def train(args):
writer = SummaryWriter()
logger = make_logger(args.log_file)
if args.zs:
packed = args.packed_pkl_zs
else:
packed = args.packed_pkl_nozs
data = ZSIH_dataloader(args.sketch_dir, args.image_dir, args.stats_file, args.embedding_file, packed, zs=args.zs)
print(len(data))
dataloader_train = DataLoader(dataset=data, num_workers=args.num_worker, \
batch_size=args.batch_size,
shuffle=args.shuffle)
logger.info('Building the model ...')
model = ZSIM(args.hidden_size, args.hashing_bit, args.semantics_size, data.pretrain_embedding.float(),
adj_scaler=args.adj_scaler, dropout=args.dropout, fix_cnn=args.fix_cnn,
fix_embedding=args.fix_embedding, logger=logger)
logger.info('Building the optimizer ...')
optimizer = Adam(params=model.parameters(), lr=args.lr)
#optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.9)
l1_regularization = _Regularization(model, 1, p=1, logger=logger)
l2_regularization = _Regularization(model, 0.005, p=2, logger=logger)
if args.start_from is not None:
logger.info('Loading pretrained model from {} ...'.format(args.start_from))
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
if args.gpu_id != -1:
model.cuda(args.gpu_id)
batch_acm = 0
global_step = 0
loss_p_xz_acm, loss_q_zx_acm, loss_image_l2_acm, loss_sketch_l2_acm, loss_reg_l2_acm, loss_reg_l1_acm = 0., 0., 0., 0., 0., 0.,
best_precision = 0.
best_iter = 0
patience = args.patience
logger.info('Hyper-Parameter:')
logger.info(args)
logger.info('Model Structure:')
logger.info(model)
logger.info('Begin Training !')
while True:
if patience <= 0:
break
for sketch_batch, image_batch, semantics_batch in dataloader_train:
if global_step % args.print_every == 0 % args.print_every and global_step and batch_acm % args.cum_num == 0:
logger.info('Iter {}, Loss/p_xz {:.3f}, Loss/q_zx {:.3f}, Loss/image_l2 {:.3f}, Loss/sketch_l2 {:.3f}, Loss/reg_l2 {:.3f}, Loss/reg_l1 {:.3f}'.format(global_step, \
loss_p_xz_acm/args.print_every/args.cum_num, \
loss_q_zx_acm/args.print_every/args.cum_num, \
loss_image_l2_acm/args.print_every/args.cum_num, \
loss_sketch_l2_acm/args.print_every/args.cum_num, \
loss_reg_l2_acm/args.print_every/args.cum_num, \
loss_reg_l1_acm/args.print_every/args.cum_num))
loss_p_xz_acm, loss_q_zx_acm, loss_image_l2_acm, loss_sketch_l2_acm, loss_reg_l2_acm, loss_reg_l1_acm = 0., 0., 0., 0., 0., 0.,
if global_step % args.save_every == 0 % args.save_every and batch_acm % args.cum_num == 0 and global_step :
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()},
'{}/Iter_{}.pkl'.format(args.save_dir,global_step))
### Evaluation
model.eval()
image_label = list()
image_feature = list()
for image, label in data.load_test_images(batch_size=args.batch_size):
image = image.cuda(args.gpu_id)
image_label += label
tmp_feature = model.hash(image, 1).cpu().detach().numpy()
image_feature.append(tmp_feature)
image_feature = np.vstack(image_feature)
sketch_label = list()
sketch_feature = list()
for sketch, label in data.load_test_sketch(batch_size=args.batch_size):
sketch = sketch.cuda(args.gpu_id)
sketch_label += label
tmp_feature = model.hash(sketch, 0).cpu().detach().numpy()
sketch_feature.append(tmp_feature)
sketch_feature = np.vstack(sketch_feature)
dists_cosine = cdist(image_feature, sketch_feature, 'hamming')
rank_cosine = np.argsort(dists_cosine, 0)
for n in [5, 100, 200]:
ranksn_cosine = rank_cosine[:n, :].T
classesn_cosine = np.array([[image_label[i] == sketch_label[r] \
for i in ranksn_cosine[r]] for r in range(len(ranksn_cosine))])
precision_cosine = np.mean(classesn_cosine)
writer.add_scalar('Precision_{}/cosine'.format(n),
precision_cosine, global_step)
logger.info('Iter {}, Precision_{}/cosine {}'.format(global_step, n, precision_cosine))
if best_precision < precision_cosine:
patience = args.patience
best_precision = precision_cosine
best_iter = global_step
writer.add_scalar('Best/Precision_200', best_precision, best_iter)
logger.info('Iter {}, Best Precision_200 {}'.format(global_step, best_precision))
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()}, '{}/Best.pkl'.format(args.save_dir))
else:
patience -= 1
if patience <= 0:
break
model.train()
