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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset == "miotcd":
dataset = datasets.get_miotcd_dataset()
cfg.MODEL.NUM_CLASSES = 12
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset.startswith("bogota"):
dataset = datasets.get_bogota_dataset()
cfg.MODEL.NUM_CLASSES = 12
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True, device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dataset_result = {}
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
try:
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN, im, timers=timers)
boxes_, segme_ , keyps_ ,clasies= convert_from_cls_format(cls_boxes,cls_segms,cls_keyps)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
dataset_result[im_name] = localize_obj_in_image(im_name,boxes_,clasies)
except(e):
import pdb
pdb.set_trace()
np.save('dictionary_answer.npy', dataset_result)
tmp = args.image_dir.split('/')
txt = str(tmp[-2:])+'_'+str(tmp[-1:])+'.csv'
save_localization_result(dataset_result,txt)
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_from_file(args.cfg_file)
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 == "monuseg2018":
cfg.TRAIN.DATASETS = ('monuseg_2018_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "monuseg_all":
cfg.TRAIN.DATASETS = ('monuseg_all_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_all_gan":
cfg.TRAIN.DATASETS = ('monuseg_all_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_baseline":
cfg.TRAIN.DATASETS = ('monuseg_baseline_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 6000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_baseline_best":
cfg.TRAIN.DATASETS = ('monuseg_baseline_best_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_baseline_best_gan":
cfg.TRAIN.DATASETS = ('monuseg_baseline_best_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_0":
cfg.TRAIN.DATASETS = ('monuseg_0_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_1":
cfg.TRAIN.DATASETS = ('monuseg_1_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_2":
cfg.TRAIN.DATASETS = ('monuseg_2_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_3":
cfg.TRAIN.DATASETS = ('monuseg_3_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_4":
cfg.TRAIN.DATASETS = ('monuseg_4_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_5":
cfg.TRAIN.DATASETS = ('monuseg_5_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_6":
cfg.TRAIN.DATASETS = ('monuseg_6_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_0_gan":
cfg.TRAIN.DATASETS = ('monuseg_0_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_1_gan":
cfg.TRAIN.DATASETS = ('monuseg_1_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_2_gan":
cfg.TRAIN.DATASETS = ('monuseg_2_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_3_gan":
cfg.TRAIN.DATASETS = ('monuseg_3_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_4_gan":
cfg.TRAIN.DATASETS = ('monuseg_4_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_5_gan":
cfg.TRAIN.DATASETS = ('monuseg_5_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_6_gan":
cfg.TRAIN.DATASETS = ('monuseg_6_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_all":
cfg.TRAIN.DATASETS = ('BNS_all_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_all_gan":
cfg.TRAIN.DATASETS = ('BNS_all_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_0":
cfg.TRAIN.DATASETS = ('BNS_0_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_1":
cfg.TRAIN.DATASETS = ('BNS_1_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_2":
cfg.TRAIN.DATASETS = ('BNS_2_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_3":
cfg.TRAIN.DATASETS = ('BNS_3_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_4":
cfg.TRAIN.DATASETS = ('BNS_4_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_5":
cfg.TRAIN.DATASETS = ('BNS_5_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_6":
cfg.TRAIN.DATASETS = ('BNS_6_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_0_gan":
cfg.TRAIN.DATASETS = ('BNS_0_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_1_gan":
cfg.TRAIN.DATASETS = ('BNS_1_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_2_gan":
cfg.TRAIN.DATASETS = ('BNS_2_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_3_gan":
