def save_models(net, check_point_file, model_file, image_h_w, onnx_file, opt_level):
map_location = (lambda storage, loc: storage)
data = torch.load(check_point_file, map_location=map_location)
models = dict(data['state_dicts'][0])
dummy_input = torch.randn(10, 3, image_h_w[0], image_h_w[1], device='cuda').half()
model = Model(net, pretrained=False)
model.load_state_dict(models)
model.cuda()
optimizer = optim.Adam(model.parameters())
model, optimizer = amp.initialize(model, optimizer,
opt_level=opt_level,
#loss_scale=cfg.loss_scale
)
input_names = [ "actual_input_1" ] + [ "learned_%d" % i for i in range(16) ]
output_names = [ "output1" ]
torch.save(model.state_dict(), model_file)
torch.onnx.export(model.base, dummy_input, onnx_file, verbose=True, input_names=input_names, output_names=output_names)
def main(opt):
dataset = VideoDataset(opt, 'test')
dataloader = DataLoader(dataset,
collate_fn=test_collate_fn,
batch_size=opt['batch_size'],
shuffle=False)
opt['cms_vocab_size'] = dataset.get_cms_vocab_size()
opt['cap_vocab_size'] = dataset.get_cap_vocab_size()
if opt['cms'] == 'int':
cms_text_length = opt['int_max_len']
elif opt['cms'] == 'eff':
cms_text_length = opt['eff_max_len']
else:
cms_text_length = opt['att_max_len']
model = Model(dataset.get_cap_vocab_size(),
dataset.get_cms_vocab_size(),
cap_max_seq=opt['cap_max_len'],
cms_max_seq=cms_text_length,
tgt_emb_prj_weight_sharing=True,
vis_emb=opt['dim_vis_feat'],
rnn_layers=opt['rnn_layer'],
d_k=opt['dim_head'],
d_v=opt['dim_head'],
d_model=opt['dim_model'],
d_word_vec=opt['dim_word'],
d_inner=opt['dim_inner'],
n_layers=opt['num_layer'],
n_head=opt['num_head'],
dropout=opt['dropout'])
if len(opt['load_checkpoint']) != 0:
state_dict = torch.load(opt['load_checkpoint'])
# for name, param in model.state_dict().items():
# print(name, param.size())
#
# print('=================')
# print(state_dict.keys())
model.load_state_dict(state_dict)
if opt['cuda']:
model = model.cuda()
model.eval()
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print(params)
test(dataloader, model, opt, dataset.get_cap_vocab(),
dataset.get_cms_vocab())
def main(opt):
dataset = VideoDataset(opt, 'test')
dataloader = DataLoader(dataset,
batch_size=opt['batch_size'],
shuffle=False)
opt['cms_vocab_size'] = dataset.get_cms_vocab_size()
opt['cap_vocab_size'] = dataset.get_cap_vocab_size()
if opt['cms'] == 'int':
cms_text_length = opt['int_max_len']
elif opt['cms'] == 'eff':
cms_text_length = opt['eff_max_len']
else:
cms_text_length = opt['att_max_len']
model = Model(dataset.get_cap_vocab_size(),
dataset.get_cms_vocab_size(),
cap_max_seq=opt['cap_max_len'],
cms_max_seq=cms_text_length,
tgt_emb_prj_weight_sharing=True,
vis_emb=opt['dim_vis_feat'],
rnn_layers=opt['rnn_layer'],
d_k=opt['dim_head'],
d_v=opt['dim_head'],
d_model=opt['dim_model'],
d_word_vec=opt['dim_word'],
d_inner=opt['dim_inner'],
n_layers=opt['num_layer'],
n_head=opt['num_head'],
dropout=opt['dropout'])
if len(opt['load_checkpoint']) != 0:
state_dict = torch.load(opt['load_checkpoint'])
model.load_state_dict(state_dict)
model = model.cuda()
model.eval()
test(dataloader, model, opt, dataset.get_cap_vocab(),
dataset.get_cms_vocab())
def main(opt):
# load and define dataloader
dataset = VideoDataset(opt, 'train')
dataloader = DataLoader(dataset, batch_size=opt['batch_size'], shuffle=True)
opt['cms_vocab_size'] = dataset.get_cms_vocab_size()
opt['cap_vocab_size'] = dataset.get_cap_vocab_size()
if opt['cms'] == 'int':
cms_text_length = opt['int_max_len']
elif opt['cms'] == 'eff':
cms_text_length = opt['eff_max_len']
else:
cms_text_length = opt['att_max_len']
