def _initModel(self):
# Current dataset name:
dname = self.dataset_nm
# Number of classes
num_classes = self.num_classes_dict[dname]
# Parameters
cfg = self.cfg
# ReID model
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
model = Model(
last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes
)
# Model wrapper
model_w = DataParallel(model)
# Criteria and Optimizers
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [p for n, p in model.named_parameters()
if not n.startswith('base.')]
param_groups = [{'params': finetuned_params, \
'lr': cfg.finetuned_params_lr}, \
{'params': new_params, 'lr': cfg.new_params_lr}]
optimizer = optim.SGD(
param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
# May Transfer Models and Optims to Specified Device.
# Transferring optimizer is to cope with the case when
# you load the checkpoint to a new device.
TMO(modules_optims)
self.model_w = model_w
self.modules_optims = modules_optims
self.criterion = criterion
self.TVT = TVT
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
# Lazily create SummaryWriter
writer = None
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
train_set = create_dataset(**cfg.train_set_kwargs)
num_classes = len(train_set.ids2labels)
# The combined dataset does not provide val set currently.
val_set = None if cfg.dataset == 'combined' else create_dataset(
**cfg.val_set_kwargs)
test_sets = []
test_set_names = []
if cfg.dataset == 'combined':
for name in ['market1501', 'cuhk03', 'duke']:
cfg.test_set_kwargs['name'] = name
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(name)
else:
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(cfg.dataset)
###########
# Models #
###########
model = Model(last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [
p for n, p in model.named_parameters() if not n.startswith('base.')
]
param_groups = [{
'params': finetuned_params,
'lr': cfg.finetuned_params_lr
}, {
'params': new_params,
'lr': cfg.new_params_lr
}]
optimizer = optim.SGD(param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
################################
# May Resume Models and Optims #
################################
if cfg.resume:
resume_ep, scores = load_ckpt(modules_optims, cfg.ckpt_file)
# May Transfer Models and Optims to Specified Device. Transferring optimizer
# is to cope with the case when you load the checkpoint to a new device.
TMO(modules_optims)
########
# Test #
########
def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file,
map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(
cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(normalize_feat=True, verbose=True)
def validate():
if val_set.extract_feat_func is None:
val_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n===== Test on validation set =====\n')
mAP, cmc_scores, _, _ = val_set.eval(normalize_feat=True,
to_re_rank=False,
verbose=True)
print()
return mAP, cmc_scores[0]
if cfg.only_test:
test(load_model_weight=True)
return
############
# Training #
############
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
adjust_lr_staircase(optimizer.param_groups,
[cfg.finetuned_params_lr, cfg.new_params_lr],
ep + 1, cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
# For recording loss
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_var = Variable(TVT(torch.from_numpy(labels).long()))
_, logits_list = model_w(ims_var)
loss = torch.sum(
torch.cat(
[criterion(logits, labels_var) for logits in logits_list]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1,
time.time() - step_st, loss_meter.val)
print(log)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(ep + 1,
time.time() - ep_st,
loss_meter.avg)
print(log)
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars('val scores', dict(mAP=mAP, Rank1=Rank1), ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ), ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
# Lazily create SummaryWriter
writer = None
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
train_set = create_dataset(**cfg.train_set_kwargs)
num_classes = len(train_set.ids2labels)
# The combined dataset does not provide val set currently.
val_set = None if cfg.dataset == 'combined' else create_dataset(**cfg.val_set_kwargs)
test_sets = []
test_set_names = []
if cfg.dataset == 'combined':
for name in ['market1501', 'cuhk03', 'duke']:
cfg.test_set_kwargs['name'] = name
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(name)
else:
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(cfg.dataset)
###########
# Models #
###########
model = Model(
last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes
)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [p for n, p in model.named_parameters()
if not n.startswith('base.')]
param_groups = [{'params': finetuned_params, 'lr': cfg.finetuned_params_lr},
{'params': new_params, 'lr': cfg.new_params_lr}]
optimizer = optim.SGD(
param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
################################
# May Resume Models and Optims #
################################
if cfg.resume:
resume_ep, scores = load_ckpt(modules_optims, cfg.ckpt_file)
