUTPUT_DIR 'logs/2019.8.14/bj/baseline'
-
-# CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
-# DATASETS.NAMES '("market1501","duke","cuhk03","beijing")'  \
-# DATASETS.TEST_NAMES 'bj' \
-# INPUT.DO_LIGHTING 'True' \
-# OUTPUT_DIR 'logs/2019.8.9/bj/lighting'
-
-# CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
-# DATASETS.NAMES '("market1501","duke","cuhk03","beijing")'  \
-# DATASETS.TEST_NAMES 'bj' \
-# MODEL.BACKBONE 'resnet50_ibn' \
-# MODEL.PRETRAIN_PATH '/export/home/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
-# INPUT.DO_LIGHTING 'True' \
-# OUTPUT_DIR 'logs/2019.8.14/bj/lighting_ibn7_1'

scripts/train_market.sh

@@ -3,10 +3,10 @@ gpu=0
 CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
 DATASETS.NAMES '("market1501",)'  \
 DATASETS.TEST_NAMES 'market1501' \
-MODEL.BACKBONE 'resnet50_ibn' \
-MODEL.PRETRAIN_PATH '/export/home/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
-INPUT.DO_LIGHTING 'True' \
-OUTPUT_DIR 'logs/2019.8.14/market/lighting_ibn_7_1'
+MODEL.BACKBONE 'resnet50' \
+MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50-19c8e357.pth' \
+INPUT.DO_LIGHTING 'False' \
+OUTPUT_DIR 'logs/2019.8.14/market/baseline'
 
 # CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
 # DATASETS.NAMES '("market1501",)'  \

solver/__init__.py

@@ -1,7 +0,0 @@
-# encoding: utf-8
-"""
-@author:  sherlock
-@contact: sherlockliao01@gmail.com
-"""
-
-from .adabound import *

solver/build.py

@@ -1,15 +0,0 @@
-# encoding: utf-8
-"""
-@author:  sherlock
-@contact: sherlockliao01@gmail.com
-"""
-
-from fastai.vision import *
-
-
-def make_optimizer(cfg):
-    if cfg.SOLVER.OPTIMIZER_NAME == 'SGD':
-        opt = partial(getattr(torch.optim, cfg.SOLVER.OPTIMIZER_NAME), momentum=cfg.SOLVER.MOMENTUM)
-    else:
-        opt = partial(getattr(torch.optim, cfg.SOLVER.OPTIMIZER_NAME))
-    return opt

tools/train.py

@@ -17,7 +17,6 @@ from engine.trainer import do_train
 from fastai.vision import *
 from layers import make_loss
 from modeling import build_model
-from solver import *
 from utils.logger import setup_logger
 
 
@@ -29,7 +28,7 @@ def train(cfg):
     # prepare model
     model = build_model(cfg, data_bunch.c)
 
-    opt_func = partial(torch.optim.Adam)
+    opt_fns = partial(getattr(torch.optim, cfg.SOLVER.OPTIMIZER_NAME))
 
     def warmup_multistep(start: float, end: float, pct: float):
         warmup_factor = 1
@@ -41,9 +40,7 @@ def train(cfg):
             warmup_factor = cfg.SOLVER.WARMUP_FACTOR * (1 - alpha) + alpha
         return start * warmup_factor * gamma ** bisect_right(milestones, pct)
 
-    # lr = cfg.SOLVER.BASE_LR * (cfg.SOLVER.IMS_PER_BATCH // 64)
-    lr = cfg.SOLVER.BASE_LR
-    lr_sched = Scheduler(lr, cfg.SOLVER.MAX_EPOCHS, warmup_multistep)
+    lr_sched = Scheduler(cfg.SOLVER.BASE_LR, cfg.SOLVER.MAX_EPOCHS, warmup_multistep)
 
     loss_func = make_loss(cfg)
 
@@ -52,7 +49,7 @@ def train(cfg):
         model,
         data_bunch,
         test_labels,
-        opt_func,
+        opt_fns,
         lr_sched,
         loss_func,
         num_query,
@@ -89,6 +86,5 @@ def main():
     train(cfg)
 
 
-
 if __name__ == '__main__':
     main()