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fast-reid
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1. fix minor bug 

2. update experiment results in readme
pull/43/head
liaoxingyu 2019-08-21 09:35:34 +08:00
parent aa4c8eee2c
commit 3d5f7d24aa
11 changed files with 112 additions and 62 deletions
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17
README.md
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@ -70,9 +70,18 @@ You can test your model's performance directly by running this command
python3 tools/test.py --config_file='configs/softmax.yml' TEST.WEIGHT '/save/trained_model/path'
```
## Results
## Experiment Results
| size=(256, 128) batch_size=64 (16 id x 4 imgs) | | | | | |
| :------: | :-----: | :-----: | :--: | :---: | :----: |
| softmax? | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ |
| triplet? | | ✔︎ | | ✔︎ | ✔︎ |
| ibn? | | | ✔︎ | ✔︎ | ✔︎ |
| gcnet? | | | | | ✔︎ |
| Market1501 | 93.4 (82.9) | 94.2 (86.1) |93.3 (84.3)|94.9 (86.4)|-|
| DukeMTMC-reid | 84.7 (72.7) | 87.3 (76.0) |86.7 (74.9)|87.9 (77.1)|-|
| CUHK03 | | ||||
| cfg | market1501 | dukemtmc |
| --- | -- | -- |
| softmax+triplet, size=(256, 128), batch_size=64(16 id x 4 imgs) | 93.9 (85.9) | 86.5 (75.9) |
🔥Any other tricks are welcomed！

4
config/defaults.py
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@ -63,7 +63,7 @@ _C.INPUT.P_LIGHTING=0.75
# -----------------------------------------------------------------------------
_C.DATASETS = CN()
# List of the dataset names for training
_C.DATASETS.NAMES = ("cuhk03",)
_C.DATASETS.NAMES = ("market1501",)
# List of the dataset names for testing
_C.DATASETS.TEST_NAMES = "market1501"
@ -84,6 +84,8 @@ _C.DATALOADER.NUM_INSTANCE = 16
_C.SOLVER = CN()
_C.SOLVER.OPT = "adam"
_C.SOLVER.LOSSTYPE = ("softmax",)
_C.SOLVER.MAX_EPOCHS = 50
_C.SOLVER.BASE_LR = 3e-4

15
configs/softmax.yml
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@ -1,22 +1,23 @@
MODEL:
PRETRAIN_PATH: 'home/user01/.torch/models/resnet50-19c8e357.pth'
BACKBONE: "resnet50"
INPUT:
SIZE_TRAIN: [384, 128]
SIZE_TEST: [384, 128]
PROB: 0.5 # random horizontal flip
SIZE_TRAIN: [256, 128]
SIZE_TEST: [256, 128]
FLIP_PROB: 0.5 # random horizontal flip
PADDING: 10
DATASETS:
NAMES: ('market1501')
NAMES: ("market1501",)
TEST_NAMES: 'market1501'
DATALOADER:
SAMPLER: 'softmax'
NUM_WORKERS: 8
SOLVER:
OPTIMIZER_NAME: 'Adam'
OPT: 'adam'
LOSSTYPE: ('softmax',)
MAX_EPOCHS: 120
BASE_LR: 0.00035
BIAS_LR_FACTOR: 1

3
configs/softmax_triplet.yml
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@ -13,11 +13,12 @@ DATASETS:
TEST_NAMES: "market1501"
DATALOADER:
SAMPLER: 'softmax_triplet'
SAMPLER: 'triplet'
NUM_INSTANCE: 4
SOLVER:
OPT: 'adam'
LOSSTYPE: ('softmax', 'triplet')
MAX_EPOCHS: 150
BASE_LR: 0.00035
WEIGHT_DECAY: 0.0005

2
data/build.py
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@ -78,7 +78,7 @@ def get_data_bunch(cfg):
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:
if cfg.DATALOADER.SAMPLER == 'triplet':
data_sampler = RandomIdentitySampler(train_names, cfg.SOLVER.IMS_PER_BATCH, cfg.DATALOADER.NUM_INSTANCE)
data_bunch.train_dl = data_bunch.train_dl.new(shuffle=False, sampler=data_sampler)

26
layers/__init__.py
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@ -4,28 +4,4 @@
@contact: sherlockliao01@gmail.com
"""
import torch.nn.functional as F
from .triplet_loss import TripletLoss
def make_loss(cfg):
sampler = cfg.DATALOADER.SAMPLER
triplet = TripletLoss(cfg.SOLVER.MARGIN) # triplet loss
if sampler == 'softmax':
def loss_func(out, target):
score, feat = out
return F.cross_entropy(score, target)
elif cfg.DATALOADER.SAMPLER == 'triplet':
def loss_func(out, target):
score, feat = out
return triplet(feat, target)[0]
elif cfg.DATALOADER.SAMPLER == 'softmax_triplet':
def loss_func(out, target):
score, feat = out
return F.cross_entropy(score, target) + triplet(feat, target)[0]
else:
print('expected sampler should be softmax, triplet or softmax_triplet, '
'but got {}'.format(cfg.DATALOADER.SAMPLER))
return loss_func
from .loss import reidLoss

28
layers/loss.py 100644
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@ -0,0 +1,28 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from torch import nn
from .triplet_loss import TripletLoss
__all__ = ['reidLoss']
class reidLoss(nn.Module):
def __init__(self, lossType:list, margin:float):
super().__init__()
self.lossType = lossType
self.ce_loss = nn.CrossEntropyLoss()
self.triplet_loss = TripletLoss(margin)