batch_acm += 1
if global_step <= args.warmup_steps:
update_lr(optimizer, args.lr*global_step/args.warmup_steps)
"""
#code for testing if the images and the sketches are corresponding to each other correctly
for i in range(args.batch_size):
sk = sketch_batch[i].numpy().reshape(224, 224, 3)
im = image_batch[i].numpy().reshape(224, 224, 3)
print(label[i])
ims = np.vstack((np.uint8(sk), np.uint8(im)))
cv2.imshow('test', ims)
cv2.waitKey(3000)
"""
sketch = sketch_batch.cuda(args.gpu_id)
image = image_batch.cuda(args.gpu_id)
semantics = semantics_batch.long().cuda(args.gpu_id)
optimizer.zero_grad()
loss = model(sketch, image, semantics)
loss_l1 = l1_regularization()
loss_l2 = l2_regularization()
loss_p_xz_acm += loss['p_xz'][0].item()
loss_q_zx_acm += loss['q_zx'][0].item()
loss_image_l2_acm += loss['image_l2'][0].item()
loss_sketch_l2_acm += loss['sketch_l2'][0].item()
loss_reg_l1_acm += loss_l1.item()
loss_reg_l2_acm += (loss_l2.item() / 0.005)
writer.add_scalar('Loss/p_xz', loss['p_xz'][0].item(), global_step)
writer.add_scalar('Loss/q_zx', loss['q_zx'][0].item(), global_step)
writer.add_scalar('Loss/image_l2', loss['image_l2'][0].item(), global_step)
writer.add_scalar('Loss/sketch_l2', loss['sketch_l2'][0].item(), global_step)
writer.add_scalar('Loss/reg_l2', (loss_l2.item() / 0.005), global_step)
writer.add_scalar('Loss/reg_l1', loss_l1.item(), global_step)
loss_ = loss_l2
for item in loss.values():
loss_ += item[0]*item[1]
loss_.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if batch_acm % args.cum_num == 0:
optimizer.step()
global_step += 1
def train(args):
# srun -p gpu --gres=gpu:1 python main_dsh.py
sketch_folder, imsk_folder, im_folder, path_semantic, train_class, test_class = _parse_args_paths(
args)
logger = make_logger(join(mkdir(args.save_dir), curr_time_str() + '.log'))
if DEBUG:
train_class = train_class[:2]
test_class = test_class[:2]
args.print_every = 2
args.save_every = 8
args.steps = 20
args.batch_size = 2
args.npy_dir = NPY_FOLDER_SKETCHY
# logger.info("try loading data_train")
data_train = DSH_dataloader(folder_sk=sketch_folder,
folder_im=im_folder,
clss=train_class,
folder_nps=args.npy_dir,
folder_imsk=imsk_folder,
normalize01=False,
doaug=False,
m=args.m,
path_semantic=path_semantic,
folder_saving=join(mkdir(args.save_dir),
'train_saving'),
logger=logger)
dataloader_train = DataLoader(dataset=data_train,
batch_size=args.batch_size,
shuffle=False)
# logger.info("try loading data_test")
data_test = DSH_dataloader(folder_sk=sketch_folder,
clss=test_class,
folder_nps=args.npy_dir,
path_semantic=path_semantic,
folder_imsk=imsk_folder,
normalize01=False,
doaug=False,
m=args.m,
folder_saving=join(mkdir(args.save_dir),
'test_saving'),
logger=logger)
model = DSH(m=args.m, config=args.config)
model.cuda()
optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.9)
# logger.info("optimizer inited")
steps = _try_load(args, logger, model, optimizer)
logger.info(str(args))
args.steps += steps
dsh_loss = _DSH_loss(gamma=args.gamma)
model.train()
l2_regularization = _Regularization(model, args.l2_reg, p=2, logger=None)
loss_sum = []
# logger.info("iterations")
# iterations
while True:
# logger.info("update D")
# 1. update D
data_train.D = update_D(bi=data_train.BI,
bs=data_train.BS,
vec_bi=data_train.vec_bi,
vec_bs=data_train.vec_bs)
# logger.info("update BI/BS")
# 2. update BI/BS
feats_labels_sk, feats_labels_im = _extract_feats_sk_im(
data=data_train, model=model, batch_size=args.batch_size)
data_train.BI, data_train.BS = update_B(bi=data_train.BI,
bs=data_train.BS,
vec_bi=data_train.vec_bi,
vec_bs=data_train.vec_bs,
W=data_train.W,
D=data_train.D,
Fi=feats_labels_im[0],
Fs=feats_labels_sk[0],
lamb=args.lamb,
gamma=args.gamma)
# logger.info("update network parameters")
# 3. update network parameters
for _, (sketch, code_of_sketch, image, sketch_token,
code_of_image) in enumerate(dataloader_train):
sketch_feats, im_feats = model(sketch.cuda(), sketch_token.cuda(),
image.cuda())
loss = dsh_loss(sketch_feats, im_feats, code_of_sketch.cuda(), code_of_image.cuda()) \
+ l2_regularization()
loss = loss / args.update_every