# model initialization.
model = Model(
dataset.get_cap_vocab_size(),
dataset.get_cms_vocab_size(),
cap_max_seq=opt['cap_max_len'],
cms_max_seq=cms_text_length,
tgt_emb_prj_weight_sharing=True,
vis_emb=opt['dim_vis_feat'],
rnn_layers=opt['rnn_layer'],
d_k=opt['dim_head'],
d_v=opt['dim_head'],
d_model=opt['dim_model'],
d_word_vec=opt['dim_word'],
d_inner=opt['dim_inner'],
n_layers=opt['num_layer'],
n_head=opt['num_head'],
dropout=opt['dropout'])
# number of parameters
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('number of learnable parameters are {}'.format(params))
if opt['cuda']: model = model.cuda()
# resume from previous checkpoint if indicated
if opt['load_checkpoint'] and opt['resume']:
cap_state_dict = torch.load(opt['load_checkpoint'])
model_dict = model.state_dict()
model_dict.update(cap_state_dict)
model.load_state_dict(model_dict)
optimizer = ScheduledOptim(optim.Adam(filter(lambda x: x.requires_grad, model.parameters()),
betas=(0.9, 0.98), eps=1e-09), 512, opt['warm_up_steps'])
# note: though we set the init learning rate as np.power(d_model, -0.5),
# grid search indicates different LR may improve the results.
opt['init_lr'] = round(optimizer.init_lr, 3)
# create checkpoint output directory
dir = os.path.join(opt['checkpoint_path'], 'CMS_CAP_MODEL_INT_lr_{}_BS_{}_Layer_{}_ATTHEAD_{}_HID_{}_RNNLayer_{}'
.format(opt['init_lr'], opt['batch_size'], opt['num_layer'],
opt['num_head'], opt['dim_model'], opt['rnn_layer']))
if not os.path.exists(dir): os.makedirs(dir)
# save the model snapshot to local
info_path = os.path.join(dir, 'iteration_info_log.log')
print('model architecture saved to {} \n {}'.format(info_path, str(model)))
with open(info_path, 'a') as f:
f.write(str(model))
f.write('\n')
f.write(str(params))
f.write('\n')
# log file directory
opt['output_dir'] = dir
opt['info_path'] = info_path
opt['model_info_path'] = os.path.join(opt['output_dir'],
'checkpoint_loss_log.log')
train(dataloader, model, optimizer, opt, dataset.get_cap_vocab(), dataset.get_cms_vocab())
train=False,
test=False,
valid=True)
trainloader = DataLoaderX(trainset, batch_size=64, shuffle=True, num_workers=8)
valloader = DataLoaderX(valset, batch_size=64, shuffle=False, num_workers=8)
resnet = resnet50(pretrained=True)
resnet.fc = nn.Linear(2048, 68)
resnet.load_state_dict(
torch.load(
'/home/hh9665/Desktop/CurrentProject/AutoEncoderMPNN/ckp/model_resnet50_my_loss1.pth'
))
model = Model(resnet, AutoEncoder, MPNN, TRAIN=True, feature_d=16)
print(model)
# model.load_state_dict(torch.load('/home/hh9665/Desktop/CurrentProject/AutoEncoderMPNN/result/T20200224_0/model0.pth'))
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=0.00005, weight_decay=0)
scheduler = StepLR(optimizer, step_size=3, gamma=0.9)
N_epoch = 80
RESULTS = {
'LOSS': [],
'BCELOSS': [],
'AUTOENCODER_LOSS': [],
'ACC': [],
'mAP': [],
'miF1': [],
'maF1': [],
'LOSS_val': [],
'ACC_val': [],
'mAP_val': [],
class Solver(object):
def __init__(self, train_loader, test_loader, config, save_fold=None):
self.train_loader = train_loader
self.test_loader = test_loader
self.config = config
self.save_fold = save_fold
self.build_model()
if config.mode == 'test':
self.net_bone.eval()
def print_network(self, model, name):
num_params = 0
for p in model.parameters():
num_params += p.numel() # 返回一个tensor变量内所有元素个数
print(name)
print(model)
print("The number of parameters: {}".format(num_params))
# build the network
def build_model(self):
print('mode: {}'.format(self.config.mode))
print('------------------------------------------')
self.net_bone = Model(3, self.config.mode)
if self.config.cuda:
self.net_bone = self.net_bone.cuda()
if self.config.mode == 'train':
if self.config.model_path != '':
assert (os.path.exists(self.config.model_path)), (
'please import correct pretrained model path!')