cfg.TRAIN.DATASETS = ('BNS_3_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_4_gan":
cfg.TRAIN.DATASETS = ('BNS_4_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_5_gan":
cfg.TRAIN.DATASETS = ('BNS_5_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "BNS_6_gan":
cfg.TRAIN.DATASETS = ('BNS_6_train_gan', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "TNBC":
cfg.TRAIN.DATASETS = ('TNBC_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.FPN.RPN_ANCHOR_START_SIZE = 8
cfg.SOLVER.MAX_ITER = 3000
cfg.TRAIN.USE_FLIPPED = False
elif args.dataset == "monuseg_baseline_default":
cfg.TRAIN.DATASETS = ('monuseg_baseline_train', )
cfg.MODEL.NUM_CLASSES = 2
cfg.SOLVER.MAX_ITER = 3000
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)
print('------------------------------------')
#print (roidb[0])
print(len(roidb))
print(cfg.TRAIN.DATASETS)
print(cfg.TRAIN.PROPOSAL_FILES)
imggg = cv2.imread(roidb[0]['image'])
for iii in roidb[0]['boxes']:
xmin = iii[0]
ymin = iii[1]
xmax = iii[2]
ymax = iii[3]
imggg = cv2.rectangle(imggg, (xmin, ymin), (xmax, ymax), (0, 255, 0),
3)
cv2.imwrite('aaaaaa.png', imggg)
print('------------------------------------')
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)
############
output_dir = output_dir + '_' + args.dataset
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"""
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
print(count_parameters(maskRCNN))
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
img = np.zeros(list(im.shape), dtype=np.uint8)
img.fill(255)
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
vis_utils.vis_one_image(
img[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
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()
# load config
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
cfg_from_file('configs/baselines/e2e_mask_rcnn_R-50-C4_1x.yaml')
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
# load model
maskRCNN = Generalized_RCNN()
checkpoint = torch.load(
'/home/work/liupeng11/code/Detectron.pytorch/models/e2e_mask_rcnn_R-50-C4_1x.pth',
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
maskRCNN.eval()
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0])
# load image
img_path = "/home/work/liupeng11/code/Detectron.pytorch/demo/sample_images/img1.jpg"
im = cv2.imread(img_path)
# detect bouding boxes and segments
from core.test import im_detect_bbox, im_detect_mask, box_results_with_nms_and_limit, segm_results
scores, boxes, im_scale, blob_conv = im_detect_bbox(maskRCNN, im,
cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE, None)
scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
masks = im_detect_mask(maskRCNN, im_scale, boxes, blob_conv)
cls_segms = segm_results(cls_boxes, masks, boxes, im.shape[0], im.shape[1])
cls_keyps = None
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 == "vrd":
cfg.TRAIN.DATASETS = ('vrd_train',)
cfg.MODEL.NUM_CLASSES = 101
cfg.MODEL.NUM_PRD_CLASSES = 70 # exclude background
elif args.dataset == "vg_mini":
cfg.TRAIN.DATASETS = ('vg_train_mini',)
cfg.MODEL.NUM_CLASSES = 151
cfg.MODEL.NUM_PRD_CLASSES = 50 # 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 == "oi_rel":
cfg.TRAIN.DATASETS = ('oi_rel_train',)
# cfg.MODEL.NUM_CLASSES = 62
cfg.MODEL.NUM_CLASSES = 58
cfg.MODEL.NUM_PRD_CLASSES = 9 # rel, exclude background
elif args.dataset == "oi_rel_mini":
cfg.TRAIN.DATASETS = ('oi_rel_train_mini',)
# cfg.MODEL.NUM_CLASSES = 62
cfg.MODEL.NUM_CLASSES = 58
cfg.MODEL.NUM_PRD_CLASSES = 9 # rel, 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 ###
cfg.SOLVER.BASE_LR = 0.0033
cfg.SOLVER.GAMMA = 0.33
if args.dataset == "vrd":
cfg.SOLVER.STEPS = [1000,3000,8000,14000]
cfg.SOLVER.MAX_ITER = 22680
cfg.MODEL.STAGE_TWO = True
cfg.TRAIN.FG_REL_SIZE_PER_IM = 128
cfg.TRAIN.FG_REL_FRACTION = 0.5
if args.dataset == "vg":
cfg.SOLVER.STEPS = [0,90000,120000]
cfg.SOLVER.MAX_ITER = 125446
cfg.MODEL.STAGE_TWO = True
cfg.TRAIN.FG_REL_SIZE_PER_IM = 256
cfg.TRAIN.FG_REL_FRACTION = 0.5
#cfg.MODEL.FEATLOSS_WEIGHT = 0.05
cfg.NUM_GPUS = torch.cuda.device_count()
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)
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 ###
# 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_rel.load_ckpt_rel(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.
