def main():
"""Main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
load_config(args.cfg_file)
if cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
# np.random.seed(cfg.RNG_SEED)
# torch.manual_seed(cfg.RNG_SEED)
# if cfg.CUDA:
# torch.cuda.manual_seed_all(cfg.RNG_SEED)
torch.backends.cudnn.deterministic = True
transforms = dt_trans.Compose([
dt_trans.Normalize([102.9801, 115.9465, 122.7717]),
dt_trans.HorizontalFlip(),
dt_trans.Resize(),
dt_trans.ToTensor(),
])
if cfg.TRAIN.DATASET == 'voc_2007_trainval':
dataset = VOCDetection(cfg.DATA_DIR + '/',
year='2007',
image_set='trainval',
transforms=transforms)
elif cfg.TRAIN.DATASET == 'voc_2012_trainval':
dataset = VOCDetection(cfg.DATA_DIR + '/',
year='2012',
image_set='trainval',
transforms=transforms)
# Effective training sample size for one epoch
train_size = len(dataset) // args.batch_size * args.batch_size
batchSampler = TrainSampler(subdivision=cfg.TRAIN.ITERATION_SIZE,
batch_size=cfg.TRAIN.ITERATION_SIZE,
max_iterations=cfg.SOLVER.MAX_ITER,
num_samples=len(dataset),
image_scales=cfg.TRAIN.SCALES,
scale_interval=10)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
model = eval(args.model).loot_model(args)
if cfg.CUDA:
model.cuda()
### Optimizer ###
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in model.named_parameters():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
},
{
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
},
]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
optimizer_utils.load_ckpt(model, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
model.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS /
(cfg.NUM_GPUS * args.iter_size))
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
optimizer_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
optimizer_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
optimizer_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
model.set_inner_iter(step)
im_data = input_data[0].cuda()
labels = input_data[1].cuda()
rois = input_data[2].cuda()
net_outputs = model(im_data, rois, labels)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward(retain_graph=True)
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, model, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, model, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, model, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' % (args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' % (original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' % (original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' % (original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print('Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps, cfg.SOLVER.STEPS,
old_max_iter, cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print('Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True
)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name+'.weight')
gn_param_nameset.add(name+'.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in maskRCNN.named_parameters():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) - set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN}
]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print('train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step+1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
saveNetStructure = False
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
#set gpu device
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
[str(ids) for ids in args.device_ids])
torch.backends.cudnn.benchmark = True
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cityscapes":
cfg.TRAIN.DATASETS = ('cityscapes_semseg_train', )
cfg.MODEL.NUM_CLASSES = 19
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' %
(cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.
format(old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON:
roidb, ratio_list, ratio_index = combined_roidb_for_training_semseg(
cfg.TRAIN.DATASETS)
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch_semseg
if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON else collate_minibatch)
assert_and_infer_cfg()
#for args.step, input_data in zip(range(100), dataloader):
# data_L = input_data['data']
# data_R = input_data['data_R']
# label = input_data['disp_label_0']
# cv2.imwrite('ims_L.png', data_L[0].numpy()[0].transpose(1,2,0)[:,:,::-1]+cfg.PIXEL_MEANS)
# cv2.imwrite('ims_R.png', data_R[0].numpy()[0].transpose(1,2,0)[:,:,::-1]+cfg.PIXEL_MEANS)
# cv2.imwrite('label.png', label[0].numpy()[0])
# return
### Model ###
dispSeg = DispSeg()
if cfg.CUDA:
dispSeg.to('cuda')
pspnet_bias_params = []
pspnet_nonbias_params = []
for key, value in dict(dispSeg.pspnet.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
pspnet_bias_params.append(value)
else:
pspnet_nonbias_params.append(value)
pspnet_params = [{
'params': pspnet_nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
pspnet_bias_params,
'lr':
cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
glassGCN_bias_params = []
glassGCN_nonbias_params = []
for key, value in dict(dispSeg.glassGCN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
glassGCN_bias_params.append(value)
else:
glassGCN_nonbias_params.append(value)
segdisp3d_params = [{
'params': glassGCN_nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
glassGCN_bias_params,
'lr':
cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizerP = torch.optim.SGD(pspnet_params,
momentum=cfg.SOLVER.MOMENTUM)
optimizerS = torch.optim.SGD(segdisp3d_params,
momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizerP = torch.optim.Adam(pspnet_params)
optimizerS = torch.optim.Adam(segdisp3d_params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(pspnet, checkpoint['model'])
net_utils.load_ckpt(segdisp3d, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
lr = optimizerP.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
dispSeg = mynn.DataParallel(dispSeg,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
#from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
dispSeg.train()
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
iters_per_epoch = int(train_size / args.batch_size) # drop last
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
try:
logger.info('Training starts !')
args.step = args.start_iter
global_step = iters_per_epoch * args.start_epoch + args.step
for args.epoch in range(args.start_epoch,
args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
# adjust learning rate
if args.lr_decay_epochs and args.epoch == args.lr_decay_epochs[
0] and args.start_iter == 0:
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizerP, lr, cfg.SOLVER.GAMMA)
net_utils.decay_learning_rate(optimizerS, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for args.step, input_data in zip(
range(args.start_iter, iters_per_epoch), dataloader):
if cfg.DISP.DISP_ON:
input_data['data'] = list(
map(lambda x, y: torch.cat((x, y), dim=0),
input_data['data'], input_data['data_R']))
if cfg.SEM.DECODER_TYPE.endswith('3Ddeepsup'):
input_data['disp_scans'] = torch.arange(
0, cfg.DISP.MAX_DISPLACEMENT).float().view(
1, cfg.DISP.MAX_DISPLACEMENT, 1,
1).repeat(args.batch_size, 1, 1, 1)
input_data['semseg_scans'] = torch.arange(
0, cfg.MODEL.NUM_CLASSES).long().view(
1, cfg.MODEL.NUM_CLASSES, 1,
1).repeat(args.batch_size, 1, 1, 1)
del input_data['data_R']
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(
map(
lambda x: Variable(x, requires_grad=False).to(
'cuda'), input_data[key]))
training_stats.IterTic()
net_outputs = dispSeg(**input_data)
training_stats.UpdateIterStats(net_outputs)
#loss = net_outputs['losses']['loss_semseg']
#acc = net_outputs['metrics']['accuracy_pixel']
#print (loss.item(), acc)
#for key in net_outputs.keys():
# print(key)
loss = net_outputs['total_loss']
#print("loss.shape:",loss)
optimizerP.zero_grad()
optimizerS.zero_grad()
loss.backward()
optimizerP.step()
optimizerS.step()
training_stats.IterToc()
if args.step % args.disp_interval == 0:
#disp_image=net_outputs['disp_image']
#semseg_image=net_outputs['semseg_image']
#tblogger.add_image('disp_image',disp_image,global_step)
#tblogger.add_image('semseg_image',semseg_image,global_step)
log_training_stats(training_stats, global_step, lr)
global_step += 1
# ---- End of epoch ----
# save checkpoint
net_utils.save_ckpt(output_dir, args, dispSeg, optimizerS)
# reset starting iter number after first epoch
args.start_iter = 0
# ---- Training ends ----
#if iters_per_epoch % args.disp_interval != 0:
# log last stats at the end
# log_training_stats(training_stats, global_step, lr)
except (RuntimeError, KeyboardInterrupt):
logger.info('Save ckpt on exception ...')
net_utils.save_ckpt(output_dir, args, dispSeg, optimizerS)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' %
(cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.
format(old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
drop_last=True,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
assert checkpoint['train_size'] == train_size
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, run_name)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] > args.start_step:
decay_steps_ind = i
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
logger.info('Training starts !')
