def train():
cfg = get_args(**Cfg)
os.chdir(cfg.root_dir)
os.makedirs(cfg.log_dir, exist_ok=True)
os.makedirs(cfg.ckpt_dir, exist_ok=True)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = True
cfg.device = torch.device('cuda')
print('Setting up data...')
train_dataset = KneeDataset(cfg.train_label, cfg)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True)
val_dataset = KneeDataset(cfg.val_label, cfg, False)
eval_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=collate_fn)
center_net = CenterNet(cfg)
center_net.train(len(train_dataset), train_loader, eval_loader,
cfg.num_epochs)
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset = COCO if cfg.dataset == 'coco' else PascalVOC
train_dataset = Dataset(cfg.data_dir,
'train',
split_ratio=cfg.split_ratio,
img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = COCO_eval if cfg.dataset == 'coco' else PascalVOC_eval
val_dataset = Dataset_eval(cfg.data_dir,
'val',
test_scales=[1.],
test_flip=False)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net(num_layers=int(cfg.arch.split('_')[-1]),
num_classes=train_dataset.num_classes)
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
else:
# todo don't use this, or wrapped it with utils.losses.Loss() !
model = nn.DataParallel(model).to(cfg.device)
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.lr_step,
gamma=0.1)
def train(epoch):
print('\n%s Epoch: %d' % (datetime.now(), epoch))
model.train()
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
batch[k] = batch[k].to(device=cfg.device, non_blocking=True)
outputs = model(batch['image'])
hmap_tl, hmap_br, embd_tl, embd_br, regs_tl, regs_br = zip(
*outputs)
embd_tl = [
_tranpose_and_gather_feature(e, batch['inds_tl'])
for e in embd_tl
]
embd_br = [
_tranpose_and_gather_feature(e, batch['inds_br'])
for e in embd_br
]
regs_tl = [
_tranpose_and_gather_feature(r, batch['inds_tl'])
for r in regs_tl
]
regs_br = [
_tranpose_and_gather_feature(r, batch['inds_br'])
for r in regs_br
]
focal_loss = _neg_loss(hmap_tl, batch['hmap_tl']) + \
_neg_loss(hmap_br, batch['hmap_br'])
reg_loss = _reg_loss(regs_tl, batch['regs_tl'], batch['ind_masks']) + \
_reg_loss(regs_br, batch['regs_br'], batch['ind_masks'])
pull_loss, push_loss = _ae_loss(embd_tl, embd_br,
batch['ind_masks'])
loss = focal_loss + 0.1 * pull_loss + 0.1 * push_loss + reg_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print(
'[%d/%d-%d/%d] ' %
(epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
' focal_loss= %.5f pull_loss= %.5f push_loss= %.5f reg_loss= %.5f'
% (focal_loss.item(), pull_loss.item(), push_loss.item(),
reg_loss.item()) + ' (%d samples/sec)' %
(cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('focal_loss', focal_loss.item(),
step)
summary_writer.add_scalar('pull_loss', pull_loss.item(), step)
summary_writer.add_scalar('push_loss', push_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
return
def val_map(epoch):
print('\n%s Val@Epoch: %d' % (datetime.now(), epoch))
model.eval()
# torch.cuda.empty_cache()
results = {}
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device)
output = model(inputs[scale]['image'])[-1]
det = _decode(*output, ae_threshold=0.5, K=100, kernel=3)
det = det.reshape(det.shape[0], -1,
8).detach().cpu().numpy()
if det.shape[0] == 2:
det[1, :, [0, 2]] = inputs[scale]['fmap_size'][
0, 1] - det[1, :, [2, 0]]
det = det.reshape(1, -1, 8)
_rescale_dets(det, inputs[scale]['ratio'],
inputs[scale]['border'],
inputs[scale]['size'])
det[:, :, 0:4] /= scale
detections.append(det)
detections = np.concatenate(detections, axis=1)[0]
# reject detections with negative scores
detections = detections[detections[:, 4] > -1]
classes = detections[..., -1]
results[img_id] = {}
for j in range(val_dataset.num_classes):
keep_inds = (classes == j)
results[img_id][j +
1] = detections[keep_inds][:, 0:7].astype(
np.float32)
soft_nms_merge(results[img_id][j + 1],
Nt=0.5,
method=2,
weight_exp=10)
# soft_nms(results[img_id][j + 1], Nt=0.5, method=2)
results[img_id][j + 1] = results[img_id][j + 1][:, 0:5]
scores = np.hstack([
results[img_id][j][:, -1]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > val_dataset.max_objs:
kth = len(scores) - val_dataset.max_objs
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (results[img_id][j][:, -1] >= thresh)
results[img_id][j] = results[img_id][j][keep_inds]
eval_results = val_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
summary_writer.add_scalar('val_mAP/mAP', eval_results[0], epoch)
print('Starting training...')
for epoch in range(1, cfg.num_epochs + 1):
train_sampler.set_epoch(epoch)
train(epoch)
if cfg.val_interval > 0 and epoch % cfg.val_interval == 0:
val_map(epoch)
print(saver.save(model.module.state_dict(), 'checkpoint'))
lr_scheduler.step(epoch) # move to here after pytorch1.1.0
summary_writer.close()
def main():
best_mAP = 0.0
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print_log = logger.info
print_log(cfg)
torch.manual_seed(319)
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda:%d' % cfg.device_id)
print_log('Setting up data...')
cfg.dictionary_file = os.path.join(
cfg.dictionary_folder,
'train_dict_v{}_n{}_a{:.2f}.npy'.format(cfg.n_vertices, cfg.n_codes,
cfg.sparse_alpha))
print_log('Loading the dictionary: ' + cfg.dictionary_file)
dictionary = np.load(cfg.dictionary_file)
if 'hourglass' in cfg.arch:
cfg.padding = 127
else:
cfg.padding = 31
Dataset = COCOSEGMCMM if cfg.dataset == 'coco' else KINSSEGMCMM
train_dataset = Dataset(cfg.data_dir,
cfg.dictionary_file,
'train',
split_ratio=cfg.split_ratio,
img_size=cfg.img_size,
padding=cfg.padding,
n_coeffs=cfg.n_codes,
n_vertices=cfg.n_vertices,
sparse_alpha=cfg.sparse_alpha)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=False,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = COCO_eval_segm_cmm if cfg.dataset == 'coco' else KINS_eval_segm_cmm
val_dataset = Dataset_eval(cfg.data_dir,
cfg.dictionary_file,
'val',
test_scales=[1.],
test_flip=False,
img_size=cfg.img_size,
padding=cfg.padding,
n_coeffs=cfg.n_codes,
n_vertices=cfg.n_vertices,
fix_size=False,
sparse_alpha=cfg.sparse_alpha)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False,
collate_fn=val_dataset.collate_fn)
print_log('Creating model...')
