def read_models_from_file(model_path, heirarchy, args):
"""
initializes models and reads them from file
"""
encoder, binarizer, decoder, unet = util.get_models(
args=args, v_compress=args.v_compress,
bits=args.bits,
encoder_fuse_level=args.encoder_fuse_level,
decoder_fuse_level=args.decoder_fuse_level)
d2 = network.DecoderCell2(v_compress=args.v_compress, shrink=args.shrink,bits=args.bits,fuse_level=args.decoder_fuse_level, itrs=args.iterations).cuda()
print(d2)
nets = [d2]
if unet is not None:
nets.append(unet)
names = ['unet', 'd2']
for net_idx, net in enumerate(nets):
if net is not None:
name = names[net_idx]
checkpoint_path = '{}/{}_{}_{:08d}.pth'.format(
model_path + "/h"+ str(heirarchy), 'demo',
name, 100000)
print('Loading %s from %s...' % (name, checkpoint_path))
net.load_state_dict(torch.load(checkpoint_path))
return nets
args=args),
}
return eval_loaders
############### Model ###############
encoder, binarizer, decoder, unet = get_models(
args=args,
v_compress=args.v_compress,
bits=args.bits,
encoder_fuse_level=args.encoder_fuse_level,
decoder_fuse_level=args.decoder_fuse_level)
d2 = network.DecoderCell2(v_compress=args.v_compress,
shrink=args.shrink,
bits=args.bits,
fuse_level=args.decoder_fuse_level,
itrs=args.iterations).cuda()
nets = [encoder, binarizer, decoder, d2]
if unet is not None:
nets.append(unet)
print(nets)
gpus = [int(gpu) for gpu in args.gpus.split(',')]
if len(gpus) > 1:
print("Using GPUs {}.".format(gpus))
for net in nets:
net = nn.DataParallel(net, device_ids=gpus)