loss_avg = 0
try:
timers['train_loop'].tic()
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
lr = cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = lr_new
decay_steps_ind += 1
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
outputs = maskRCNN(**input_data)
rois_label = outputs['rois_label']
cls_score = outputs['cls_score']
bbox_pred = outputs['bbox_pred']
loss_rpn_cls = outputs['loss_rpn_cls'].mean()
loss_rpn_bbox = outputs['loss_rpn_bbox'].mean()
loss_rcnn_cls = outputs['loss_rcnn_cls'].mean()
loss_rcnn_bbox = outputs['loss_rcnn_bbox'].mean()
loss = loss_rpn_cls + loss_rpn_bbox + loss_rcnn_cls + loss_rcnn_bbox
if cfg.MODEL.MASK_ON:
loss_rcnn_mask = outputs['loss_rcnn_mask'].mean()
loss += loss_rcnn_mask
if cfg.MODEL.KEYPOINTS_ON:
loss_rcnn_keypoints = outputs['loss_rcnn_keypoints'].mean()
loss += loss_rcnn_keypoints
loss_avg += loss.data.cpu().numpy()[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
if ((step % args.disp_interval == 0 and
(step - args.start_step >= args.disp_interval))
or step == cfg.SOLVER.MAX_ITER - 1):
diff = timers['train_loop'].toc(average=False)
loss_avg /= args.disp_interval
loss_rpn_cls = loss_rpn_cls.data[0]
loss_rpn_bbox = loss_rpn_bbox.data[0]
loss_rcnn_cls = loss_rcnn_cls.data[0]
loss_rcnn_bbox = loss_rcnn_bbox.data[0]
fg_cnt = torch.sum(rois_label.data.ne(0))
bg_cnt = rois_label.data.numel() - fg_cnt
print("[ %s ][ step %d ]" % (run_name, step))
print("\t\tloss: %.4f, lr: %.2e" % (loss_avg, lr))
print("\t\tfg/bg=(%d/%d), time cost: %f" %
(fg_cnt, bg_cnt, diff))
print(
"\t\trpn_cls: %.4f, rpn_bbox: %.4f, rcnn_cls: %.4f, rcnn_bbox %.4f"
% (loss_rpn_cls, loss_rpn_bbox, loss_rcnn_cls,
loss_rcnn_bbox))
print_prefix = "\t\t"
if cfg.MODEL.MASK_ON:
loss_rcnn_mask = loss_rcnn_mask.data[0]
print("%srcnn_mask %.4f" % (print_prefix, loss_rcnn_mask))
print_prefix = ", "
if cfg.MODEL.KEYPOINTS_ON:
loss_rcnn_keypoints = loss_rcnn_keypoints.data[0]
print("%srcnn_keypoints %.4f" %
(print_prefix, loss_rcnn_keypoints))
if args.use_tfboard and not args.no_save:
info = {
'lr': lr,
'loss': loss_avg,
'loss_rpn_cls': loss_rpn_cls,
'loss_rpn_box': loss_rpn_bbox,
'loss_rcnn_cls': loss_rcnn_cls,
'loss_rcnn_box': loss_rcnn_bbox,
}
if cfg.MODEL.MASK_ON:
info['loss_rcnn_mask'] = loss_rcnn_mask
if cfg.MODEL.KEYPOINTS_ON:
info['loss_rcnn_keypoints'] = loss_rcnn_keypoints
for tag, value in info.items():
tblogger.add_scalar(tag, value, step)
loss_avg = 0
timers['train_loop'].tic()
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt) as e:
print('Save on exception:', e)
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
# ---- Training ends ----
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = GetRCNNModel()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name + '.weight')
gn_param_nameset.add(name + '.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) -
set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}, {
'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN
}]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
if cfg.TRAIN_SYNC_BN:
# Shu:For synchorinized BN
patch_replication_callback(maskRCNN)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if cfg.MODEL.LR_VIEW_ON or cfg.MODEL.GIF_ON or cfg.MODEL.LRASY_MAHA_ON:
if key != 'roidb' and key != 'data': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(
map(Variable, input_data[key]))
if key == 'data':
input_data[key] = [
torch.squeeze(item) for item in input_data[key]
]
input_data[key] = list(
map(Variable, input_data[key]))
else:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(
map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step + 1) % CHECKPOINT_PERIOD == 0:
net_utils.train_save_ckpt(output_dir, args, step, train_size,
maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
net_utils.train_save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
net_utils.train_save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' % (cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.format(
old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
args.mGPUs = (cfg.NUM_GPUS > 1)
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
assert_and_infer_cfg()
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
params = [
{'params': nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0}
]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, run_name)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
iters_per_epoch = int(train_size / args.batch_size) # drop last
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
step = 0
try:
logger.info('Training starts !')
for epoch in range(args.start_epoch, args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
loss_avg = 0
timers['train_loop'].tic()
# adjust learning rate
if args.lr_decay_epochs and epoch == args.lr_decay_epochs[0] and args.start_iter == 0:
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizer, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for step, input_data in zip(range(args.start_iter, iters_per_epoch), dataloader):
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
outputs = maskRCNN(**input_data)
rois_label = outputs['rois_label']
cls_score = outputs['cls_score']
bbox_pred = outputs['bbox_pred']
loss_rpn_cls = outputs['loss_rpn_cls'].mean()
loss_rpn_bbox = outputs['loss_rpn_bbox'].mean()
loss_rcnn_cls = outputs['loss_rcnn_cls'].mean()
loss_rcnn_bbox = outputs['loss_rcnn_bbox'].mean()
loss = loss_rpn_cls + loss_rpn_bbox + loss_rcnn_cls + loss_rcnn_bbox
if cfg.MODEL.MASK_ON:
loss_rcnn_mask = outputs['loss_rcnn_mask'].mean()
loss += loss_rcnn_mask
if cfg.MODEL.KEYPOINTS_ON:
loss_rcnn_keypoints = outputs['loss_rcnn_keypoints'].mean()
loss += loss_rcnn_keypoints
loss_avg += loss.data.cpu().numpy()[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (step+1) % ckpt_interval_per_epoch == 0:
net_utils.save_ckpt(output_dir, args, epoch, step, maskRCNN, optimizer, iters_per_epoch)
if (step+1) % args.disp_interval == 0:
if (step + 1 - args.start_iter) >= args.disp_interval: # for the case of resume
diff = timers['train_loop'].toc(average=False)
loss_avg /= args.disp_interval
loss_rpn_cls = loss_rpn_cls.data[0]
loss_rpn_bbox = loss_rpn_bbox.data[0]
loss_rcnn_cls = loss_rcnn_cls.data[0]
loss_rcnn_bbox = loss_rcnn_bbox.data[0]
fg_cnt = torch.sum(rois_label.data.ne(0))
bg_cnt = rois_label.data.numel() - fg_cnt
print("[%s][epoch %2d][iter %4d / %4d]"
% (run_name, epoch, step, iters_per_epoch))
print("\t\tloss: %.4f, lr: %.2e" % (loss_avg, lr))
print("\t\tfg/bg=(%d/%d), time cost: %f" % (fg_cnt, bg_cnt, diff))
print("\t\trpn_cls: %.4f, rpn_bbox: %.4f, rcnn_cls: %.4f, rcnn_bbox %.4f"
% (loss_rpn_cls, loss_rpn_bbox, loss_rcnn_cls, loss_rcnn_bbox))
print_prefix = "\t\t"
if cfg.MODEL.MASK_ON:
loss_rcnn_mask = loss_rcnn_mask.data[0]
print("%srcnn_mask %.4f" % (print_prefix, loss_rcnn_mask))
print_prefix = ", "
if cfg.MODEL.KEYPOINTS_ON:
loss_rcnn_keypoints = loss_rcnn_keypoints.data[0]
print("%srcnn_keypoints %.4f" % (print_prefix, loss_rcnn_keypoints))
if args.use_tfboard:
info = {
'loss': loss_avg,
'loss_rpn_cls': loss_rpn_cls,
'loss_rpn_box': loss_rpn_bbox,
'loss_rcnn_cls': loss_rcnn_cls,
'loss_rcnn_box': loss_rcnn_bbox,
}
if cfg.MODEL.MASK_ON:
info['loss_rcnn_mask'] = loss_rcnn_mask
if cfg.MODEL.KEYPOINTS_ON:
info['loss_rcnn_keypoints'] = loss_rcnn_keypoints
for tag, value in info.items():
tblogger.add_scalar(tag, value, iters_per_epoch * epoch + step)
loss_avg = 0
timers['train_loop'].tic()
# ---- End of epoch ----
# save checkpoint
net_utils.save_ckpt(output_dir, args, epoch, step, maskRCNN, optimizer, iters_per_epoch)
# reset timer
timers['train_loop'].reset()
# reset starting iter number after first epoch
args.start_iter = 0
except (RuntimeError, KeyboardInterrupt) as e:
print('Save on exception:', e)
net_utils.save_ckpt(output_dir, args, epoch, step, maskRCNN, optimizer, iters_per_epoch)
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
# ---- Training ends ----
if args.use_tfboard:
tblogger.close()
def main():
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the training code, sry bro :(.")