if 'hourglass' in cfg.arch:
model = exkp(n=5,
nstack=2,
dims=[256, 256, 384, 384, 384, 512],
modules=[2, 2, 2, 2, 2, 4],
n_codes=cfg.n_codes)
elif 'resdcn' in cfg.arch:
model = get_pose_resdcn(num_layers=int(cfg.arch.split('_')[-1]),
head_conv=64,
num_classes=train_dataset.num_classes,
num_codes=cfg.n_codes)
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
else:
model = nn.DataParallel(model, device_ids=[
cfg.local_rank,
]).to(cfg.device)
if cfg.pretrain_checkpoint is not None and os.path.isfile(
cfg.pretrain_checkpoint):
print_log('Load pretrain model from ' + cfg.pretrain_checkpoint)
model = load_model(model, cfg.pretrain_checkpoint, cfg.device_id)
torch.cuda.empty_cache()
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.lr_step,
gamma=cfg.gamma)
def train(epoch):
print_log('\n Epoch: %d' % epoch)
model.train()
# torch.autograd.set_detect_anomaly(mode=True)
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=cfg.device,
non_blocking=True)
dict_tensor = torch.from_numpy(dictionary.astype(np.float32)).to(
cfg.device, non_blocking=True)
dict_tensor.requires_grad = False
outputs = model(batch['image'])
# hmap, regs, w_h_, codes_1, codes_2, codes_3, offsets = zip(*outputs)
hmap, regs, w_h_, codes, offsets = zip(*outputs)
regs = [
_tranpose_and_gather_feature(r, batch['inds']) for r in regs
]
w_h_ = [
_tranpose_and_gather_feature(r, batch['inds']) for r in w_h_
]
codes = [
_tranpose_and_gather_feature(r, batch['inds']) for r in codes
]
# c_2 = [_tranpose_and_gather_feature(r, batch['inds']) for r in codes_2]
# c_3 = [_tranpose_and_gather_feature(r, batch['inds']) for r in codes_3]
offsets = [
_tranpose_and_gather_feature(r, batch['inds']) for r in offsets
]
hmap_loss = _neg_loss(hmap, batch['hmap'])
reg_loss = _reg_loss(regs, batch['regs'], batch['ind_masks'])
w_h_loss = _reg_loss(w_h_, batch['w_h_'], batch['ind_masks'])
offsets_loss = _reg_loss(offsets, batch['offsets'],
batch['ind_masks'])
# codes_loss = (norm_reg_loss(c_1, batch['codes'], batch['ind_masks'], sparsity=0.)
# + norm_reg_loss(c_2, batch['codes'], batch['ind_masks'], sparsity=0.)
# + norm_reg_loss(c_3, batch['codes'], batch['ind_masks'], sparsity=0.)) / 3.
if cfg.code_loss == 'norm':
codes_loss = norm_reg_loss(codes,
batch['codes'],
batch['ind_masks'],
sparsity=0.)
elif cfg.code_loss == 'adapt':
codes_loss = adapt_norm_reg_loss(codes,
batch['codes'],
batch['ind_masks'],
sparsity=0.,
norm=cfg.adapt_norm)
elif cfg.code_loss == 'wing':
codes_loss = wing_norm_reg_loss(codes,
batch['codes'],
batch['ind_masks'],
sparsity=0.,
epsilon=cfg.wing_epsilon,
omega=cfg.wing_omega)
else:
print('Loss type for code not implemented yet.')
raise NotImplementedError
loss = 1. * hmap_loss + 1. * reg_loss + 0.1 * w_h_loss + 0.1 * offsets_loss + \
cfg.code_loss_weight * codes_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print_log(
'[%d/%d-%d/%d] ' %
(epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
'Loss: hmap = %.3f reg = %.3f w_h = %.3f code = %.3f offsets = %.3f'
% (hmap_loss.item(), reg_loss.item(), w_h_loss.item(),
codes_loss.item(), offsets_loss.item()) +
' (%d samples/sec)' %
(cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('hmap_loss', hmap_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
summary_writer.add_scalar('w_h_loss', w_h_loss.item(), step)
summary_writer.add_scalar('offset_loss', offsets_loss.item(),
step)
summary_writer.add_scalar('code_loss', codes_loss.item(), step)
return
def val_map(epoch):
print_log('\n Val@Epoch: %d' % epoch)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
speed_list = []
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
start_image_time = time.time()
segmentations = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device)
# dict_tensor = torch.from_numpy(dictionary.astype(np.float32)).to(cfg.device, non_blocking=True)
# dict_tensor.requires_grad = False
# hmap, regs, w_h_, _, _, codes, offsets = model(inputs[scale]['image'])[-1]
hmap, regs, w_h_, codes, offsets = model(
inputs[scale]['image'])[-1]
output = [hmap, regs, w_h_, codes, offsets]
segms = ctsegm_inmodal_code_decode(
*output,
torch.from_numpy(dictionary.astype(np.float32)).to(
cfg.device),
K=cfg.test_topk)
segms = segms.detach().cpu().numpy().reshape(
1, -1, segms.shape[2])[0]
top_preds = {}
for j in range(cfg.n_vertices):
segms[:, 2 * j:2 * j + 2] = transform_preds(
segms[:, 2 * j:2 * j + 2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
segms[:, cfg.n_vertices * 2:cfg.n_vertices * 2 +
2] = transform_preds(
segms[:,
cfg.n_vertices * 2:cfg.n_vertices * 2 + 2],
inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'],
inputs[scale]['fmap_h']))
segms[:, cfg.n_vertices * 2 + 2:cfg.n_vertices * 2 +
4] = transform_preds(
segms[:, cfg.n_vertices * 2 +
2:cfg.n_vertices * 2 + 4],
inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'],
inputs[scale]['fmap_h']))
clses = segms[:, -1]
for j in range(val_dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = segms[inds, :cfg.n_vertices * 2 +
5].astype(np.float32)
top_preds[j + 1][:, :cfg.n_vertices * 2 + 4] /= scale
segmentations.append(top_preds)
end_image_time = time.time()
segms_and_scores = {
j: np.concatenate([d[j] for d in segmentations], axis=0)
for j in range(1, val_dataset.num_classes + 1)
}
scores = np.hstack([
segms_and_scores[j][:, cfg.n_vertices * 2 + 4]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (
segms_and_scores[j][:, cfg.n_vertices * 2 + 4] >=
thresh)
segms_and_scores[j] = segms_and_scores[j][keep_inds]
results[img_id] = segms_and_scores
speed_list.append(end_image_time - start_image_time)
eval_results = val_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print_log(eval_results)
summary_writer.add_scalar('val_mAP/mAP', eval_results[0], epoch)
print_log('Average speed on val set:{:.2f}'.format(
1. / np.mean(speed_list)))
return eval_results[0]
print_log('Starting training...')
for epoch in range(1, cfg.num_epochs + 1):
start = time.time()
train_sampler.set_epoch(epoch)
train(epoch)
if (cfg.val_interval > 0
and epoch % cfg.val_interval == 0) or epoch == 2:
stat = val_map(epoch)
if stat > best_mAP:
print('Overall mAP {:.3f} is improving ...'.format(stat))
print_log(saver.save(model.module.state_dict(), 'checkpoint'))
best_mAP = stat
lr_scheduler.step() # move to here after pytorch1.1.0
epoch_time = (time.time() - start) / 3600. / 24.
print_log('ETA:{:.2f} Days'.format(
(cfg.num_epochs - epoch) * epoch_time))
summary_writer.close()
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(319)
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda:%d' % cfg.device_id)
print('Setting up data...')