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if (steps + 1) % args.update_every == 0:
optimizer.step()
optimizer.zero_grad()
loss_sum.append(float(loss.item() * args.update_every))
if (steps + 1) % args.save_every == 0:
_test_and_save(steps=steps,
optimizer=optimizer,
data_test=data_test,
model=model,
logger=logger,
args=args,
loss_sum=loss_sum)
data_train.save_params()
if (steps + 1) % args.print_every == 0:
loss_sum = [np.mean(loss_sum)]
logger.info('step: {}, loss: {}'.format(steps, loss_sum[0]))
steps += 1
if steps >= args.steps: break
dr_dec(optimizer=optimizer, args=args)
if steps >= args.steps: break
def train(args):
# srun -p gpu --gres=gpu:1 --output=d3shape_sketchy.out python main_d3shape.py --steps 50000 --print_every 200 --npy_dir 0 --save_every 1000 --batch_size 8 --dataset sketchy --save_dir d3shape_sketchy
sketch_folder, imsk_folder, train_class, test_class = _parse_args_paths(
args)
data_train = D3Shape_dataloader(folder_sk=sketch_folder,
clss=train_class,
folder_nps=args.npy_dir,
folder_imsk=imsk_folder,
normalize01=False,
doaug=False)
dataloader_train = DataLoader(dataset=data_train,
batch_size=args.batch_size,
shuffle=False)
data_test = D3Shape_dataloader(folder_sk=sketch_folder,
clss=test_class,
folder_nps=args.npy_dir,
folder_imsk=imsk_folder,
normalize01=False,
doaug=False)
model = D3Shape()
model.cuda()
optimizer = Adam(params=model.parameters(), lr=args.lr)
logger = make_logger(join(mkdir(args.save_dir), curr_time_str() + '.log'))
steps = _try_load(args, logger, model, optimizer)
logger.info(str(args))
args.steps += steps
d3shape_loss = _D3Shape_loss(cp=args.cp, cn=args.cn)
model.train()
l2_regularization = _Regularization(model, args.l2_reg, p=2, logger=None)
while True:
loss_sum = []
for _, (sketch1, imsk1, sketch2, imsk2,
is_same) in enumerate(dataloader_train):
optimizer.zero_grad()
sketch1_feats, imsk1_feats = model(sketch1.cuda(), imsk1.cuda())
sketch2_feats, imsk2_feats = model(sketch2.cuda(), imsk2.cuda())
loss = d3shape_loss(sketch1_feats, imsk1_feats, sketch2_feats, imsk2_feats, is_same.cuda()) \
+ l2_regularization()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
loss_sum.append(float(loss.item()))
if (steps + 1) % args.save_every == 0:
model.eval()
n = 50
skip = 1
start_cpu_t = time.time()
feats_labels_sk = _extract_feats(data_test,
lambda sk: model(sk, None)[0],
SK,
skip=skip,
batch_size=args.batch_size)
feats_labels_imsk = _extract_feats(
data_test,
lambda imsk: model(None, imsk)[0],
IMSK,
skip=skip,
batch_size=args.batch_size)
pre, mAP = _eval(feats_labels_sk, feats_labels_imsk, n)
logger.info(
"Precision@{}: {}, mAP@{}: {}".format(n, pre, n, mAP) +
" " + 'step: {}, loss: {}, (eval cpu time: {}s)'.format(
steps, np.mean(loss_sum),
time.time() - start_cpu_t))
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'steps': steps,
'args': args
}, save_fn(args.save_dir, steps, pre, mAP))
model.train()
if (steps + 1) % args.print_every == 0:
logger.info('step: {}, loss: {}'.format(
steps, np.mean(loss_sum)))
loss_sum = []
steps += 1
if steps >= args.steps: break
if steps >= args.steps: break
def train(args):
# srun -p gpu --gres=gpu:1 --exclusive --output=san10.out python main_san.py --epochs 50000 --print_every 500 --save_every 2000 --batch_size 96 --dataset sketchy --margin 10 --npy_dir 0 --save_dir san_sketchy10
# srun -p gpu --gres=gpu:1 --exclusive --output=san1.out python main_san.py --epochs 50000 --print_every 500 --save_every 2000 --batch_size 96 --dataset sketchy --margin 1 --npy_dir 0 --save_dir san_sketchy1
# srun -p gpu --gres=gpu:1 --output=san_sketchy03.out python main_san.py --epochs 30000 --print_every 200 --save_every 3000 --batch_size 96 --dataset sketchy --margin 0.3 --npy_dir 0 --save_dir san_sketchy03 --lr 0.0001
sketch_folder, image_folder, path_semantic, train_class, test_class = _parse_args_paths(
args)
if DEBUG:
args.back_bone = 'default'
args.npy_dir = NPY_FOLDER_SKETCHY
args.ni_path = PATH_NAMES
args.print_every = 1
args.save_every = 5
args.paired = True
args.epochs = 20000
# args.lr = 0.001
args.sz = 32
# args.l2_reg = 0.0001
args.back_bone = 'default'
args.batch_size = 32
args.h = 500
