def additional_transforms(self, stage_transforms: List[Transforms]) -> Compose:
""" Returns the transforms in `stage_transforms` that are additional transforms
from those in `self.transforms`.
For example, if:
```
setting.transforms = Compose([Transforms.Resize(32), Transforms.ToTensor])
setting.train_transforms = Compose([Transforms.Resize(32), Transforms.ToTensor, Transforms.RandomGrayscale])
```
Then:
```
setting.additional_transforms(setting.train_transforms)
# will give:
Compose([Transforms.RandomGrayscale])
```
"""
reference_transforms = self.transforms
if len(stage_transforms) < len(reference_transforms):
# Assume no overlap, return all the 'stage' transforms.
return Compose(stage_transforms)
if stage_transforms == reference_transforms:
# Complete overlap, return an empty list.
return Compose([])
# IDEA: Only add the additional transforms, compared to the 'base' transforms.
# As soon as one is different, break.
i = 0
for i, t_a, t_b in enumerate(zip(stage_transforms, self.transforms)):
if t_a != t_b:
break
return Compose(stage_transforms[i:])
def test_observation_wrapper_applied_to_passive_environment():
""" Test that when we apply a gym wrapper to a PassiveEnvironment, it also
affects the observations / actions / rewards produced when iterating on the
env.
"""
batch_size = 5
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
dataset = MNIST("data", transform=transforms)
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
dataset.classes
env = PassiveEnvironment(
dataset, n_classes=10, batch_size=batch_size, observation_space=obs_space,
)
assert env.observation_space == Image(0, 1, (batch_size, 3, 28, 28))
assert env.action_space.shape == (batch_size,)
assert env.reward_space == env.action_space
env.seed(123)
check_env(env)
# Apply a transformation that changes the observation space.
env = TransformObservation(env=env, f=Compose([Transforms.resize_64x64]))
assert env.observation_space == Image(0, 1, (batch_size, 3, 64, 64))
assert env.action_space.shape == (batch_size,)
assert env.reward_space.shape == (batch_size,)
env.seed(123)
check_env(env)
env.close()
def test_passive_environment_without_pretend_to_be_active():
""" Test the gym.Env-style interaction with a PassiveEnvironment.
"""
batch_size = 5
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
max_samples = 100
dataset = Subset(dataset, list(range(max_samples)))
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
env = PassiveEnvironment(
dataset,
n_classes=10,
batch_size=batch_size,
observation_space=obs_space,
pretend_to_be_active=False,
)
assert env.observation_space == Image(0, 1, (batch_size, 3, 28, 28))
assert env.action_space.shape == (batch_size, )
assert env.reward_space == env.action_space
env.seed(123)
obs = env.reset()
assert obs in env.observation_space
obs, reward, done, info = env.step(env.action_space.sample())
assert reward is not None
for i, (obs, reward) in enumerate(env):
assert reward is not None
other_reward = env.send(env.action_space.sample())
assert (other_reward == reward).all()
assert i == max_samples // batch_size - 1
def test_multiple_epochs_dataloader_with_split_batch_fn():
""" Test that we can iterate on the dataloader more than once. """
max_epochs = 3
max_samples = 200
batch_size = 5
def split_batch_fn(batch):
x, y, = batch
# some dummy function.
return torch.zeros_like(x), y
max_batches = max_samples // batch_size
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = PassiveEnvironment(dataset,
n_classes=10,
batch_size=batch_size,
split_batch_fn=split_batch_fn)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
total_steps = 0
for epoch in range(max_epochs):
for obs, reward in env:
assert obs.shape == (batch_size, 3, 28, 28)
assert torch.all(obs == 0)
assert reward.shape == (batch_size, )
total_steps += 1
assert total_steps == max_batches * max_epochs
def test_mnist_as_gym_env(self):
# from continuum.datasets import MNIST
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
batch_size = 4
env = self.PassiveEnvironment(dataset,
n_classes=10,
batch_size=batch_size)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
env.seed(123)
obs = env.reset()
assert obs.shape == (batch_size, 3, 28, 28)
for i in range(10):
obs, reward, done, info = env.step(env.action_space.sample())
assert obs.shape == (batch_size, 3, 28, 28)
assert reward.shape == (batch_size, )
assert not done
env.close()
def test_multiple_epochs_dataloader(self):
""" Test that we can iterate on the dataloader more than once. """
max_epochs = 3
max_samples = 200
batch_size = 5
max_batches = max_samples // batch_size
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = self.PassiveEnvironment(dataset,
n_classes=10,
batch_size=batch_size)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
total_steps = 0
for epoch in range(max_epochs):
for obs, reward in env:
assert obs.shape == (batch_size, 3, 28, 28)
assert reward.shape == (batch_size, )
total_steps += 1
assert total_steps == max_batches * max_epochs
def test_env_gives_done_on_last_item():
# from continuum.datasets import MNIST
max_samples = 100
batch_size = 1
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = PassiveEnvironment(dataset, n_classes=10, batch_size=batch_size)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
env.seed(123)
obs = env.reset()
assert obs.shape == (batch_size, 3, 28, 28)
# Starting at 1 since reset() gives one observation already.
for i in range(1, max_samples):
obs, reward, done, info = env.step(env.action_space.sample())
assert obs.shape == (batch_size, 3, 28, 28)
assert reward.shape == (batch_size, )
assert done == (i == max_samples - 1), i
if done:
break
else:
assert False, "Should have reached done=True!"