else:
cfg.CUDA = True
cfg.NUM_GPUS = torch.cuda.device_count()
#######~~~.Parameters stuff.~~~#######
args = parse_args()
print('Called with args:\n', args)
# Enables fixed seed
if args.fixed_seed:
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
if cfg.CUDA:
torch.cuda.manual_seed_all(cfg.RNG_SEED)
torch.backends.cudnn.deterministic = True
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
check_and_overwrite_params(cfg, args)
assert_and_infer_cfg()
#indentificantion of a specific running
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args)
save_training_config(output_dir, cfg, args)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
else:
tblogger = None
#######~~~.Dataset.~~~#######
timers = defaultdict(Timer)
# Roi datasets
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
########~~~.Model.~~~#######
model = eval(args.model).loot_model(args)
if cfg.CUDA:
model.cuda()
optimizer = OptimizerHandler(model, cfg)
load_ckpt(args.load_ckpt, model, optimizer)
#######~~~.Training Loop.~~~#######
try:
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS /
(cfg.NUM_GPUS * cfg.TRAIN.ITERATION_SIZE))
training_stats = TrainingStats(args, tblogger)
model.train()
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
optimizer.update_learning_rate(step)
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(cfg.TRAIN.ITERATION_SIZE):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
# input_data['inner_iter'] = torch.tensor((inner_iter))
model.set_inner_iter(step)
im_data = input_data['data'][0].cuda()
rois = input_data['rois'][0].cuda().type(im_data.dtype)
labels = input_data['labels'][0].cuda().type(im_data.dtype)
net_outputs = model(im_data, rois, labels)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward(retain_graph=True)
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, optimizer.get_lr())
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, model, optimizer)
# Training ends, saves the last checkpoint
save_ckpt(output_dir, args, step, train_size, model, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, model, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: # TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
# maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
# minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
cfg.DATASET = args.dataset
if args.dataset == "vg80k":
cfg.TRAIN.DATASETS = ('vg80k_train', )
cfg.TEST.DATASETS = ('vg80k_val', )
cfg.MODEL.NUM_CLASSES = 53305 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 29086 # excludes background
elif args.dataset == "vg8k":
cfg.TRAIN.DATASETS = ('vg8k_train', )
cfg.TEST.DATASETS = ('vg8k_val', )
cfg.MODEL.NUM_CLASSES = 5331 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 2000 # excludes background
elif args.dataset == "vrd":
cfg.TRAIN.DATASETS = ('vrd_train', )
cfg.TEST.DATASETS = ('vrd_val', )
cfg.MODEL.NUM_CLASSES = 101
cfg.MODEL.NUM_PRD_CLASSES = 70 # exclude background
elif args.dataset == "vg":
cfg.TRAIN.DATASETS = ('vg_train', )
cfg.TEST.DATASETS = ('vg_val', )
cfg.MODEL.NUM_CLASSES = 151
cfg.MODEL.NUM_PRD_CLASSES = 50 # exclude background
elif args.dataset == "gvqa20k":
cfg.TRAIN.DATASETS = ('gvqa20k_train', )
cfg.TEST.DATASETS = ('gvqa20k_val', )
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
elif args.dataset == "gvqa10k":
cfg.TRAIN.DATASETS = ('gvqa10k_train', )
cfg.TEST.DATASETS = ('gvqa10k_val', )
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
elif args.dataset == "gvqa":
cfg.TRAIN.DATASETS = ('gvqa_train', )
cfg.TEST.DATASETS = ('gvqa_val', )
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if args.seed:
cfg.RNG_SEED = args.seed
# Some imports need to be done after loading the config to avoid using default values
from datasets.roidb_rel import combined_roidb_for_training
from modeling.model_builder_rel import Generalized_RCNN
from core.test_engine_rel import run_eval_inference, run_inference
from core.test_engine_rel import get_inference_dataset, get_roidb_and_dataset
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
# record backbone params, i.e., conv_body and box_head params
gn_params = []
backbone_bias_params = []
backbone_bias_param_names = []
prd_branch_bias_params = []
prd_branch_bias_param_names = []
backbone_nonbias_params = []
backbone_nonbias_param_names = []
prd_branch_nonbias_params = []
prd_branch_nonbias_param_names = []
if cfg.MODEL.DECOUPLE:
for key, value in dict(maskRCNN.named_parameters()).items():
if not 'so_sem_embeddings.2' in key:
value.requires_grad = False
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'gn' in key:
gn_params.append(value)
elif 'Conv_Body' in key or 'Box_Head' in key or 'Box_Outs' in key or 'RPN' in key:
if 'bias' in key:
backbone_bias_params.append(value)
backbone_bias_param_names.append(key)
else:
backbone_nonbias_params.append(value)
backbone_nonbias_param_names.append(key)
else:
if 'bias' in key:
prd_branch_bias_params.append(value)
prd_branch_bias_param_names.append(key)
else:
prd_branch_nonbias_params.append(value)
prd_branch_nonbias_param_names.append(key)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': backbone_nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
backbone_bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}, {
'params': prd_branch_nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
prd_branch_bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}, {
'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
load_ckpt_dir = './'
### Load checkpoint
if args.load_ckpt_dir:
load_name = get_checkpoint_resume_file(args.load_ckpt_dir)
load_ckpt_dir = args.load_ckpt_dir
elif args.load_ckpt:
load_name = args.load_ckpt
load_ckpt_dir = os.path.dirname(args.load_ckpt)
if args.load_ckpt or args.load_ckpt_dir:
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
if cfg.MODEL.DECOUPLE:
del checkpoint['model']['RelDN.so_sem_embeddings.2.weight']
del checkpoint['model']['RelDN.so_sem_embeddings.2.bias']
del checkpoint['model']['RelDN.prd_sem_embeddings.2.weight']
del checkpoint['model']['RelDN.prd_sem_embeddings.2.bias']
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[2][
'lr'] # lr of non-backbone parameters, for commmand line outputs.
backbone_lr = optimizer.param_groups[0][
'lr'] # lr of backbone parameters, for commmand line outputs.
prd_categories = maskRCNN.prd_categories
obj_categories = maskRCNN.obj_categories
prd_freq_dict = maskRCNN.prd_freq_dict
obj_freq_dict = maskRCNN.obj_freq_dict
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step_with_prd_cls_v' + str(
cfg.MODEL.SUBTYPE)
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
ckpt_dir = os.path.join(output_dir, 'ckpt')
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
# if os.path.exists(os.path.join(ckpt_dir, 'best.json')):
# best = json.load(open(os.path.join(ckpt_dir, 'best.json')))
if args.resume and os.path.exists(
os.path.join(load_ckpt_dir, 'best.json')):
logger.info('Loading best json from :' +
os.path.join(load_ckpt_dir, 'best.json'))
best = json.load(open(os.path.join(load_ckpt_dir, 'best.json')))
json.dump(best, open(os.path.join(ckpt_dir, 'best.json'), 'w'))
else:
best = {}
best['avg_per_class_acc'] = 0.0
best['iteration'] = 0
best['accuracies'] = []
json.dump(best, open(os.path.join(ckpt_dir, 'best.json'), 'w'))
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
args.output_dir = output_dir
args.do_val = True
args.use_gt_boxes = True
args.use_gt_labels = True
logger.info('Creating val roidb')
val_dataset_name, val_proposal_file = get_inference_dataset(0)
val_roidb, val_dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
val_dataset_name, val_proposal_file, None, args.do_val)
logger.info('Done')
### Training Loop ###
maskRCNN.train()
# CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# CHECKPOINT_PERIOD = cfg.SOLVER.MAX_ITER / cfg.TRAIN.SNAPSHOT_FREQ
CHECKPOINT_PERIOD = 10000
EVAL_PERIOD = cfg.TRAIN.EVAL_PERIOD
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
maskRCNN.train()
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate_rel(optimizer, lr, lr_new)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate_rel(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate_rel(optimizer, lr, lr_new)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, backbone_lr)
if (step + 1) % EVAL_PERIOD == 0 or (step
== cfg.SOLVER.MAX_ITER - 1):
logger.info('Validating model')
eval_model = maskRCNN.module
eval_model = mynn.DataParallel(
eval_model,
cpu_keywords=['im_info', 'roidb'],
device_ids=[0],
minibatch=True)
eval_model.eval()
all_results = run_eval_inference(eval_model,
val_roidb,
args,
val_dataset,
val_dataset_name,
val_proposal_file,
ind_range=None,
multi_gpu_testing=False,
check_expected_results=True)
csv_path = os.path.join(output_dir, 'eval.csv')
all_results = all_results[0]
generate_csv_file_from_det_obj(all_results, csv_path,
obj_categories, prd_categories,
obj_freq_dict, prd_freq_dict)
overall_metrics, per_class_metrics = get_metrics_from_csv(
csv_path)
obj_acc = per_class_metrics[(csv_path, 'obj', 'top1')]
sbj_acc = per_class_metrics[(csv_path, 'sbj', 'top1')]
prd_acc = per_class_metrics[(csv_path, 'rel', 'top1')]
avg_obj_sbj = (obj_acc + sbj_acc) / 2.0
avg_acc = (prd_acc + avg_obj_sbj) / 2.0
best = json.load(open(os.path.join(ckpt_dir, 'best.json')))
if avg_acc > best['avg_per_class_acc']:
print('Found new best validation accuracy at {:2.2f}%'.
format(avg_acc))
print('Saving best model..')
best['avg_per_class_acc'] = avg_acc
best['iteration'] = step
best['per_class_metrics'] = {
'obj_top1':
per_class_metrics[(csv_path, 'obj', 'top1')],
'sbj_top1':
per_class_metrics[(csv_path, 'sbj', 'top1')],
'prd_top1':
per_class_metrics[(csv_path, 'rel', 'top1')]
}
best['overall_metrics'] = {
'obj_top1': overall_metrics[(csv_path, 'obj', 'top1')],
'sbj_top1': overall_metrics[(csv_path, 'sbj', 'top1')],
'prd_top1': overall_metrics[(csv_path, 'rel', 'top1')]
}
save_best_ckpt(output_dir, args, step, train_size,
maskRCNN, optimizer)
json.dump(best,
open(os.path.join(ckpt_dir, 'best.json'), 'w'))
if (step + 1) % CHECKPOINT_PERIOD == 0:
print('Saving Checkpoint..')