test_class = train_class[5:7]
train_class = train_class[:5]
logger = make_logger(join(mkdir(args.save_dir), curr_time_str() + '.log'))
data_train = CMT_dataloader(
folder_sk=sketch_folder,
clss=train_class,
folder_nps=args.npy_dir,
path_semantic=path_semantic,
paired=args.paired,
names=args.ni_path,
folder_im=image_folder,
normalize01=False,
doaug=False,
logger=logger,
sz=None if args.back_bone == 'vgg' else args.sz)
dataloader_train = DataLoader(dataset=data_train,
batch_size=args.batch_size,
shuffle=True)
data_test = CMT_dataloader(folder_sk=sketch_folder,
clss=test_class,
folder_nps=args.npy_dir,
path_semantic=path_semantic,
folder_im=image_folder,
normalize01=False,
doaug=False,
logger=logger,
sz=None if args.back_bone == 'vgg' else args.sz)
model = CMT(d=data_train.d(),
h=args.h,
back_bone=args.back_bone,
batch_normalization=args.bn,
sz=args.sz)
model.cuda()
if not args.ft:
model.fix_vgg()
optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.6)
epochs = _try_load(args, logger, model, optimizer)
logger.info(str(args))
args.epochs += epochs
cmt_loss = _CMT_loss()
model.train()
l2_regularization = _Regularization(model, args.l2_reg, p=2, logger=None)
loss_sum = [[0], [0]]
logger.info(
"Start training:\n train_classes: {}\n test_classes: {}".format(
train_class, test_class))
_test_and_save(epochs=epochs,
optimizer=optimizer,
data_test=data_test,
model=model,
logger=logger,
args=args,
loss_sum=loss_sum)
while True:
for mode, get_feat in [[IM, lambda data: model(im=data)],
[SK, lambda data: model(sk=data)]]:
data_train.mode = mode
for _, (data, semantics) in enumerate(dataloader_train):
# Skip one-element batch in consideration of batch normalization
if data.shape[0] == 1:
continue
# print(data.shape)
optimizer.zero_grad()
loss = cmt_loss(get_feat(data.cuda()),
semantics.cuda()) \
+ l2_regularization()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
loss_sum[mode].append(float(loss.item()))
epochs += 1
dr_dec(optimizer=optimizer, args=args)
if (epochs + 1) % args.save_every == 0:
_test_and_save(epochs=epochs,
optimizer=optimizer,
data_test=data_test,
model=model,
logger=logger,
args=args,
loss_sum=loss_sum)
if (epochs + 1) % args.print_every == 0:
logger.info('epochs: {}, loss_sk: {}, loss_im: {},'.format(
epochs, np.mean(loss_sum[SK]), np.mean(loss_sum[IM])))
loss_sum = [[], []]
if epochs >= args.epochs: break
def train(args):
writer = SummaryWriter()
logger = make_logger(args.log_file)
if args.zs:
packed = args.packed_pkl_zs
else:
packed = args.packed_pkl_nozs
logger.info('Loading the data ...')
data = CMDTrans_data(args.sketch_dir,
args.image_dir,
args.stats_file,
args.embedding_file,
packed,
args.preprocess_data,
args.raw_data,
zs=args.zs,
sample_time=1,
cvae=True,
paired=False,
cut_part=False)
dataloader_train = DataLoader(dataset=data, num_workers=args.num_worker, \
batch_size=args.batch_size,
shuffle=args.shuffle)
logger.info('Training sketch size: {}'.format(
len(data.path2class_sketch.keys())))
logger.info('Training image size: {}'.format(
len(data.path2class_image.keys())))
logger.info('Testing sketch size: {}'.format(
len(data.path2class_sketch_test.keys())))
logger.info('Testing image size: {}'.format(
len(data.path2class_image_test.keys())))
logger.info('Building the model ...')
model = Regressor(args.raw_size,
args.hidden_size,
dropout_prob=args.dropout,
logger=logger)
logger.info('Building the optimizer ...')
optimizer = Adam(params=model.parameters(), lr=args.lr, betas=(0.5, 0.999))
l1_regularization = _Regularization(model,
args.l1_weight,
p=1,
logger=logger)
l2_regularization = _Regularization(model,
args.l2_weight,
p=2,
logger=logger)
if args.start_from is not None:
logger.info('Loading pretrained model from {} ...'.format(
args.start_from))
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
if args.gpu_id != -1:
model.cuda(args.gpu_id)
optimizer.zero_grad()
loss_tri_acm = 0.
loss_l1_acm = 0.
loss_l2_acm = 0.
batch_acm = 0
global_step = 0
best_precision = 0.
best_iter = 0
patience = args.patience
logger.info('Hyper-Parameter:')
logger.info(args)
logger.info('Model Structure:')
logger.info(model)
logger.info('Begin Training !')
