def test(config):
log_dir = os.path.join(config.log_dir, config.name + '_stage_2')
val_path = os.path.join(config.data, "*/test")
val_dataset = MultiviewImgDataset(val_path,
scale_aug=False,
rot_aug=False,
num_views=config.num_views)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.stage2_batch_size,
shuffle=False,
num_workers=0)
pretraining = not config.no_pretraining
cnet = SVCNN(config.name,
nclasses=config.num_classes,
cnn_name=config.cnn_name,
pretraining=pretraining)
cnet_2 = MVCNN(config.name,
cnet,
nclasses=config.num_classes,
cnn_name=config.cnn_name,
num_views=config.num_views)
cnet_2.load(
os.path.join(log_dir, config.snapshot_prefix + str(config.weights)))
optimizer = optim.Adam(cnet_2.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay,
betas=(0.9, 0.999))
trainer = ModelNetTrainer(cnet_2,
None,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
config,
log_dir,
num_views=config.num_views)
labels, predictions = trainer.update_validation_accuracy(config.weights,
test=True)
import Evaluation_tools as et
eval_file = os.path.join(config.log_dir, '{}.txt'.format(config.name))
et.write_eval_file(config.data, eval_file, predictions, labels,
config.name)
et.make_matrix(config.data, eval_file, config.log_dir)
pretraining = not args.no_pretraining
cnet = SVCNN(args.name,
nclasses=40,
pretraining=pretraining,
cnn_name=args.cnn_name)
cnet_2 = MVCNN('mvcnn_run2',
cnet,
nclasses=40,
cnn_name=args.cnn_name,
num_views=args.num_views)
del cnet
cnet_2.cuda()
cnet_2.load(path, modelfile)
cnet_2.eval()
n_models_train = args.num_models * args.num_views
log_dir = None
train_dataset = MultiviewImgDataset(args.train_path,
scale_aug=False,
rot_aug=False,
num_models=n_models_train,
num_views=args.num_views)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0
) # shuffle needs to be false! it's done within the trainer
val_dataset = MultiviewImgDataset(args.val_path,