# May Transfer Models and Optims to Specified Device. Transferring optimizer
# is to cope with the case when you load the checkpoint to a new device.
TMO(modules_optims)
########
# Test #
########
def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(
normalize_feat=True,
verbose=True)
def validate():
if val_set.extract_feat_func is None:
val_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n===== Test on validation set =====\n')
mAP, cmc_scores, _, _ = val_set.eval(
normalize_feat=True,
to_re_rank=False,
verbose=True)
print()
return mAP, cmc_scores[0]
if cfg.only_test:
test(load_model_weight=True)
return
############
# Training #
############
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
adjust_lr_staircase(
optimizer.param_groups,
[cfg.finetuned_params_lr, cfg.new_params_lr],
ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
# For recording loss
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch()
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_var = Variable(TVT(torch.from_numpy(labels).long()))
_, logits_list = model_w(ims_var)
loss = torch.sum(
torch.cat([criterion(logits, labels_var) for logits in logits_list]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1, time.time() - step_st, loss_meter.val)
print(log)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(
ep + 1, time.time() - ep_st, loss_meter.avg)
print(log)
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars(
'val scores',
dict(mAP=mAP,
Rank1=Rank1),
ep)
writer.add_scalars(
'loss',
dict(loss=loss_meter.avg, ),
ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
TVT, TMO = set_devices(cfg.sys_device_ids)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
test_set = create_dataset(**cfg.test_set_kwargs)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=0)
# Model wrapper
model_w = DataParallel(model)
# May Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
# To first load weights to CPU
map_location = (lambda storage, loc: storage)
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=map_location)
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
test_set.set_feat_func(ExtractFeature(model_w, TVT))
with measure_time('Extracting feature...', verbose=True):
feat, ids, cams, im_names, marks = test_set.extract_feat(True,
verbose=True)
#######################
# Select Query Images #
#######################
# Fix some query images, so that the visualization for different models can
# be compared.
# Sort in the order of image names
inds = np.argsort(im_names)
feat, ids, cams, im_names, marks = \
feat[inds], ids[inds], cams[inds], im_names[inds], marks[inds]
# query, gallery index mask
is_q = marks == 0
is_g = marks == 1
prng = np.random.RandomState(1)
# selected query indices
sel_q_inds = prng.permutation(range(np.sum(is_q)))[:cfg.num_queries]
q_ids = ids[is_q][sel_q_inds]
q_cams = cams[is_q][sel_q_inds]
q_feat = feat[is_q][sel_q_inds]
q_im_names = im_names[is_q][sel_q_inds]
####################
# Compute Distance #
####################
# query-gallery distance
q_g_dist = compute_dist(q_feat, feat[is_g], type='euclidean')
###########################
# Save Rank List as Image #
###########################
q_im_paths = [ospj(test_set.im_dir, n) for n in q_im_names]
save_paths = [ospj(cfg.exp_dir, 'rank_lists', n) for n in q_im_names]
g_im_paths = [ospj(test_set.im_dir, n) for n in im_names[is_g]]
for dist_vec, q_id, q_cam, q_im_path, save_path in zip(
q_g_dist, q_ids, q_cams, q_im_paths, save_paths):
rank_list, same_id = get_rank_list(dist_vec, q_id, q_cam, ids[is_g],
cams[is_g], cfg.rank_list_size)
save_rank_list_to_im(rank_list, same_id, q_im_path, g_im_paths,
save_path)
from PIL import Image import cv2 import os from bpm.model.PCBModel import PCBModel as Model from bpm.dataset.PreProcessImage import PreProcessIm from bpm.utils.utils import load_state_dict from bpm.utils.utils import set_devices ############# # Arguments # ############# sys_device_ids = (2, ) TVT, TMO = set_devices(sys_device_ids) # image input scale_im = True im_mean = [0.486, 0.459, 0.408] im_std = [0.229, 0.224, 0.225] resize_h_w = (384, 128) # model last_conv_stride = 1 last_conv_dilation = 1 num_stripes = 6 local_conv_out_channels = 256 # weight file model_weight_file = "/mnt/md1/lztao/deecamp/ReID-beyond_part_model/baseline_log/ckpt.pth"
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
# Lazily create SummaryWriter
writer = None
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
#####################################################
# Dataset #
# create train dataset for pcb loss#
#####################################################
print('start to create pcb dataset.....')
