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Update according to issue #12

pull/19/head
shaoniangu 2019-03-26 16:22:05 +08:00
parent 2b4296a2c5
commit 8cbe846f9d
15 changed files with 94 additions and 25 deletions
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11
Experiment-pretrain_choice-all_tricks-tri_center-market.sh 100644
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@ -0,0 +1,11 @@
# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005
# Dataset 1: market1501
# imagesize: 256x128
# batchsize: 16x4
# warmup_step 10
# random erase prob 0.5
# labelsmooth: on
# last stride 1
# bnneck on
# with center loss
python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('2')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" MODEL.PRETRAIN_PATH "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-pretrain_choice_all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_2.pth')" OUTPUT_DIR "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-pretrain_choice_all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005')"

2
Test-all_tricks-tri_center-feat_after_bn-cos-duke.sh
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@ -9,4 +9,4 @@
# bnneck on
# with center loss
# without re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

2
Test-all_tricks-tri_center-feat_after_bn-cos-market.sh
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@ -9,4 +9,4 @@
# bnneck on
# with center loss
# without re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

2
Test-all_tricks-without_center-feat_after_bn-cos-duke.sh
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@ -9,4 +9,4 @@
# bnneck on
# without center loss
# without re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on/resnet50_model_120.pth')"

2
Test-all_tricks-without_center-feat_after_bn-cos-market.sh
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@ -9,4 +9,4 @@
# bnneck on
# without center loss
# without re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on/resnet50_model_120.pth')"

2
Test-reranking-all_tricks-tri_center-feat_after_bn-cos-duke.sh
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@ -9,4 +9,4 @@
# bnneck on
# with center loss
# with re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" TEST.RE_RANKING "('yes')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" DATASETS.NAMES "('dukemtmc')" TEST.RE_RANKING "('yes')" DATASETS.ROOT_DIR "('/home/haoluo/data')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

2
Test-reranking-all_tricks-tri_center-feat_after_bn-cos-market.sh
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@ -9,4 +9,4 @@
# bnneck on
# with center loss
# with re-ranking
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.RE_RANKING "('yes')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" TEST.RE_RANKING "('yes')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005/resnet50_model_120.pth')"

3
config/defaults.py
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@ -27,6 +27,9 @@ _C.MODEL.NAME = 'resnet50'
_C.MODEL.LAST_STRIDE = 1
# Path to pretrained model of backbone
_C.MODEL.PRETRAIN_PATH = ''
# Use ImageNet pretrained model to initialize backbone or use self trained model to initialize the whole model
# Options: 'imagenet' or 'self'
_C.MODEL.PRETRAIN_CHOICE = 'imagenet'
# If train with BNNeck, options: 'bnneck' or 'no'
_C.MODEL.NECK = 'bnneck'
# If train loss include center loss, options: 'yes' or 'no'. Loss with center loss has different optimizer configuration

1
configs/baseline.yml
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@ -1,4 +1,5 @@
MODEL:
PRETRAIN_CHOICE: 'imagenet'
PRETRAIN_PATH: '/home/haoluo/.torch/models/resnet50-19c8e357.pth'
LAST_STRIDE: 2
NECK: 'no'

1
configs/softmax_triplet.yml
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@ -1,4 +1,5 @@
MODEL:
PRETRAIN_CHOICE: 'imagenet'
PRETRAIN_PATH: '/home/haoluo/.torch/models/resnet50-19c8e357.pth'
METRIC_LOSS_TYPE: 'triplet'
IF_LABELSMOOTH: 'on'

1
configs/softmax_triplet_with_center.yml
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@ -1,4 +1,5 @@
MODEL:
PRETRAIN_CHOICE: 'imagenet'
PRETRAIN_PATH: '/home/haoluo/.torch/models/resnet50-19c8e357.pth'
METRIC_LOSS_TYPE: 'triplet_center'
IF_LABELSMOOTH: 'on'

18
engine/trainer.py
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@ -130,7 +130,8 @@ def do_train(
optimizer,
scheduler,
loss_fn,
num_query
num_query,
start_epoch
):
log_period = cfg.SOLVER.LOG_PERIOD
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
@ -155,6 +156,10 @@ def do_train(
RunningAverage(output_transform=lambda x: x[0]).attach(trainer, 'avg_loss')
RunningAverage(output_transform=lambda x: x[1]).attach(trainer, 'avg_acc')
@trainer.on(Events.STARTED)
def start_training(engine):
engine.state.epoch = start_epoch
@trainer.on(Events.EPOCH_STARTED)
def adjust_learning_rate(engine):
scheduler.step()
@ -201,7 +206,8 @@ def do_train_with_center(
optimizer_center,
scheduler,
loss_fn,
num_query
num_query,
start_epoch
):
log_period = cfg.SOLVER.LOG_PERIOD
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
@ -218,7 +224,9 @@ def do_train_with_center(
timer = Timer(average=True)
trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'model': model.state_dict(),
'optimizer': optimizer.state_dict()})
'optimizer': optimizer.state_dict(),
'optimizer_center': optimizer_center.state_dict()})
timer.attach(trainer, start=Events.EPOCH_STARTED, resume=Events.ITERATION_STARTED,
pause=Events.ITERATION_COMPLETED, step=Events.ITERATION_COMPLETED)
@ -226,6 +234,10 @@ def do_train_with_center(
RunningAverage(output_transform=lambda x: x[0]).attach(trainer, 'avg_loss')
RunningAverage(output_transform=lambda x: x[1]).attach(trainer, 'avg_acc')
@trainer.on(Events.STARTED)
def start_training(engine):
engine.state.epoch = start_epoch
@trainer.on(Events.EPOCH_STARTED)
def adjust_learning_rate(engine):
scheduler.step()