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 = cfg.SOLVER.MAX_ITER / 8
# 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
superview = []
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_rel.update_learning_rate_rel(optimizer, lr, lr_new)
# lr = optimizer.param_groups[0]['lr']
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_rel.update_learning_rate_rel(optimizer, lr, cfg.SOLVER.BASE_LR)
# lr = optimizer.param_groups[0]['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_rel.update_learning_rate_rel(optimizer, lr, lr_new)
# lr = optimizer.param_groups[0]['lr']
lr = optimizer.param_groups[2]['lr']
backbone_lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
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)
optimizer.zero_grad()
training_stats.UpdateIterStats(net_outputs, step, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
if step == 20000:
cfg.immutable(False)
cfg.MODEL.MEMORY_SAVE_UPDATE = True
cfg.immutable(True)
if step == 40000:
cfg.immutable(False)
cfg.MODEL.MEMORY_TRAIN_UPDATE = True
cfg.immutable(True)
class_count = maskRCNN.module.RelDN.class_count.clone()
class_count[class_count==0] = 1
memory_ini = maskRCNN.module.RelDN.memory_save / class_count.float().unsqueeze(1)
maskRCNN.module.RelDN.memory_train.data = memory_ini.clone()
np.save('memory_ini.npy',memory_ini.cpu().numpy())
maskRCNN.module.RelDN.mix_scores.fc_hallucinator.load_state_dict(maskRCNN.module.RelDN.mix_scores.linear_classifier.state_dict())
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, backbone_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)
np.save('result{}.npy'.format(step),superview)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
np.save('result{}.npy'.format(step),superview)
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
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
break
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"""
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
prefix_path = args.output_dir + '_results'
if os.path.exists(prefix_path):
shutil.rmtree(prefix_path)
os.mkdir(prefix_path)
else:
os.mkdir(prefix_path)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
for i in tqdm(range(num_images)):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
boxes, _, _, classes = convert_from_cls_format(cls_boxes, cls_segms,
cls_keyps)
if classes == []:
continue
voc_boxes = np.zeros_like(boxes)
voc_boxes[:, 0:1] = boxes[:, 4:5]
voc_boxes[:, 1:3] = boxes[:, 0:2] + 1
voc_boxes[:, 3:5] = boxes[:, 2:4] + 1
for instance_idx, cls_idx in enumerate(classes):
cls_name = dataset.classes[cls_idx]
if cls_name == 'motorcycle':
cls_name = 'motorbike'
f = open(os.path.join(prefix_path, cls_name + ".txt"), "a+")
f.write("%s " % im_name)
for item in voc_boxes[instance_idx]:
f.write("%f " % item)
f.write("\n")
f.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"""
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)
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
#Do not use RPN.