train_set = create_dataset(**cfg.pcb_train_set_kwargs)
print('train_set shape:{}'.format(len(train_set.im_names)))
num_classes = len(train_set.ids2labels)
#####################################################
# Dataset #
# create val_set test_set for pcb loss#
#####################################################
# The combined dataset does not provide val set currently.
val_set = None if cfg.pcb_dataset == 'combined' else create_dataset(
**cfg.val_set_kwargs)
test_sets = []
test_set_names = []
if cfg.pcb_dataset == 'combined':
for name in ['market1501', 'cuhk03', 'duke']:
cfg.test_set_kwargs['name'] = name
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(name)
else:
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(cfg.pcb_dataset)
########
# Test #
########
def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file,
map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(
cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(normalize_feat=True, verbose=True)
def validate():
if val_set.extract_feat_func is None:
val_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n===== Test on validation set =====\n')
mAP, cmc_scores, _, _ = val_set.eval(normalize_feat=True,
to_re_rank=False,
verbose=True)
print()
return mAP, cmc_scores[0]
#############################################################################
# train pcb model for 60eps #
#############################################################################
if not cfg.only_triplet:
model = Model(last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
num_cols=cfg.num_cols,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [
p for n, p in model.named_parameters() if not n.startswith('base.')
]
param_groups = [{
'params': finetuned_params,
'lr': cfg.finetuned_params_lr
}, {
'params': new_params,
'lr': cfg.new_params_lr
}]
optimizer = optim.SGD(param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
################################
# May Resume Models and Optims #
################################
if cfg.resume:
resume_ep, scores = load_ckpt(modules_optims, cfg.ckpt_file)
# May Transfer Models and Optims to Specified Device. Transferring optimizer
# is to cope with the case when you load the checkpoint to a new device.
TMO(modules_optims)
if cfg.only_test:
test(load_model_weight=True)
return
print(
'#####################Begin to train pcb model##############################'
)
############
# Training #
############
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.pcb_epochs):
# Adjust Learning Rate
adjust_lr_staircase(optimizer.param_groups,
[cfg.finetuned_params_lr, cfg.new_params_lr],
ep + 1, cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
may_set_mode(modules_optims, 'train')
# For recording loss
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_var = Variable(TVT(torch.from_numpy(labels).long()))
_, logits_list = model_w(ims_var)
loss = torch.sum(
torch.cat([
criterion(logits, labels_var) for logits in logits_list
]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
del logits_list, ims, im_names, labels
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1,
time.time() - step_st, loss_meter.val)
print(log)
################
# adjust the lr#
################
# scheduler.step(loss_meter.avg)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(ep + 1,
time.time() - ep_st,
loss_meter.avg)
print(log)
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars('val scores', dict(mAP=mAP, Rank1=Rank1),
ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ), ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.pcb_ckpt_file)
################
# Test the pcb #
################
test(load_model_weight=False)
#####################################################
# Dataset #
# create train_set val_set test_set for triplet loss#
# todo #
#####################################################
print('start to create triplet_all dataset.....')
train_set_anchor = create_dataset_tri(**cfg.train_set_anchor_kwargs)
print('train_anchor_im_names:{}'.format(
len(train_set_anchor.get_im_names())))
train_set_positive = create_dataset_tri(**cfg.train_set_positive_kwargs)
print('train_positive_im_names:{}'.format(
len(train_set_positive.get_im_names())))
train_set_negative = create_dataset_tri(**cfg.train_set_negative_kwargs)
print('train_negative_im_names:{}'.format(
len(train_set_negative.get_im_names())))
num_classes = len(train_set_anchor.ids2labels)
print('finish creating....num_classes:{}\n '.format(num_classes))
if not cfg.only_all:
print('come into triplet*********************************************')
##################################################################################
# Models for triplet 5 eps #
###################################################################################
model = Model(last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
num_cols=cfg.num_cols,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
#load the checkpoint
if osp.isfile(cfg.pcb_ckpt_file):
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.pcb_ckpt_file, map_location=map_location)
model_dict = model.state_dict()
sd_load = {
k: v
for k, v in (sd['state_dicts'][0]).items() if k in model_dict
}
model_dict.update(sd_load)
model.load_state_dict(model_dict)
# # Optimizer
# if hasattr(model.module, 'base'):
# base_param_ids = set(map(id, model.base.parameters()))
# conv_list_ids = set(map(id,model.local_conv_list.parameters()))
# fc_list_ids = set(map(id,model.fc_list.parameters()))
# new_params = [p for p in model.parameters() if
# id(p) not in base_param_ids and id(p) not in fc_list_ids]
# param_groups = [{'params': new_params, 'lr': 1.0}]
# else:
# param_groups = model.parameters()
# To finetune from ImageNet weights
# finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [
p for n, p in model.named_parameters()
if not n.startswith('base.') and not n.startswith('fc_list.')