dictionary = np.load(cfg.dictionary_file)
Dataset = COCOSEGMSHIFT if cfg.dataset == 'coco' else PascalVOC
train_dataset = Dataset(cfg.data_dir,
cfg.dictionary_file,
'train',
split_ratio=cfg.split_ratio,
img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=False,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = COCO_eval_segm_shift if cfg.dataset == 'coco' else PascalVOC_eval
val_dataset = Dataset_eval(cfg.data_dir,
cfg.dictionary_file,
'val',
test_scales=[1.],
test_flip=False)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False,
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net(num_layers=int(cfg.arch.split('_')[-1]),
num_classes=train_dataset.num_classes)
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
else:
model = nn.DataParallel(model, device_ids=[
cfg.local_rank,
]).to(cfg.device)
if cfg.pretrain_checkpoint is not None and os.path.isfile(
cfg.pretrain_checkpoint):
print('Load pretrain model from ' + cfg.pretrain_checkpoint)
model = load_model(model, cfg.pretrain_checkpoint, cfg.device_id)
torch.cuda.empty_cache()
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.lr_step,
gamma=0.1)
def train(epoch):
print('\n Epoch: %d' % epoch)
model.train()
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=cfg.device,
non_blocking=True)
outputs = model(batch['image'])
hmap, regs, w_h_, codes_ = zip(*outputs)
regs = [
_tranpose_and_gather_feature(r, batch['inds']) for r in regs
]
w_h_ = [
_tranpose_and_gather_feature(r, batch['inds']) for r in w_h_
]
codes_ = [
_tranpose_and_gather_feature(r, batch['inds']) for r in codes_
]
hmap_loss = _neg_loss(hmap, batch['hmap'])
reg_loss = _reg_loss(regs, batch['regs'], batch['ind_masks'])
w_h_loss = _reg_loss(w_h_, batch['w_h_'], batch['ind_masks'])
codes_loss = norm_reg_loss(codes_, batch['codes'],
batch['ind_masks'])
# codes_loss = mse_reg_loss(codes_, batch['codes'], batch['ind_masks'])
loss = hmap_loss + 1 * reg_loss + 0.1 * w_h_loss + cfg.code_loss_weight * codes_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print(
'[%d/%d-%d/%d] ' %
(epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
' hmap_loss= %.3f reg_loss= %.3f w_h_loss= %.3f code_loss= %.3f'
% (hmap_loss.item(), reg_loss.item(), w_h_loss.item(),
codes_loss.item()) + ' (%d samples/sec)' %
(cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('hmap_loss', hmap_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
summary_writer.add_scalar('w_h_loss', w_h_loss.item(), step)
summary_writer.add_scalar('code_loss', codes_loss.item(), step)
return
def val_map(epoch):
print('\n Val@Epoch: %d' % epoch)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
input_scales = {}
speed_list = []
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
start_image_time = time.time()
segmentations = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device)
if scale == 1. and img_id not in input_scales.keys(
): # keep track of the input image Sizes
_, _, input_h, input_w = inputs[scale]['image'].shape
input_scales[img_id] = {'h': input_h, 'w': input_w}
output = model(inputs[scale]['image'])[-1]
segms = ctsegm_shift_decode(
*output,
torch.from_numpy(dictionary.astype(np.float32)).to(
cfg.device),
K=cfg.test_topk)
segms = segms.detach().cpu().numpy().reshape(
1, -1, segms.shape[2])[0]
top_preds = {}
for j in range(cfg.n_vertices):
segms[:, 2 * j:2 * j + 2] = transform_preds(
segms[:, 2 * j:2 * j + 2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
segms[:, cfg.n_vertices * 2:cfg.n_vertices * 2 +
2] = transform_preds(
segms[:,
cfg.n_vertices * 2:cfg.n_vertices * 2 + 2],
inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'],
inputs[scale]['fmap_h']))
segms[:, cfg.n_vertices * 2 + 2:cfg.n_vertices * 2 +
4] = transform_preds(
segms[:, cfg.n_vertices * 2 +
2:cfg.n_vertices * 2 + 4],
inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'],
inputs[scale]['fmap_h']))
clses = segms[:, -1]
for j in range(val_dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = segms[inds, :cfg.n_vertices * 2 +
5].astype(np.float32)
top_preds[j + 1][:, :cfg.n_vertices * 2 + 4] /= scale
segmentations.append(top_preds)
end_image_time = time.time()
segms_and_scores = {
j: np.concatenate([d[j] for d in segmentations], axis=0)
for j in range(1, val_dataset.num_classes + 1)
}
scores = np.hstack([
segms_and_scores[j][:, cfg.n_vertices * 2 + 4]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (
segms_and_scores[j][:, cfg.n_vertices * 2 + 4] >=
thresh)
segms_and_scores[j] = segms_and_scores[j][keep_inds]
results[img_id] = segms_and_scores
speed_list.append(end_image_time - start_image_time)
eval_results = val_dataset.run_eval(results,
input_scales,
save_dir=cfg.ckpt_dir)
print(eval_results)
summary_writer.add_scalar('val_mAP/mAP', eval_results[0], epoch)
print('Average speed on val set:{:.2f}'.format(1. /
np.mean(speed_list)))
print('Starting training...')
for epoch in range(1, cfg.num_epochs + 1):
start = time.time()
train_sampler.set_epoch(epoch)
train(epoch)
if (cfg.val_interval > 0
and epoch % cfg.val_interval == 0) or epoch == 3:
val_map(epoch)
print(saver.save(model.module.state_dict(), 'checkpoint'))
lr_scheduler.step(epoch) # move to here after pytorch1.1.0
epoch_time = (time.time() - start) / 3600. / 24.
print('ETA:{:.2f} Days'.format((cfg.num_epochs - epoch) * epoch_time))
summary_writer.close()
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
# disable this if OOM at beginning of training
torch.backends.cudnn.benchmark = True
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset = COCO if cfg.dataset == 'coco' else PascalVOC
train_dataset = Dataset(cfg.data_dir,
'train',
split_ratio=cfg.split_ratio,
img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = COCO_eval if cfg.dataset == 'coco' else PascalVOC_eval
val_dataset = Dataset_eval(cfg.data_dir,
'test',
test_scales=[1.],
test_flip=False)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net(num_layers=int(cfg.arch.split('_')[-1]),
num_classes=train_dataset.num_classes)
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
else:
model = nn.DataParallel(model).to(cfg.device)
if os.path.isfile(cfg.pretrain_dir):
model = load_model(model, cfg.pretrain_dir)
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.lr_step,
gamma=0.1)
def train(epoch):
print('\n Epoch: %d' % epoch)
model.train()
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=cfg.device,
non_blocking=True)
outputs = model(batch['image'])
# 得到heat map, reg, wh 三个变量
hmap, regs, w_h_ = zip(*outputs)
regs = [
_tranpose_and_gather_feature(r, batch['inds']) for r in regs
]
w_h_ = [
_tranpose_and_gather_feature(r, batch['inds']) for r in w_h_
]
# 分别计算loss
hmap_loss = _neg_loss(hmap, batch['hmap'])
reg_loss = _reg_loss(regs, batch['regs'], batch['ind_masks'])
w_h_loss = _reg_loss(w_h_, batch['w_h_'], batch['ind_masks'])
# 进行loss加权，得到最终loss
loss = hmap_loss + 1 * reg_loss + 0.1 * w_h_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print('[%d/%d-%d/%d] ' %
(epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
' hmap_loss= %.5f reg_loss= %.5f w_h_loss= %.5f' %
(hmap_loss.item(), reg_loss.item(), w_h_loss.item()) +
' (%d samples/sec)' %
(cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('hmap_loss', hmap_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
summary_writer.add_scalar('w_h_loss', w_h_loss.item(), step)
return
def val_map(epoch):
print('\n Val@Epoch: %d' % epoch)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device)
output = model(inputs[scale]['image'])[-1]
dets = ctdet_decode(*output, K=cfg.test_topk)
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(
dets[:, 0:2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(
dets[:, 2:4], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
clses = dets[:, -1]
for j in range(val_dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {
j: np.concatenate([d[j] for d in detections], axis=0)
for j in range(1, val_dataset.num_classes + 1)
}
scores = np.hstack([
bbox_and_scores[j][:, 4]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
results[img_id] = bbox_and_scores
eval_results = val_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
summary_writer.add_scalar('val_mAP/mAP', eval_results[0], epoch)
print('Starting training...')