assert i == max_samples - 1
env.close()
def test_is_proxy_to(use_wrapper: bool):
import numpy as np
from sequoia.common.transforms import Compose, Transforms
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
from sequoia.common.spaces import Image
from torchvision.datasets import MNIST
batch_size = 32
dataset = MNIST("data", transform=transforms)
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
env_type = ProxyPassiveEnvironment if use_wrapper else PassiveEnvironment
env: Iterable[Tuple[Tensor, Tensor]] = env_type(
dataset,
batch_size=batch_size,
n_classes=10,
observation_space=obs_space,
)
if use_wrapper:
assert isinstance(env, EnvironmentProxy)
assert issubclass(type(env), EnvironmentProxy)
assert is_proxy_to(env, PassiveEnvironment)
else:
assert not is_proxy_to(env, PassiveEnvironment)
def __init__(self, env: gym.Env, f: Union[Callable, Compose]):
if isinstance(f, list) and not callable(f):
f = Compose(f)
super().__init__(env, f=f)
self.f: Transform
# try:
self.observation_space = self(self.env.observation_space)
if has_tensor_support(self.env.observation_space):
self.observation_space = add_tensor_support(self.observation_space)
def __init__(self, env: gym.Env, f: Callable[[Union[gym.Env, Space]], Union[gym.Env, Space]]):
if isinstance(f, list) and not callable(f):
f = Compose(f)
super().__init__(env)
self.f: Compose = f
# Modify the action space by applying the transform onto it.
self.action_space = self.env.action_space
if isinstance(self.f, Transform):
self.action_space = self.f(self.env.action_space)
def test_compose_on_image_space():
in_space = Image(0, 255, shape=(64, 64, 3), dtype=np.uint8)
transform = Compose([Transforms.to_tensor, Transforms.three_channels])
expected = Image(0, 1., shape=(3, 64, 64), dtype=np.float32)
actual = transform(in_space)
assert actual == expected
env = gym.make("MetaMonsterKong-v0")
assert env.observation_space == gym.spaces.Box(0, 255, (64, 64, 3), np.uint8)
assert env.observation_space == in_space
wrapped_env = TransformObservation(env, transform)
assert wrapped_env.observation_space == expected
def test_passive_environment_needs_actions_to_be_sent():
""" Test the 'active dataloader' style interaction.
"""
batch_size = 10
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
max_samples = 105
dataset = Subset(dataset, list(range(max_samples)))
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
env = PassiveEnvironment(
dataset,
n_classes=10,
batch_size=batch_size,
observation_space=obs_space,
pretend_to_be_active=True,
strict=True,
)
with pytest.raises(RuntimeError):
for i, (obs, _) in enumerate(env):
pass
env = PassiveEnvironment(
dataset,
n_classes=10,
batch_size=batch_size,
observation_space=obs_space,
pretend_to_be_active=True,
)
for i, (obs, _) in enumerate(env):
assert isinstance(obs, Tensor)
action = env.action_space.sample()[:obs.shape[0]]
rewards = env.send(action)
assert rewards is not None
assert rewards.shape[0] == action.shape[0]
def test_env_requires_reset_before_step():
# from continuum.datasets import MNIST
max_samples = 100
batch_size = 5
max_batches = max_samples // batch_size
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = PassiveEnvironment(dataset, n_classes=10, batch_size=batch_size)
with pytest.raises(gym.error.ResetNeeded):
env.step(env.action_space.sample())
def __post_init__(self,
observation_space: gym.Space = None,
action_space: gym.Space = None,
reward_space: gym.Space = None):
""" Initializes the fields of the setting that weren't set from the
command-line.
"""
logger.debug(f"__post_init__ of Setting")
if len(self.train_transforms) == 1 and isinstance(self.train_transforms[0], list):
self.train_transforms = self.train_transforms[0]
if len(self.val_transforms) == 1 and isinstance(self.val_transforms[0], list):
self.val_transforms = self.val_transforms[0]
if len(self.test_transforms) == 1 and isinstance(self.test_transforms[0], list):
self.test_transforms = self.test_transforms[0]