]
param_groups = [{'params': new_params, 'lr': cfg.new_params_lr}]
optimizer = optim.SGD(param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Model wrapper
model_w = DataParallel(model)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
TMO(modules_optims)
############
# Training #
############
print(
'#####################Begin to train triplet model##############################'
)
start_ep = resume_ep if cfg.resume else 0
triplet_st = time.time()
for ep in range(start_ep, cfg.triplet_epochs):
# Adjust Learning Rate
adjust_lr_staircase(optimizer.param_groups,
[cfg.triplet_finetuned_params_lr], ep + 1,
cfg.staircase_decay_at_epochs,
cfg.triplet_staircase_decay_multiply_factor)
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
may_set_mode(modules_optims, 'train')
# For recording loss
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims_a, im_names_a, labels_a, mirrored_a, epoch_done = train_set_anchor.next_batch(
)
ims_p, im_names_p, labels_p, mirrored_p, epoch_done = train_set_positive.next_batch(
)
ims_n, im_names_n, labels_n, mirrored_n, epoch_done = train_set_negative.next_batch(
)
ims_var_a,ims_var_p,ims_var_n = Variable(TVT(torch.from_numpy(ims_a).float())),Variable(TVT(torch.from_numpy(ims_p).float())),\
Variable(TVT(torch.from_numpy(ims_n).float()))
labels_a_var = Variable(TVT(torch.from_numpy(labels_a).long()))
labels_n_var = Variable(TVT(torch.from_numpy(labels_n).long()))
local_feat_list_a, logits_list_a = model_w(ims_var_a)
local_feat_list_p, logits_list_p = model_w(ims_var_p)
local_feat_list_n, logits_list_n = model_w(ims_var_n)
loss_triplet = []
#print('shape of local_feat:{} {}'.format(len(local_feat_list_a),local_feat_list_a[0].shape))
# print('Ep{}: '.format(ep+1))
loss_local = Variable(torch.Tensor([0]))
for i in range(cfg.parts_num):
#print(i)
loss_triplet.append(
TripletMarginLoss(cfg.margin).forward(
local_feat_list_a[i], local_feat_list_p[i],
local_feat_list_n[i]))
# print('the {}th local loss: {}'.format(i,loss_triplet[i]))
# loss_local = loss_local+loss_triplet[i].data
if cfg.parts_num == 6:
#get the local loss
#loss_local_all =0.1* loss_triplet[0]+0.2*loss_triplet[1]+0.2*loss_triplet[2]+0.2*loss_triplet[3]+0.2*loss_triplet[4]+0.1*loss_triplet[5]
loss_local_all = loss_triplet[0] + loss_triplet[
1] + loss_triplet[2] + loss_triplet[3] + loss_triplet[
4] + loss_triplet[5]
elif cfg.parts_num == 12:
loss_local_all =loss_triplet[0]+loss_triplet[1]+loss_triplet[2]+loss_triplet[3]+loss_triplet[4]+loss_triplet[5]+\
loss_triplet[6]+loss_triplet[7]+loss_triplet[8]+loss_triplet[9]+loss_triplet[10]+loss_triplet[11]
elif cfg.parts_num == 18:
loss_local_all =loss_triplet[0]+loss_triplet[1]+loss_triplet[2]+loss_triplet[3]+loss_triplet[4]+loss_triplet[5]+\
loss_triplet[6]+loss_triplet[7]+loss_triplet[8]+loss_triplet[9]+loss_triplet[10]+loss_triplet[11]+\
loss_triplet[12]+loss_triplet[13]+loss_triplet[14]+loss_triplet[15]+loss_triplet[16]+loss_triplet[17]
elif cfg.parts_num == 3:
loss_local_all = loss_triplet[0] + loss_triplet[
1] + loss_triplet[2]
loss = torch.div(loss_local_all, cfg.parts_num)
# loss = torch.div(torch.sum(local for local in loss_triplet),cfg.parts_num)
print('loss:{}'.format(loss))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# del local_feat_list_a,local_feat_list_p,local_feat_list_n,logits_list_a,logits_list_p,logits_list_n
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1,
time.time() - step_st, loss_meter.val)
print(log)
################
# adjust the lr by torch function#
################
# scheduler.step(loss_meter.avg)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(ep + 1,
time.time() - ep_st,
loss_meter.avg)
print(log)
##############
# RPP module #
##############
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars('val scores', dict(mAP=mAP, Rank1=Rank1),
cfg.pcb_epochs + ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ),
ep + cfg.pcb_epochs)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + cfg.pcb_epochs + 1, 0,
cfg.triplet_ckpt_file)
triplet_time = time.time() - triplet_st
########
# Test #
########
test(load_model_weight=False)
print('come into all train********************************************88')
##########################################################################
# train all for 5 eps #
###########################################################################
model = Model(last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
num_cols=cfg.num_cols,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes)
#####################################################
# Dataset #
# create train dataset for pcb loss#
#####################################################
print('start to create all dataset.....')