while True:
if patience <= 0:
break
for sketch_batch, image_p_batch, image_n_batch, _semantics_batch in dataloader_train:
sketch_batch = sketch_batch.float()
image_p_batch = image_p_batch.float()
image_n_batch = image_n_batch.float()
if global_step % args.print_every == 0 % args.print_every and global_step and batch_acm % args.cum_num == 0:
logger.info('*** Iter {} ***'.format(global_step))
logger.info(' Loss/Triplet {:.3}'.format(
loss_tri_acm / args.print_every / args.cum_num))
logger.info(' Loss/L1 {:.3}'.format(
loss_l1_acm / args.print_every / args.cum_num))
logger.info(' Loss/L2 {:.3}'.format(
loss_l2_acm / args.print_every / args.cum_num))
loss_tri_acm = 0.
loss_l1_acm = 0.
loss_l2_acm = 0.
if global_step % args.save_every == 0 % args.save_every and batch_acm % args.cum_num == 0 and global_step:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save(
{
'args': args,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}, '{}/Iter_{}.pkl'.format(args.save_dir, global_step))
### Evaluation
model.eval()
image_label = list()
image_feature = list()
for image, label in data.load_test_images(
batch_size=args.batch_size):
image = image.float()
if args.gpu_id != -1:
image = image.cuda(args.gpu_id)
image_label += label
tmp_feature = model.inference_image(
image).cpu().detach().numpy()
image_feature.append(tmp_feature)
image_feature = np.vstack(image_feature)
sketch_label = list()
sketch_feature = list()
for sketch, label in data.load_test_sketch(
batch_size=args.batch_size):
sketch = sketch.float()
if args.gpu_id != -1:
sketch = sketch.cuda(args.gpu_id)
sketch_label += label
tmp_feature = model.inference_sketch(
sketch).cpu().detach().numpy()
sketch_feature.append(tmp_feature)
sketch_feature = np.vstack(sketch_feature)
Precision, mAP, = cal_matrics_single(image_feature,
image_label,
sketch_feature,
sketch_label)
writer.add_scalar('Precision_200/cosine', Precision,
global_step)
writer.add_scalar('mAP_200/cosine', mAP, global_step)
logger.info('*** Evaluation Iter {} ***'.format(global_step))
logger.info(' Precision {:.3}'.format(Precision))
logger.info(' mAP {:.3}'.format(mAP))
if best_precision < Precision:
patience = args.patience
best_precision = Precision
best_iter = global_step
writer.add_scalar('Best/Precision_200', best_precision,
best_iter)
logger.info('Iter {}, Best Precision_200 {:.3}'.format(
global_step, best_precision))
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()}, '{}/Best.pkl'.format(args.save_dir))
else:
patience -= 1
if patience <= 0:
break
model.train()
batch_acm += 1
if global_step <= args.warmup_steps:
update_lr(optimizer, args.lr * global_step / args.warmup_steps)
if args.gpu_id != -1:
sketch_batch = sketch_batch.cuda(args.gpu_id)
image_p_batch = image_p_batch.cuda(args.gpu_id)
image_n_batch = image_n_batch.cuda(args.gpu_id)
loss = model(sketch_batch, image_p_batch, image_n_batch)
loss_l1 = l1_regularization()
loss_l2 = l2_regularization()
loss_tri = loss.item()
loss_l1_acm += (loss_l1.item() / args.l1_weight)
loss_l2_acm += (loss_l2.item() / args.l2_weight)
loss_tri_acm += loss_tri
writer.add_scalar('Loss/Triplet', loss_tri, global_step)
writer.add_scalar('Loss/Reg_l1', (loss_l1.item() / args.l1_weight),
global_step)
writer.add_scalar('Loss/Reg_l2', (loss_l2.item() / args.l2_weight),
global_step)
loss_ = 0
loss_ += loss
loss_.backward()
if batch_acm % args.cum_num == 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
global_step += 1
optimizer.zero_grad()
def train(args):
writer = SummaryWriter()
logger = make_logger(args.log_file)
if args.zs:
packed = args.packed_pkl_zs
else:
packed = args.packed_pkl_nozs
data = Siamese_dataloader(args.sketch_dir,
args.image_dir,
args.stats_file,
packed,
zs=args.zs)
print(len(data))
dataloader_train = DataLoader(dataset=data, num_workers=args.num_worker, \
batch_size=args.batch_size,
shuffle=args.shuffle)
logger.info('Building the model ...')
model = Siamese(args.margin,
args.loss_type,
args.distance_type,
batch_normalization=False,
from_pretrain=True,
logger=logger)
logger.info('Building the optimizer ...')
