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():
args = parse_args()
print('Called with args:')
print(args)
cfg = set_configs(args)
timers = defaultdict(Timer)
### --------------------------------------------------------------------------------
### Dataset Training ###
### --------------------------------------------------------------------------------
timers['roidb_training'].tic()
roidb_training, ratio_list_training, ratio_index_training, category_to_id_map, prd_category_to_id_map = combined_roidb_for_training(
cfg.TRAIN.DATASETS)
timers['roidb_training'].toc()
roidb_size_training = len(roidb_training)
logger.info('{:d} training roidb entries'.format(roidb_size_training))
logger.info('Takes %.2f sec(s) to construct training roidb',
timers['roidb_training'].average_time)
batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
dataset_training = RoiDataLoader(roidb_training,
cfg.MODEL.NUM_CLASSES,
training=True,
dataset=cfg.TRAIN.DATASETS)
dataloader_training = torch.utils.data.DataLoader(
dataset_training,
batch_size=batch_size,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch,
shuffle=True,
drop_last=True)
dataiterator_training = iter(dataloader_training)
### --------------------------------------------------------------------------------
### Dataset Validation ###
### --------------------------------------------------------------------------------
timers['roidb_val'].tic()
roidb_val, ratio_list_val, ratio_index_val, _, _ = combined_roidb_for_training(
cfg.VAL.DATASETS)
timers['roidb_val'].toc()
roidb_size_val = len(roidb_val)
logger.info('{:d} val roidb entries'.format(roidb_size_val))
logger.info('Takes %.2f sec(s) to construct val roidb',
timers['roidb_val'].average_time)
dataset_val = RoiDataLoader(roidb_val,
cfg.MODEL.NUM_CLASSES,
training=False,
dataset=cfg.VAL.DATASETS)
dataloader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=batch_size,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch,
drop_last=True)
### --------------------------------------------------------------------------------
### Dataset Test ###
### --------------------------------------------------------------------------------
timers['roidb_test'].tic()
roidb_test, ratio_list_test, ratio_index_test, _, _ = combined_roidb_for_training(
cfg.TEST.DATASETS)
timers['roidb_test'].toc()
roidb_size_test = len(roidb_test)
logger.info('{:d} test roidb entries'.format(roidb_size_test))
logger.info('Takes %.2f sec(s) to construct test roidb',
timers['roidb_test'].average_time)
dataset_test = RoiDataLoader(roidb_test,
cfg.MODEL.NUM_CLASSES,
training=False,
dataset=cfg.TEST.DATASETS)
dataloader_test = torch.utils.data.DataLoader(
dataset_test,
batch_size=batch_size,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch,
drop_last=True)
### --------------------------------------------------------------------------------
### Dataset Unseen ###
### --------------------------------------------------------------------------------
if args.dataset == 'vhico':
timers['roidb_unseen'].tic()
roidb_unseen, ratio_list_unseen, ratio_index_unseen, _, _ = combined_roidb_for_training(
cfg.UNSEEN.DATASETS)
timers['roidb_unseen'].toc()
roidb_size_unseen = len(roidb_unseen)
logger.info('{:d} test unseen roidb entries'.format(roidb_size_unseen))
logger.info('Takes %.2f sec(s) to construct test roidb',
timers['roidb_unseen'].average_time)
dataset_unseen = RoiDataLoader(roidb_unseen,
cfg.MODEL.NUM_CLASSES,
training=False,
dataset=cfg.UNSEEN.DATASETS)
dataloader_unseen = torch.utils.data.DataLoader(
dataset_unseen,
batch_size=batch_size,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch,
drop_last=True)
### --------------------------------------------------------------------------------
### Model ###
### --------------------------------------------------------------------------------
maskRCNN = Generalized_RCNN(category_to_id_map=category_to_id_map,
prd_category_to_id_map=prd_category_to_id_map,
args=args)
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 = []
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 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'])
print(
'--------------------------------------------------------------------------------'
)
print('loading checkpoint %s' % load_name)
print(
'--------------------------------------------------------------------------------'
)
if args.resume:
print('resume')
args.start_step = checkpoint['step'] + 1
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
else:
print('args.load_ckpt', args.load_ckpt)
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 = args.out_dir
output_dir = misc_utils.get_output_dir(args, args.out_dir)
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
tblogger = SummaryWriter(output_dir)
### --------------------------------------------------------------------------------
### Training Loop ###
### --------------------------------------------------------------------------------
maskRCNN.train()
# 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, True)
val_stats = ValStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None, False)
test_stats = TestStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None, False)
best_total_loss = np.inf
best_eval_result = 0
### --------------------------------------------------------------------------------
### EVAL ###
### --------------------------------------------------------------------------------
if cfg.EVAL_SUBSET == 'unseen':
print('testing unseen ...')
is_best, best_eval_result = run_eval(args,
cfg,
maskRCNN,
dataloader_unseen,
step=0,
output_dir=output_dir,
test_stats=test_stats,
best_eval_result=best_eval_result,
eval_subset=cfg.EVAL_SUBSET)
return
elif cfg.EVAL_SUBSET == 'test':
print('testing ...')
is_best, best_eval_result = run_eval(args,
cfg,
maskRCNN,
dataloader_test,
step=0,
output_dir=output_dir,
test_stats=test_stats,
best_eval_result=best_eval_result,
eval_subset=cfg.EVAL_SUBSET)
return
### --------------------------------------------------------------------------------
### TRAIN ###
### --------------------------------------------------------------------------------
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_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
#########################################################################################################################
## train
#########################################################################################################################
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator_training)
except StopIteration:
print('recurrence data loader')
dataiterator_training = iter(dataloader_training)
input_data = next(dataiterator_training)
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['gt_label'],
inner_iter)
loss = net_outputs['gt_label']['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, backbone_lr)
if (step + 1) % cfg.SAVE_MODEL_ITER == 0:
save_ckpt(output_dir, args, step, batch_size, maskRCNN,
optimizer, False, best_total_loss)
# ---- Training ends ----
save_ckpt(output_dir, args, step, batch_size, maskRCNN, optimizer,
False, best_total_loss)
except (RuntimeError, KeyboardInterrupt):
del dataiterator_training
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, batch_size, maskRCNN, optimizer,
False, best_total_loss)
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 == "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()