all_train_set = create_dataset(**cfg.all_train_set_kwargs)
print('train_set shape:{}'.format(len(all_train_set.im_names)))
num_classes = len(train_set.ids2labels)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
#load the checkpoint
if osp.isfile(cfg.triplet_ckpt_file):
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.triplet_ckpt_file, map_location=map_location)
model_dict = model.state_dict()
sd_load = {
k: v
for k, v in (sd['state_dicts'][0]).items() if k in model_dict
}
model_dict.update(sd_load)
model.load_state_dict(model_dict)
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
# new_params = [p for n, p in model.named_parameters()
# if not n.startswith('base.')]
new_params = [
p for n, p in model.named_parameters()
if not n.startswith('base.') and not n.startswith('local_conv_list.')
]
param_groups = [{
'params': finetuned_params,
'lr': cfg.finetuned_params_lr * 0.1
}, {
'params': new_params,
'lr': cfg.finetuned_params_lr
}]
optimizer = optim.SGD(param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Model wrapper
model_w = DataParallel(model)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
TMO(modules_optims)
############
# Training #
############
print(
'#####################Begin to train all model##############################'
)
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
adjust_lr_staircase(optimizer.param_groups, [
cfg.all_base_finetuned_params_lr, cfg.all_new_finetuned_params_lr
], ep + 1, cfg.all_staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
may_set_mode(modules_optims, 'train')
# For recording loss
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
# step += 1
# step_st = time.time()
# ims_a, im_names_a, labels_a, mirrored_a, epoch_done = train_set_anchor.next_batch()
# ims_p, im_names_p, labels_p, mirrored_p, epoch_done = train_set_positive.next_batch()
# ims_n, im_names_n, labels_n, mirrored_n, epoch_done = train_set_negative.next_batch()
# ims_var_a,ims_var_p,ims_var_n = Variable(TVT(torch.from_numpy(ims_a).float())),Variable(TVT(torch.from_numpy(ims_p).float())),\
# Variable(TVT(torch.from_numpy(ims_n).float()))
# labels_a_var = Variable(TVT(torch.from_numpy(labels_a).long()))
# labels_n_var = Variable(TVT(torch.from_numpy(labels_n).long()))
# local_feat_list_a, logits_list_a = model_w(ims_var_a)
# local_feat_list_p, logits_list_p = model_w(ims_var_p)
# local_feat_list_n, logits_list_n = model_w(ims_var_n)
# loss_triplet = []
# #print('shape of local_feat:{} {}'.format(len(local_feat_list_a),local_feat_list_a[0].shape))
# # print('Ep{}: '.format(ep+1))
# loss_local = Variable(torch.Tensor([0]))
# for i in range(cfg.parts_num):
# #print(i)
# loss_triplet.append(TripletMarginLoss(cfg.margin).forward(local_feat_list_a[i], local_feat_list_p[i], local_feat_list_n[i]))
# # print('the {}th local loss: {}'.format(i,loss_triplet[i]))
# # loss_local = loss_local+loss_triplet[i].data
# if cfg.parts_num == 6:
# #get the local loss
# #loss_local_all =0.1* loss_triplet[0]+0.2*loss_triplet[1]+0.2*loss_triplet[2]+0.2*loss_triplet[3]+0.2*loss_triplet[4]+0.1*loss_triplet[5]
# loss_local_all =loss_triplet[0]+loss_triplet[1]+loss_triplet[2]+loss_triplet[3]+loss_triplet[4]+loss_triplet[5]
# elif cfg.parts_num == 12:
# loss_local_all =loss_triplet[0]+loss_triplet[1]+loss_triplet[2]+loss_triplet[3]+loss_triplet[4]+loss_triplet[5]+\
# loss_triplet[6]+loss_triplet[7]+loss_triplet[8]+loss_triplet[9]+loss_triplet[10]+loss_triplet[11]
# elif cfg.parts_num == 18:
# loss_local_all =loss_triplet[0]+loss_triplet[1]+loss_triplet[2]+loss_triplet[3]+loss_triplet[4]+loss_triplet[5]+\
# loss_triplet[6]+loss_triplet[7]+loss_triplet[8]+loss_triplet[9]+loss_triplet[10]+loss_triplet[11]+\
# loss_triplet[12]+loss_triplet[13]+loss_triplet[14]+loss_triplet[15]+loss_triplet[16]+loss_triplet[17]
# loss_local = torch.div(loss_local_all,cfg.parts_num)
# # print('for loss_local:{}'.format(loss_local))
# #get the id loss
# loss_id_a = torch.sum(
# torch.cat([criterion(logits, labels_a_var) for