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)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batchSize,
shuffle=False,
num_workers=0)
print('num_train_files: ' + str(len(train_dataset.filepaths)))
print('num_val_files: ' + str(len(val_dataset.filepaths)))
optimizer = None
trainer = ModelNetTrainer(cnet_2,
train_loader,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
'mvcnn',
log_dir,
num_views=args.num_views)
loss, val_overall_acc, val_mean_class_acc = trainer.update_validation_accuracy(
None)
# total_seen = 0
# total_correct = 0
# cnet_2.eval()
# for _, data in enumerate(val_loader,0):
#
# N, V, C, H, W = data[1].size()
# total_seen = total_seen + N
# in_data = Variable(data[1]).view(-1, C, H, W).cuda()
# target = Variable(data[0]).cuda()
#
# out_data = cnet_2(in_data)
# pred = torch.max(out_data, 1)[1]
# correct = (pred==target).float().sum()
# total_correct = total_correct+correct