# Actually compose the list of Transforms or callables into a single transform.
self.train_transforms: Compose = Compose(self.train_transforms)
self.val_transforms: Compose = Compose(self.val_transforms)
self.test_transforms: Compose = Compose(self.test_transforms)
LightningDataModule.__init__(self,
train_transforms=self.train_transforms,
val_transforms=self.val_transforms,
test_transforms=self.test_transforms,
)
self._observation_space = observation_space
self._action_space = action_space
self._reward_space = reward_space
# TODO: It's a bit confusing to also have a `config` attribute on the
# Setting. Might want to change this a bit.
self.config: Config = None
self.train_env: Environment = None # type: ignore
self.val_env: Environment = None # type: ignore
self.test_env: Environment = None # type: ignore
def test_passive_environment_as_dataloader():
batch_size = 1
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
dataset = MNIST("data", transform=transforms)
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
env: Iterable[Tuple[Tensor, Tensor]] = PassiveEnvironment(
dataset, batch_size=batch_size, n_classes=10, observation_space=obs_space,
)
for x, y in env:
assert x.shape == (batch_size, 3, 28, 28)
x = x.permute(0, 2, 3, 1)
assert y.tolist() == [5]
break
def test_multiple_epochs_env(self):
max_epochs = 3
max_samples = 100
batch_size = 5
max_batches = max_samples // batch_size
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = self.PassiveEnvironment(dataset,
n_classes=10,
batch_size=batch_size)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
env.seed(123)
total_steps = 0
for epoch in range(max_epochs):
obs = env.reset()
total_steps += 1
assert obs.shape == (batch_size, 3, 28, 28)
# Starting at 1 since reset() gives one observation already.
for i in range(1, max_batches):
obs, reward, done, info = env.step(env.action_space.sample())
assert obs.shape == (batch_size, 3, 28, 28)
assert reward.shape == (batch_size, )
assert done == (i == max_batches - 1), i
total_steps += 1
if done:
break
else:
assert False, "Should have reached done=True!"
assert i == max_batches - 1
assert total_steps == max_batches * max_epochs
env.close()
def test_cant_iterate_after_closing_passive_env(self):
max_epochs = 3
max_samples = 200
batch_size = 5
max_batches = max_samples // batch_size
dataset = MNIST("data",
transform=Compose(
[Transforms.to_tensor, Transforms.three_channels]))
dataset = Subset(dataset, list(range(max_samples)))
env = self.PassiveEnvironment(dataset,
n_classes=10,
batch_size=batch_size,
num_workers=4)
assert env.observation_space.shape == (batch_size, 3, 28, 28)
assert env.action_space.shape == (batch_size, )
assert env.reward_space.shape == (batch_size, )
total_steps = 0
for epoch in range(max_epochs):
for obs, reward in env:
assert obs.shape == (batch_size, 3, 28, 28)
assert reward.shape == (batch_size, )
total_steps += 1
assert total_steps == max_batches * max_epochs
env.close()
with pytest.raises(gym.error.ClosedEnvironmentError):
for _ in zip(range(3), env):
pass
with pytest.raises(gym.error.ClosedEnvironmentError):
env.reset()
with pytest.raises(gym.error.ClosedEnvironmentError):
env.get_next_batch()
with pytest.raises(gym.error.ClosedEnvironmentError):
env.step(env.action_space.sample())
def __init__(self, env: gym.Env, f: Union[Callable, Compose]):
if isinstance(f, list) and not callable(f):
f = Compose(f)
super().__init__(env, f=f)
self.f: Compose
# Modify the reward space, if it exists.
if hasattr(self.env, "reward_space"):
self.reward_space = self.env.reward_space
else:
self.reward_space = spaces.Box(
low=self.env.reward_range[0],
high=self.env.reward_range[1],
shape=(),
)
try:
self.reward_space = self.f(self.reward_space)
logger.debug(f"New reward space after transform: {self.reward_space}")
except Exception as e:
logger.warning(UserWarning(
f"Don't know how the transform {self.f} will impact the "
f"observation space! (Exception: {e})"
))
def test_issue_204():
""" Test that reproduces the issue #204, which was that some zombie processes
appeared to be created when iterating using an EnvironmentProxy.
The issue appears to have been caused by calling `self.__environment.reset()` in
`__iter__`, which I think caused another dataloader iterator to be created?
"""
transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
batch_size = 2048
num_workers = 12
dataset = MNIST("data", transform=transforms)
obs_space = Image(0, 255, (1, 28, 28), np.uint8)
obs_space = transforms(obs_space)
current_process = psutil.Process()
print(
f"Current process is using {current_process.num_threads()} threads, with "
f" {len(current_process.children(recursive=True))} child processes.")
starting_threads = current_process.num_threads()
starting_processes = len(current_process.children(recursive=True))
for use_wrapper in [False, True]:
threads = current_process.num_threads()
processes = len(current_process.children(recursive=True))
assert threads == starting_threads
assert processes == starting_processes
env_type = ProxyPassiveEnvironment if use_wrapper else PassiveEnvironment
env: Iterable[Tuple[Tensor, Tensor]] = env_type(
dataset,
batch_size=batch_size,
n_classes=10,
observation_space=obs_space,
num_workers=num_workers,
persistent_workers=True,
)
for i, _ in enumerate(env):
threads = current_process.num_threads()
processes = len(current_process.children(recursive=True))
assert threads == starting_threads + num_workers
assert processes == starting_processes + num_workers
print(f"Current process is using {threads} threads, with "
f" {processes} child processes.")
