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[BugFix] Fix CompositeSpec.to_numpy method #931

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merged 7 commits into from
Feb 21, 2023
Merged

[BugFix] Fix CompositeSpec.to_numpy method #931

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97ddd1a
Replace _to_numpy by to_numpy
riiswa Feb 21, 2023
2d0cfb1
Add unit test
riiswa Feb 21, 2023
f02d7e0
init
vmoens Feb 21, 2023
c2b7659
amend
vmoens Feb 21, 2023
287de7f
amend
vmoens Feb 21, 2023
358807d
amend
vmoens Feb 21, 2023
7469d01
Merge branch 'fix_envpool' into fix/composite_spec_to_numpy
vmoens Feb 21, 2023
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8 changes: 8 additions & 0 deletions test/test_specs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -446,6 +446,14 @@ def test_is_in(self, is_complete, device, dtype):
r = ts.rand()
assert ts.is_in(r)

def test_to_numpy(self, is_complete, device, dtype):
ts = self._composite_spec(is_complete, device, dtype)
for _ in range(100):
r = ts.rand()
for key, value in ts.to_numpy(r).items():
spec = ts[key]
assert (spec.to_numpy(r[key]) == value).all()

@pytest.mark.parametrize("shape", [[], [3]])
def test_project(self, is_complete, device, dtype, shape):
ts = self._composite_spec(is_complete, device, dtype)
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2 changes: 1 addition & 1 deletion torchrl/data/tensor_specs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1947,7 +1947,7 @@ def clone(self) -> CompositeSpec:
)

def to_numpy(self, val: TensorDict, safe: bool = True) -> dict:
return {key: self[key]._to_numpy(val) for key, val in val.items()}
return {key: self[key].to_numpy(val) for key, val in val.items()}

def zero(self, shape=None) -> TensorDictBase:
if shape is None:
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57 changes: 11 additions & 46 deletions torchrl/envs/vec_env.py
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Expand Up @@ -33,11 +33,6 @@
# Libraries necessary for MultiThreadedEnv
import envpool

try:
import gym
except ModuleNotFoundError:
import gymnasium as gym

import treevalue

_has_envpool = True
Expand Down Expand Up @@ -1181,66 +1176,36 @@ def _get_input_spec(self) -> TensorSpec:
# DM_Control-compatible specs as env.spec.action_spec(). We use the Gym ones.

# Gym specs produced by EnvPool don't contain batch_size, we add it to satisfy checks in EnvBase
action_spec = self._add_shape_to_spec(self._env.spec.action_space)
transformed_spec = _gym_to_torchrl_spec_transform(
action_spec,
action_spec = _gym_to_torchrl_spec_transform(
self._env.spec.action_space,
device=self.device,
categorical_action_encoding=True,
)
if not transformed_spec.shape:
transformed_spec.shape = (self.num_workers,)
action_spec = self._add_shape_to_spec(action_spec)
return CompositeSpec(
action=transformed_spec,
shape=transformed_spec.shape,
action=action_spec,
shape=(self.num_workers,),
)

def _get_observation_spec(self) -> TensorSpec:
# Gym specs produced by EnvPool don't contain batch_size, we add it to satisfy checks in EnvBase
obs_spec = self._add_shape_to_spec(self._env.spec.observation_space)
observation_spec = _gym_to_torchrl_spec_transform(
obs_spec,
self._env.spec.observation_space,
device=self.device,
categorical_action_encoding=True,
)
if isinstance(observation_spec, CompositeSpec):
observation_spec.shape = (self.num_workers,)
observation_spec = self._add_shape_to_spec(observation_spec)
return CompositeSpec(
observation=observation_spec,
shape=observation_spec.shape,
shape=(self.num_workers,),
)

def _add_shape_to_spec(
self, spec: gym.spaces.space.Space
) -> gym.spaces.space.Space:
if isinstance(spec, gym.spaces.Box):
return gym.spaces.Box(
low=np.stack([spec.low] * self.num_workers),
high=np.stack([spec.high] * self.num_workers),
dtype=spec.dtype,
shape=(self.num_workers, *spec.shape),
)
if isinstance(spec, gym.spaces.dict.Dict):
spec_dict = {}
for key in spec.keys():
if isinstance(spec[key], gym.spaces.Box):
spec_dict[key] = gym.spaces.Box(
low=np.stack([spec[key].low] * self.num_workers),
high=np.stack([spec[key].high] * self.num_workers),
dtype=spec[key].dtype,
shape=(self.num_workers, *spec[key].shape),
)
elif isinstance(spec[key], gym.spaces.dict.Dict):
# If needed, we could add support by applying this function recursively
raise TypeError("Nested specs with depth > 1 are not supported.")
return spec_dict
if isinstance(spec, gym.spaces.discrete.Discrete):
# Discrete spec in Gym doesn't have shape, so nothing to change
return spec
raise TypeError(f"Unsupported spec type {spec.__class__}.")
def _add_shape_to_spec(self, spec: TensorSpec) -> TensorSpec:
return spec.expand((self.num_workers, *spec.shape))

def _get_reward_spec(self) -> TensorSpec:
return UnboundedContinuousTensorSpec(
device=self.device, shape=(self.num_workers,)
device=self.device, shape=(self.num_workers, 1)
)

def __repr__(self) -> str:
Expand Down