def test_beholder_video(self):
LOG_DIRECTORY = '/tmp/beholder-demo-recording'
tensor_and_name = namedtuple('tensor_and_name', 'tensor, name')
fake_param = [
tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)
]
arrays = [
tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)
]
beholder = beholder_lib.Beholder(logdir=LOG_DIRECTORY)
pkl = fio.read_pickle(LOG_DIRECTORY + '/plugins/beholder/config.pkl')
pkl['is_recording'] = True
fio.write_pickle(pkl, LOG_DIRECTORY + '/plugins/beholder/config.pkl')
for i in range(3):
if i == 2:
pkl = fio.read_pickle(LOG_DIRECTORY +
'/plugins/beholder/config.pkl')
pkl['is_recording'] = False
fio.write_pickle(
pkl, LOG_DIRECTORY + '/plugins/beholder/config.pkl')
beholder.update(
trainable=fake_param,
arrays=arrays,
frame=np.random.randn(128, 128),
)
def beholder_pytorch():
for i in range(1000):
fake_param = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)]
arrays = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)]
beholder = beholder_lib.Beholder(logdir=LOG_DIRECTORY)
beholder.update(
trainable=fake_param,
arrays=arrays,
frame=np.random.randn(128, 128),
)
time.sleep(0.1)
print(i)
def test_beholder(self):
LOG_DIRECTORY = '/tmp/beholder-demo'
tensor_and_name = namedtuple('tensor_and_name', 'tensor, name')
fake_param = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)]
arrays = [tensor_and_name(np.random.randn(128, 768, 3), 'test' + str(i))
for i in range(5)]
beholder = beholder_lib.Beholder(logdir=LOG_DIRECTORY)
beholder.update(
trainable=fake_param,
arrays=arrays,
frame=np.random.randn(128, 128),
)