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Fix support of 0-dim numpy array (#89)

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* Fix support of 0-dim numpy array.

* Do not raise exception if Batch index does not make sense since it breaks existing code.

Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
This commit is contained in:
Alexis DUBURCQ 2020-06-24 00:55:24 +02:00 committed by GitHub
parent d7dd3105bc
commit ebc551a25e
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 64 additions and 25 deletions
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23
test/base/test_batch.py
View File

@ -34,6 +34,18 @@ def test_batch():
assert batch_item.a.c == batch_dict['c']
assert isinstance(batch_item.a.d, torch.Tensor)
assert batch_item.a.d == batch_dict['d']
batch2 = Batch(a=[{
'b': np.float64(1.0),
'c': np.zeros(1),
'd': Batch(e=np.array(3.0))}])
assert len(batch2) == 1
assert list(batch2[1].keys()) == ['a']
assert len(batch2[-2].a.d.keys()) == 0
assert len(batch2[-1].keys()) > 0
assert batch2[0][0].a.c == 0.0
assert isinstance(batch2[0].a.c, np.ndarray)
assert isinstance(batch2[0].a.b, np.float64)
assert isinstance(batch2[0].a.d.e, np.float64)
def test_batch_over_batch():
@ -60,15 +72,18 @@ def test_batch_over_batch():
def test_batch_cat_and_stack():
b1 = Batch(a=[{'b': np.array([1.0]), 'd': Batch(e=np.array([3.0]))}])
b2 = Batch(a=[{'b': np.array([4.0]), 'd': Batch(e=np.array([6.0]))}])
b1 = Batch(a=[{'b': np.float64(1.0), 'd': Batch(e=np.array(3.0))}])
b2 = Batch(a=[{'b': np.float64(4.0), 'd': {'e': np.array(6.0)}}])
b_cat_out = Batch.cat((b1, b2))
b_cat_in = copy.deepcopy(b1)
b_cat_in.cat_(b2)
assert np.all(b_cat_in.a.d.e == b_cat_out.a.d.e)
assert b_cat_in.a.d.e.ndim == 2
assert np.all(b_cat_in.a.d.e == b_cat_out.a.d.e)
assert isinstance(b_cat_in.a.d.e, np.ndarray)
assert b_cat_in.a.d.e.ndim == 1
b_stack = Batch.stack((b1, b2))
assert b_stack.a.d.e.ndim == 3
assert isinstance(b_stack.a.d.e, np.ndarray)
assert b_stack.a.d.e.ndim == 2
def test_batch_over_batch_to_torch():

66
tianshou/data/batch.py
View File

@ -78,15 +78,23 @@ class Batch:
batch_dict: Optional[
Union[dict, Tuple[dict], List[dict], np.ndarray]] = None,
**kwargs) -> None:
if isinstance(batch_dict, (list, tuple, np.ndarray)) \
and len(batch_dict) > 0 and isinstance(batch_dict[0], dict):
def _is_batch_set(data: Any) -> bool:
if isinstance(data, (list, tuple)):
if len(data) > 0 and isinstance(data[0], dict):
return True
elif isinstance(data, np.ndarray):
if isinstance(data.item(0), dict):
return True
return False
if isinstance(batch_dict, np.ndarray) and batch_dict.ndim == 0:
batch_dict = batch_dict[()]
if _is_batch_set(batch_dict):
for k, v in zip(batch_dict[0].keys(),
zip(*[e.values() for e in batch_dict])):
if isinstance(v[0], dict) \
or (isinstance(v, (list, tuple, np.ndarray))
and len(v) > 0 and isinstance(v[0], dict)):
if isinstance(v[0], dict) or _is_batch_set(v[0]):
self.__dict__[k] = Batch(v)
elif isinstance(v[0], np.ndarray):
elif isinstance(v[0], (np.generic, np.ndarray)):
self.__dict__[k] = np.stack(v, axis=0)
elif isinstance(v[0], torch.Tensor):
self.__dict__[k] = torch.stack(v, dim=0)
@ -96,9 +104,7 @@ class Batch:
self.__dict__[k] = list(v)
elif isinstance(batch_dict, dict):
for k, v in batch_dict.items():
if isinstance(v, dict) \
or (isinstance(v, (list, tuple, np.ndarray))
and len(v) > 0 and isinstance(v[0], dict)):
if isinstance(v, dict) or _is_batch_set(v):
self.__dict__[k] = Batch(v)
else:
self.__dict__[k] = v
@ -124,18 +130,32 @@ class Batch:
"""
self.__init__(**state)
def __getitem__(self, index: Union[str, slice]) -> Union['Batch', dict]:
def __getitem__(self, index: Union[
str, slice, int, np.integer, np.ndarray, List[int]]) -> 'Batch':
"""Return self[index]."""
def _valid_bounds(length: int, index: Union[
slice, int, np.integer, np.ndarray, List[int]]) -> bool:
if isinstance(index, (int, np.integer)):
return -length <= index and index < length
elif isinstance(index, (list, np.ndarray)):
return _valid_bounds(length, min(index)) and \
_valid_bounds(length, max(index))
elif isinstance(index, slice):
return _valid_bounds(length, index.start) and \
_valid_bounds(length, index.stop - 1)
if isinstance(index, str):
return self.__getattr__(index)
b = Batch()
for k, v in self.__dict__.items():
if hasattr(v, '__len__'):
try:
b.__dict__.update(**{k: v[index]})
except IndexError:
continue
return b
else:
b = Batch()
for k, v in self.__dict__.items():
if isinstance(v, Batch):
b.__dict__[k] = v[index]
elif hasattr(v, '__len__') and (not isinstance(
v, (np.ndarray, torch.Tensor)) or v.ndim > 0):
if _valid_bounds(len(v), index):
b.__dict__[k] = v[index]
return b
def __getattr__(self, key: str) -> Union['Batch', Any]:
"""Return self.key"""
@ -198,7 +218,7 @@ class Batch:
device = torch.device(device)
for k, v in self.__dict__.items():
if isinstance(v, np.ndarray):
if isinstance(v, (np.generic, np.ndarray)):
v = torch.from_numpy(v).to(device)
if dtype is not None:
v = v.type(dtype)
@ -236,7 +256,7 @@ class Batch:
continue
if not hasattr(self, k) or self.__dict__[k] is None:
self.__dict__[k] = copy.deepcopy(v)
elif isinstance(v, np.ndarray):
elif isinstance(v, np.ndarray) and v.ndim > 0:
self.__dict__[k] = np.concatenate([self.__dict__[k], v])
elif isinstance(v, torch.Tensor):
self.__dict__[k] = torch.cat([self.__dict__[k], v])
@ -274,7 +294,11 @@ class Batch:
def __len__(self) -> int:
"""Return len(self)."""
r = [len(v) for k, v in self.__dict__.items() if hasattr(v, '__len__')]
r = []
for v in self.__dict__.values():
if hasattr(v, '__len__') and (not isinstance(
v, (np.ndarray, torch.Tensor)) or v.ndim > 0):
r.append(len(v))
return max(r) if len(r) > 0 else 0
def split(self, size: Optional[int] = None,