#cfg.MODEL.FASTER_RCNN = False
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
train_db = davis_db.DAVIS_imdb(split='val')
for seq_idx in range(train_db.get_num_sequence()):
train_db.set_to_sequence(seq_idx)
seq_name = train_db.get_current_seq_name()
save_dir = osp.join(args.output_dir, seq_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
else:
merge_out_path = '{}/results.pdf'.format(save_dir)
if osp.exists(merge_out_path):
continue
for idx in range(train_db.get_current_seq_length()):
im_name = '%02d.pdf' % (idx)
print(osp.join(save_dir, im_name))
if osp.exists(osp.join(save_dir, im_name)):
continue
im = train_db.get_image_cv2(idx)
boxes = train_db.get_bboxes(idx)
new_boxes = []
for bbox in boxes:
new_box = []
new_box.extend(bbox)
new_box[2] = new_box[0] + new_box[2]
new_box[3] = new_box[1] + new_box[3]
new_boxes.append(new_box)
boxes = np.array(new_boxes, np.float32)
print(boxes.shape)
if boxes.shape[0] > 0:
device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
#boxes = torch.tensor(boxes,device = device)
maskRCNN_predictor_with_boxes = Generalized_RCNN_Predictor_with_Boxes(
)
if args.cuda:
maskRCNN_predictor_with_boxes.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(
load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN_predictor_with_boxes,
checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN_predictor_with_boxes,
args.load_detectron)
maskRCNN_predictor_with_boxes = mynn.DataParallel(
maskRCNN_predictor_with_boxes,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN_predictor_with_boxes.eval()
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(
maskRCNN_predictor_with_boxes,
im,
timers=timers,
box_proposals=boxes)
else:
cls_boxes = None
cls_segms = None
cls_keyps = None
im_name = '%02d' % (idx)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
save_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.01,
kp_thresh=2)
if args.merge_pdfs:
merge_out_path = '{}/results.pdf'.format(save_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(save_dir, merge_out_path)
subprocess.call(command, shell=True)
correct_state_dict = {k:tmp_state_dict['module.'+k] for k in fasterRCNN.state_dict()}
fasterRCNN.load_state_dict(correct_state_dict)
# fasterRCNN.load_state_dict(checkpoint['model'])
if 'pooling_mode' in checkpoint.keys():
cfg.POOLING_MODE = checkpoint['pooling_mode']
print('load model successfully!')
if args.cuda:
cfg.CUDA = True
if args.cuda:
fasterRCNN.cuda()
fasterRCNN = mynn.DataParallel(fasterRCNN, minibatch=True)
start = time.time()
max_per_image = 100
vis = args.vis
if vis:
thresh = 0.05
else:
thresh = 0.0
save_name = 'faster_rcnn_{}_{}_{}'.format(args.checksession, args.checkepoch, args.checkpoint)
num_images = len(imdb.image_index)
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset.startswith("gangjin"):
dataset = datasets.get_gangjin_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
img_ids = []
rects = []
for i in range(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
cls_boxes, cls_segms, cls_keyps)
if boxes is not None:
for j in range(len(boxes)):
# print(boxes[j][-1])
if float(boxes[j][-1]) < 0.99: # 阀值
continue
xmin = float(boxes[j, 0])
xmax = float(boxes[j, 2])
ymin = float(boxes[j, 1])
ymax = float(boxes[j, 3])
img_ids.append(os.path.basename(imglist[i]))
rects.append(
str(xmin) + " " + str(ymin) + " " + str(xmax) + " " +
str(ymax))
# im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# vis_utils.vis_one_image(
# im[:, :, ::-1], # BGR -> RGB for visualization
# im_name,
# args.output_dir,
# cls_boxes,
# cls_segms,
# cls_keyps,
# dataset=dataset,
# box_alpha=0.3,
# show_class=False,
# thresh=0.99,
# kp_thresh=2,
# ext="jpg"
# )
result_dict = {"ID": img_ids, "rects": rects}
import pandas as pd
result = pd.DataFrame.from_dict(result_dict)
result.to_csv('submit/submit1.csv', header=None, index=False)
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
prefix_path = args.output_dir
os.makedirs(prefix_path, exist_ok=True)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
writen_results = []
# validate
demo_im = cv2.imread(imglist[0])
print(np.shape(demo_im))
h, w, _ = np.shape(demo_im)
#print(h)
#print(args.height)
assert h == args.height
assert w == args.width
h_scale = 720 / args.height
w_scale = 1280 / args.width
for i in tqdm(range(num_images)):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# boxs = [[x1, y1, x2, y2, cls], ...]