def forward(self, out, target):
scores, feats = out
loss = 0
if 'softmax' in self.lossType: loss += self.ce_loss(scores, target)
if 'triplet' in self.lossType: loss += self.triplet_loss(feats, target)[0]
return loss

5
modeling/backbones/resnet.py
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@ -33,7 +33,7 @@ __all__ = ['ResNet']
class IBN(nn.Module):
def __init__(self, planes):
super(IBN, self).__init__()
half1 = int(planes/2)
half1 = int(planes/8)
self.half = half1
half2 = planes - half1
self.IN = nn.InstanceNorm2d(half1, affine=True)
@ -42,8 +42,7 @@ class IBN(nn.Module):
def forward(self, x):
split = torch.split(x, self.half, 1)
out1 = self.IN(split[0].contiguous())
# out2 = self.BN(torch.cat(split[1:], dim=1).contiguous())
out2 = self.BN(split[1].contiguous())
out2 = self.BN(torch.cat(split[1:], dim=1).contiguous())
out = torch.cat((out1, out2), 1)
return out

2
scripts/train_duke.sh
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@ -1,6 +1,6 @@
gpu=0
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax.yml' \
DATASETS.NAMES '("duke",)' \
DATASETS.TEST_NAMES 'duke' \
MODEL.BACKBONE 'resnet50' \

68
scripts/train_market.sh
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@ -1,27 +1,61 @@
gpu=0
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax.yml' \
DATASETS.NAMES '("market1501",)' \
DATASETS.TEST_NAMES 'market1501' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'False' \
OUTPUT_DIR 'logs/2019.8.20/market/resnet_softmax'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
DATASETS.NAMES '("market1501",)' \
DATASETS.TEST_NAMES 'market1501' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'False' \
INPUT.DO_LIGHTING 'False' \
SOLVER.OPT 'radam' \
OUTPUT_DIR 'logs/2019.8.17/market/resnet_radam_nowarmup'
OUTPUT_DIR 'logs/2019.8.20/market/resnet_softmax_triplet'
# MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax.yml' \
DATASETS.NAMES '("market1501",)' \
DATASETS.TEST_NAMES 'market1501' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'True' \
MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
OUTPUT_DIR 'logs/2019.8.20/market/resnet_ibn_softmax'
# CUDA_VISIBLE_DEVICES=$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 'False' \
# OUTPUT_DIR 'logs/2019.8.13/market/ibn7_1'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
DATASETS.NAMES '("market1501",)' \
DATASETS.TEST_NAMES 'market1501' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'True' \
MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
OUTPUT_DIR 'logs/2019.8.20/market/resnet_ibn_softmax_triplet'
# CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
# DATASETS.NAMES '("market1501",)' \
# DATASETS.TEST_NAMES 'market1501' \
# SOLVER.IMS_PER_BATCH '64' \
# INPUT.DO_LIGHTING 'True' \
# OUTPUT_DIR 'logs/market/bs64'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax.yml' \
DATASETS.NAMES '("duke",)' \
DATASETS.TEST_NAMES 'duke' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'False' \
OUTPUT_DIR 'logs/2019.8.20/duke/resnet_softmax'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
DATASETS.NAMES '("duke",)' \
DATASETS.TEST_NAMES 'duke' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'False' \
OUTPUT_DIR 'logs/2019.8.20/duke/resnet_softmax_triplet'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax.yml' \
DATASETS.NAMES '("duke",)' \
DATASETS.TEST_NAMES 'duke' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'True' \
MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
OUTPUT_DIR 'logs/2019.8.20/duke/resnet_ibn_softmax'
CUDA_VISIBLE_DEVICES=$gpu python tools/train.py -cfg='configs/softmax_triplet.yml' \
DATASETS.NAMES '("duke",)' \
DATASETS.TEST_NAMES 'duke' \
MODEL.BACKBONE 'resnet50' \
MODEL.IBN 'True' \
MODEL.PRETRAIN_PATH '/home/user01/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar' \
OUTPUT_DIR 'logs/2019.8.20/duke/resnet_ibn_softmax_triplet'

4
tools/train.py
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@ -15,7 +15,7 @@ from config import cfg
from data import get_data_bunch
from engine.trainer import do_train
from fastai.vision import *
from layers import make_loss
from layers import reidLoss
from modeling import build_model
from solver import *
from utils.logger import setup_logger
@ -44,7 +44,7 @@ def train(cfg):
lr_sched = Scheduler(cfg.SOLVER.BASE_LR, cfg.SOLVER.MAX_EPOCHS, lr_multistep)
loss_func = make_loss(cfg)
loss_func = reidLoss(cfg.SOLVER.LOSSTYPE, cfg.SOLVER.MARGIN)
do_train(
cfg,