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except Exception as e:
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
# ADE20k as a detection dataset
elif args.dataset == "ade_train":
cfg.TRAIN.DATASETS = ('ade_train', )
cfg.MODEL.NUM_CLASSES = 446
# Noisy CS6+WIDER datasets
elif args.dataset == 'cs6_noise020+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise020', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise050+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise050', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise080+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise080', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise085+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise085', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise090+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise090', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise095+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise095', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise100+WIDER':
cfg.TRAIN.DATASETS = ('cs6_noise100', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
# Just Noisy CS6 datasets
elif args.dataset == 'cs6_noise020':
cfg.TRAIN.DATASETS = ('cs6_noise020', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise030':
cfg.TRAIN.DATASETS = ('cs6_noise030', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise040':
cfg.TRAIN.DATASETS = ('cs6_noise040', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise050':
cfg.TRAIN.DATASETS = ('cs6_noise050', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise060':
cfg.TRAIN.DATASETS = ('cs6_noise060', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise070':
cfg.TRAIN.DATASETS = ('cs6_noise070', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise080':
cfg.TRAIN.DATASETS = ('cs6_noise080', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise085':
cfg.TRAIN.DATASETS = ('cs6_noise085', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise090':
cfg.TRAIN.DATASETS = ('cs6_noise090', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise095':
cfg.TRAIN.DATASETS = ('cs6_noise095', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'cs6_noise100':
cfg.TRAIN.DATASETS = ('cs6_noise100', )
cfg.MODEL.NUM_CLASSES = 2
# Cityscapes 7 classes
elif args.dataset == "cityscapes":
cfg.TRAIN.DATASETS = ('cityscapes_train', )
cfg.MODEL.NUM_CLASSES = 8
# BDD 7 classes
elif args.dataset == "bdd_any_any_any":
cfg.TRAIN.DATASETS = ('bdd_any_any_any_train', )
cfg.MODEL.NUM_CLASSES = 8
elif args.dataset == "bdd_any_any_daytime":
cfg.TRAIN.DATASETS = ('bdd_any_any_daytime_train', )
cfg.MODEL.NUM_CLASSES = 8
elif args.dataset == "bdd_clear_any_daytime":
cfg.TRAIN.DATASETS = ('bdd_clear_any_daytime_train', )
cfg.MODEL.NUM_CLASSES = 8
# Cistyscapes Pedestrian sets
elif args.dataset == "cityscapes_peds":
cfg.TRAIN.DATASETS = ('cityscapes_peds_train', )
cfg.MODEL.NUM_CLASSES = 2
# Cityscapes Car sets
elif args.dataset == "cityscapes_cars_HPlen3+kitti_car_train":
cfg.TRAIN.DATASETS = ('cityscapes_cars_HPlen3', 'kitti_car_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cityscapes_cars_HPlen5+kitti_car_train":
cfg.TRAIN.DATASETS = ('cityscapes_cars_HPlen5', 'kitti_car_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cityscapes_car_train+kitti_car_train":
cfg.TRAIN.DATASETS = ('cityscapes_car_train', 'kitti_car_train')
cfg.MODEL.NUM_CLASSES = 2
# KITTI Car set
elif args.dataset == "kitti_car_train":
cfg.TRAIN.DATASETS = ('kitti_car_train', )
cfg.MODEL.NUM_CLASSES = 2
# BDD pedestrians sets
elif args.dataset == "bdd_peds":
cfg.TRAIN.DATASETS = ('bdd_peds_train',
) # bdd peds: clear_any_daytime
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "bdd_peds_full":
cfg.TRAIN.DATASETS = ('bdd_peds_full_train', ) # bdd peds: any_any_any
cfg.MODEL.NUM_CLASSES = 2
# Pedestrians with constraints
elif args.dataset == "bdd_peds_not_clear_any_daytime":
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train', )
cfg.MODEL.NUM_CLASSES = 2
# Ashish's 20k samples videos
elif args.dataset == "bdd_peds_not_clear_any_daytime_20k":
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_20k_train', )
cfg.MODEL.NUM_CLASSES = 2
# Source domain + Target domain detections
elif args.dataset == "bdd_peds+DETS_20k":
cfg.TRAIN.DATASETS = ('bdd_peds_dets_20k_target_domain',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
# Source domain + Target domain detections -- same 18k images as HP18k
elif args.dataset == "bdd_peds+DETS18k":
cfg.TRAIN.DATASETS = ('bdd_peds_dets18k_target_domain',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
# Only Dets
elif args.dataset == "DETS20k":
cfg.TRAIN.DATASETS = ('bdd_peds_dets_20k_target_domain', )
cfg.MODEL.NUM_CLASSES = 2
# Only Dets18k - same images as HP18k
elif args.dataset == 'DETS18k':
cfg.TRAIN.DATASETS = ('bdd_peds_dets18k_target_domain', )
cfg.MODEL.NUM_CLASSES = 2
# Only HP
elif args.dataset == 'HP':
cfg.TRAIN.DATASETS = ('bdd_peds_HP_target_domain', )
cfg.MODEL.NUM_CLASSES = 2
# Only HP 18k videos
elif args.dataset == 'HP18k':
cfg.TRAIN.DATASETS = ('bdd_peds_HP18k_target_domain', )
cfg.MODEL.NUM_CLASSES = 2
# Source domain + Target domain HP
elif args.dataset == 'bdd_peds+HP':
cfg.TRAIN.DATASETS = ('bdd_peds_train', 'bdd_peds_HP_target_domain')
cfg.MODEL.NUM_CLASSES = 2
# Source domain + Target domain HP 18k videos
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k':
cfg.TRAIN.DATASETS = ('bdd_peds_HP18k_target_domain', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
#### Source domain + Target domain with different conf threshold theta ####
elif args.dataset == 'bdd_peds+HP18k_thresh-050':
cfg.TRAIN.DATASETS = ('bdd_HP18k_thresh-050', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_thresh-060':
cfg.TRAIN.DATASETS = ('bdd_HP18k_thresh-060', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_thresh-070':
cfg.TRAIN.DATASETS = ('bdd_HP18k_thresh-070', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_thresh-090':
cfg.TRAIN.DATASETS = ('bdd_HP18k_thresh-090', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
##############################
#### Data distillation on BDD -- for rebuttal
elif args.dataset == 'bdd_peds+bdd_data_dist_small':
cfg.TRAIN.DATASETS = ('bdd_data_dist_small', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+bdd_data_dist_mid':
cfg.TRAIN.DATASETS = ('bdd_data_dist_mid', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+bdd_data_dist':
cfg.TRAIN.DATASETS = ('bdd_data_dist', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
##############################
#### Source domain + **Labeled** Target domain with varying number of images
elif args.dataset == 'bdd_peds+labeled_100':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_100',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_075':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_075',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_050':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_050',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_025':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_025',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_010':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_010',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_005':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_005',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+labeled_001':
cfg.TRAIN.DATASETS = ('bdd_peds_not_clear_any_daytime_train_001',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
##############################
# Source domain + Target domain HP tracker bboxes only
elif args.dataset == 'bdd_peds+HP18k_track_only':
cfg.TRAIN.DATASETS = ('bdd_HP18k_track_only', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
##### subsets of bdd_HP18k with different constraints
# Source domain + HP tracker images at NIGHT
elif args.dataset == 'bdd_peds+HP18k_any_any_night':
cfg.TRAIN.DATASETS = ('bdd_HP18k_any_any_night', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_rainy_any_daytime':
cfg.TRAIN.DATASETS = ('bdd_HP18k_rainy_any_daytime', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_rainy_any_night':
cfg.TRAIN.DATASETS = ('bdd_HP18k_rainy_any_night', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_overcast,rainy_any_daytime':
cfg.TRAIN.DATASETS = ('bdd_HP18k_overcast,rainy_any_daytime',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_overcast,rainy_any_night':
cfg.TRAIN.DATASETS = ('bdd_HP18k_overcast,rainy_any_night',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_overcast,rainy,snowy_any_daytime':
cfg.TRAIN.DATASETS = ('bdd_HP18k_overcast,rainy,snowy_any_daytime',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
############# end of bdd constraned subsets #####################
# Source domain + Target domain HP18k -- after histogram matching
elif args.dataset == 'bdd_peds+HP18k_remap_hist':
cfg.TRAIN.DATASETS = ('bdd_peds_HP18k_target_domain_remap_hist',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+HP18k_remap_cityscape_hist':
cfg.TRAIN.DATASETS = (
'bdd_peds_HP18k_target_domain_remap_cityscape_hist',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
# Source domain + Target domain HP18k -- after histogram matching
elif args.dataset == 'bdd_peds+HP18k_remap_random':
cfg.TRAIN.DATASETS = ('bdd_peds_HP18k_target_domain_remap_random',