for epoch in range(1, cfg.num_epochs + 1):
train_sampler.set_epoch(epoch)
train(epoch)
if cfg.val_interval > 0 and epoch % cfg.val_interval == 0:
val_map(epoch)
print(saver.save(model.module.state_dict(), 'checkpoint'))
lr_scheduler.step(epoch) # move to here after pytorch1.1.0
summary_writer.close()
def main():
logger = create_logger(save_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = False
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset_eval = KAIST_eval
dataset = Dataset_eval(cfg.data_dir,
'test',
test_scales=cfg.test_scales,
test_flip=cfg.test_flip)
val_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
else:
raise NotImplementedError
model = nn.DataParallel(model).to(cfg.device)
if (os.path.exists(cfg.pretrain_dir)):
model.load_state_dict(torch.load(cfg.pretrain_dir))
print('loaded pretrained model from %s !' % cfg.pretrain_dir)
print('test starts at %s' % datetime.now())
model.eval()
results = {}
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['img_rgb'] = inputs[scale]['img_rgb'].to(
cfg.device)
inputs[scale]['img_ir'] = inputs[scale]['img_ir'].to(
cfg.device)
output = model(
(inputs[scale]['img_rgb'], inputs[scale]['img_ir']))[-1]
dets = _decode(*output,
ae_threshold=cfg.ae_threshold,
K=cfg.topk,
kernel=3)
dets = dets.reshape(dets.shape[0], -1,
8).detach().cpu().numpy()
if dets.shape[0] == 2:
dets[1, :,
[0, 2]] = inputs[scale]['fmap_size'][0,
1] - dets[1, :,
[2, 0]]
dets = dets.reshape(1, -1, 8)
_rescale_dets(dets, inputs[scale]['ratio'],
inputs[scale]['border'], inputs[scale]['size'])
dets[:, :, 0:4] /= scale
detections.append(dets)
detections = np.concatenate(detections, axis=1)[0]
# reject detections with negative scores
detections = detections[detections[:, 4] > -1]
classes = detections[..., -1]
results[img_id] = {}
for j in range(dataset.num_classes):
keep_inds = (classes == j)
results[img_id][j + 1] = detections[keep_inds][:, 0:7].astype(
np.float32)
bboxes = results[img_id][j + 1]
bboxes = bboxes[(bboxes[:, 5] != 0.0) & (bboxes[:, 6] != 0.0)]
print(img_id)
soft_nms_merge(bboxes,
Nt=cfg.nms_threshold,
method=2,
weight_exp=cfg.w_exp)
# soft_nms(results[img_id][j + 1], Nt=0.5, method=2)
results[img_id][j + 1] = results[img_id][j + 1][:, 0:5]
scores = np.hstack([
results[img_id][j][:, -1]
for j in range(1, dataset.num_classes + 1)
])
if len(scores) > dataset.max_objs:
kth = len(scores) - dataset.max_objs
thresh = np.partition(scores, kth)[kth]
for j in range(1, dataset.num_classes + 1):
keep_inds = (results[img_id][j][:, -1] >= thresh)
results[img_id][j] = results[img_id][j][keep_inds]
lamr = dataset.run_eval(results, run_dir=cfg.ckpt_dir)
print('log-average miss rate = {}'.format(lamr))
print('test ends at %s' % datetime.now())
def main():
logger = create_logger(save_dir=cfg.log_dir)
print = logger.info
print(cfg)
cfg.device = torch.device('cuda')
torch.backends.cudnn.benchmark = False
max_per_image = 100
Dataset_eval = Damage_eval
dataset = Dataset_eval(cfg.data_dir,
split='train',
test_scales=cfg.test_scales,
test_flip=cfg.test_flip) # split test
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net_resdcn(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet':
model = get_pose_net(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'res_CBAM':
model = get_pose_net_resnet_CBAM(num_layers=18,
head_conv=64,
num_classes=3)
elif cfg.arch == 'resnet_PAM':
model = get_pose_net_resnet_PAM(num_layers=18,
head_conv=64,
num_classes=3)
elif cfg.arch == 'resnet_SE':
model = get_pose_net_resnet_SE(num_layers=18,
head_conv=64,
num_classes=3)
def Evaluate(epoch, model):
print('\n Evaluate@Epoch: %d' % epoch)
start_time = time.clock()
print('Start time %s Seconds' % start_time)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
with torch.no_grad():
for inputs in data_loader:
img_id, inputs, img_path = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device) # (1,3)
output = model(
inputs[scale]['image'])[-1] # hmap, regs, pxpy
dets = ctdet_decode(
*output, K=cfg.test_topk
) # torch.cat([bboxes, scores, clses], dim=2)
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(
dets[:, 0:2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(
dets[:, 2:4], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
clses = dets[:, -1]
for j in range(dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {
j: np.concatenate([d[j] for d in detections], axis=0)
for j in range(1, dataset.num_classes + 1)
}
scores = np.hstack([
bbox_and_scores[j][:, 4]
for j in range(1, dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
results[img_id] = bbox_and_scores
end_time = time.clock()
eval_results = dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
print('End time %s Seconds' % end_time)
Run_time = end_time - start_time
FPS = 100 / Run_time # replace 100 with the number of images
print('FPS %s ' % FPS)
#summary_writer.add_scalar('Evaluate_mAP/mAP', eval_results[0], epoch)
return eval_results[0]
num_epochs = 60 # replace 60 with the number of epoch
Max_mAP = 0
for epoch in range(1, num_epochs + 1):
cfg.pretrain_dir = os.path.join(cfg.ckpt_dir, 'checkpoint_epoch' +
str(epoch) + '.t7') # the address
model = load_model(model, cfg.pretrain_dir)
model = model.to(cfg.device)
mAP = Evaluate(epoch, model)
if mAP > Max_mAP:
Max_mAP = mAP
print('Max_AP=%s' % Max_mAP)
def main():
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
num_gpus = torch.cuda.device_count()
if cfg.dist:
device = torch.device('cuda:%d' % cfg.local_rank) if cfg.dist else torch.device('cuda')
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl', init_method='env://',
world_size=num_gpus, rank=cfg.local_rank)
else:
device = torch.device('cuda')
print('==> Preparing data..')
train_dataset = ImgNet_split(root=os.path.join(cfg.data_dir, 'train'),
transform=imgnet_transform(is_training=True))
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=num_gpus,
rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist
else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
sampler=train_sampler if cfg.dist else None)
val_dataset = ImgNet_split(root=os.path.join(cfg.data_dir, 'val'),
transform=imgnet_transform(is_training=False))
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers)
print('==> Building model..')