for i, _ in enumerate(env):
threads = current_process.num_threads()
processes = len(current_process.children(recursive=True))
assert threads == starting_threads + num_workers
assert processes == starting_processes + num_workers
print(f"Current process is using {threads} threads, with "
f" {processes} child processes.")
obs = env.reset()
done = False
while not done:
obs, reward, done, info = env.step(env.action_space.sample())
# env.render(mode="human")
threads = current_process.num_threads()
processes = len(current_process.children(recursive=True))
if not done:
assert threads == starting_threads + num_workers
assert processes == starting_processes + num_workers
print(f"Current process is using {threads} threads, with "
f" {processes} child processes.")
env.close()
import time
# Need to give it a second (or so) to cleanup.
time.sleep(1)
threads = current_process.num_threads()
processes = len(current_process.children(recursive=True))
assert threads == starting_threads
assert processes == starting_processes
class Setting(
SettingABC,
Parseable,
Serializable,
LightningDataModule,
Generic[EnvironmentType],
metaclass=SettingMeta,
):
""" Base class for all research settings in ML: Root node of the tree.
A 'setting' is loosely defined here as a learning problem with a specific
set of assumptions, restrictions, and an evaluation procedure.
For example, Reinforcement Learning is a type of Setting in which we assume
that an Agent is able to observe an environment, take actions upon it, and
receive rewards back from the environment. Some of the assumptions include
that the reward is dependant on the action taken, and that the actions have
an impact on the environment's state (and on the next observations the agent
will receive). The evaluation procedure consists in trying to maximize the
reward obtained from an environment over a given number of steps.
This 'Setting' class should ideally represent the most general learning
problem imaginable, with almost no assumptions about the data or evaluation
procedure.
This is a dataclass. Its attributes are can also be used as command-line
arguments using `simple_parsing`.
Abstract (required) methods:
- **apply** Applies a given Method on this setting to produce Results.
- **prepare_data** (things to do on 1 GPU/TPU not on every GPU/TPU in distributed mode).
- **setup** (things to do on every accelerator in distributed mode).
- **train_dataloader** the training environment/dataloader.
- **val_dataloader** the val environments/dataloader(s).
- **test_dataloader** the test environments/dataloader(s).
"Abstract"-ish (required) class attributes:
- `Results`: The class of Results that are created when applying a Method on
this setting.
- `Observations`: The type of Observations that will be produced in this
setting.
- `Actions`: The type of Actions that are expected from this setting.
- `Rewards`: The type of Rewards that this setting will (potentially) return
upon receiving an action from the method.
"""
# ---------- Class Variables -------------
# Fields in this block are class attributes. They don't create command-line
# arguments.
# Type of Observations that the dataloaders (a.k.a. "environments") will
# produce for this type of Setting.
Observations: ClassVar[Type[Observations]] = Observations
# Type of Actions that the dataloaders (a.k.a. "environments") will receive
# through their `send` method, for this type of Setting.
Actions: ClassVar[Type[Actions]] = Actions
# Type of Rewards that the dataloaders (a.k.a. "environments") will return
# after receiving an action, for this type of Setting.
Rewards: ClassVar[Type[Rewards]] = Rewards
# The type of Results that are given back when a method is applied on this
# Setting. The `Results` class basically defines the 'evaluation metric' for
# a given type of setting. See the `Results` class for more info.
Results: ClassVar[Type[Results]] = Results
available_datasets: ClassVar[Dict[str, Any]] = {}
# Transforms to be applied to the observatons of the train/valid/test
# environments.
transforms: Optional[List[Transforms]] = None
# Transforms to be applied to the training datasets.
train_transforms: Optional[List[Transforms]] = None
# Transforms to be applied to the validation datasets.
val_transforms: Optional[List[Transforms]] = None
# Transforms to be applied to the testing datasets.
test_transforms: Optional[List[Transforms]] = None
# Fraction of training data to use to create the validation set.
# (Only applicable in Passive settings.)
val_fraction: float = 0.2
# TODO: Still not sure where exactly we should be adding the 'batch_size'
# and 'num_workers' arguments. Adding it here for now with cmd=False, so
# that they can be passed to the constructor of the Setting.
batch_size: Optional[int] = field(default=None, cmd=False)
num_workers: Optional[int] = field(default=None, cmd=False)
# # TODO: Add support for semi-supervised training.
# # Fraction of the dataset that is labeled.
# labeled_data_fraction: int = 1.0
# # Number of labeled examples.
# n_labeled_examples: Optional[int] = None
def __post_init__(
self,
observation_space: gym.Space = None,
action_space: gym.Space = None,
reward_space: gym.Space = None,
):
""" Initializes the fields of the setting that weren't set from the
command-line.
