def base_tester(model, impl, observation_shape, action_size=2):
# dummy impl object
model._impl = impl
# check save model
impl.save_model = Mock()
model.save_model("model.pt")
impl.save_model.assert_called_with("model.pt")
# check load model
impl.load_model = Mock()
model.load_model("mock.pt")
impl.load_model.assert_called_with("mock.pt")
# check get_params
params = model.get_params(deep=False)
clone = model.__class__(**params)
for key, val in clone.get_params(deep=False).items():
assert params[key] is val
# check deep flag
deep_params = model.get_params(deep=True)
assert deep_params["impl"] is not impl
# check set_params
clone = model.__class__()
for key, val in params.items():
if np.isscalar(val) and not isinstance(val, str):
params[key] = val + np.random.random()
# set_params returns itself
assert clone.set_params(**params) is clone
for key, val in clone.get_params(deep=False).items():
assert params[key] is val
# check fit and fitter
update_backup = model.update
model.update = Mock(return_value={"loss": np.random.random()})
n_episodes = 4
episode_length = 25
n_batch = 32
n_steps = 10
n_steps_per_epoch = 5
n_epochs = n_steps // n_steps_per_epoch
data_size = n_episodes * episode_length
model._batch_size = n_batch
shape = (data_size, ) + observation_shape
if len(observation_shape) == 3:
observations = np.random.randint(256, size=shape, dtype=np.uint8)
else:
observations = np.random.random(shape).astype("f4")
if model.get_action_type() == ActionSpace.CONTINUOUS:
actions = np.random.random((data_size, action_size))
else:
actions = np.random.randint(action_size, size=data_size)
rewards = np.random.random(data_size)
terminals = np.zeros(data_size)
for i in range(n_episodes):
terminals[(i + 1) * episode_length - 1] = 1.0
dataset = MDPDataset(observations, actions, rewards, terminals)
# check fit
results = model.fit(
dataset.episodes,
n_steps=n_steps,
n_steps_per_epoch=n_steps_per_epoch,
logdir="test_data",
verbose=False,
show_progress=False,
)
assert isinstance(results, list)
assert len(results) == n_epochs
# check if the correct number of iterations are performed
assert len(model.update.call_args_list) == n_steps
# check arguments at each iteration
for i, call in enumerate(model.update.call_args_list):
epoch = i // n_steps_per_epoch
assert isinstance(call[0][0], TransitionMiniBatch)
assert len(call[0][0]) == n_batch
# check fitter
fitter = model.fitter(
dataset.episodes,
n_steps=n_steps,
n_steps_per_epoch=n_steps_per_epoch,
logdir="test_data",
verbose=False,
show_progress=False,
)
for epoch, metrics in fitter:
assert isinstance(epoch, int)
assert isinstance(metrics, dict)
assert epoch == n_epochs
# save params.json
logger = D3RLPyLogger("test", root_dir="test_data", verbose=False)
# save parameters to test_data/test/params.json
model.save_params(logger)
# load params.json
json_path = os.path.join(logger.logdir, "params.json")
new_model = model.__class__.from_json(json_path)
assert new_model.impl is not None
assert new_model.impl.observation_shape == observation_shape
assert new_model.impl.action_size == action_size
assert type(model.scaler) == type(new_model.scaler)
# check __setattr__ override
prev_batch_size = model.impl.batch_size
model.batch_size = prev_batch_size + 1
assert model.impl.batch_size == model.batch_size
# check builds
model._impl = None
model.build_with_dataset(dataset)
assert model.impl.observation_shape == dataset.get_observation_shape()
assert model.impl.action_size == dataset.get_action_size()
# set backed up methods
model._impl = None
model.update = update_backup
return dataset
def base_tester(model, impl, observation_shape, action_size=2):
# dummy impl object
model.impl = impl
# check save model
impl.save_model = Mock()
model.save_model('model.pt')
impl.save_model.assert_called_with('model.pt')
# check load model
impl.load_model = Mock()
model.load_model('mock.pt')
impl.load_model.assert_called_with('mock.pt')
# check get_params
params = model.get_params(deep=False)
clone = model.__class__(**params)
for key, val in clone.get_params(deep=False).items():
assert params[key] is val
# check deep flag
deep_params = model.get_params(deep=True)
assert deep_params['impl'] is not impl
# check set_params
clone = model.__class__()
for key, val in params.items():
if np.isscalar(val) and not isinstance(val, str):
params[key] = val + np.random.random()
# set_params returns itself
assert clone.set_params(**params) is clone
for key, val in clone.get_params(deep=False).items():
assert params[key] is val
# check fit
update_backup = model.update
model.update = Mock(return_value=range(len(model._get_loss_labels())))
n_episodes = 4
episode_length = 25
n_batch = 32
n_epochs = 3
data_size = n_episodes * episode_length
model.batch_size = n_batch
model.n_epochs = n_epochs
shape = (data_size, ) + observation_shape
if len(observation_shape) == 3:
observations = np.random.randint(256, size=shape, dtype=np.uint8)
else:
observations = np.random.random(shape).astype('f4')
actions = np.random.random((data_size, action_size))
rewards = np.random.random(data_size)
terminals = np.zeros(data_size)
for i in range(n_episodes):
terminals[(i + 1) * episode_length - 1] = 1.0
dataset = MDPDataset(observations, actions, rewards, terminals)
model.fit(dataset.episodes,
logdir='test_data',
verbose=False,
show_progress=False,
tensorboard=False)
# check if the correct number of iterations are performed
assert len(model.update.call_args_list) == data_size // n_batch * n_epochs
# check arguments at each iteration
for i, call in enumerate(model.update.call_args_list):
epoch = i // (data_size // n_batch)
total_step = i
assert call[0][0] == epoch
assert call[0][1] == total_step
assert isinstance(call[0][2], TransitionMiniBatch)
assert len(call[0][2]) == n_batch
# save params.json
logger = D3RLPyLogger('test',
root_dir='test_data',
verbose=False,
tensorboard=False)
# save parameters to test_data/test/params.json
model._save_params(logger)
# load params.json
json_path = os.path.join(logger.logdir, 'params.json')
new_model = model.__class__.from_json(json_path)
assert new_model.impl is not None
assert new_model.impl.observation_shape == observation_shape
assert new_model.impl.action_size == action_size
assert type(model.scaler) == type(new_model.scaler)
# check __setattr__ override
prev_batch_size = model.impl.batch_size
model.batch_size = prev_batch_size + 1
assert model.impl.batch_size == model.batch_size
# set backed up methods
model.impl = None
model.update = update_backup
return dataset