genotype = torch.load(os.path.join(cfg.ckpt_dir, 'genotype.pickle'))['genotype']
model = NetworkImageNet(genotype, cfg.init_ch, cfg.num_cells, cfg.auxiliary, num_classes=1000)
if not cfg.dist:
model = nn.DataParallel(model).to(device)
else:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device)
model = nn.parallel.DistributedDataParallel(model,
device_ids=[cfg.local_rank, ],
output_device=cfg.local_rank)
optimizer = torch.optim.SGD(model.parameters(), cfg.lr, momentum=0.9, weight_decay=cfg.wd)
criterion = CrossEntropyLabelSmooth(num_classes=1000, epsilon=cfg.label_smooth).to(device)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.97)
warmup = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=2)
# Training
def train(epoch):
model.train()
start_time = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device, non_blocking=True)
outputs, outputs_aux = model(inputs)
loss = criterion(outputs, targets)
loss_aux = criterion(outputs_aux, targets)
loss += cfg.auxiliary * loss_aux
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 5.0)
optimizer.step()
if batch_idx % cfg.log_interval == 0:
step = len(train_loader) * epoch + batch_idx
duration = time.time() - start_time
print('[%d/%d - %d/%d] cls_loss= %.5f (%d samples/sec)' %
(epoch, cfg.max_epochs, batch_idx, len(train_loader),
loss.item(), cfg.batch_size * cfg.log_interval / duration))
start_time = time.time()
summary_writer.add_scalar('cls_loss', loss.item(), step)
summary_writer.add_scalar('learning rate', optimizer.param_groups[0]['lr'], step)
def val(epoch):
# switch to evaluate mode
model.eval()
top1 = 0
top5 = 0
with torch.no_grad():
for i, (inputs, targets) in enumerate(val_loader):
inputs, targets = inputs.to(device), targets.to(device, non_blocking=True)
output, _ = model(inputs)
# measure accuracy and record loss
_, pred = output.data.topk(5, dim=1, largest=True, sorted=True)
pred = pred.t()
correct = pred.eq(targets.view(1, -1).expand_as(pred))
top1 += correct[:1].view(-1).float().sum(0, keepdim=True).item()
top5 += correct[:5].view(-1).float().sum(0, keepdim=True).item()
top1 *= 100 / len(val_dataset)
top5 *= 100 / len(val_dataset)
print(' Precision@1 ==> %.2f%% Precision@1: %.2f%%\n' % (top1, top5))
summary_writer.add_scalar('Precision@1', top1, epoch)
summary_writer.add_scalar('Precision@5', top5, epoch)
return
for epoch in range(cfg.max_epochs):
print('\nEpoch: %d lr: %.5f drop_path_prob: %.3f' %
(epoch, scheduler.get_lr()[0], cfg.drop_path_prob * epoch / cfg.max_epochs))
model.module.drop_path_prob = cfg.drop_path_prob * epoch / cfg.max_epochs
train_sampler.set_epoch(epoch)
train(epoch)
val(epoch)
if epoch < 5:
warmup.step(epoch)
else:
scheduler.step(epoch) # move to here after pytorch1.1.0
print(model.module.genotype())
if cfg.local_rank == 0:
torch.save(model.state_dict(), os.path.join(cfg.ckpt_dir, 'checkpoint.t7'))
summary_writer.close()
count_parameters(model)
count_flops(model, input_size=224)
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl', init_method='env://',
world_size=num_gpus, rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset = KAIST
train_dataset = Dataset(cfg.data_dir, 'train', img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=num_gpus,
rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size // num_gpus
if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(model,
device_ids=[cfg.local_rank, ],
output_device=cfg.local_rank)
else:
model = nn.DataParallel(model).to(cfg.device)
if(os.path.exists(cfg.pretrain_dir)):
model.module.load_state_dict(torch.load(cfg.pretrain_dir))
print('loaded pretrained model from %s !' % cfg.pretrain_dir)
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, cfg.lr_step, gamma=0.1)
def train(epoch):
print('\n%s Epoch: %d' % (datetime.now(), epoch))
model.train()
tic = time.perf_counter()
epoch_start = True
for batch_idx, batch in enumerate(train_loader):
for k in batch:
batch[k] = batch[k].to(device=cfg.device, non_blocking=True)
outputs = model((batch['img_rgb'], batch["img_ir"]))
hmap_tl, hmap_br, embd_tl, embd_br, regs_tl, regs_br = zip(*outputs)
embd_tl = [_tranpose_and_gather_feature(e, batch['inds_tl']) for e in embd_tl]
embd_br = [_tranpose_and_gather_feature(e, batch['inds_br']) for e in embd_br]
regs_tl = [_tranpose_and_gather_feature(r, batch['inds_tl']) for r in regs_tl]
regs_br = [_tranpose_and_gather_feature(r, batch['inds_br']) for r in regs_br]
focal_loss = _neg_loss(hmap_tl, batch['hmap_tl']) + \
_neg_loss(hmap_br, batch['hmap_br'])
reg_loss = _reg_loss(regs_tl, batch['regs_tl'], batch['ind_masks']) + \
_reg_loss(regs_br, batch['regs_br'], batch['ind_masks'])
pull_loss, push_loss = _ae_loss(embd_tl, embd_br, batch['ind_masks'])
loss = focal_loss + 0.1 * pull_loss + 0.1 * push_loss + reg_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print('[%d/%d-%d/%d] ' % (epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
' focal_loss= %.5f pull_loss= %.5f push_loss= %.5f reg_loss= %.5f' %
(focal_loss.item(), pull_loss.item(), push_loss.item(), reg_loss.item()) +
' (%d samples/sec)' % (cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('focal_loss', focal_loss.item(), step)
summary_writer.add_scalar('pull_loss', pull_loss.item(), step)
summary_writer.add_scalar('push_loss', push_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
return
print('Starting training...')
for epoch in range(1, cfg.num_epochs + 1):
train_sampler.set_epoch(epoch)
train(epoch)
print(saver.save(model.state_dict(), 'checkpoint'))
lr_scheduler.step(epoch)
summary_writer.close()
def main():
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
num_gpus = torch.cuda.device_count()
if cfg.dist:
device = torch.device(
'cuda:%d' % cfg.local_rank) if cfg.dist else torch.device('cuda')
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
device = torch.device('cuda')
print('==> Preparing data..')
cifar = 100 if 'cifar100' in cfg.log_name else 10
train_dataset = CIFAR_split(cifar=cifar,
root=cfg.data_dir,
split='train',
ratio=1.0,
transform=cifar_search_transform(
is_training=True, cutout=cfg.cutout))
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
sampler=train_sampler if cfg.dist else None)
test_dataset = CIFAR_split(
cifar=cifar,
root=cfg.data_dir,
split='test',
transform=cifar_search_transform(is_training=False))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers)
print('==> Building model..')
print(os.path.join(cfg.ckpt_dir, 'seed-14880-best-genotype.pth'))
#genotype = torch.load(os.path.join(cfg.ckpt_dir, 'seed-14880-best-genotype.pth'))
genotype = seed14880
model = NetworkCIFAR(genotype,
cfg.init_ch,
cfg.num_cells,
cfg.auxiliary,
num_classes=cifar)
if not cfg.dist:
model = nn.DataParallel(model).to(device)
else:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
optimizer = torch.optim.SGD(model.parameters(),
cfg.lr,
momentum=0.9,
weight_decay=cfg.wd)
criterion = nn.CrossEntropyLoss().to(device)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, cfg.max_epochs)
# Training
def train(epoch):
model.train()
start_time = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device,
non_blocking=True)
# very important
outputs, outputs_aux = model(inputs)
loss = criterion(outputs, targets)
loss_aux = criterion(outputs_aux, targets)
loss += cfg.auxiliary * loss_aux
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 5.0)
optimizer.step()
if batch_idx % cfg.log_interval == 0:
step = len(train_loader) * epoch + batch_idx
duration = time.time() - start_time
print('[%d/%d - %d/%d] cls_loss= %.5f (%d samples/sec)' %