#optimizer = Adam(params=model.parameters(), lr=args.lr)
optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.9)
siamese_loss = _Siamese_loss()
l1_regularization = _Regularization(model, 0.1, p=1, logger=logger)
l2_regularization = _Regularization(model, 1e-4, p=2, logger=logger)
if args.start_from is not None:
logger.info('Loading pretrained model from {} ...'.format(
args.start_from))
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
if args.gpu_id != -1:
model.cuda(args.gpu_id)
batch_acm = 0
global_step = 0
loss_siamese_acm, sim_acm, dis_sim_acm, loss_l1_acm, loss_l2_acm = 0., 0., 0., 0., 0.,
best_precision = 0.
best_iter = 0
patience = args.patience
logger.info('Hyper-Parameter:')
logger.info(args)
logger.info('Model Structure:')
logger.info(model)
logger.info('Begin Training !')
while True:
if patience <= 0:
break
for sketch_batch, image_batch, label_batch in dataloader_train:
if global_step % args.print_every == 0 % args.print_every and global_step and batch_acm % args.cum_num == 0:
logger.info('Iter {}, Loss/siamese {:.3f}, Loss/l1 {:.3f}, Loss/l2 {:.3f}, Siamese/sim {:.3f}, Siamese/dis_sim {:.3f}'.format(global_step, \
loss_siamese_acm/args.print_every/args.cum_num, \
loss_l1_acm/args.print_every/args.cum_num, \
loss_l2_acm/args.print_every/args.cum_num, \
sim_acm/args.print_every/args.cum_num, \
dis_sim_acm/args.print_every/args.cum_num))
loss_siamese_acm, sim_acm, dis_sim_acm, loss_l1_acm, loss_l2_acm = 0., 0., 0., 0., 0.,
if global_step % args.save_every == 0 % args.save_every and batch_acm % args.cum_num == 0 and global_step:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()},
'{}/Iter_{}.pkl'.format(args.save_dir,global_step))
### Evaluation
model.eval()
image_label = list()
image_feature = list()
for image, label in data.load_test_images(
batch_size=args.batch_size):
image = image.cuda(args.gpu_id)
image_label += label
tmp_feature = model.get_feature(
image).cpu().detach().numpy()
image_feature.append(tmp_feature)
image_feature = np.vstack(image_feature)
sketch_label = list()
sketch_feature = list()
for sketch, label in data.load_test_sketch(
batch_size=args.batch_size):
sketch = sketch.cuda(args.gpu_id)
sketch_label += label
tmp_feature = model.get_feature(
sketch).cpu().detach().numpy()
sketch_feature.append(tmp_feature)
sketch_feature = np.vstack(sketch_feature)
dists_cosine = cdist(image_feature, sketch_feature, 'cosine')
print(dists_cosine.shape)
dists_euclid = cdist(image_feature, sketch_feature,
'euclidean')
rank_cosine = np.argsort(dists_cosine, 0)
rank_euclid = np.argsort(dists_euclid, 0)
for n in [5, 200]:
ranksn_cosine = rank_cosine[:n, :].T
ranksn_euclid = rank_euclid[:n, :].T
classesn_cosine = np.array([[image_label[i] == sketch_label[r] \
for i in ranksn_cosine[r]] for r in range(len(ranksn_cosine))])
classesn_euclid = np.array([[image_label[i] == sketch_label[r] \
for i in ranksn_euclid[r]] for r in range(len(ranksn_euclid))])
precision_cosine = np.mean(classesn_cosine)
precision_euclid = np.mean(classesn_euclid)
writer.add_scalar('Precision_{}/cosine'.format(n),
precision_cosine, global_step)
writer.add_scalar('Precision_{}/euclid'.format(n),
precision_euclid, global_step)
logger.info('Iter {}, Precision_{}/cosine {}'.format(
global_step, n, precision_cosine))
logger.info('Iter {}, Precision_{}/euclid {}'.format(
global_step, n, precision_euclid))
if best_precision < precision_cosine:
patience = args.patience
best_precision = precision_cosine
best_iter = global_step
writer.add_scalar('Best/Precision_200', best_precision,
best_iter)
logger.info('Iter {}, Best Precision_200 {}'.format(
global_step, best_precision))
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()}, '{}/Best.pkl'.format(args.save_dir))
else:
patience -= 1
if patience <= 0:
break
model.train()
batch_acm += 1
if global_step <= args.warmup_steps:
update_lr(optimizer, args.lr * global_step / args.warmup_steps)
"""
#code for testing if the images and the sketches are corresponding to each other correctly
for i in range(args.batch_size):
sk = sketch_batch[i].numpy().reshape(224, 224, 3)
im = image_batch[i].numpy().reshape(224, 224, 3)
print(label[i])
ims = np.vstack((np.uint8(sk), np.uint8(im)))
cv2.imshow('test', ims)
cv2.waitKey(3000)
"""
sketch = sketch_batch.cuda(args.gpu_id)
image = image_batch.cuda(args.gpu_id)
label = label_batch.float().cuda(args.gpu_id)
optimizer.zero_grad()
sketch_feature, image_feature = model(sketch, image)
loss_siamese, sim, dis_sim = siamese_loss(
sketch_feature,
image_feature,
label,
args.margin,
loss_type=args.loss_type,
distance_type=args.distance_type)
loss_l1 = l1_regularization()
loss_l2 = l2_regularization()
loss_siamese_acm += loss_siamese.item()
sim_acm += sim.item()
dis_sim_acm += dis_sim.item()
loss_l1_acm += loss_l1.item()
loss_l2_acm += loss_l2.item()
writer.add_scalar('Loss/Siamese', loss_siamese.item(), global_step)
writer.add_scalar('Loss/L1', loss_l1.item(), global_step)
writer.add_scalar('Loss/L2', loss_l2.item(), global_step)
writer.add_scalar('Siamese/Similar', sim.item(), global_step)
writer.add_scalar('Siamese/Dis-Similar', dis_sim.item(),
global_step)
loss = loss_siamese + loss_l2
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if batch_acm % args.cum_num == 0:
optimizer.step()
global_step += 1
def train(args):
relu = nn.ReLU(inplace=True)
writer = SummaryWriter()
logger = make_logger(args.log_file)
if args.zs:
packed = args.packed_pkl_zs
else:
packed = args.packed_pkl_nozs
logger.info('Loading the data ...')