boxes, _, _, classes = convert_from_cls_format(cls_boxes, cls_segms,
cls_keyps)
if boxes is None:
continue
# scale
boxes[:, 0] = boxes[:, 0] * w_scale
boxes[:, 2] = boxes[:, 2] * w_scale
boxes[:, 1] = boxes[:, 1] * h_scale
boxes[:, 3] = boxes[:, 3] * h_scale
if classes == []:
continue
for instance_idx, cls_idx in enumerate(classes):
cls_name = dataset.classes[cls_idx]
if cls_name == 'motorcycle':
cls_name = 'motor'
elif cls_name == 'stop sign':
cls_name = 'traffic sign'
elif cls_name == 'bicycle':
cls_name = 'bike'
if cls_name not in bdd_category:
continue
writen_results.append({
"name": imglist[i].split('/')[-1],
"timestamp": 1000,
"category": cls_name,
"bbox": boxes[instance_idx, :4],
"score": boxes[instance_idx, -1]
})
with open(os.path.join(prefix_path, args.name + '.json'),
'w') as outputfile:
json.dump(writen_results, outputfile, cls=MyEncoder)
from __future__ import absolute_import
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
dataset = datasets.get_hospital_dataset()
cfg.MODEL.NUM_CLASSES = 20 # with bg
num_class = cfg.MODEL.NUM_CLASSES
sents = dataset.sents
th_cls = dataset.th_cls
cls2eng = dataset.cls2eng
eng2type = dataset.eng2type
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images): # for each image
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
# segmentation
# d = segment(im)
# pdb.set_trace()
timers = defaultdict(Timer)
# detection
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
# first we collect boxes from all classes
dets_total = np.empty([0, 6], dtype=np.float32)
for cls in range(1, num_class): # for each cls
dets = cls_boxes[cls]
if dets.shape[0] == 0:
continue
dets_extend = np.pad(
dets,
((0, 0),
(0, 1)), # add 0 rows above, below and left, but 1 row right
mode='constant',
constant_values=cls) # append cls to dets
dets_total = np.vstack((dets_total, dets_extend))
# then use a loose NMS to make each region has only one symptom
keep = box_utils.nms(dets_total, 0.7)
nms_dets = dets_total[keep, :]
# iterate through remained boxes
report, healthy = '', True
have_sym_of_cls = [False for _ in range(num_class)]
n = nms_dets.shape[0]
final_results = [] # return to the web
for idx in range(n): # for each region
th, cls = nms_dets[idx, -2], int(nms_dets[idx, -1])
if th > th_cls[cls]: # diagnosed to have the sym
report += sents[cls][1]
have_sym_of_cls[cls] = True
healthy = False
ename = cls2eng[int(cls)]
_type = eng2type[ename]
final_results.append({
'name': ename,
'type': _type,
'box': list(nms_dets[idx, 0:4])
})
for cls in range(1, num_class): # for each cls
if not have_sym_of_cls[cls]: # if have no sym of this cls
report += sents[cls][0]
if healthy:
report = sents[0][0]
print(report)
pdb.set_trace()
# healthy = True # flag indicating healthy or not
# for cls in range(1, num_class): # for each cls
# dets = cls_boxes[cls]
# if dets.shape[0] == 0:
# report += sents[cls][0]
# continue
# n = dets.shape[0]
# flag = False # indicates if the sym exists
# for k in range(n): # for each region
# if dets[k, -1] > th_cls[cls]: # above threshold for this cls, means have this cls of symptom
# report += sents[cls][1]
# flag = True
# healthy = False
# if not flag: # don't have this symptom
# report += sents[cls][0]
#
# if healthy: # use the report for healthy people
# report = sents[0][0]
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# vis_utils.vis_one_image(
# im[:, :, ::-1], # BGR -> RGB for visualization
# im_name,
# args.output_dir,
# cls_boxes,
# cls_segms,
# cls_keyps,
# dataset=dataset,
# box_alpha=0.3,
# show_class=True,
# thresh=0.05,
# kp_thresh=2
# )
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
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()
cfg_file = 'configs/baselines/e2e_pspnet-50_2x.yaml'
cfg_from_file(cfg_file)
#cfg_from_list(cfg_file)
assert_and_infer_cfg()
devices_ids = [1, 2]
