def main():
args = parser()
update_config(args.cfg)
if args.set_cfg_list:
update_config_from_list(args.set_cfg_list)
context = [mx.gpu(int(gpu)) for gpu in config.gpus.split(',')]
if not os.path.isdir(config.output_path):
os.mkdir(config.output_path)
# Create roidb
roidb, imdb = load_proposal_roidb(config.dataset.dataset, config.dataset.test_image_set, config.dataset.root_path,
config.dataset.dataset_path,
proposal=config.dataset.proposal, only_gt=True, flip=False,
result_path=config.output_path,
proposal_path=config.proposal_path, get_imdb=True)
# Creating the Logger
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
print(output_path)
model_prefix = os.path.join(output_path, args.save_prefix)
arg_params, aux_params = load_param(model_prefix, config.TEST.TEST_EPOCH,
convert=True, process=True)
sym_inst = eval('{}.{}'.format(config.symbol, config.symbol))
if config.TEST.EXTRACT_PROPOSALS:
imdb_proposal_extraction_wrapper(sym_inst, config, imdb, roidb, context, arg_params, aux_params, args.vis)
else:
imdb_detection_wrapper(sym_inst, config, imdb, roidb, context, arg_params, aux_params, args.vis)
def main():
args = parser()
update_config(args.cfg)
if args.set_cfg_list:
update_config_from_list(args.set_cfg_list)
if not os.path.isdir(config.output_path):
os.mkdir(config.output_path)
# Create roidb
#print(config)
roidb, imdb = load_proposal_roidb(config.dataset.dataset, config.dataset.test_image_set, config.dataset.root_path,
config.dataset.dataset_path,
proposal=config.dataset.proposal, only_gt=True, flip=False,
result_path=config.output_path,
proposal_path=config.proposal_path, get_imdb=True)
#roidb = roidb[:100]
#check_point = torch.load('output/faster_rcnn_1220_0_19000.pth')
load_name = 'output/nofix_3_15000.pth'
#load_name = 'output/faster_rcnn_jwyang.pth'
# faster-rcnn
fasterRCNN = resnet(config.dataset.NUM_CLASSES, 101, pretrained=True, class_agnostic=config.CLASS_AGNOSTIC)
# init weight
fasterRCNN.create_architecture()
print("load checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['model'].items():
if k[0:6] == 'module':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
fasterRCNN.load_state_dict(new_state_dict)
fasterRCNN.cuda()
fasterRCNN = nn.DataParallel(fasterRCNN)
fasterRCNN.eval()
if config.TEST.EXTRACT_PROPOSALS:
imdb_proposal_extraction_wrapper(sym_inst, config, imdb, roidb, context, arg_params, aux_params, args.vis)
else:
imdb_detection_wrapper(fasterRCNN, config, imdb, roidb)
if args.set_cfg_list:
update_config_from_list(args.set_cfg_list)
context = [mx.gpu(int(gpu)) for gpu in config.gpus.split(',')]
nGPUs = len(context)
batch_size = nGPUs * config.TRAIN.BATCH_IMAGES
print("batch size is", batch_size)
if not os.path.isdir(config.output_path):
os.mkdir(config.output_path)
# Create roidb
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [load_proposal_roidb(config.dataset.dataset, image_set, config.dataset.root_path,
config.dataset.dataset_path,
proposal=config.dataset.proposal, append_gt=True, flip=config.TRAIN.FLIP,
result_path=config.output_path,
proposal_path=config.proposal_path, load_mask=config.TRAIN.WITH_MASK, only_gt=not config.TRAIN.USE_NEG_CHIPS)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
bbox_means, bbox_stds = add_bbox_regression_targets(roidb, config)
print('Creating Iterator with {} Images'.format(len(roidb)))
train_iter = MNIteratorE2E(roidb=roidb, config=config, batch_size=batch_size, nGPUs=nGPUs,
threads=config.TRAIN.NUM_THREAD, pad_rois_to=400) #, crop_size=(config.TRAIN.SCALES[-1],config.TRAIN.SCALES[-1]))
print('The Iterator has {} samples!'.format(len(train_iter)))
if __name__ == '__main__':
args = parser()
update_config(args.cfg)
context = [mx.gpu(int(gpu)) for gpu in config.gpus.split(',')]
nGPUs = len(context)
batch_size = nGPUs * config.TRAIN.BATCH_IMAGES
if not os.path.isdir(config.output_path):
os.mkdir(config.output_path)
# Create roidb
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [load_proposal_roidb(config.dataset.dataset, image_set, config.dataset.root_path,
config.dataset.dataset_path,
proposal=config.dataset.proposal, append_gt=True, flip=config.TRAIN.FLIP,
result_path=config.output_path,
proposal_path=config.proposal_path, load_mask=config.TRAIN.WITH_MASK)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
bbox_means, bbox_stds = add_bbox_regression_targets(roidb, config)
print('Creating Iterator with {} Images'.format(len(roidb)))
train_iter = MNIteratorE2E(roidb=roidb, config=config, batch_size=batch_size, nGPUs=nGPUs,
threads=config.TRAIN.NUM_THREAD, pad_rois_to=400)
print('The Iterator has {} samples!'.format(len(train_iter)))