logits in logits_list_a]))
# loss_id_p = torch.sum(
# torch.cat([criterion(logits, labels_a_var) for logits in logits_list_p]))
# loss_id_n = torch.sum(
# torch.cat([criterion(logits, labels_n_var) for logits in logits_list_n]))
# """
# get the id using the whole feature
# """
# # loss_id_a = criterion(logits_list_a, labels_a_var)
# # loss_id_p = criterion(logits_list_a, labels_a_var)
# # loss_id_n = criterion(logits_list_n, labels_n_var)
# loss_id = torch.div(loss_id_a+loss_id_n+loss_id_p,3)
# # print('for id loss：{}'.format(loss_id))
# loss = loss_id+5*loss_local
# print('loss:{}'.format(loss))
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = all_train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_var = Variable(TVT(torch.from_numpy(labels).long()))
_, logits_list = model_w(ims_var)
loss = torch.sum(
torch.cat(
[criterion(logits, labels_var) for logits in logits_list]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1,
time.time() - step_st, loss_meter.val)
print(log)
################
# adjust the lr by torch function#
################
# scheduler.step(loss_meter.avg)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(ep + 1,
time.time() - ep_st,
loss_meter.avg)
print(log)
##############
# RPP module #
##############
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars('val scores', dict(mAP=mAP, Rank1=Rank1),
cfg.pcb_epochs + cfg.triplet_epochs + ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ),
ep + cfg.pcb_epochs + cfg.triplet_epochs)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims,
ep + cfg.pcb_epochs + 1 + cfg.triplet_epochs, 0,
cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)
print('over**************************************************')
print('{} spends {} s'.format(cfg.triplet_dataset, triplet_time))
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
TVT, TMO = set_devices(cfg.sys_device_ids)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
test_set = create_dataset(**cfg.test_set_kwargs)
#########
# Model #
#########
model = Model(
last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=0
)
# Model wrapper
model_w = DataParallel(model)
# May Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
# To first load weights to CPU
map_location = (lambda storage, loc: storage)
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=map_location)
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
test_set.set_feat_func(ExtractFeature(model_w, TVT))
with measure_time('Extracting feature...', verbose=True):
feat, ids, cams, im_names, marks = test_set.extract_feat(True, verbose=True)
#######################
# Select Query Images #
#######################
# Fix some query images, so that the visualization for different models can
# be compared.
# Sort in the order of image names
inds = np.argsort(im_names)
feat, ids, cams, im_names, marks = \
feat[inds], ids[inds], cams[inds], im_names[inds], marks[inds]
# query, gallery index mask
is_q = marks == 0
is_g = marks == 1
prng = np.random.RandomState(1)
# selected query indices
sel_q_inds = prng.permutation(range(np.sum(is_q)))[:cfg.num_queries]
q_ids = ids[is_q][sel_q_inds]
q_cams = cams[is_q][sel_q_inds]
q_feat = feat[is_q][sel_q_inds]
q_im_names = im_names[is_q][sel_q_inds]
####################
# Compute Distance #
####################
# query-gallery distance
q_g_dist = compute_dist(q_feat, feat[is_g], type='euclidean')
###########################
# Save Rank List as Image #
###########################
q_im_paths = [ospj(test_set.im_dir, n) for n in q_im_names]
save_paths = [ospj(cfg.exp_dir, 'rank_lists', n) for n in q_im_names]
g_im_paths = [ospj(test_set.im_dir, n) for n in im_names[is_g]]
for dist_vec, q_id, q_cam, q_im_path, save_path in zip(
q_g_dist, q_ids, q_cams, q_im_paths, save_paths):
rank_list, same_id = get_rank_list(
dist_vec, q_id, q_cam, ids[is_g], cams[is_g], cfg.rank_list_size)
save_rank_list_to_im(rank_list, same_id, q_im_path, g_im_paths, save_path)