(epoch, cfg.max_epochs, batch_idx, len(train_loader),
loss.item(),
cfg.batch_size * cfg.log_interval / duration))
start_time = time.time()
summary_writer.add_scalar('cls_loss', loss.item(), step)
summary_writer.add_scalar('learning rate',
optimizer.param_groups[0]['lr'],
step)
def test(epoch):
model.eval()
correct = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True)
outputs, _ = model(inputs)
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum().item()
acc = 100. * correct / len(test_loader.dataset)
print(' Precision@1 ==> %.2f%% \n' % acc)
summary_writer.add_scalar('Precision@1', acc, global_step=epoch)
return
for epoch in range(cfg.max_epochs):
print('\nEpoch: %d lr: %.5f drop_path_prob: %.3f' %
(epoch, scheduler.get_lr()[0],
cfg.drop_path_prob * epoch / cfg.max_epochs))
model._modules[
'module'].drop_path_prob = cfg.drop_path_prob * epoch / cfg.max_epochs
train_sampler.set_epoch(epoch)
train(epoch)
test(epoch)
scheduler.step(epoch) # move to here after pytorch1.1.0
#print(model.module.genotype())
if cfg.local_rank == 0:
torch.save(model.state_dict(),
os.path.join(cfg.ckpt_dir, 'checkpoint.t7'))
summary_writer.close()
count_parameters(model)
count_flops(model, input_size=32)
def main():
logger = create_logger(save_dir=cfg.log_dir)
print = logger.info
print(cfg)
cfg.device = torch.device('cuda')
torch.backends.cudnn.benchmark = False
max_per_image = 100
Dataset_eval = Damage_eval # your own data set
# Crack RE Spalling
dataset = Dataset_eval(cfg.data_dir, split='val', test_scales=cfg.test_scales, test_flip=cfg.test_flip) # split test
data_loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
num_workers=1, pin_memory=True,
collate_fn=dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net_resdcn(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet':
model = get_pose_net(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'res_CBAM':
model = get_pose_net_resnet_CBAM(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet_PAM':
model = get_pose_net_resnet_PAM(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet_SE':
model = get_pose_net_resnet_SE(num_layers=18, head_conv=64, num_classes=3)
model = load_model(model, cfg.pretrain_dir)
model = model.to(cfg.device)
model.eval()
results = {}
with torch.no_grad():
for inputs in tqdm(data_loader):
img_id, inputs,img_path = inputs[0]
print('id%s ',img_id)
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(cfg.device)
output = model(inputs[scale]['image'])[-1]
dets = ctdet_decode(*output, K=cfg.test_topk)
dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(dets[:, 0:2],
inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(dets[:, 2:4],
inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
cls = dets[:, -1]
for j in range(dataset.num_classes):
inds = (cls == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {}
for j in range(1, dataset.num_classes + 1):
bbox_and_scores[j] = np.concatenate([d[j] for d in detections], axis=0)
if len(dataset.test_scales) > 1:
soft_nms(bbox_and_scores[j], Nt=0.5, method=2)
scores = np.hstack([bbox_and_scores[j][:, 4] for j in range(1, dataset.num_classes + 1)])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
images_test = cv2.imread(img_path)
fig = plt.figure(0)
colors = COCO_COLORS
names = COCO_NAMES
#cv2.imwrite('E:/test1.png',images_test)
plt.imshow(cv2.cvtColor(images_test, cv2.COLOR_BGR2RGB))
for lab in bbox_and_scores:
for boxes in bbox_and_scores[lab]:
x1, y1, x2, y2, score = boxes
if (x1 < 0):
x1 = 0
if (y1 < 0):
y1 = 0
if (x2 > 511):
x2 = 511
if (y2 > 511):
y2 = 511
if score > 0.2:
plt.gca().add_patch(Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, edgecolor=colors[lab], facecolor='none'))
plt.text(x1 -12 , y1 - 12 , names[lab], bbox=dict(facecolor=colors[lab], alpha=0.5), fontsize=7, color='k')
fig.patch.set_visible(False)
Save_dir = 'data/damage/Predict_images' # save images
Image_name = img_path[-10:]
Save_dir = os.path.join(Save_dir, Image_name)
plt.axis('off')
plt.savefig(Save_dir, dpi=400, transparent=True, bbox_inches="tight", pad_inches=0.1) # 保存
plt.close(0)
results[img_id] = bbox_and_scores
eval_results = dataset.run_eval(results, cfg.ckpt_dir)
print(eval_results)
def main():
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
num_gpus = torch.cuda.device_count()
if cfg.dist:
device = torch.device(
'cuda:%d' % cfg.local_rank) if cfg.dist else torch.device('cuda')
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
device = torch.device('cuda')
print('==> Preparing data..')
cifar = 100 if 'cifar100' in cfg.log_name else 10
train_dataset = CIFAR_split(
cifar=cifar,
root=cfg.data_dir,
split='train',
ratio=0.5,
transform=cifar_search_transform(is_training=True))
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
sampler=train_sampler if cfg.dist else None)
val_dataset = CIFAR_split(
cifar=cifar,
root=cfg.data_dir,
split='val',
ratio=0.5,
transform=cifar_search_transform(is_training=False))
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
sampler=val_sampler if cfg.dist else None)
print('==> Building model..')
model = Network(C=cfg.init_ch,
num_cells=cfg.num_cells,
num_nodes=cfg.num_nodes,
multiplier=cfg.num_nodes,
num_classes=cifar)
if not cfg.dist:
model = nn.DataParallel(model).to(device)
else:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
# proxy_model is used for 2nd order update
if cfg.order == '2nd':
proxy_model = Network(cfg.init_ch, cfg.num_cells, cfg.num_nodes).cuda()
count_parameters(model)
weights = [v for k, v in model.named_parameters() if 'alpha' not in k]
alphas = [v for k, v in model.named_parameters() if 'alpha' in k]
optimizer_w = optim.SGD(weights,
cfg.w_lr,
momentum=0.9,
weight_decay=cfg.w_wd)
optimizer_a = optim.Adam(alphas,
lr=cfg.a_lr,
betas=(0.5, 0.999),
weight_decay=cfg.a_wd)
criterion = nn.CrossEntropyLoss().cuda()
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer_w,
cfg.max_epochs,
eta_min=cfg.w_min_lr)
alphas = []
def train(epoch):
model.train()
print('\nEpoch: %d lr: %f' % (epoch, scheduler.get_lr()[0]))
alphas.append([])
start_time = time.time()
for batch_idx, ((inputs_w, targets_w), (inputs_a, targets_a)) \
in enumerate(zip(train_loader, val_loader)):
inputs_w, targets_w = inputs_w.to(device), targets_w.to(
device, non_blocking=True)
inputs_a, targets_a = inputs_a.to(device), targets_a.to(
device, non_blocking=True)
# 1. update alpha
if epoch > cfg.a_start:
optimizer_a.zero_grad()
if cfg.order == '1st':
# using 1st order update
outputs = model(inputs_a)
val_loss = criterion(outputs, targets_a)
val_loss.backward()
else:
# using 2nd order update
val_loss = update(model, proxy_model, criterion,
optimizer_w, inputs_a, targets_a,
inputs_w, targets_w)
optimizer_a.step()
else:
val_loss = torch.tensor([0]).cuda()
# 2. update weights
outputs = model(inputs_w)
cls_loss = criterion(outputs, targets_w)
optimizer_w.zero_grad()
cls_loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 5.0)
optimizer_w.step()
if batch_idx % cfg.log_interval == 0:
step = len(train_loader) * epoch + batch_idx
duration = time.time() - start_time
print(
'[%d/%d - %d/%d] cls_loss: %5f val_loss: %5f (%d samples/sec)'
% (epoch, cfg.max_epochs, batch_idx, len(train_loader),
cls_loss.item(), val_loss.item(),
cfg.batch_size * cfg.log_interval / duration))
start_time = time.time()
summary_writer.add_scalar('cls_loss', cls_loss.item(), step)
summary_writer.add_scalar('val_loss', val_loss.item(), step)
summary_writer.add_scalar('learning rate',
optimizer_w.param_groups[0]['lr'],
step)
alphas[-1].append(
model.module.alpha_normal.detach().cpu().numpy())