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
[str(ids) for ids in devices_ids])
torch.backends.cudnn.benchmark = True
#torch.cuda.set_device(3)
len_gpus = len(devices_ids)
batch_size = 2 * len_gpus
#net = mynn.DataParallel(load_net().to('cuda'), minibatch=True)
net = mynn.DataParallel(Generalized_SEGDISP().to('cuda'), minibatch=True)
optimizer = optim.SGD(net.parameters(), lr=0.000875, momentum=0.9)
criterion = nn.NLLLoss(ignore_index=255)
dataloader = dataloader(batch_size, len_gpus)
#for i in range(10):
for i, inputs in zip(range(1000), dataloader):
#data, label= dataloader(batch_size, len_gpus)
#data = Variable(data).to('cuda')
#data = torch.chunk(data, chunks=len_gpus, dim=0)
#label = Variable(label).to('cuda')
#assert torch.all(data >= 0) and torch.all(data < 19), 'label is error'
for key in inputs:
inputs[key] = list(map(lambda x: Variable(x).to('cuda'), inputs[key]))
data, label = inputs['data'], inputs['semseg_label_0']
#cv2.imwrite('ims.png', data[0].cpu().numpy()[0].transpose(1,2,0)[:,:,::-1])
label = torch.cat(label, 0)
def main():
"""main function"""
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
params = list(maskRCNN.parameters())
k = 0
for i in params:
l = 1
for j in i.size():
l *= j
k = k + l
print('zonghe:' + str(k))
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
start = time.time()
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
class_result_boxes = []
for index, class_boxes in enumerate(cls_boxes):
if len(class_boxes) != 0:
class_boxes = class_boxes.tolist()
results_oneclass = threeD_detect(imglist[i], class_boxes,
index)
class_result_boxes.append(results_oneclass)
save_image = im
color_class = {
'Car': [0, 255, 255],
'Cyclist': [255, 0, 0],
'Pedestrian': [0, 0, 255]
}
for result_boxes in class_result_boxes:
for box in result_boxes:
cv2.rectangle(save_image, (box[0], box[1]), (box[2], box[3]),
color_class[box[-1]], 2)
height = round(box[-2][0], 2)
width = round(box[-2][1], 2)
length = round(box[-2][2], 2)
threeD_info = str(height) + ' ' + str(width) + ' ' + str(
length)
cv2.putText(save_image, threeD_info, (box[0], box[1] - 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2)
_, imagename = os.path.split(imglist[i])
imagename2 = imagename.split('.')[0]
cv2.imwrite('../output1/%s.png' % imagename2, save_image)
end = time.time()
print(end - start)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
return image
cfg_file = 'configs/baselines/e2e_pspnet-101_2x.yaml'
cfg_from_file(cfg_file)
devices_ids = [5]
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
[str(ids) for ids in devices_ids])
pretrained_model = './output/pspnet50_2gpu_single_scale/Oct20-12-41-16_localhost.localdomain/ckpt/model_17_1486.pth'
checkpoint = torch.load(pretrained_model)
net = Generalized_SEMSEG()
net.load_state_dict(checkpoint['model'])
net = mynn.DataParallel(net.to('cuda'), minibatch=True)
net.eval()
# params = net.state_dict() #查看权重是否导入
# params_ckpt = checkpoint['model']
# a = list(params.values())
# b = list(params_ckpt.values())
# keys = list(params.keys())
# print(is_equal(a, b, keys))
len_gpus = len(devices_ids)
batch_size = 1 * len_gpus
dataloader = dataloader(batch_size, len_gpus)
loader = transforms.Compose([
transforms.ToTensor(),
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_trainval":
cfg.TRAIN.DATASETS = ('cityscapes_trainval', )
cfg.MODEL.NUM_CLASSES = 19
elif args.dataset == "cityscapes_all":
cfg.TRAIN.DATASETS = ('cityscapes_all', )
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 if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON else collate_minibatch,
# drop_last=False,
# shuffle=True) # when load image will be shuffle in each epoch
## Dataset and Loader
#dataset_train = TrainDataset(