2
modeling/__init__.py
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@ -10,5 +10,5 @@ from .baseline import Baseline
def build_model(cfg, num_classes):
# if cfg.MODEL.NAME == 'resnet50':
# model = Baseline(num_classes, cfg.MODEL.LAST_STRIDE, cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.NECK, cfg.TEST.NECK_FEAT)
model = Baseline(num_classes, cfg.MODEL.LAST_STRIDE, cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.NECK, cfg.TEST.NECK_FEAT, cfg.MODEL.NAME)
model = Baseline(num_classes, cfg.MODEL.LAST_STRIDE, cfg.MODEL.PRETRAIN_PATH, cfg.MODEL.NECK, cfg.TEST.NECK_FEAT, cfg.MODEL.NAME, cfg.MODEL.PRETRAIN_CHOICE)
return model

14
modeling/baseline.py
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@ -37,7 +37,7 @@ def weights_init_classifier(m):
class Baseline(nn.Module):
in_planes = 2048
def __init__(self, num_classes, last_stride, model_path, neck, neck_feat, model_name):
def __init__(self, num_classes, last_stride, model_path, neck, neck_feat, model_name, pretrain_choice):
super(Baseline, self).__init__()
if model_name == 'resnet18':
self.base = ResNet(last_stride=last_stride,
@ -117,14 +117,14 @@ class Baseline(nn.Module):
last_stride=last_stride)
elif model_name == 'senet154':
self.base = SENet(block=SEBottleneck,
layers=[3, 8, 36, 3],
layers=[3, 8, 36, 3],
groups=64,
reduction=16,
dropout_p=0.2,
last_stride=last_stride)
self.base.load_param(model_path)
if pretrain_choice == 'imagenet':
self.base.load_param(model_path)
self.gap = nn.AdaptiveAvgPool2d(1)
# self.gap = nn.AdaptiveMaxPool2d(1)
self.num_classes = num_classes
@ -167,6 +167,6 @@ class Baseline(nn.Module):
def load_param(self, trained_path):
param_dict = torch.load(trained_path)
for i in param_dict:
if 'classifier' in i:
continue
self.state_dict()[i].copy_(param_dict[i])
# if 'classifier' in i:
# continue
self.state_dict()[i].copy_(param_dict[i])

56
tools/train.py
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@ -7,6 +7,7 @@
import argparse
import os
import sys
import torch
from torch.backends import cudnn
@ -31,11 +32,28 @@ def train(cfg):
if cfg.MODEL.IF_WITH_CENTER == 'no':
print('Train without center loss, the loss type is', cfg.MODEL.METRIC_LOSS_TYPE)
optimizer = make_optimizer(cfg, model)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
loss_func = make_loss(cfg, num_classes) # modified by gu
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD, start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
else:
print('Only support pretrain_choice for imagenet and self, but got {}'.format(cfg.MODEL.PRETRAIN_CHOICE))
arguments = {}
do_train(
@ -44,19 +62,40 @@ def train(cfg):
train_loader,
val_loader,
optimizer,
scheduler,
scheduler, # modify for using self trained model
loss_func,
num_query
num_query,
start_epoch # add for using self trained model
)
elif cfg.MODEL.IF_WITH_CENTER == 'yes':
print('Train with center loss, the loss type is', cfg.MODEL.METRIC_LOSS_TYPE)
loss_func, center_criterion = make_loss_with_center(cfg, num_classes) # modified by gu
optimizer, optimizer_center = make_optimizer_with_center(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
# scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
# cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
arguments = {}
# Add for using self trained model
if cfg.MODEL.PRETRAIN_CHOICE == 'self':
start_epoch = eval(cfg.MODEL.PRETRAIN_PATH.split('/')[-1].split('.')[0].split('_')[-1])
print('Start epoch:', start_epoch)
path_to_optimizer = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer')
print('Path to the checkpoint of optimizer:', path_to_optimizer)
path_to_optimizer_center = cfg.MODEL.PRETRAIN_PATH.replace('model', 'optimizer_center')
print('Path to the checkpoint of optimizer_center:', path_to_optimizer_center)
model.load_state_dict(torch.load(cfg.MODEL.PRETRAIN_PATH))
optimizer.load_state_dict(torch.load(path_to_optimizer))
optimizer_center.load_state_dict(torch.load(path_to_optimizer_center))
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD, start_epoch)
elif cfg.MODEL.PRETRAIN_CHOICE == 'imagenet':
start_epoch = 0
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_ITERS, cfg.SOLVER.WARMUP_METHOD)
else:
print('Only support pretrain_choice for imagenet and self, but got {}'.format(cfg.MODEL.PRETRAIN_CHOICE))
do_train_with_center(
cfg,
model,
@ -65,9 +104,10 @@ def train(cfg):
val_loader,
optimizer,
optimizer_center,
scheduler,
scheduler, # modify for using self trained model
loss_func,
num_query
num_query,
start_epoch # add for using self trained model
)
else:
print("Unsupported value for cfg.MODEL.IF_WITH_CENTER {}, only support yes or no!\n".format(cfg.MODEL.IF_WITH_CENTER))