data = CMDTrans_data(args.sketch_dir,
args.image_dir,
args.stats_file,
args.embedding_file,
packed,
args.preprocess_data,
args.raw_data,
zs=args.zs,
sample_time=1,
cvae=True,
paired=True,
cut_part=False,
ranking=True,
tu_berlin=args.tu_berlin,
strong_pair=args.strong_pair)
dataloader_train = DataLoader(dataset=data,
num_workers=args.num_worker,
batch_size=args.batch_size,
shuffle=args.shuffle)
logger.info('Training sketch size: {}'.format(
len(data.path2class_sketch.keys())))
logger.info('Training image size: {}'.format(
len(data.path2class_image.keys())))
logger.info('Testing sketch size: {}'.format(
len(data.path2class_sketch_test.keys())))
logger.info('Testing image size: {}'.format(
len(data.path2class_image_test.keys())))
logger.info('Building the model ...')
model = CMDTrans_model(args.pca_size,
args.raw_size,
args.hidden_size,
args.semantics_size,
data.pretrain_embedding.float(),
dropout_prob=args.dropout,
fix_embedding=args.fix_embedding,
seman_dist=args.seman_dist,
triplet_dist=args.triplet_dist,
margin1=args.margin1,
margin2=args.margin2,
logger=logger)
logger.info('Building the optimizer ...')
optimizer = Adam(params=model.parameters(), lr=args.lr, betas=(0.5, 0.999))
#optimizer = SGD(params=model.parameters(), lr=args.lr, momentum=0.9)
l1_regularization = _Regularization(model,
args.l1_weight,
p=1,
logger=logger)
l2_regularization = _Regularization(model,
args.l2_weight,
p=2,
logger=logger)
if args.start_from is not None:
logger.info('Loading pretrained model from {} ...'.format(
args.start_from))
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
if args.gpu_id != -1:
model.cuda(args.gpu_id)
optimizer.zero_grad()
# six design loss and two reg loss
loss_triplet_acm = 0.
loss_orth_acm = 0.
loss_kl_acm = 0.
loss_img_acm = 0.
loss_ske_acm = 0.
loss_l1_acm = 0.
loss_l2_acm = 0.
# loading batch and optimization step
batch_acm = 0
global_step = 0
# best recoder
best_precision = 0.
best_iter = 0
patience = args.patience
logger.info('Hyper-Parameter:')
logger.info(args)
logger.info('Model Structure:')
logger.info(model)
logger.info('Begin Training !')
loss_weight = dict([('kl', 1.0), ('triplet', 1.0), ('orthogonality', 0.01),
('image', 1.0), ('sketch', 10.0)])
while True:
if patience <= 0:
break
for sketch_batch, image_pair_batch, image_unpair_batch, image_n_batch in dataloader_train:
if global_step % args.print_every == 0 % args.print_every and global_step and batch_acm % args.cum_num == 0:
logger.info('*** Iter {} ***'.format(global_step))
logger.info(' Loss/Triplet {:.3}'.format(
loss_triplet_acm / args.print_every / args.cum_num))
logger.info(' Loss/Orthogonality {:.3}'.format(
loss_orth_acm / args.print_every / args.cum_num))
logger.info(' Loss/KL {:.3}'.format(
loss_kl_acm / args.print_every / args.cum_num))
logger.info(' Loss/Image {:.3}'.format(
loss_img_acm / args.print_every / args.cum_num))
logger.info(' Loss/Sketch {:.3}'.format(
loss_ske_acm / args.print_every / args.cum_num))
logger.info(' Loss/L1 {:.3}'.format(
loss_l1_acm / args.print_every / args.cum_num))
logger.info(' Loss/L2 {:.3}'.format(
loss_l2_acm / args.print_every / args.cum_num))
loss_triplet_acm = 0.
loss_orth_acm = 0.
loss_kl_acm = 0.
loss_img_acm = 0.
loss_ske_acm = 0.
loss_l1_acm = 0.
loss_l2_acm = 0.
