def test_phase_minibatch_generator():
for step, minibatch in enumerate(
get_while_running(test_process, test_queue)):
if (num_minibatches is not None) and (step
== num_minibatches):
break
yield minibatch
def test_net():
''' Evaluate the network '''
# Make result directory and the result file.
result_dir = os.path.join(cfg.DIR.OUT_PATH, cfg.TEST.EXP_NAME)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_fn = os.path.join(result_dir, 'result.mat')
print("Exp file will be written to: " + result_fn)
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
print('Network definition: \n')
print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass(compute_grad=False)
net.load(cfg.CONST.WEIGHTS)
solver = Solver(net)
# set constants
batch_size = cfg.CONST.BATCH_SIZE
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(
dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue,
data_pair,
1,
repeat=False,
train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / batch_size)
# prepare result container
results = {'cost': np.zeros(num_batch)}
for thresh in cfg.TEST.VOXEL_THRESH:
results[str(thresh)] = np.zeros((num_batch, batch_size, 5))
# Get all test data
batch_idx = 0
for batch_img, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
pred, loss, activations = solver.test_output(batch_img, batch_voxel)
print('%d/%d, cost is: %f' % (batch_idx, num_batch, loss))
for i, thresh in enumerate(cfg.TEST.VOXEL_THRESH):
for j in range(batch_size):
r = evaluate_voxel_prediction(pred[j, ...],
batch_voxel[j, ...], thresh)
results[str(thresh)][batch_idx, j, :] = r
# record result for the batch
results['cost'][batch_idx] = float(loss)
batch_idx += 1
print('Total loss: %f' % np.mean(results['cost']))
sio.savemat(result_fn, results)
def demo(args):
''' Evaluate the network '''
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
print('Network definition: \n')
print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass(compute_grad=False)
net.load(cfg.CONST.WEIGHTS)
solver = Solver(net)
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue, data_pair, 1, repeat=False, train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / args.batch_size)
# Get all test data
batch_idx = 0
for batch_img, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
pred, loss, activations = solver.test_output(batch_img, batch_voxel)
if (batch_idx < args.exportNum):
# Save the prediction to an OBJ file (mesh file).
print('saving {}/{}'.format(batch_idx, args.exportNum - 1))
voxel2obj('out/prediction_{}b_{}.obj'.format(args.batch_size, batch_idx),
pred[0, :, 1, :, :] > cfg.TEST.VOXEL_THRESH)
else:
break
batch_idx += 1
if args.file:
# Use meshlab or other mesh viewers to visualize the prediction.
# For Ubuntu>=14.04, you can install meshlab using
# `sudo apt-get install meshlab`
if cmd_exists('meshlab'):
call(['meshlab', 'obj/{}.obj'.format(args.file)])
else:
print('Meshlab not found: please use visualization of your choice to view %s' %
args.file)
def demo(args):
''' Evaluate the network '''
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
print('Network definition: \n')
print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass(compute_grad=False)
net.load(cfg.CONST.WEIGHTS)
solver = Solver(net)
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(
dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue,
data_pair,
1,
repeat=False,
train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / args.batch_size)
# Get all test data
batch_idx = 0
for batch_img, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
pred, loss, activations = solver.test_output(batch_img, batch_voxel)
if (batch_idx < args.exportNum):
# Save the prediction to an OBJ file (mesh file).
print('saving {}/{}'.format(batch_idx, args.exportNum - 1))
# voxel2obj('out/prediction_{}b_{}.obj'.format(args.batch_size, batch_idx),
# pred[0, :, 1, :, :] > cfg.TEST.VOXEL_THRESH)
else:
break
batch_idx += 1
def test_phase_minibatch_generator(test_process, test_queue):
for step, minibatch in enumerate(get_while_running(test_process, test_queue)):
yield minibatch
def test_net():
''' Evaluate the network '''
# Make result directory and the result file.
result_dir = os.path.join(cfg.DIR.OUT_PATH, cfg.TEST.EXP_NAME)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_fn = os.path.join(result_dir, 'result.mat')
print("Exp file will be written to: " + result_fn)
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
#print('Network definition: \n')
#print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass()
net.cuda()
solver = Solver(net)
solver.load(cfg.CONST.WEIGHTS)
# set constants
batch_size = cfg.CONST.BATCH_SIZE
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue, data_pair, 1, repeat=False, train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / batch_size)
# prepare result container
results = {'cost': np.zeros(num_batch),
'mAP': np.zeros((num_batch, batch_size))}
# Save results for various thresholds
for thresh in cfg.TEST.VOXEL_THRESH:
results[str(thresh)] = np.zeros((num_batch, batch_size, 5))
# Get all test data
batch_idx = 0
for batch_img, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
#activations is a list of torch.cuda.FloatTensor
pred, loss, activations = solver.test_output(batch_img, batch_voxel)
#convert pytorch tensor to numpy array
pred = pred.data.cpu().numpy()
loss = loss.data.cpu().numpy()
for j in range(batch_size):
# Save IoU per thresh
for i, thresh in enumerate(cfg.TEST.VOXEL_THRESH):
r = evaluate_voxel_prediction(pred[j, ...], batch_voxel[j, ...], thresh)
results[str(thresh)][batch_idx, j, :] = r
# Compute AP
precision = sklearn.metrics.average_precision_score(
batch_voxel[j, 1].flatten(), pred[j, 1].flatten())
results['mAP'][batch_idx, j] = precision
# record result for the batch
results['cost'][batch_idx] = float(loss)
print('%d/%d, costs: %f, mAP: %f' %
(batch_idx, num_batch, loss, np.mean(results['mAP'][batch_idx])))
batch_idx += 1
print('Total loss: %f' % np.mean(results['cost']))
print('Total mAP: %f' % np.mean(results['mAP']))
sio.savemat(result_fn, results)