'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
# Source+Noisy Target domain -- prevent domain adv from using HP roi info
elif args.dataset == 'bdd_peds+bdd_HP18k_noisy_100k':
cfg.TRAIN.DATASETS = ('bdd_HP18k_noisy_100k', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+bdd_HP18k_noisy_080':
cfg.TRAIN.DATASETS = ('bdd_HP18k_noisy_080', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+bdd_HP18k_noisy_060':
cfg.TRAIN.DATASETS = ('bdd_HP18k_noisy_060', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == 'bdd_peds+bdd_HP18k_noisy_070':
cfg.TRAIN.DATASETS = ('bdd_HP18k_noisy_070', 'bdd_peds_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "wider_train":
cfg.TRAIN.DATASETS = ('wider_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-subset":
cfg.TRAIN.DATASETS = ('cs6-subset', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-subset-score":
cfg.TRAIN.DATASETS = ('cs6-subset-score', )
elif args.dataset == "cs6-subset-gt":
cfg.TRAIN.DATASETS = ('cs6-subset-gt', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-3013-gt":
cfg.TRAIN.DATASETS = ('cs6-3013-gt',
) # DEBUG: overfit on one video annots
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-subset-gt+WIDER":
cfg.TRAIN.DATASETS = ('cs6-subset-gt', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-subset+WIDER":
cfg.TRAIN.DATASETS = ('cs6-subset', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-gt":
cfg.TRAIN.DATASETS = ('cs6-train-gt', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-gt-noisy-0.3":
cfg.TRAIN.DATASETS = ('cs6-train-gt-noisy-0.3', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-gt-noisy-0.5":
cfg.TRAIN.DATASETS = ('cs6-train-gt-noisy-0.5', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-det-score":
cfg.TRAIN.DATASETS = ('cs6-train-det-score', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-det-score-0.5":
cfg.TRAIN.DATASETS = ('cs6-train-det-score-0.5', )
elif args.dataset == "cs6-train-det":
cfg.TRAIN.DATASETS = ('cs6-train-det', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-det-0.5":
cfg.TRAIN.DATASETS = ('cs6-train-det-0.5', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-hp":
cfg.TRAIN.DATASETS = ('cs6-train-hp', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-easy-gt":
cfg.TRAIN.DATASETS = ('cs6-train-easy-gt', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-easy-gt-sub":
cfg.TRAIN.DATASETS = ('cs6-train-easy-gt-sub', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-easy-hp":
cfg.TRAIN.DATASETS = ('cs6-train-easy-hp', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-easy-det":
cfg.TRAIN.DATASETS = ('cs6-train-easy-det', )
cfg.MODEL.NUM_CLASSES = 2
# Joint training with CS6 and WIDER
elif args.dataset == "cs6-train-easy-gt-sub+WIDER":
cfg.TRAIN.DATASETS = ('cs6-train-easy-gt-sub', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-gt+WIDER":
cfg.TRAIN.DATASETS = ('cs6-train-gt', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-hp+WIDER":
cfg.TRAIN.DATASETS = ('cs6-train-hp', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-dummy+WIDER":
cfg.TRAIN.DATASETS = ('cs6-train-dummy', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cs6-train-det+WIDER":
cfg.TRAIN.DATASETS = ('cs6-train-det', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
# Dets dataset created by removing tracker results from the HP json
elif args.dataset == "cs6_train_det_from_hp+WIDER":
cfg.TRAIN.DATASETS = ('cs6_train_det_from_hp', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
# Dataset created by removing det results from the HP json -- HP tracker only
elif args.dataset == "cs6_train_hp_tracker_only+WIDER":
cfg.TRAIN.DATASETS = ('cs6_train_hp_tracker_only', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
# HP dataset with noisy labels: used to prevent DA from getting any info from HP
elif args.dataset == "cs6_train_hp_noisy_100+WIDER":
cfg.TRAIN.DATASETS = ('cs6_train_hp_noisy_100', 'wider_train')
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
"""def _init_fn(worker_id):
random.seed(999)
np.random.seed(999)
torch.cuda.manual_seed(999)
torch.cuda.manual_seed_all(999)
torch.manual_seed(999)
torch.backends.cudnn.deterministic = True
"""
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
if cfg.TRAIN.JOINT_TRAINING:
if len(cfg.TRAIN.DATASETS) == 2:
print('Joint training on two datasets')
else:
raise NotImplementedError
joint_training_roidb = []
for i, dataset_name in enumerate(cfg.TRAIN.DATASETS):
# ROIDB construction
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
(dataset_name), cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
if i == 0:
roidb_size = len(roidb)
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
#worker_init_fn=_init_fn)
# decrease num-threads when using two dataloaders
dataiterator = iter(dataloader)
joint_training_roidb.append({
'dataloader': dataloader,
'dataiterator': dataiterator,
'dataset_name': dataset_name
})
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
#worker_init_fn=init_fn)
dataiterator = iter(dataloader)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
if cfg.TRAIN.JOINT_SELECTIVE_FG:
orig_fg_batch_ratio = cfg.TRAIN.FG_FRACTION
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name + '.weight')
gn_param_nameset.add(name + '.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) -
set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}, {
'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN
}]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_' + str(
cfg.TRAIN.DATASETS) + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
"""
random.seed(cfg.RNG_SEED)
np.random.seed(cfg.RNG_SEED)
torch.cuda.manual_seed(cfg.RNG_SEED)
torch.cuda.manual_seed_all(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
torch.backends.cudnn.deterministic = True
"""
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
# use a iter counter for optional alternating batches
if args.iter_size == 1:
iter_counter = step
else:
iter_counter = inner_iter
if cfg.TRAIN.JOINT_TRAINING:
# alternate batches between dataset[0] and dataset[1]
if iter_counter % 2 == 0:
if True: #DEBUG:
print('Dataset: %s' %
joint_training_roidb[0]['dataset_name'])
dataloader = joint_training_roidb[0]['dataloader']
dataiterator = joint_training_roidb[0]['dataiterator']
# NOTE: if available FG samples cannot fill minibatch
# then batchsize will be smaller than cfg.TRAIN.BATCH_SIZE_PER_IM.
else:
if True: #DEBUG:
print('Dataset: %s' %
joint_training_roidb[1]['dataset_name'])
dataloader = joint_training_roidb[1]['dataloader']
dataiterator = joint_training_roidb[1]['dataiterator']
try:
input_data = next(dataiterator)
except StopIteration:
# end of epoch for dataloader
dataiterator = iter(dataloader)
input_data = next(dataiterator)
if cfg.TRAIN.JOINT_TRAINING:
if iter_counter % 2 == 0:
joint_training_roidb[0][
'dataiterator'] = dataiterator
else:
joint_training_roidb[1][
'dataiterator'] = dataiterator
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
# [p.data.get_device() for p in maskRCNN.parameters()]
# [(name, p.data.get_device()) for name, p in maskRCNN.named_parameters()]
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "davis2017":
cfg.TRAIN.DATASETS = ('davis_train', )
#For davis, coco category is used.
cfg.MODEL.NUM_CLASSES = 81 #80 foreground + 1 background
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if cfg.MODEL.IDENTITY_TRAINING and cfg.MODEL.IDENTITY_REPLACE_CLASS:
cfg.MODEL.NUM_CLASSES = 145
cfg.MODEL.IDENTITY_TRAINING = False
cfg.MODEL.ADD_UNKNOWN_CLASS = False
#Add unknow class type if necessary.
if cfg.MODEL.ADD_UNKNOWN_CLASS is True:
cfg.MODEL.NUM_CLASSES += 1
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
cfg.SOLVER.BASE_LR *= 1.0 / cfg.MODEL.SEQUENCE_LENGTH
print(
'Adjust BASE_LR linearly according to batch_size change and sequence length change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
merged_roidb, seq_num, seq_start_end = sequenced_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES, load_inv_db=True)
timers['roidb'].toc()
roidb_size = len(merged_roidb)
logger.info('{:d} roidbs sequences.'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidbs',
timers['roidb'].average_time)
# Effective training sample size for one epoch, number of sequences.
train_size = roidb_size // args.batch_size * args.batch_size
### Model ###
maskRCNN = vos_model_builder.Generalized_VOS_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name + '.weight')
gn_param_nameset.add(name + '.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in maskRCNN.named_parameters():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) -
set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}, {
'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN
}]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt_no_mapping(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN,
args.load_detectron,
force_load_all=False)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
dataloader, dataiterator, warmup_length = gen_sequence_data_sampler(
merged_roidb, seq_num, seq_start_end, use_seq_warmup=False)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
if cfg.TRAIN.USE_SEQ_WARMUP is True and step >= cfg.TRAIN.ITER_BEFORE_USE_SEQ_WARMUP and (
(step - cfg.TRAIN.ITER_BEFORE_USE_SEQ_WARMUP) %
cfg.TRAIN.WARMUP_LENGTH_CHANGE_STEP) == 0:
#TODO better dataiterator creator.
raise NotImplementedError('not able to delete.')