# args.list_train, args, batch_per_gpu=args.batch_size_per_gpu)
#args.epoch_iters=dataset_train.num_sample//(args.num_gpus*args.batch_size_per_gpu)
dataloader = torchdata.DataLoader(
dataset,
batch_size=args.batch_size, # we have modified data_parallel
collate_fn=collate_minibatch_semseg,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
drop_last=True,
shuffle=True,
maxsize=cfg.TRAIN.IMS_PER_BATCH * 2)
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 'deform_conv' in cfg.MODEL.CONV_BODY or 'deeplab' in cfg.SEM.DECODER_TYPE:
elif 'deeplab' in cfg.SEM.DECODER_TYPE and 'uber' in cfg.SEM.PSPNET_PRETRAINED_WEIGHTS:
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=False)
else:
maskRCNN.load_state_dict(pretrained, strict=False)
print("weights load success")
if cfg.SEM.SPN_ON:
maskRCNN.pspnet.eval()
for p in maskRCNN.pspnet.parameters():
p.requires_grad = False
if cfg.CUDA:
maskRCNN.to('cuda')
#print(maskRCNN)
### 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.
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 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"""
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)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset == "pascal_parts_heads":
dataset = datasets.get_head_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "scuthead_a":
dataset = datasets.get_head_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("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.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
outputfile = os.path.join(args.output_dir, im_name)
head_boxes = cls_boxes[1]
print('img :', i, ' num_heads :', len(head_boxes), ' img_path :',
imglist[i])
np.save(outputfile, head_boxes)
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 = 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
warmup_factor_trans = 1.0
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)
if not cfg.TRAIN.HOMOSCEDASTIC:
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 = 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()
dataset,
batch_size=bs,
sampler=sampler,
num_workers=gpus,
collate_fn=collate_minibatch_semseg)
return dataloader
# return torch.randn(bs*gpus, 3, 720, 720), \
# torch.LongTensor(np.random.randint(0, 19, (bs*gpus, 90, 90), dtype=np.long))
devices_ids=[3,4]
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
torch.backends.cudnn.benchmark=True
#torch.cuda.set_device(3)
len_gpus = len(devices_ids)
batch_size = 2 * len_gpus
net = mynn.DataParallel(load_net().to('cuda'), minibatch=True)
optimizer = optim.SGD(net.parameters(), lr=0.000875, momentum=0.9)
criterion = nn.NLLLoss(ignore_index=255)
dataloader= dataloader(batch_size, len_gpus)
#for i in range(10):
for i, inputs in zip(range(1000), dataloader):
#data, label= dataloader(batch_size, len_gpus)
#data = Variable(data).to('cuda')
#data = torch.chunk(data, chunks=len_gpus, dim=0)
#label = Variable(label).to('cuda')
#assert torch.all(data >= 0) and torch.all(data < 19), 'label is error'
for key in inputs:
inputs[key] = list(map(lambda x:Variable(x).to('cuda'), inputs[key]))
data, label = inputs['data'], inputs['semseg_label_0']
print(i)
#cv2.imwrite('ims.png', data[0].cpu().numpy()[0].transpose(1,2,0)[:,:,::-1])
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 == "fis_cell":
cfg.TRAIN.DATASETS = ('fis_cell_train_val',)
cfg.MODEL.NUM_CLASSES = 2
elif 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))
if args.close_fpn:
args.cfg_file = "configs/few_shot/e2e_mask_rcnn_R-50-C4_1x_{}.yaml".format(args.group)
cfg.OUTPUT_DIR = 'Outputs_wo_fpn'
else:
args.cfg_file = "configs/few_shot/e2e_mask_rcnn_R-50-FPN_1x_{}.yaml".format(args.group)
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.RNG_SEED = args.random_seed
if cfg.RNG_SEED is None:
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
else:
print('Make the experiment results deterministic.')