self.net_bone.load_pretrain_model(self.config.model_path)
else:
assert (self.config.model_path !=
''), ('Test mode, please import pretrained model path!')
assert (os.path.exists(self.config.model_path)), (
'please import correct pretrained model path!')
self.net_bone.load_pretrain_model(self.config.model_path)
self.lr_bone = p['lr_bone']
self.lr_branch = p['lr_branch']
self.optimizer_bone = Adam(filter(lambda p: p.requires_grad,
self.net_bone.parameters()),
lr=self.lr_bone,
weight_decay=p['wd'])
print('------------------------------------------')
self.print_network(self.net_bone, 'DSNet')
print('------------------------------------------')
def test(self):
if not os.path.exists(self.save_fold):
os.makedirs(self.save_fold)
for i, data_batch in enumerate(self.test_loader):
image, flow, name, split, size = data_batch['image'], data_batch[
'flow'], data_batch['name'], data_batch['split'], data_batch[
'size']
dataset = data_batch['dataset']
if self.config.cuda:
image, flow = image.cuda(), flow.cuda()
with torch.no_grad():
pre, pre2, pre3, pre4 = self.net_bone(image, flow)
for i in range(self.config.test_batch_size):
presavefold = os.path.join(self.save_fold, dataset[i],
split[i])
if not os.path.exists(presavefold):
os.makedirs(presavefold)
pre1 = torch.nn.Sigmoid()(pre[i])
pre1 = (pre1 - torch.min(pre1)) / (torch.max(pre1) -
torch.min(pre1))
pre1 = np.squeeze(pre1.cpu().data.numpy()) * 255
pre1 = cv2.resize(pre1, (size[0][1], size[0][0]))
cv2.imwrite(presavefold + '/' + name[i], pre1)
def train(self):
# 一个epoch中训练iter_num个batch
iter_num = len(self.train_loader.dataset) // self.config.batch_size
aveGrad = 0
if not os.path.exists(tmp_path):
os.mkdir(tmp_path)
for epoch in range(self.config.epoch):
r_img_loss, r_flo_loss, r_pre_loss, r_sal_loss, r_sum_loss = 0, 0, 0, 0, 0
self.net_bone.zero_grad()
for i, data_batch in enumerate(self.train_loader):
image, label, flow = data_batch['image'], data_batch[
'label'], data_batch['flow']
if image.size()[2:] != label.size()[2:]:
print("Skip this batch")
continue
if self.config.cuda:
image, label, flow = image.cuda(), label.cuda(), flow.cuda(
)
sal_loss1 = []
sal_loss2 = []
sal_loss3 = []
sal_loss4 = []
pre1, pre2, pre3, pre4 = self.net_bone(image, flow)
sal_loss1.append(
F.binary_cross_entropy_with_logits(pre1,
label,
reduction='sum'))
sal_loss2.append(
F.binary_cross_entropy_with_logits(pre2,
label,
reduction='sum'))
sal_loss3.append(
F.binary_cross_entropy_with_logits(pre3,
label,
reduction='sum'))
sal_loss4.append(
