def initialize_model_from_cfg(args, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
set to evaluation mode.
"""
model = model_builder.Generalized_RCNN()
model.eval()
if args.cuda:
model.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
logger.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(model, checkpoint['model'])
if args.load_detectron:
logger.info("loading detectron weights %s", args.load_detectron)
load_detectron_weight(model, args.load_detectron)
model = mynn.DataParallel(model,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
return model
def initialize_model_from_cfg(args, gpu_id=0):
'''
Initialize a model from the global cfg. Loads test-time weights and
set to evaluation mode.
'''
model = model_builder.Generalized_RCNN()
model.eval()
# pdb.set_trace()
if args.cuda:
model.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
logger.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
#pdb.set_trace() # checkpoint['model'].keys()
net_utils.load_ckpt(model, checkpoint['model'])
if args.load_detectron:
logger.info("loading detectron weights %s", args.load_detectron)
load_detectron_weight(model, args.load_detectron)
# for key, vaule in model.named_parameters(): # LJ output required_grad parameter
# pdb.set_trace()
# print(key)
model = mynn.DataParallel(model,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
return model
def initialize_model_from_cfg(args, roidb=None, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
set to evaluation mode.
"""
model = model_builder.Generalized_RCNN()
model.eval()
cfg.immutable(False)
cfg.TEST.CLASS_SPLIT = {'source': roidb[0]['source'], 'target': roidb[0]['target']}
cfg.immutable(True)
if 'word_embeddings' in roidb[0]:
model.Box_Outs.set_word_embedding(torch.tensor(roidb[0]['word_embeddings']))
if cfg.MODEL.IGNORE_CLASSES:
if cfg.MODEL.IGNORE_CLASSES == 'all':
roidb[0]['all'] = roidb[0]['source'] + roidb[0]['target']
model._ignore_classes = roidb[0][cfg.MODEL.IGNORE_CLASSES]
model.Box_Outs._ignore_classes = roidb[0][cfg.MODEL.IGNORE_CLASSES]
if True:
tmp = {}
for rel in roidb[0]['relationships']:
tmp[(rel['subject_id'], rel['object_id'])] = \
tmp.get((rel['subject_id'], rel['object_id']), []) + [rel['rel_id']]
if cfg.MODEL.RELATION_COOCCUR:
for k in tmp:
tmp[k] = [1]
if cfg.MODEL.NUM_RELATIONS > 0:
model.Rel_Outs.relationship_dict = tmp
if args.cuda:
model.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
logger.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(model, checkpoint['model'])
if args.load_detectron:
logger.info("loading detectron weights %s", args.load_detectron)
load_detectron_weight(model, args.load_detectron)
model = mynn.DataParallel(model, cpu_keywords=['im_info', 'roidb'], minibatch=True)
return model
def reload_ckpt(load_ckpt):
#checkpoint = torch.load(load_ckpt, map_location=lambda storage, loc: storage)
#net_utils.load_ckpt(model, checkpoint['model'])
model = model_builder.Generalized_RCNN()
model.eval()
model.cuda()
load_name = load_ckpt
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(model, checkpoint['model'])
model = mynn.DataParallel(model,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
print("load checkpoint: {}".format(load_ckpt))
return model
def initialize_model_from_cfg(args, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
set to evaluation mode.
"""
model = model_builder.Generalized_RCNN()
model.eval()
# model.train()
if args.cuda:
model.cuda()
if cfg.PRM_ON:
model = PeakResponseMapping_3d(model)
model.inference()
if args.load_ckpt:
load_name = args.load_ckpt
logger.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
# net_utils.load_ckpt(model, checkpoint['model'])
if cfg.MODEL.RPN_ONLY:
model_dic = model.state_dict()
pretrained_dict = {
k: v
for k, v in checkpoint['model'].items() if k in model_dic
}
model_dic.update(pretrained_dict)
model.load_state_dict(model_dic)
else:
model.load_state_dict(checkpoint['model'])
if args.load_detectron:
logger.info("loading detectron weights %s", args.load_detectron)
load_detectron_weight(model, args.load_detectron)
model = mynn.DataParallel(model,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
return model