dataloader, dataiterator, warmup_length = gen_sequence_data_sampler(
merged_roidb, seq_num, seq_start_end, use_seq_warmup=True)
print('update warmup length:', warmup_length)
try:
input_data_sequence = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data_sequence = next(dataiterator)
# clean hidden states before training.
maskRCNN.module.clean_hidden_states()
maskRCNN.module.clean_flow_features()
assert len(input_data_sequence['data']
) == cfg.MODEL.SEQUENCE_LENGTH + warmup_length, print(
len(input_data_sequence['data']), '!=',
cfg.MODEL.SEQUENCE_LENGTH + warmup_length)
# if train_part == 0: train backbone.
# else train_part == 1: train heads.
train_part = 1
if cfg.TRAIN.ALTERNATE_TRAINING:
if step % 2 == 0:
maskRCNN.module.freeze_conv_body_only()
train_part = 1
else:
maskRCNN.module.train_conv_body_only()
train_part = 0
# this is used for longer sequence training.
# when reach maximum trainable length, detach the hidden states.
cnter_for_detach_hidden_states = 0
for inner_iter in range(cfg.MODEL.SEQUENCE_LENGTH + warmup_length):
#get input_data
input_data = {}
for key in input_data_sequence.keys():
input_data[key] = input_data_sequence[key][
inner_iter:inner_iter + 1]
for key in input_data:
if key != 'roidb' and key != 'data_flow': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
if key == 'data_flow':
if inner_iter != 0 and input_data[key][0][0][
0] is not None: # flow is not None.
input_data[key] = [
Variable(
torch.tensor(
np.expand_dims(
np.squeeze(
np.array(input_data[key],
dtype=np.float32)),
0),
device=input_data['data'][0].device))
]
assert input_data['data'][0].shape[
-2:] == input_data[key][0].shape[
-2:], "Spatial shape of image and flow are not equal."
else:
input_data[key] = [None]
if cfg.TRAIN.USE_SEQ_WARMUP and inner_iter < warmup_length:
maskRCNN.module.set_stop_after_hidden_states(stop=True)
net_outputs = maskRCNN(**input_data)
assert net_outputs is None
maskRCNN.module.detach_hidden_states()
maskRCNN.module.set_stop_after_hidden_states(stop=False)
continue
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
if train_part == 0 or cnter_for_detach_hidden_states >= cfg.TRAIN.MAX_TRAINABLE_SEQ_LENGTH or inner_iter == cfg.MODEL.SEQUENCE_LENGTH + warmup_length - 1:
# if reach the max trainable length or end of the sequence. free the graph and detach the hidden states.
loss.backward()
maskRCNN.module.detach_hidden_states()
cnter_for_detach_hidden_states = 0
else:
loss.backward(retain_graph=True)
cnter_for_detach_hidden_states += 1
#TODO step every time?
if cnter_for_detach_hidden_states == 0:
optimizer.step()
optimizer.zero_grad()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
except (KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on Keyboard Interrupt ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' % (args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' % (original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' % (original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' % (original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print('Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps, cfg.SOLVER.STEPS,
old_max_iter, cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print('Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True
)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name+'.weight')
gn_param_nameset.add(name+'.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) - set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN}
]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print('train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step+1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "coco2014":
cfg.TRAIN.DATASETS = ('coco_2014_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_trainval':
cfg.TRAIN.DATASETS = ('vcoco_trainval', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_train':
cfg.TRAIN.DATASETS = ('vcoco_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_val':
cfg.TRAIN.DATASETS = ('vcoco_val', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'keypoints_coco2014':
cfg.TRAIN.DATASETS = ('keypoints_coco_2014_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if args.vcoco_kp_on:
cfg.VCOCO.KEYPOINTS_ON = True
cfg.NETWORK_NAME = args.net_name # network name
print('Network name:', args.net_name)
cfg.MODEL.CONV_BODY = args.conv_body # backbone network name
print('Conv_body name:', args.conv_body)
cfg.TRAIN.FG_THRESH = args.fg_thresh
print('Train fg thresh:', args.fg_thresh)
cfg.RESNETS.FREEZE_AT = args.freeze_at
print('Freeze at: ', args.freeze_at)
cfg.VCOCO.MLP_HEAD_DIM = args.mlp_head_dim
print('MLP head dim: ', args.mlp_head_dim)
cfg.SOLVER.MAX_ITER = args.max_iter
print('MAX iter: ', args.max_iter)
cfg.TRAIN.SNAPSHOT_ITERS = args.snapshot
print('Snapshot Iters: ', args.snapshot)
if args.solver_steps is not None:
cfg.SOLVER.STEPS = args.solver_steps
print('Solver_steps: ', cfg.SOLVER.STEPS)
cfg.VCOCO.TRIPLETS_NUM_PER_IM = args.triplets_num_per_im
print('triplets_num_per_im: ', cfg.VCOCO.TRIPLETS_NUM_PER_IM)
cfg.VCOCO.HEATMAP_KERNEL_SIZE = args.heatmap_kernel_size
print('heatmap_kernel_size: ', cfg.VCOCO.HEATMAP_KERNEL_SIZE)
cfg.VCOCO.PART_CROP_SIZE = args.part_crop_size
print('part_crop_size: ', cfg.VCOCO.PART_CROP_SIZE)
print('use use_kps17 for part Align: ', args.use_kps17)
if args.use_kps17:
cfg.VCOCO.USE_KPS17 = True
else:
cfg.VCOCO.USE_KPS17 = False
print('MULTILEVEL_ROIS: ', cfg.FPN.MULTILEVEL_ROIS)
if args.vcoco_use_spatial:
cfg.VCOCO.USE_SPATIAL = True
if args.vcoco_use_union_feat:
cfg.VCOCO.USE_UNION_FEAT = True
if args.use_precomp_box:
cfg.VCOCO.USE_PRECOMP_BOX = True
cfg.DEBUG_TEST_WITH_GT = True
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH # 16
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
print(' FG_THRESH: ', cfg.TRAIN.FG_THRESH)
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
cfg.SOLVER.VAL_ITER = int(cfg.SOLVER.VAL_ITER * step_scale + 0.5)
cfg.TRAIN.SNAPSHOT_ITERS = int(cfg.TRAIN.SNAPSHOT_ITERS * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
# ipdb.set_trace()
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
# ToDo: shuffle?
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
# dataiterator = iter(dataloader)
### Model ###
from modeling.model_builder import Generalized_RCNN
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_hoi_params = []
bias_hoi_param_names = []
bias_faster_params = []
bias_faster_param_names = []
nobias_hoi_params = []
nobias_hoi_param_names = []
nobias_faster_params = []
nobias_faster_param_names = []
# bias_params = []
# bias_param_names = []
# nonbias_params = []
# nonbias_param_names = []
#base_model = torch.load('Outputs/baseline/baseline_512_32_nogt_1o3/ckpt/model_step47999.pth')
nograd_param_names = []
for key, value in maskRCNN.named_parameters():
#if key in base_model['model'].keys():
# value.requires_grad = False
#print('the key xxx:', key)
# Fix RPN module same as the paper
# ToDo: or key.startswith('Box')
# if 'affinity' not in key:
# value.requires_grad = False
print(key, value.size(), value.requires_grad)
if value.requires_grad:
if 'bias' in key:
if 'HOI_Head' in key:
bias_hoi_params.append(value)
bias_hoi_param_names.append(key)
else:
bias_faster_params.append(value)
bias_faster_param_names.append(key)
else:
if 'HOI_Head' in key:
nobias_hoi_params.append(value)
nobias_hoi_param_names.append(key)
else:
nobias_faster_params.append(value)
nobias_faster_param_names.append(key)
else:
nograd_param_names.append(key)
#del base_model
#ipdb.set_trace()
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{
'params': nobias_hoi_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
},
{
'params': nobias_faster_params,
'lr': 0 * cfg.SOLVER.FASTER_RCNN_WEIGHT,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
},
{
'params':
bias_hoi_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
},
{
'params':
bias_faster_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1) *
cfg.SOLVER.FASTER_RCNN_WEIGHT,
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
},
]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
if args.krcnn_from_faster:
net_utils.load_krcnn_from_faster(maskRCNN, checkpoint['model'])
else:
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
print('Original model loaded....')
if args.resume:
print('Resume, loaded step\n\n\n: ', checkpoint['step'])
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
#output_dir = misc_utils.get_output_dir(args, args.run_name)
output_dir = os.path.join('Outputs', args.expDir, args.expID)
os.makedirs(output_dir, exist_ok=True)
args.cfg_filename = os.path.basename(args.cfg_file)
tblogger = None
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
if args.expID.__contains__('base'):
os.environ['FABRICATOR'] = 'base'
else:
os.environ['FABRICATOR'] = 'fcl'
print('log', os.environ['FABRICATOR'])
### Training Loop ###
train_val(maskRCNN, args, optimizer, lr, dataloader, train_size,
output_dir, tblogger)
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "common":
cfg.TRAIN.DATASETS = ('common_train', )
cfg.MODEL.NUM_CLASSES = 81
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' %
(cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.
format(old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
assert_and_infer_cfg()
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
params = [{
'params': nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
iters_per_epoch = int(train_size / args.batch_size) # drop last
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
try:
logger.info('Training starts !')
args.step = args.start_iter
global_step = iters_per_epoch * args.start_epoch + args.step
for args.epoch in range(args.start_epoch,
args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
# adjust learning rate
if args.lr_decay_epochs and args.epoch == args.lr_decay_epochs[
0] and args.start_iter == 0:
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizer, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for args.step, input_data in zip(
range(args.start_iter, iters_per_epoch), dataloader):
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
training_stats.IterTic()
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs)
loss = net_outputs['total_loss']
optimizer.zero_grad()
loss.backward()
optimizer.step()
training_stats.IterToc()
if (args.step + 1) % ckpt_interval_per_epoch == 0:
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
if args.step % args.disp_interval == 0:
log_training_stats(training_stats, global_step, lr)
global_step += 1
# ---- End of epoch ----
# save checkpoint
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
# reset starting iter number after first epoch
args.start_iter = 0
# ---- Training ends ----
if iters_per_epoch % args.disp_interval != 0:
# log last stats at the end
log_training_stats(training_stats, global_step, lr)
except (RuntimeError, KeyboardInterrupt):
logger.info('Save ckpt on exception ...')