F.binary_cross_entropy_with_logits(pre4,
label,
reduction='sum'))
sal_img = sum(sal_loss3) / (nAveGrad * self.config.batch_size)
sal_flo = sum(sal_loss4) / (nAveGrad * self.config.batch_size)
sal_pre = sum(sal_loss2) / (nAveGrad * self.config.batch_size)
sal_final = sum(sal_loss1) / (nAveGrad *
self.config.batch_size)
r_img_loss += sal_img.data
r_flo_loss += sal_flo.data
r_pre_loss += sal_pre.data
r_sal_loss += sal_final.data
sal_loss = (sum(sal_loss1) + sum(sal_loss2) + sum(sal_loss3) +
sum(sal_loss4)) / (nAveGrad *
self.config.batch_size)
r_sum_loss += sal_loss.data
loss = sal_loss
loss.backward()
aveGrad += 1
if aveGrad % nAveGrad == 0:
self.optimizer_bone.step()
self.optimizer_bone.zero_grad()
aveGrad = 0
if i % showEvery == 0:
print(
'epoch: [%2d/%2d], iter: [%5d/%5d] Loss || img : %10.4f || flo : %10.4f || pre : %10.4f || sal : %10.4f || sum : %10.4f'
% (epoch, self.config.epoch, i, iter_num, r_img_loss *
(nAveGrad * self.config.batch_size) / showEvery,
r_flo_loss * (nAveGrad * self.config.batch_size) /
showEvery, r_pre_loss *
(nAveGrad * self.config.batch_size) / showEvery,
r_sal_loss * (nAveGrad * self.config.batch_size) /
showEvery, r_sum_loss *
(nAveGrad * self.config.batch_size) / showEvery))
print('Learning rate: ' + str(self.lr_bone))
r_img_loss, r_flo_loss, r_pre_loss, r_sal_loss, r_sum_loss = 0, 0, 0, 0, 0
if i % 50 == 0:
vutils.save_image(torch.sigmoid(pre1.data),
tmp_path + '/iter%d-sal-0.jpg' % i,
normalize=True,
padding=0)
# vutils.save_image(torch.sigmoid(edge_out.data), tmp_path + '/iter%d-edge-0.jpg' % i,
# normalize=True, padding=0)
vutils.save_image(image.data,
tmp_path + '/iter%d-sal-data.jpg' % i,
padding=0)
vutils.save_image(label.data,
tmp_path + '/iter%d-sal-target.jpg' % i,
padding=0)
if (epoch + 1) % self.config.epoch_save == 0:
torch.save(
self.net_bone.state_dict(), '%s/models/epoch_%d_bone.pth' %
(self.config.save_fold, epoch + 1))
if epoch in lr_decay_epoch:
self.lr_bone = self.lr_bone * 0.2
self.optimizer_bone = Adam(filter(lambda p: p.requires_grad,
self.net_bone.parameters()),
lr=self.lr_bone,
weight_decay=p['wd'])
torch.save(self.net_bone.state_dict(),
'%s/models/final_bone.pth' % self.config.save_fold)
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(cfg.net,
path_to_predefined='',
pretrained=False,
last_conv_stride=cfg.last_conv_stride)
model.cuda()
r'''
This is compeletly useless, but since I used apex, and its optimization level
has different effect on each layer of networ and optimizer is mandatory argument, I created this optimizer.