"""
logger.debug("__post_init__ of Setting")
# BUG: simple-parsing sometimes parses a list with a single item, itself the
# list of transforms. Not sure if this still happens.
def is_list_of_list(v: Any) -> bool:
return isinstance(v, list) and len(v) == 1 and isinstance(v[0], list)
if is_list_of_list(self.train_transforms):
self.train_transforms = self.train_transforms[0]
if is_list_of_list(self.val_transforms):
self.val_transforms = self.val_transforms[0]
if is_list_of_list(self.test_transforms):
self.test_transforms = self.test_transforms[0]
if all(
t is None
for t in [
self.transforms,
self.train_transforms,
self.val_transforms,
self.test_transforms,
]
):
# Use these two transforms by default if no transforms are passed at all.
# TODO: Remove this after the competition perhaps.
self.transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
# If the constructor is called with just the `transforms` argument, like this:
# <SomeSetting>(dataset="bob", transforms=foo_transform)
# Then we use this value as the default for the train, val and test transforms.
if self.transforms and not any(
[self.train_transforms, self.val_transforms, self.test_transforms]
):
if not isinstance(self.transforms, list):
self.transforms = Compose([self.transforms])
self.train_transforms = self.transforms.copy()
self.val_transforms = self.transforms.copy()
self.test_transforms = self.transforms.copy()
if self.train_transforms is not None and not isinstance(
self.train_transforms, list
):
self.train_transforms = [self.train_transforms]
if self.val_transforms is not None and not isinstance(
self.val_transforms, list
):
self.val_transforms = [self.val_transforms]
if self.test_transforms is not None and not isinstance(
self.test_transforms, list
):
self.test_transforms = [self.test_transforms]
# Actually compose the list of Transforms or callables into a single transform.
self.train_transforms: Compose = Compose(self.train_transforms or [])
self.val_transforms: Compose = Compose(self.val_transforms or [])
self.test_transforms: Compose = Compose(self.test_transforms or [])
LightningDataModule.__init__(
self,
train_transforms=self.train_transforms,
val_transforms=self.val_transforms,
test_transforms=self.test_transforms,
)
self._observation_space = observation_space
self._action_space = action_space
self._reward_space = reward_space
# TODO: It's a bit confusing to also have a `config` attribute on the
# Setting. Might want to change this a bit.
self.config: Config = None
self.train_env: Environment = None # type: ignore
self.val_env: Environment = None # type: ignore
self.test_env: Environment = None # type: ignore
@abstractmethod
def apply(self, method: Method, config: Config = None) -> "Setting.Results":
# NOTE: The actual train/test loop should be defined in a more specific
# setting. This is just here as an illustration of what that could look
# like.
assert False, "this is just here for illustration purposes. "
method.fit(
train_env=self.train_dataloader(), valid_env=self.val_dataloader(),
)
# Test loop:
test_env = self.test_dataloader()
test_metrics = []
# Number of episodes to test on:
n_test_episodes = 1
# Perform a set number of episodes in the test environment.
for episode in range(n_test_episodes):
# Get initial observations.
observations = test_env.reset()
for i in itertools.count():
# Get the predictions/actions for a batch of observations.
actions = method.get_actions(observations, test_env.action_space)
observations, rewards, done, info = test_env.step(actions)
# Calculate the 'metrics' (TODO: This should be done be in the env!)
batch_metrics = ...
test_metrics.append(batch_metrics)
if done:
break
return self.Results(test_metrics=test_metrics)
def get_metrics(self, actions: Actions, rewards: Rewards) -> Union[float, Metrics]:
""" Calculate the "metric" from the model predictions (actions) and the true labels (rewards).
In this example, we return a 'Metrics' object:
- `ClassificationMetrics` for classification problems,
- `RegressionMetrics` for regression problems.
We use these objects because they are awesome (they basically simplify
making plots, wandb logging, and serialization), but you can also just
return floats if you want, no problem.
TODO: This is duplicated from Incremental. Need to fix this.
"""
from sequoia.common.metrics import get_metrics
# In this particular setting, we only use the y_pred from actions and
# the y from the rewards.
if isinstance(actions, Actions):
actions = torch.as_tensor(actions.y_pred)
if isinstance(rewards, Rewards):
rewards = torch.as_tensor(rewards.y)
# TODO: At the moment there's this problem, ClassificationMetrics wants
# to create a confusion matrix, which requires 'logits' (so it knows how
# many classes.
if isinstance(actions, Tensor):
actions = actions.cpu().numpy()
if isinstance(rewards, Tensor):
rewards = rewards.cpu().numpy()
if isinstance(self.action_space, spaces.Discrete):
batch_size = rewards.shape[0]
actions = torch.as_tensor(actions)
if len(actions.shape) == 1 or (
actions.shape[-1] == 1 and self.action_space.n != 2
):
fake_logits = torch.zeros([batch_size, self.action_space.n], dtype=int)