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
cfg.DATASET = args.dataset
if args.dataset == "vrd":
cfg.TRAIN.DATASETS = ('vrd_train',)
cfg.MODEL.NUM_CLASSES = 101
cfg.MODEL.NUM_PRD_CLASSES = 70 # exclude background
elif args.dataset == "vg":
cfg.TRAIN.DATASETS = ('vg_train',)
cfg.MODEL.NUM_CLASSES = 151
cfg.MODEL.NUM_PRD_CLASSES = 50 # exclude background
elif args.dataset == "vg80k":
cfg.TRAIN.DATASETS = ('vg80k_train',)
cfg.MODEL.NUM_CLASSES = 53305 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 29086 # excludes background
elif args.dataset == "gvqa20k":
cfg.TRAIN.DATASETS = ('gvqa20k_train',)
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
elif args.dataset == "gvqa10k":
cfg.TRAIN.DATASETS = ('gvqa10k_train',)
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
elif args.dataset == "gvqa":
cfg.TRAIN.DATASETS = ('gvqa_train',)
cfg.MODEL.NUM_CLASSES = 1704 # includes background
cfg.MODEL.NUM_PRD_CLASSES = 310 # exclude background
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' % (args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' % (original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' % (original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' % (original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print('Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps, cfg.SOLVER.STEPS,
old_max_iter, cfg.SOLVER.MAX_ITER))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True
)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
# record backbone params, i.e., conv_body and box_head params
gn_params = []
backbone_bias_params = []
backbone_bias_param_names = []
prd_branch_bias_params = []
prd_branch_bias_param_names = []
backbone_nonbias_params = []
backbone_nonbias_param_names = []
prd_branch_nonbias_params = []
prd_branch_nonbias_param_names = []
classifier_params = []
classifier_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'gn' in key:
gn_params.append(value)
elif 'Conv_Body' in key or 'Box_Head' in key or 'Box_Outs' in key or 'RPN' in key:
if 'bias' in key:
backbone_bias_params.append(value)
backbone_bias_param_names.append(key)
else:
backbone_nonbias_params.append(value)
backbone_nonbias_param_names.append(key)
#elif 'classifier' in key:
# classifier_params.append(value)
# classifier_param_names.append(value)
else:
if 'bias' in key:
prd_branch_bias_params.append(value)
prd_branch_bias_param_names.append(key)
else:
prd_branch_nonbias_params.append(value)
prd_branch_nonbias_param_names.append(key)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': backbone_nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': backbone_bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': prd_branch_nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': prd_branch_bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN}#,
#{'params': classifier_params,
# 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0005}
]
# print('Initializing model optimizer.')
# if cfg.SOLVER.TYPE == "SGD":
# optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
# elif cfg.SOLVER.TYPE == "Adam":
# optimizer = torch.optim.Adam(params)
print('Initializing model and classifier optimizers.')
#classifier_optim_param = {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0005}
#params.append({'params': maskRCNN.classifier.parameters(),
# 'lr': classifier_optim_param['lr']),
# 'momentum': classifier_optim_param['momentum'],
# 'weight_decay': classifier_optim_param['weight_decay']})
#params.append({'params': maskRCNN.prd_classifier.parameters(),
# 'lr': classifier_optim_param['lr'],
# 'momentum': classifier_optim_param['momentum'],
# 'weight_decay': classifier_optim_param['weight_decay']})
if cfg.MODEL.MEMORY_MODULE_STAGE == 1:
step_size = 10
elif cfg.MODEL.MEMORY_MODULE_STAGE == 2:
step_size = 20
else:
raise NotImplementedError
scheduler_params = {'step_size': step_size, 'gamma': 0.1}
optimizer, optimizer_scheduler = init_optimizers(params, scheduler_params)
criterion_optimizer, criterion_optimizer_scheduler = None, None
if cfg.MODEL.MEMORY_MODULE_STAGE == 2:
print('Initializing criterion optimizer.')
feat_loss_optim_param = {'lr': 0.01, 'momentum': 0.9, 'weight_decay': 0.0005}
optim_params = feat_loss_optim_param
optim_params = [{'params': maskRCNN.feature_loss_sbj_obj.parameters(),
'lr': optim_params['lr'],
'momentum': optim_params['momentum'],
'weight_decay': optim_params['weight_decay']},
{'params': maskRCNN.feature_loss_prd.parameters(),
'lr': optim_params['lr'],
'momentum': optim_params['momentum'],
'weight_decay': optim_params['weight_decay']}
]
# Initialize criterion optimizer and scheduler
criterion_optimizer, criterion_optimizer_scheduler = init_optimizers(optim_params, scheduler_params)
if cfg.MODEL.MEMORY_MODULE_STAGE == 2:
weights_path = 'Outputs/e2e_relcnn_VGG16_8_epochs_gvqa_y_loss_only_1_gpu/gvqa/Feb07-10-55-03_login104-09_step_with_prd_cls_v3/ckpt/model_step1439.pth'
weights = torch.load(weights_path)
#print('weights', weights['model'].keys())
#print(maskRCNN.state_dict().keys())
maskRCNN.load_state_dict(weights['model'], strict=False)
#print(list(maskRCNN.parameters()))
#print(maskRCNN.state_dict().keys())
#print(maskRCNN.state_dict()['prd_classifier.fc_hallucinator.weight'] == weights['model']['prd_classifier.fc.weight'])
#print(torch.all(torch.eq(maskRCNN.state_dict()['prd_classifier.fc_hallucinator.weight'], weights['model']['prd_classifier.fc.weight'])))
#print(torch.all(torch.eq(maskRCNN.state_dict()['Box_Head.heads.0.weight'], weights['model']['Box_Head.heads.0.weight'])))
#exit()
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print('train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[2]['lr'] # lr of non-backbone parameters, for commmand line outputs.
backbone_lr = optimizer.param_groups[0]['lr'] # lr of backbone parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step_with_prd_cls_v' + str(cfg.MODEL.SUBTYPE)
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
# CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# CHECKPOINT_PERIOD = cfg.SOLVER.MAX_ITER / cfg.TRAIN.SNAPSHOT_FREQ
CHECKPOINT_PERIOD = 200000
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# optimizer_scheduler.step()
# if criterion_optimizer:
# criterion_optimizer_scheduler.step()
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate_rel(optimizer, lr, lr_new)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate_rel(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate_rel(optimizer, lr, lr_new)
if criterion_optimizer:
net_utils.update_learning_rate_rel(criterion_optimizer, lr, lr_new)
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
if criterion_optimizer:
criterion_optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
#print('input_data', [torch.isnan(x) for x in input_data.values()])
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
#with autograd.detect_anomaly():
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
if criterion_optimizer:
criterion_optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, backbone_lr)
if (step+1) % CHECKPOINT_PERIOD == 0:
print('Saving Checkpoint..')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except Exception as e:
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
saveNetStructure=False
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
#set gpu device
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(ids) for ids in args.device_ids])
torch.backends.cudnn.benchmark=True
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cityscapes":
cfg.TRAIN.DATASETS = ('cityscapes_semseg_train', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscape_train_on_val":
cfg.TRAIN.DATASETS = ('cityscape_train_on_val', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscapes_coarse":
cfg.TRAIN.DATASETS = ('cityscapes_coarse', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscapes_all":
cfg.TRAIN.DATASETS = ('cityscapes_all', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscapes_trainval":
cfg.TRAIN.DATASETS = ('cityscapes_trainval', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscapes_fineturn":
cfg.TRAIN.DATASETS = ('cityscapes_fineturn', )
cfg.MODEL.NUM_CLASSES = 19
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' % (cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.format(
old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON:
roidb, ratio_list, ratio_index = combined_roidb_for_training_semseg(
cfg.TRAIN.DATASETS)
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
#sampler = MinibatchSampler(ratio_list, ratio_index)
sampler = None
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch_semseg_all if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON else collate_minibatch,
drop_last=False,
shuffle=True,
pin_memory=True)
assert_and_infer_cfg()
#for data in dataloader:
# image = data['data'][0][0].numpy()
# print (image.shape)
# image=image.transpose(1,2,0)+cfg.PIXEL_MEANS
# cv2.imwrite('image.png', image[:,:,::-1])
# cv2.imwrite('label.png',10*data['semseg_label_0'][0][0].numpy())
# return
maskRCNN = eval(cfg.MODEL.TYPE)()
if len(cfg.SEM.PSPNET_PRETRAINED_WEIGHTS)>1:
print("loading pspnet weights")
state_dict={}
pretrained=torch.load(cfg.SEM.PSPNET_PRETRAINED_WEIGHTS, map_location=lambda storage, loc: storage)
pretrained = pretrained['model']
if cfg.SEM.SPN_ON:
maskRCNN.pspnet.load_state_dict(pretrained,strict=True)
elif 'deeplab' in cfg.SEM.DECODER_TYPE:
encoder = dict()
for k, v in pretrained.items():
if 'decoder' in k:
continue
encoder[k.replace('encoder.','')] = v
maskRCNN.encoder.load_state_dict(encoder,strict=True)
del encoder
else:
maskRCNN.load_state_dict(pretrained,strict=True)
del pretrained
print("weights load success")
if cfg.SEM.SPN_ON:
maskRCNN.pspnet.eval()
for p in maskRCNN.pspnet.parameters():
p.requires_grad = False
# load nets into gpu
maskRCNN = UserScatteredDataParallel(maskRCNN)
# For sync bn
patch_replication_callback(maskRCNN)
if cfg.CUDA:
maskRCNN.to('cuda')
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
params = [
{'params': nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0}
]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
print("Using STEP as Lr reduce policy!")