'''
optimizer = optim.Adam(model.parameters())
model, optimizer = amp.initialize(
model,
optimizer,
opt_level=cfg.opt_level,
#loss_scale=cfg.loss_scale
)
print(model)
# 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(2)
# 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 = list()
for n in q_im_names:
if isinstance(n, bytes):
n = n.decode("utf-8")
q_im_paths.append(ospj(test_set.im_dir, n))
save_paths = list()
for n in q_im_names:
if isinstance(n, bytes):
n = n.decode("utf-8")
save_paths.append(ospj(cfg.exp_dir, 'rank_lists', n))
g_im_paths = list()
for n in im_names[is_g]:
if isinstance(n, bytes):
n = n.decode("utf-8")
g_im_paths.append(ospj(test_set.im_dir, n))
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)
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 #
###########
if not cfg.only_test:
train_set = create_dataset(**cfg.train_set_kwargs)
# 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 #
###########
if cfg.only_test:
model = Model(cfg.net, pretrained=False, last_conv_stride=cfg.last_conv_stride)
else:
model = Model(cfg.net, path_to_predefined=cfg.net_pretrained_path, last_conv_stride=cfg.last_conv_stride) # This is a ShuffleNet Network. Model(last_conv_stride=cfg.last_conv_stride)
#############################
# Criteria and Optimizers #
#############################
tri_loss = TripletLoss(margin=cfg.margin)
optimizer = optim.Adam(model.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
#optimizer = optimizers.FusedAdam(model.parameters(),
# lr=cfg.base_lr,
# weight_decay=cfg.weight_decay)
#optimizer = torch.optim.SGD(model.parameters(), cfg.base_lr,
# nesterov=True,
# momentum=cfg.momentum,
# weight_decay=cfg.weight_decay)
model.cuda()
model, optimizer = amp.initialize(model, optimizer,
opt_level=cfg.opt_level,
keep_batchnorm_fp32=cfg.keep_batchnorm_fp32,
#loss_scale=cfg.loss_scale
)
amp.init() # Register function
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
# Model wrapper
model_w = DataParallel(model)
################################
# 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):
feature_map = ExtractFeature(model_w, TVT)
test_set.set_feat_func(feature_map)
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(
normalize_feat=cfg.normalize_feature,
verbose=True)
def validate():
if val_set.extract_feat_func is None:
feature_map = ExtractFeature(model_w, TVT)
val_set.set_feat_func(feature_map)
print('\n=========> Test on validation set <=========\n')
mAP, cmc_scores, _, _ = val_set.eval(
normalize_feat=cfg.normalize_feature,
to_re_rank=False,
verbose=False)
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
if cfg.lr_decay_type == 'exp':
adjust_lr_exp(
optimizer,
cfg.base_lr,
ep + 1,
cfg.total_epochs,
cfg.exp_decay_at_epoch)
else:
adjust_lr_staircase(
optimizer,
cfg.base_lr,
ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
# For recording precision, satisfying margin, etc
prec_meter = AverageMeter()
sm_meter = AverageMeter()
dist_ap_meter = AverageMeter()
dist_an_meter = AverageMeter()
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_t = TVT(torch.from_numpy(labels).long())
feat = model_w(ims_var)
loss, p_inds, n_inds, dist_ap, dist_an, dist_mat = global_loss(
tri_loss, feat, labels_t,
normalize_feature=cfg.normalize_feature)
optimizer.zero_grad()
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
############
# Step Log #
############
# precision
prec = (dist_an > dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
sm = (dist_an > dist_ap + cfg.margin).data.float().mean()
# average (anchor, positive) distance
d_ap = dist_ap.data.mean()
# average (anchor, negative) distance
d_an = dist_an.data.mean()
prec_meter.update(prec)
sm_meter.update(sm)
dist_ap_meter.update(d_ap)
dist_an_meter.update(d_an)
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
time_log = '\tStep {}/Ep {}, {:.2f}s'.format(
step, ep + 1, time.time() - step_st, )
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, '
'loss {:.4f}'.format(
prec_meter.val, sm_meter.val,
dist_ap_meter.val, dist_an_meter.val,
loss_meter.val, ))
log = time_log + tri_log
print(log)
#############
# Epoch Log #
#############
time_log = 'Ep {}, {:.2f}s'.format(ep + 1, time.time() - ep_st)
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, '
'loss {:.4f}'.format(
prec_meter.avg, sm_meter.avg,
dist_ap_meter.avg, dist_an_meter.avg,
loss_meter.avg, ))
log = time_log + tri_log
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)
writer.add_scalars(
'precision',
dict(precision=prec_meter.avg, ),
ep)
writer.add_scalars(
'satisfy_margin',
dict(satisfy_margin=sm_meter.avg, ),
ep)
writer.add_scalars(
'average_distance',
dict(dist_ap=dist_ap_meter.avg,
dist_an=dist_an_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)