# FIXME: There must be a smarter way to do this indexing.
for i, action in enumerate(actions):
fake_logits[i, action] = 1
actions = fake_logits
return get_metrics(y_pred=actions, y=rewards)
@property
def image_space(self) -> Optional[gym.Space]:
if isinstance(self.observation_space, spaces.Box):
return self.observation_space
if isinstance(self.observation_space, spaces.Tuple):
assert isinstance(self.observation_space[0], spaces.Box)
return self.observation_space[0]
if isinstance(self.observation_space, spaces.Dict):
return self.observation_space.spaces["x"]
logger.warning(
f"Don't know what the image space is. "
f"(self.observation_space={self.observation_space})"
)
return None
@property
def observation_space(self) -> gym.Space:
return self._observation_space
@observation_space.setter
def observation_space(self, value: gym.Space) -> None:
"""Sets a the observation space.
NOTE: This also changes the value of the `dims` attribute and the result
of the `size()` method from LightningDataModule.
"""
if not isinstance(value, gym.Space):
raise RuntimeError(f"Value must be a `gym.Space` (got {value})")
if not self._dims:
if isinstance(value, spaces.Box):
self.dims = value.shape
elif isinstance(value, spaces.Tuple):
self.dims = tuple(space.shape for space in value.spaces)
elif isinstance(value, spaces.Dict) and "x" in value.spaces:
self.dims = value.spaces["x"].shape
else:
raise NotImplementedError(
f"Don't know how to set the 'dims' attribute using "
f"observation space {value}"
)
self._observation_space = value
@property
def action_space(self) -> gym.Space:
return self._action_space
@action_space.setter
def action_space(self, value: gym.Space) -> None:
self._action_space = value
@property
def reward_space(self) -> gym.Space:
return self._reward_space
@reward_space.setter
def reward_space(self, value: gym.Space) -> None:
self._reward_space = value
@classmethod
def get_available_datasets(cls) -> Iterable[str]:
""" Returns an iterable of strings which represent the names of datasets. """
return cls.available_datasets
@classmethod
def main(cls, argv: Optional[Union[str, List[str]]] = None) -> Results:
from sequoia.main import Experiment
experiment: Experiment
# Create the Setting object from the command-line:
setting = cls.from_args(argv)
# Then create the 'Experiment' from the command-line, which makes it
# possible to choose between all the methods.
experiment = Experiment.from_args(argv)
# fix the setting attribute to be the one parsed above.
experiment.setting = setting
results: ResultsType = experiment.launch(argv)
return results
def apply_all(
self, argv: Union[str, List[str]] = None
) -> Dict[Type["Method"], Results]:
applicable_methods = self.get_applicable_methods()
from sequoia.methods import Method
all_results: Dict[Type[Method], Results] = {}
config = Config.from_args(argv)
for method_type in applicable_methods:
method = method_type.from_args(argv)
results = self.apply(method, config)
all_results[method_type] = results
logger.info(f"All results for setting of type {type(self)}:")
logger.info(
{
method.get_name(): (results.get_metric() if results else "crashed")
for method, results in all_results.items()
}
)
return all_results
@classmethod
def get_path_to_source_file(cls: Type) -> Path:
from sequoia.utils.utils import get_path_to_source_file
return get_path_to_source_file(cls)
def _check_environments(self):
""" Do a quick check to make sure that interacting with the envs/dataloaders
works correctly.
"""
# Check that the env's spaces are batched versions of the settings'.
from gym.vector.utils import batch_space
from sequoia.settings.passive import PassiveEnvironment
batch_size = self.batch_size
for loader_method in [
self.train_dataloader,
self.val_dataloader,
self.test_dataloader,
]:
print(f"\n\nChecking loader method {loader_method.__name__}\n\n")
env = loader_method(batch_size=batch_size)
batch_size = env.batch_size
# We could compare the spaces directly, but that's a bit messy, and
# would be depends on the type of spaces for each. Instead, we could
# check samples from such spaces on how the spaces are batched.
if batch_size:
expected_observation_space = batch_space(
self.observation_space, n=batch_size
)
expected_action_space = batch_space(self.action_space, n=batch_size)
expected_reward_space = batch_space(self.reward_space, n=batch_size)
else:
expected_observation_space = self.observation_space
expected_action_space = self.action_space
expected_reward_space = self.reward_space
# TODO: Batching the 'Sparse' makes it really ugly, so just
# comparing the 'image' portion of the space for now.
assert (
env.observation_space[0].shape == expected_observation_space[0].shape
), (env.observation_space[0], expected_observation_space[0])
assert env.action_space == expected_action_space, (
env.action_space,
expected_action_space,
)
assert env.reward_space == expected_reward_space, (
env.reward_space,
expected_reward_space,
)
# Check that the 'gym API' interaction is working correctly.
reset_obs: Observations = env.reset()
self._check_observations(env, reset_obs)
for i in range(5):
actions = env.action_space.sample()
self._check_actions(env, actions)
step_observations, step_rewards, done, info = env.step(actions)
self._check_observations(env, step_observations)
self._check_rewards(env, step_rewards)
if batch_size:
assert not any(done)
else:
assert not done
# assert not (done if isinstance(done, bool) else any(done))
for batch in take(env, 5):
observations: Observations
rewards: Optional[Rewards]
if isinstance(env, PassiveEnvironment):
observations, rewards = batch
else:
# in RL atm, the 'dataset' gives back only the observations.
# Coul
observations, rewards = batch, None
self._check_observations(env, observations)
if rewards is not None:
self._check_rewards(env, rewards)
if batch_size:
actions = tuple(
self.action_space.sample() for _ in range(batch_size)
)
else:
actions = self.action_space.sample()
# actions = self.Actions(torch.as_tensor(actions))
rewards = env.send(actions)
self._check_rewards(env, rewards)
env.close()
def _check_observations(self, env: Environment, observations: Any):
""" Check that the given observation makes sense for the given environment.
TODO: This should probably not be in this file here. It's more used for
testing than anything else.
"""
assert isinstance(observations, self.Observations), observations
images = observations.x
assert isinstance(images, (torch.Tensor, np.ndarray))
if isinstance(images, Tensor):
images = images.cpu().numpy()
# Find the 'image' space:
if isinstance(env.observation_space, spaces.Box):
image_space = env.observation_space
elif isinstance(env.observation_space, spaces.Tuple):
image_space = env.observation_space[0]
else:
raise RuntimeError(
f"Don't know how to find the image space in the "
f"env's obs space ({env.observation_space})."
)
assert images in image_space
def _check_actions(self, env: Environment, actions: Any):
if isinstance(actions, Actions):
assert isinstance(actions, self.Actions)
actions = actions.y_pred.cpu().numpy()
elif isinstance(actions, Tensor):
actions = actions.cpu().numpy()
elif isinstance(actions, np.ndarray):
actions = actions
assert actions in env.action_space
def _check_rewards(self, env: Environment, rewards: Any):
if isinstance(rewards, Rewards):
assert isinstance(rewards, self.Rewards)
rewards = rewards.y
if isinstance(rewards, Tensor):
rewards = rewards.cpu().numpy()
if isinstance(rewards, np.ndarray):
rewards = rewards
if isinstance(rewards, (int, float)):
rewards = np.asarray(rewards)
assert rewards in env.reward_space, (rewards, env.reward_space)
# Just to make type hinters stop throwing errors when using the constructor
# to create a Setting.
def __new__(cls, *args, **kwargs):
return super().__new__(cls, *args, **kwargs)
@classmethod
def load_benchmark(
cls: Type[SettingType], benchmark: Union[str, Path]
) -> SettingType:
""" Load the given "benchmark" (pre-configured Setting) of this type.
Parameters
----------
cls : Type[SettingType]
Type of Setting to create.
benchmark : Union[str, Path]
Either the name of a benchmark (e.g. "cartpole_state", "monsterkong", etc.)
or a path to a json/yaml file.
Returns
-------
SettingType
Setting of type `cls`, appropriately populated according to the chosen
benchmark.
Raises
------
RuntimeError
If `benchmark` isn't an existing file or a known preset.
RuntimeError
If any command-line arguments are present in sys.argv which would be ignored
when creating this setting.
"""
# If the provided benchmark isn't a path, try to get the value from
# the `setting_presets` dict. If it isn't in the dict, raise an
# error.
if not Path(benchmark).is_file():
if benchmark in setting_presets:
benchmark = setting_presets[benchmark]
else:
raise RuntimeError(
f"Could not find benchmark '{benchmark}': it "
f"is neither a path to a file or a key of the "
f"`setting_presets` dictionary. \n"
f"(Available presets: {setting_presets}) "
)
# Creating an experiment for the given setting, loaded from the
# config file.
# TODO: IDEA: Do the same thing for loading the Method?
logger.info(
f"Will load the options for setting {cls} from the file "
f"at path {benchmark}."
)
# Raise an error if any of the args in sys.argv would have been used
# up by the Setting, just to prevent any ambiguities.
_, unused_args = cls.from_known_args()
consumed_args = list(set(sys.argv[1:]) - set(unused_args))
if consumed_args:
# TODO: This could also be trigerred if there were arguments
# in the method with the same name as some from the Setting.
raise RuntimeError(
f"Cannot pass command-line arguments for the Setting when "
f"loading a benchmark, since these arguments whould have been "
f"ignored when creating the setting of type {cls} "
f"anyway: {consumed_args}"
)
drop_extras = False
# Actually load the setting from the file.
setting = cls.load(path=benchmark, drop_extra_fields=drop_extras)
return setting
def __post_init__(
self,
observation_space: gym.Space = None,
action_space: gym.Space = None,
reward_space: gym.Space = None,
):
""" Initializes the fields of the setting that weren't set from the
command-line.