if cfg.SOLVER.TYPE == 'SGD' and cfg.SOLVER.LR_POLICY == 'ReduceLROnPlateau':
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,'min',patience=10)
print("Using ReduceLROnPlateau as Lr reduce policy!")
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
elif "poly" in cfg.SOLVER.TYPE:
optimizer = create_optimizers(maskRCNN,args)
print("Using Poly as Lr reduce policy!")
args.max_iters = (int(train_size / args.batch_size)) * args.num_epochs
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
if cfg.SOLVER.TYPE=='step_poly':
lr = cfg.SOLVER.BASE_LR / (cfg.SOLVER.GAMMA**len(args.lr_decay_epochs))
else:
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
### Training Setups ###
args.run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
#from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
iters_per_epoch = int(train_size / args.batch_size) # drop last
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
try:
logger.info('Training starts !')
args.step = args.start_iter
global_step = iters_per_epoch * args.start_epoch + args.step
for args.epoch in range(args.start_epoch, args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
# adjust learning rate
if args.lr_decay_epochs and args.epoch == args.lr_decay_epochs[0] and args.start_iter == 0 and cfg.SOLVER.LR_POLICY=='steps_with_decay' :
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizer, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for args.step, input_data in zip(range(args.start_iter, iters_per_epoch), dataloader):
#if cfg.DISP.DISP_ON:
# input_data['data'] = list(map(lambda x,y: torch.cat((x,y), dim=0),
# input_data['data'], input_data['data_R']))
# if cfg.SEM.DECODER_TYPE.endswith('3D'):
# input_data['disp_scans'] = torch.arange(1,
# cfg.DISP.MAX_DISPLACEMENT+1).float().view(1,cfg.DISP.MAX_DISPLACEMENT).repeat(args.batch_size,1)
# del input_data['data_R']
#for key in input_data:
# if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
# input_data[key] = list(map(lambda x: Variable(x, requires_grad=False).to('cuda'), input_data[key]))
training_stats.IterTic()
net_outputs = maskRCNN(input_data)
training_stats.UpdateIterStats(net_outputs)
#loss = net_outputs['losses']['loss_semseg']
#acc = net_outputs['metrics']['accuracy_pixel']
#print (loss.item(), acc)
#for key in net_outputs.keys():
# print(key)
loss = net_outputs['total_loss']
#print("loss.shape:",loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if cfg.SOLVER.TYPE=='poly':
lr = adjust_learning_rate(optimizer, global_step, args)
if cfg.SOLVER.TYPE=='step_poly':
lr = step_adjust_learning_rate(optimizer, lr, global_step, args)
training_stats.IterToc()
if args.step % args.disp_interval == 0:
disp_image=''
semseg_image=''
#tblogger.add_image('disp_image',disp_image,global_step)
#tblogger.add_image('semseg_image',semseg_image,global_step)
log_training_stats(training_stats, global_step, lr)
global_step += 1
# ---- End of epoch ----
# save checkpoint
if cfg.SOLVER.TYPE == 'SGD' and cfg.SOLVER.LR_POLICY == 'ReduceLROnPlateau':
lr_scheduler.step(loss)
lr = optimizer.param_groups[0]['lr']
if (args.epoch+1) % args.ckpt_num_per_epoch ==0:
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
# reset starting iter number after first epoch
args.start_iter = 0
# ---- Training ends ----
#if iters_per_epoch % args.disp_interval != 0:
# log last stats at the end
# log_training_stats(training_stats, global_step, lr)
# save final model
if (args.epoch+1) % args.ckpt_num_per_epoch:
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
logger.info('Save ckpt on exception ...')
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
merge_cfg_from_file(args.cfg_file)
# Some manual adjustment for the ApolloScape dataset parameters here
cfg.OUTPUT_DIR = args.output_dir
cfg.TRAIN.DATASETS = 'Car3D'
cfg.MODEL.NUM_CLASSES = 8
if cfg.CAR_CLS.SIM_MAT_LOSS:
cfg.MODEL.NUMBER_CARS = 79
else:
# Loss is only cross entropy, hence, we detect only car categories in the training set.
cfg.MODEL.NUMBER_CARS = 34
cfg.TRAIN.MIN_AREA = 196 # 14*14
cfg.TRAIN.USE_FLIPPED = False # Currently I don't know how to handle the flipped case
cfg.TRAIN.IMS_PER_BATCH = 1
cfg.NUM_GPUS = torch.cuda.device_count()
effective_batch_size = cfg.TRAIN.IMS_PER_BATCH * cfg.NUM_GPUS * args.iter_size
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
assert (args.batch_size %
cfg.NUM_GPUS) == 0, 'batch_size: %d, NUM_GPUS: %d' % (
args.batch_size, cfg.NUM_GPUS)
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print(
'Adjust BASE_LR linearly according to batch_size change:\n BASE_LR: {} --> {}'
.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
if cfg.MODEL.LOSS_3D_2D_ON:
roidb, ratio_list, ratio_index, ds = combined_roidb_for_training(
cfg.TRAIN.DATASETS, args.dataset_dir)
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, args.dataset_dir)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb,
cfg.MODEL.NUM_CLASSES,
training=True,
valid_keys=[
'has_visible_keypoints', 'boxes', 'seg_areas',
'gt_classes', 'gt_overlaps',
'box_to_gt_ind_map', 'is_crowd',
'car_cat_classes', 'poses', 'quaternions',
'im_info'
])
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
drop_last=True,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
if cfg.MODEL.LOSS_3D_2D_ON:
maskRCNN = Generalized_RCNN(ds.Car3D)
else:
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN,
checkpoint['model'],
ignore_list=args.ckpt_ignore_head)
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: # TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
# output_dir = os.path.join('/media/SSD_1TB/zzy/ApolloScape/ECCV2018_apollo/train', args.run_name)
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
# warmup_factor_trans = 1.0
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
# warmup_factor_trans = cfg.SOLVER.WARM_UP_FACTOR_TRANS * (1 - alpha) + alpha
# warmup_factor_trans *= cfg.TRANS_HEAD.LOSS_BETA
warmup_factor_trans = 1.0
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(
cfg.SOLVER.STEPS
) and step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
net_outputs['losses'][
'loss_car_cls'] *= cfg.CAR_CLS.CAR_CLS_LOSS_BETA
net_outputs['losses']['loss_rot'] *= cfg.CAR_CLS.ROT_LOSS_BETA
if cfg.MODEL.TRANS_HEAD_ON:
net_outputs['losses'][
'loss_trans'] *= cfg.TRANS_HEAD.TRANS_LOSS_BETA
training_stats.UpdateIterStats_car_3d(net_outputs)
# start training
# loss_car_cls: 2.233790, loss_rot: 0.296853, loss_trans: ~100
loss = net_outputs['losses']['loss_car_cls'] + net_outputs[
'losses']['loss_rot']
if cfg.MODEL.TRANS_HEAD_ON:
loss += net_outputs['losses']['loss_trans']
if cfg.MODEL.LOSS_3D_2D_ON:
loss += net_outputs['losses']['UV_projection_loss']
if not cfg.TRAIN.FREEZE_CONV_BODY and not cfg.TRAIN.FREEZE_RPN and not cfg.TRAIN.FREEZE_FPN:
loss += net_outputs['total_loss_conv']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, warmup_factor_trans)
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()