alphas[-1].append(
model.module.alpha_reduce.detach().cpu().numpy())
return
def eval(epoch):
model.eval()
correct = 0
total_loss = 0
with torch.no_grad():
for step, (inputs, targets) in enumerate(val_loader):
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True)
outputs = model(inputs)
total_loss += criterion(outputs, targets).item()
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum().item()
acc = 100. * correct / len(val_loader.dataset)
total_loss = total_loss / len(val_loader)
print('Val_loss==> %.5f Precision@1 ==> %.2f%% \n' %
(total_loss, acc))
summary_writer.add_scalar('Precision@1', acc, global_step=epoch)
summary_writer.add_scalar('val_loss_per_epoch',
total_loss,
global_step=epoch)
return
for epoch in range(cfg.max_epochs):
train_sampler.set_epoch(epoch)
val_sampler.set_epoch(epoch)
train(epoch)
eval(epoch)
scheduler.step(epoch) # move to here after pytorch1.1.0
print(model.module.genotype())
if cfg.local_rank == 0:
torch.save(alphas, os.path.join(cfg.ckpt_dir, 'alphas.t7'))
torch.save(model.state_dict(),
os.path.join(cfg.ckpt_dir, 'search_checkpoint.t7'))
torch.save({'genotype': model.module.genotype()},
os.path.join(cfg.ckpt_dir, 'genotype.t7'))
summary_writer.close()
def main():
logger = create_logger(save_dir=cfg.log_dir)
print = logger.info
print(cfg)
cfg.device = torch.device('cuda')
torch.backends.cudnn.benchmark = False
max_per_image = 100
Dataset_eval = COCO_eval if cfg.dataset == 'coco' else PascalVOC_eval
dataset = Dataset_eval(cfg.data_dir,
split='val',
img_size=cfg.img_size,
test_scales=cfg.test_scales,
test_flip=cfg.test_flip)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False,
collate_fn=dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net(num_layers=int(cfg.arch.split('_')[-1]),
num_classes=dataset.num_classes)
else:
raise NotImplementedError
model = load_model(model, cfg.pretrain_dir)
model = model.to(cfg.device)
model.eval()
results = {}
with torch.no_grad():
for inputs in data_loader:
img_id, inputs = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(cfg.device)
output = model(inputs[scale]['image'])[-1]
dets = ctdet_decode(*output, K=cfg.test_topk)
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(
dets[:,
0:2], inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(
dets[:,
2:4], inputs[scale]['center'], inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
cls = dets[:, -1]
for j in range(dataset.num_classes):
inds = (cls == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {}
for j in range(1, dataset.num_classes + 1):
bbox_and_scores[j] = np.concatenate([d[j] for d in detections],
axis=0)
if len(dataset.test_scales) > 1:
soft_nms(bbox_and_scores[j], Nt=0.5, method=2)
scores = np.hstack([
bbox_and_scores[j][:, 4]
for j in range(1, dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
results[img_id] = bbox_and_scores
eval_results = dataset.run_eval(results, cfg.ckpt_dir)
print(eval_results)
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl', init_method='env://',
world_size=num_gpus, rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset = COCO if cfg.dataset == 'coco' else PascalVOC
train_dataset = Dataset(cfg.data_dir, 'train', split_ratio=cfg.split_ratio, img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=num_gpus,
rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size // num_gpus
if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = COCO_eval if cfg.dataset == 'coco' else PascalVOC_eval
val_dataset = Dataset_eval(cfg.data_dir, 'val', test_scales=[1.], test_flip=False)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1,
shuffle=False, num_workers=1, pin_memory=True,
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net(num_layers=int(cfg.arch.split('_')[-1]), num_classes=train_dataset.num_classes)
else:
raise NotImplementedError
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(model,
device_ids=[cfg.local_rank, ],
output_device=cfg.local_rank)
else:
model = nn.DataParallel(model).to(cfg.device)
if os.path.isfile(cfg.pretrain_dir):
model = load_model(model, cfg.pretrain_dir)
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, cfg.lr_step, gamma=0.1)
def train(epoch):
print('\n Epoch: %d' % epoch)
model.train()
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=cfg.device, non_blocking=True)
def main():
saver = create_saver(cfg.local_rank, save_dir=cfg.ckpt_dir)
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(300)
torch.backends.cudnn.benchmark = True
'''
# you can also set like this. If you do like this, the random seed will be fixed.
torch.manual_seed(350)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True # consistent results on the cpu and gpu
'''
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=num_gpus,
rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
print('Setting up data...')
Dataset = Damage
train_dataset = Dataset(cfg.data_dir,
'train',
split_ratio=cfg.split_ratio,
img_size=cfg.img_size)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=num_gpus, rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True,
sampler=train_sampler if cfg.dist else None)
Dataset_eval = Damage_eval
test_dataset = Dataset_eval(cfg.data_dir,
'test',
test_scales=[1.],
test_flip=False)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1, # 测试集的batch_size
shuffle=False,
num_workers=1,
pin_memory=True, # 测试集的num_workers
collate_fn=test_dataset.collate_fn)
val_dataset = Dataset_eval(cfg.data_dir,
'val',
test_scales=[1.],
test_flip=False)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1, # 验证集的batch_size
shuffle=False,
num_workers=1,
pin_memory=True, # 验证集的num_workers
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
elif 'resdcn' in cfg.arch:
model = get_pose_net_resdcn(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet':
model = get_pose_net(num_layers=18, head_conv=64, num_classes=3)
elif cfg.arch == 'resnet_CBAM':
model = get_pose_net_resnet_CBAM(num_layers=18,
head_conv=64,
num_classes=3)
elif cfg.arch == 'resnet_PAM':
model = get_pose_net_resnet_PAM(num_layers=18,
head_conv=64,
num_classes=3)
elif cfg.arch == 'resnet_SE':
model = get_pose_net_resnet_SE(num_layers=18,
head_conv=64,
num_classes=3)
if cfg.dist:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(cfg.device)
model = nn.parallel.DistributedDataParallel(
model, device_ids=[
cfg.local_rank,
], output_device=cfg.local_rank)
else:
model = nn.DataParallel(model).to(cfg.device)
#if os.path.isfile(cfg.pretrain_dir):
# model = load_model(model, cfg.pretrain_dir) # 不加载预训练模型
optimizer = torch.optim.Adam(model.parameters(), cfg.lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.lr_step,
gamma=0.1) # adjust lr
def train(epoch):
print('\n Epoch: %d' % epoch)
model.train()
tic = time.perf_counter()
for batch_idx, batch in enumerate(train_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].to(device=cfg.device,
non_blocking=True)
outputs = model(batch['image'])
hmap, regs, w_h_, pxpy = zip(*outputs)
# batch * C（channel） * W * H
regs = [
_tranpose_and_gather_feature(r, batch['inds']) for r in regs
]
pxpy = [
_tranpose_and_gather_feature(r, batch['inds']) for r in pxpy
]
w_h_ = [
_tranpose_and_gather_feature(r, batch['inds']) for r in w_h_
]
# batch * K * C= batch * 128 *2
hmap_loss = _neg_loss(hmap, batch['hmap'])
reg_loss = _SmoothL1Loss(regs, batch['regs'], batch['ind_masks'])
pxpy_loss = _reg_loss(pxpy, batch['pxpy'], batch['ind_masks'])
w_h_loss = _SmoothL1Loss(w_h_, batch['w_h_'], batch['ind_masks'])
loss = hmap_loss + 10 * reg_loss + 0.1 * w_h_loss + 0.1 * pxpy_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % cfg.log_interval == 0:
duration = time.perf_counter() - tic
tic = time.perf_counter()
print(
'[%d/%d-%d/%d] ' %
(epoch, cfg.num_epochs, batch_idx, len(train_loader)) +
' hmap_loss= %.5f reg_loss= %.5f w_h_loss= %.5f pxpy_loss= %.5f'
% (hmap_loss.item(), reg_loss.item(), w_h_loss.item(),
pxpy_loss.item()) + ' (%d samples/sec)' %
(cfg.batch_size * cfg.log_interval / duration))
step = len(train_loader) * epoch + batch_idx
summary_writer.add_scalar('hmap_loss', hmap_loss.item(), step)
summary_writer.add_scalar('reg_loss', reg_loss.item(), step)
summary_writer.add_scalar('w_h_loss', w_h_loss.item(), step)
summary_writer.add_scalar('pxpy_loss', pxpy_loss.item(), step)
return
#--------------------test set--------------------#
def test_map(epoch):
print('\n Test@Epoch: %d' % epoch)
start_time = time.clock()
print('Start time %s Seconds' % start_time)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
with torch.no_grad():
for inputs in test_loader:
img_id, inputs, img_path = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device) # (1,3)
output = model(inputs[scale]['image'])[-1]
dets = ctdet_decode(
*output, K=cfg.test_topk
) # torch.cat([bboxes, scores, clses], dim=2)
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(
dets[:, 0:2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(
dets[:, 2:4], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
clses = dets[:, -1]
for j in range(test_dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {
j: np.concatenate([d[j] for d in detections], axis=0)
for j in range(1, test_dataset.num_classes + 1)
}
scores = np.hstack([
bbox_and_scores[j][:, 4]
for j in range(1, test_dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, test_dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
results[img_id] = bbox_and_scores
end_time = time.clock()
eval_results = test_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
print('End time %s Seconds' % end_time)
Run_time = end_time - start_time
FPS = 100 / Run_time # replace 100 with the number of images
print('FPS %s ' % FPS)
summary_writer.add_scalar('test_mAP/mAP', eval_results[0], epoch)
return eval_results[0]
#--------------------end of test set--------------------#
#--------------------validation set--------------------#
def val_map(epoch):
print('\n Val@Epoch: %d' % epoch)
start_time = time.clock()
print('Start time %s Seconds' % start_time)
model.eval()
torch.cuda.empty_cache()
max_per_image = 100
results = {}
with torch.no_grad():
for inputs in val_loader:
img_id, inputs, img_path = inputs[0]
detections = []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(
cfg.device) # (1,3)
output = model(
inputs[scale]['image'])[-1] # hmap, regs, pxpy
dets = ctdet_decode(
*output, K=cfg.test_topk
) # torch.cat([bboxes, scores, clses], dim=2)
dets = dets.detach().cpu().numpy().reshape(
1, -1, dets.shape[2])[0]
top_preds = {}
dets[:, :2] = transform_preds(
dets[:, 0:2], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
dets[:, 2:4] = transform_preds(
dets[:, 2:4], inputs[scale]['center'],
inputs[scale]['scale'],
(inputs[scale]['fmap_w'], inputs[scale]['fmap_h']))
clses = dets[:, -1]
for j in range(val_dataset.num_classes):
inds = (clses == j)
top_preds[j + 1] = dets[inds, :5].astype(np.float32)
top_preds[j + 1][:, :4] /= scale
detections.append(top_preds)
bbox_and_scores = {
j: np.concatenate([d[j] for d in detections], axis=0)
for j in range(1, val_dataset.num_classes + 1)
}
scores = np.hstack([
bbox_and_scores[j][:, 4]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (bbox_and_scores[j][:, 4] >= thresh)
bbox_and_scores[j] = bbox_and_scores[j][keep_inds]
results[img_id] = bbox_and_scores
end_time = time.clock()
eval_results = val_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
print('End time %s Seconds' % end_time)
Run_time = end_time - start_time
FPS = 100 / Run_time # replace 100 with the number of images
print('FPS %s ' % FPS)
summary_writer.add_scalar('val_mAP/mAP', eval_results[0], epoch)
return eval_results[0]
#--------------------end of validation set--------------------#
print('Starting training...')
Max_test_AP = 0 # max test AP
Max_val_AP = 0 # max validation AP
flag_epoch = 1
for epoch in range(1, cfg.num_epochs + 1):
train_sampler.set_epoch(epoch)
train(epoch)
if epoch >= flag_epoch:
test_mAP = test_map(epoch)
val_mAP = val_map(epoch)
if (test_mAP > Max_test_AP):
Max_test_AP = test_mAP
if (val_mAP > Max_val_AP):
print(
saver.save(model.module.state_dict(),
'checkpoint_MaxAP_epoch' + str(epoch)))
Max_val_AP = val_mAP
print(saver.save(model.module.state_dict(),
'checkpoint')) # save current epoch
total = sum([param.nelement()
for param in model.parameters()]) # calculate parameters
print("Number of parameter: %.2fM" % (total / 1e6))
print('Max_test_AP=%s' % Max_test_AP)
print('Max_val_AP=%s' % Max_val_AP)
lr_scheduler.step(epoch) # move to here after pytorch1.1.0
summary_writer.close()
def main():
logger = create_logger(save_dir=cfg.log_dir)
print = logger.info
print(cfg)
torch.manual_seed(317)
torch.backends.cudnn.benchmark = False
cfg.device = torch.device('cuda')
print('Setting up data...')
val_dataset = COCO_eval(cfg.data_dir,
'val',
test_scales=cfg.test_scales,
test_flip=cfg.test_flip)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=val_dataset.collate_fn)
print('Creating model...')
if 'hourglass' in cfg.arch:
model = get_hourglass[cfg.arch]
else:
raise NotImplementedError
model = model.to(cfg.device)
model.load_state_dict(torch.load(cfg.pretrain_dir))
print('loaded pretrained model from %s !' % cfg.pretrain_dir)
print('validation starts at %s' % datetime.now())
model.eval()
results = {}
with torch.no_grad():
for inputs in val_loader:
img_id, inputs = inputs[0]
detections, centers = [], []
for scale in inputs:
inputs[scale]['image'] = inputs[scale]['image'].to(cfg.device)
output = model(inputs[scale]['image'])[-1]
dets, cts = _decode(*output,
ae_threshold=cfg.ae_threshold,
K=cfg.topk,
kernel=3)
dets = dets.reshape(dets.shape[0], -1,
8).detach().cpu().numpy()
cts = cts.reshape(cts.shape[0], -1, 4).detach().cpu().numpy()
if dets.shape[0] == 2:
dets[1, :,
[0, 2]] = inputs[scale]['fmap_size'][0,
1] - dets[1, :,
[2, 0]]
cts[1, :,
[0]] = inputs[scale]['fmap_size'][0, 1] - cts[1, :,
[0]]
dets = dets.reshape(1, -1, 8)
cts = cts.reshape(1, -1, 4)
_rescale_dets(dets, cts, inputs[scale]['ratio'],
inputs[scale]['border'], inputs[scale]['size'])
dets[:, :, 0:4] /= scale
cts[:, :, 0:2] /= scale
detections.append(dets)
if scale == 1:
centers.append(cts)
detections = np.concatenate(detections, axis=1)[0]
centers = np.concatenate(centers, axis=1)[0]
detections, classes = center_match(detections, centers)
results[img_id] = {}
for j in range(val_dataset.num_classes):
keep_inds = (classes == j)
results[img_id][j + 1] = detections[keep_inds][:, 0:7].astype(
np.float32)
soft_nms_merge(results[img_id][j + 1],
Nt=cfg.nms_threshold,
method=2,
weight_exp=cfg.w_exp)
# soft_nms(results[img_id][j + 1], Nt=0.5, method=2)
results[img_id][j + 1] = results[img_id][j + 1][:, 0:5]
scores = np.hstack([
results[img_id][j][:, -1]
for j in range(1, val_dataset.num_classes + 1)
])
if len(scores) > val_dataset.max_objs:
kth = len(scores) - val_dataset.max_objs
thresh = np.partition(scores, kth)[kth]
for j in range(1, val_dataset.num_classes + 1):
keep_inds = (results[img_id][j][:, -1] >= thresh)
results[img_id][j] = results[img_id][j][keep_inds]
eval_results = val_dataset.run_eval(results, save_dir=cfg.ckpt_dir)
print(eval_results)
print('validation ends at %s' % datetime.now())