"""
logger.debug("__post_init__ of Setting")
# BUG: simple-parsing sometimes parses a list with a single item, itself the
# list of transforms. Not sure if this still happens.
def is_list_of_list(v: Any) -> bool:
return isinstance(v, list) and len(v) == 1 and isinstance(v[0], list)
if is_list_of_list(self.train_transforms):
self.train_transforms = self.train_transforms[0]
if is_list_of_list(self.val_transforms):
self.val_transforms = self.val_transforms[0]
if is_list_of_list(self.test_transforms):
self.test_transforms = self.test_transforms[0]
if all(
t is None
for t in [
self.transforms,
self.train_transforms,
self.val_transforms,
self.test_transforms,
]
):
# Use these two transforms by default if no transforms are passed at all.
# TODO: Remove this after the competition perhaps.
self.transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
# If the constructor is called with just the `transforms` argument, like this:
# <SomeSetting>(dataset="bob", transforms=foo_transform)
# Then we use this value as the default for the train, val and test transforms.
if self.transforms and not any(
[self.train_transforms, self.val_transforms, self.test_transforms]
):
if not isinstance(self.transforms, list):
self.transforms = Compose([self.transforms])
self.train_transforms = self.transforms.copy()
self.val_transforms = self.transforms.copy()
self.test_transforms = self.transforms.copy()
if self.train_transforms is not None and not isinstance(
self.train_transforms, list
):
self.train_transforms = [self.train_transforms]
if self.val_transforms is not None and not isinstance(
self.val_transforms, list
):
self.val_transforms = [self.val_transforms]
if self.test_transforms is not None and not isinstance(
self.test_transforms, list
):
self.test_transforms = [self.test_transforms]
# Actually compose the list of Transforms or callables into a single transform.
self.train_transforms: Compose = Compose(self.train_transforms or [])
self.val_transforms: Compose = Compose(self.val_transforms or [])
self.test_transforms: Compose = Compose(self.test_transforms or [])
LightningDataModule.__init__(
self,
train_transforms=self.train_transforms,
val_transforms=self.val_transforms,
test_transforms=self.test_transforms,
)
self._observation_space = observation_space
self._action_space = action_space
self._reward_space = reward_space
# TODO: It's a bit confusing to also have a `config` attribute on the
# Setting. Might want to change this a bit.
self.config: Config = None
self.train_env: Environment = None # type: ignore
self.val_env: Environment = None # type: ignore
self.test_env: Environment = None # type: ignore
def test_split_batch_fn():
# from continuum.datasets import MNIST
batch_size = 5
max_batches = 10
def split_batch_fn(
batch: Tuple[Tensor, Tensor, Tensor]
) -> Tuple[Tuple[Tensor, Tensor], Tensor]:
x, y, t = batch
return (x, t), y
# dataset = MNIST("data", transform=Compose([Transforms.to_tensor, Transforms.three_channels]))
from continuum import ClassIncremental
from continuum.datasets import MNIST
from continuum.tasks import split_train_val
scenario = ClassIncremental(
MNIST("data", download=True, train=True),
increment=2,
transformations=Compose(
[Transforms.to_tensor, Transforms.three_channels]),
)
classes_per_task = scenario.nb_classes // scenario.nb_tasks
print(f"Number of classes per task {classes_per_task}.")
for i, task_dataset in enumerate(scenario):
env = PassiveEnvironment(
task_dataset,
n_classes=classes_per_task,
batch_size=batch_size,
split_batch_fn=split_batch_fn,
# Need to pass the observation space, in this case.
observation_space=spaces.Tuple([
spaces.Box(low=0, high=1, shape=(3, 28, 28)),
spaces.Discrete(scenario.nb_tasks), # task label
]),
action_space=spaces.Box(
low=np.array([i * classes_per_task]),
high=np.array([(i + 1) * classes_per_task]),
dtype=int,
),
)
assert spaces.Box(
low=np.array([i * classes_per_task]),
high=np.array([(i + 1) * classes_per_task]),
dtype=int,
).shape == (1, )
assert isinstance(env.observation_space[0], spaces.Box)
assert env.observation_space[0].shape == (batch_size, 3, 28, 28)
assert env.observation_space[1].shape == (batch_size, )
assert env.action_space.shape == (batch_size, 1)
assert env.reward_space.shape == (batch_size, 1)
env.seed(123)
obs = env.reset()
assert len(obs) == 2
x, t = obs
assert x.shape == (batch_size, 3, 28, 28)
assert t.shape == (batch_size, )
obs, reward, done, info = env.step(env.action_space.sample())
assert x.shape == (batch_size, 3, 28, 28)
assert t.shape == (batch_size, )
assert reward.shape == (batch_size, )
assert not done
env.close()