random.seed(cfg.RNG_SEED)
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
torch.cuda.manual_seed_all(cfg.RNG_SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
cfg.SEEN = args.seen
if args.close_co_atten:
cfg.CO_ATTEN = False
if not args.close_fpn:
cfg.OUTPUT_DIR = 'Outputs_wo_co_atten'
if args.close_relation_rcnn:
cfg.RELATION_RCNN = False
if not args.close_fpn:
cfg.FAST_RCNN.ROI_BOX_HEAD = 'fast_rcnn_heads.roi_2mlp_head'
cfg.MRCNN.ROI_MASK_HEAD = 'mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs'
else:
cfg.FAST_RCNN.ROI_BOX_HEAD = 'torchResNet.ResNet_roi_conv5_head'
cfg.MRCNN.ROI_MASK_HEAD = 'mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare'
if not args.close_co_atten and not args.close_fpn:
cfg.OUTPUT_DIR = 'Outputs_wo_relation_rcnn'
if args.output_dir is not None:
cfg.OUTPUT_DIR = args.output_dir
if args.deform_conv:
cfg.MODEL.USE_DEFORM = True
### 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()
imdb, roidb, ratio_list, ratio_index, query, cat_list = combined_roidb(
cfg.TRAIN.DATASETS, True)
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, ratio_list, ratio_index, query,
cfg.MODEL.NUM_CLASSES,
training=True, cat_list=cat_list, shot=args.shot)
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.shot = checkpoint['shot']
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)
from loggers.logger import Logger
tblogger = Logger(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:
#for p in maskRCNN.module.Conv_Body.parameters():
# p.requires_grad = False
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:
#for p in maskRCNN.module.Conv_Body.parameters():
# p.requires_grad = True
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' and key != 'query': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
if key == 'query':
input_data[key] = [list(map(Variable, q)) for q in input_data[key]]
with torch.autograd.detect_anomaly():
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
torch.nn.utils.clip_grad_value_(maskRCNN.module.parameters(), clip_value=0.4)
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, input_data, args.shot)
if (step+1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
if (step+1) % args.disp_interval == 0:
log_training_stats(training_stats, step, lr)
# ---- 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():
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 !")
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 == "pascal_parts_heads":
cfg.TRAIN.DATASETS = ('pascal_parts_heads', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "scuthead_a":
cfg.TRAIN.DATASETS = ('scuthead_a', )
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
timers = defaultdict(Timer)
### Dataset ###
print("Checkpoint 0")
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
print("Checkpoint 1")
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
print(
str(iters_per_epoch) + '= int(' + str(train_size) + '/' +
str(args.batch_size) + ')')
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
print(
str(ckpt_interval_per_epoch) + '=' + str(iters_per_epoch) + '//' +
str(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 ----
print(args.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()
cfg_file = 'e2e_segdisp-R-50_3Dpool_1x.yaml'
cfg_from_file(cfg_file)
print(cfg.SEM)
print(cfg.DISP)
#cfg_from_list(cfg_file)
#assert_and_infer_cfg()
devices_ids = [5]
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
[str(ids) for ids in devices_ids])
torch.backends.cudnn.benchmark = True
#torch.cuda.set_device(3)
len_gpus = len(devices_ids)
batch_size = 2 * len_gpus
#net = mynn.DataParallel(load_net().to('cuda'), minibatch=True)
net = mynn.DataParallel(DispSeg().to('cuda'), minibatch=True)
optimizer = optim.SGD(net.parameters(), lr=0.000875, momentum=0.9)
criterion = nn.NLLLoss(ignore_index=255)
#dataloader= dataloader(batch_size, len_gpus)
for i in range(10):
#for i, inputs in zip(range(1000), dataloader):
inputs = dataloader(batch_size, len_gpus)
for key in inputs:
inputs[key] = torch.chunk(inputs[key], chunks=len_gpus, dim=0)
optimizer.zero_grad()
loss = net(**inputs)
optimizer.step()
for k in loss['losses'].keys():
print(loss['losses'][k].item())
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 !")
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()