if global_step % args.save_every == 0 % args.save_every and batch_acm % args.cum_num == 0 and global_step:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save(
{
'args': args,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}, '{}/Iter_{}.pkl'.format(args.save_dir, global_step))
### Evaluation
model.eval()
image_label = list()
image_feature1 = list() # S
image_feature2 = list() # G
for image, label in data.load_test_images(
batch_size=args.batch_size):
image = relu(image)
if args.gpu_id != -1:
image = image.float().cuda(args.gpu_id)
image_label += label
tmp_feature1 = model.inference_structure(
image, 'image').detach() # S
tmp_feature2 = image.detach() # G
image_feature1.append(tmp_feature1)
image_feature2.append(tmp_feature2)
image_feature1 = torch.cat(image_feature1)
image_feature2 = torch.cat(image_feature2)
sketch_label = list()
sketch_feature1 = list() # S
sketch_feature2 = list() # G
for sketch, label in data.load_test_sketch(
batch_size=args.batch_size):
sketch = relu(sketch)
if args.gpu_id != -1:
sketch = sketch.float().cuda(args.gpu_id)
sketch_label += label
tmp_feature1 = model.inference_structure(
sketch, 'sketch').detach() # S
tmp_feature2 = model.inference_generation(
sketch).detach() # G
sketch_feature1.append(tmp_feature1)
sketch_feature2.append(tmp_feature2)
sketch_feature1 = torch.cat(sketch_feature1)
sketch_feature2 = torch.cat(sketch_feature2)
dists_cosine1 = cosine_distance(
image_feature1, sketch_feature1).cpu().detach().numpy()
dists_cosine2 = cosine_distance(
image_feature2, sketch_feature2).cpu().detach().numpy()
Precision_list, mAP_list, lambda_list, Precision_c, mAP_c = \
cal_matrics(dists_cosine1, dists_cosine2, image_label, sketch_label)
logger.info('*** Evaluation Iter {} ***'.format(global_step))
for idx, item in enumerate(lambda_list):
writer.add_scalar('Precision_200/{}'.format(item),
Precision_list[idx], global_step)
writer.add_scalar('mAP_200/{}'.format(item), mAP_list[idx],
global_step)
logger.info(' Precision/{} {:.3}'.format(
item, Precision_list[idx]))
logger.info(' mAP/{} {:.3}'.format(
item, mAP_list[idx]))
writer.add_scalar('Precision_200/Compare', Precision_c,
global_step)
writer.add_scalar('mAP_200/Compare', mAP_c, global_step)
logger.info(
' Precision/Compare {:.3}'.format(Precision_c))
logger.info(' mAP/Compare {:.3}'.format(mAP_c))
Precision_list.append(Precision_c)
Precision = max(Precision_list)
if best_precision < Precision:
patience = args.patience
best_precision = Precision
best_iter = global_step
writer.add_scalar('Best/Precision_200', best_precision,
best_iter)
logger.info(
'=== Iter {}, Best Precision_200 {:.3} ==='.format(
global_step, best_precision))
torch.save({'args':args, 'model':model.state_dict(), \
'optimizer':optimizer.state_dict()}, '{}/Best.pkl'.format(args.save_dir))
else:
patience -= 1
if patience <= 0:
break
model.train()
batch_acm += 1
if global_step <= args.warmup_steps:
update_lr(optimizer, args.lr * global_step / args.warmup_steps)
if args.gpu_id != -1:
sketch_batch = relu(sketch_batch).float().cuda(args.gpu_id)
image_pair_batch = relu(image_pair_batch).float().cuda(
args.gpu_id)
image_unpair_batch = relu(image_unpair_batch).float().cuda(
args.gpu_id)
image_n_batch = relu(image_n_batch).float().cuda(args.gpu_id)
loss = model(sketch_batch, image_pair_batch, image_unpair_batch,
image_n_batch)
loss_l1 = l1_regularization()
loss_l2 = l2_regularization()
loss_kl = loss['kl'].item()
loss_orth = loss['orthogonality'].item()
loss_triplet = loss['triplet'].item()
loss_img = loss['image'].item()
loss_ske = loss['sketch'].item()
loss_l1_acm += (loss_l1.item() / args.l1_weight)
loss_l2_acm += (loss_l2.item() / args.l2_weight)
loss_kl_acm += loss_kl
loss_orth_acm += loss_orth
loss_triplet_acm += loss_triplet
loss_img_acm += loss_img
loss_ske_acm += loss_ske
writer.add_scalar('Loss/KL', loss_kl, global_step)
writer.add_scalar('Loss/Orthogonality', loss_orth, global_step)
writer.add_scalar('Loss/Triplet', loss_triplet, global_step)
writer.add_scalar('Loss/Image', loss_img, global_step)
writer.add_scalar('Loss/Sketch', loss_ske, global_step)
writer.add_scalar('Loss/Reg_l1', (loss_l1.item() / args.l1_weight),
global_step)
writer.add_scalar('Loss/Reg_l2', (loss_l2.item() / args.l2_weight),
global_step)
loss_ = 0
loss_ += loss['image'] * loss_weight['image']
loss_ += loss['sketch'] * loss_weight['sketch']
loss_ += loss['triplet'] * loss_weight['triplet']
loss_ += loss['kl'] * loss_weight['kl']
#loss_ += loss['orthogonality']*loss_weight['orthogonality']
loss_.backward()
if batch_acm % args.cum_num == 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
global_step += 1
optimizer.zero_grad()
