def concat_samples(samples: List[Dict[str, TensorType]]) -> \
Union["SampleBatch", "MultiAgentBatch"]:
"""Concatenates n data dicts or MultiAgentBatches.
Args:
samples (List[Dict[TensorType]]]): List of dicts of data (numpy).
Returns:
Union[SampleBatch, MultiAgentBatch]: A new (compressed)
SampleBatch or MultiAgentBatch.
"""
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
seq_lens = []
concat_samples = []
for s in samples:
if s.count > 0:
concat_samples.append(s)
if s.seq_lens is not None:
seq_lens.extend(s.seq_lens)
out = {}
for k in concat_samples[0].keys():
out[k] = concat_aligned([s[k] for s in concat_samples],
time_major=concat_samples[0].time_major)
return SampleBatch(out,
_seq_lens=seq_lens,
_time_major=concat_samples[0].time_major)
def concat(self, other: "SampleBatch") -> "SampleBatch":
"""Returns a new SampleBatch with each data column concatenated.
Args:
other (SampleBatch): The other SampleBatch object to concat to this
one.
Returns:
SampleBatch: The new SampleBatch, resulting from concating `other`
to `self`.
Examples:
>>> b1 = SampleBatch({"a": [1, 2]})
>>> b2 = SampleBatch({"a": [3, 4, 5]})
>>> print(b1.concat(b2))
{"a": [1, 2, 3, 4, 5]}
"""
if self.keys() != other.keys():
raise ValueError(
"SampleBatches to concat must have same columns! {} vs {}".
format(list(self.keys()), list(other.keys())))
out = {}
for k in self.keys():
out[k] = concat_aligned([self[k], other[k]])
return SampleBatch(out)
def concat_samples(samples):
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
out = {}
samples = [s for s in samples if s.count > 0]
for k in samples[0].keys():
out[k] = concat_aligned([s[k] for s in samples])
return SampleBatch(out)
def concat(self, other):
"""Returns a new SampleBatch with each data column concatenated.
Examples:
>>> b1 = SampleBatch({"a": [1, 2]})
>>> b2 = SampleBatch({"a": [3, 4, 5]})
>>> print(b1.concat(b2))
{"a": [1, 2, 3, 4, 5]}
"""
assert self.keys() == other.keys(), "must have same columns"
out = {}
for k in self.keys():
out[k] = concat_aligned([self[k], other[k]])
return SampleBatch(out)
def concat_samples(samples: List["SampleBatch"]) -> \
Union["SampleBatch", "MultiAgentBatch"]:
"""Concatenates n data dicts or MultiAgentBatches.
Args:
samples (List[Dict[str, TensorType]]]): List of dicts of data
(numpy).
Returns:
Union[SampleBatch, MultiAgentBatch]: A new (compressed)
SampleBatch or MultiAgentBatch.
"""
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
seq_lens = []
concat_samples = []
zero_padded = samples[0].zero_padded
max_seq_len = samples[0].max_seq_len
for s in samples:
if s.count > 0:
assert s.zero_padded == zero_padded
if zero_padded:
assert s.max_seq_len == max_seq_len
concat_samples.append(s)
if s.get("seq_lens") is not None:
seq_lens.extend(s["seq_lens"])
# If we don't have any samples (no or only empty SampleBatches),
# return an empty SampleBatch here.
if len(concat_samples) == 0:
return SampleBatch()
# Collect the concat'd data.
concatd_data = {}
for k in concat_samples[0].keys():
concatd_data[k] = concat_aligned(
[s[k] for s in concat_samples],
time_major=concat_samples[0].time_major)
# Return a new (concat'd) SampleBatch.
return SampleBatch(
concatd_data,
seq_lens=seq_lens,
_time_major=concat_samples[0].time_major,
_zero_padded=zero_padded,
_max_seq_len=max_seq_len,
)
def concat_samples(samples):
"""Concatenates n data dicts or MultiAgentBatches.
Args:
samples (List[Dict[np.ndarray]]]): List of dicts of data (numpy).
Returns:
Union[SampleBatch,MultiAgentBatch]: A new (compressed) SampleBatch/
MultiAgentBatch.
"""
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
out = {}
samples = [s for s in samples if s.count > 0]
for k in samples[0].keys():
out[k] = concat_aligned([s[k] for s in samples])
return SampleBatch(out)
def concat(self, other):
"""Returns a new SampleBatch with each data column concatenated.
Examples:
>>> b1 = SampleBatch({"a": [1, 2]})
>>> b2 = SampleBatch({"a": [3, 4, 5]})
>>> print(b1.concat(b2))
{"a": [1, 2, 3, 4, 5]}
"""
if self.keys() != other.keys():
raise ValueError(
"SampleBatches to concat must have same columns! {} vs {}".
format(list(self.keys()), list(other.keys())))
out = {}
for k in self.keys():
out[k] = concat_aligned([self[k], other[k]])
return SampleBatch(out)
def concat_samples(samples: List["SampleBatch"]) -> \
Union["SampleBatch", "MultiAgentBatch"]:
"""Concatenates n data dicts or MultiAgentBatches.
Args:
samples (List[Dict[TensorType]]]): List of dicts of data (numpy).
Returns:
Union[SampleBatch, MultiAgentBatch]: A new (compressed)
SampleBatch or MultiAgentBatch.
"""
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
seq_lens = []
concat_samples = []
zero_padded = samples[0].zero_padded
max_seq_len = samples[0].max_seq_len
for s in samples:
if s.count > 0:
assert s.zero_padded == zero_padded
if zero_padded:
assert s.max_seq_len == max_seq_len
concat_samples.append(s)
if s.seq_lens is not None:
seq_lens.extend(s.seq_lens)
out = {}
for k in concat_samples[0].keys():
out[k] = concat_aligned(
[s[k] for s in concat_samples],
time_major=concat_samples[0].time_major)
return SampleBatch(
out,
_seq_lens=np.array(seq_lens, dtype=np.int32),
_time_major=concat_samples[0].time_major,
_dont_check_lens=True,
_zero_padded=zero_padded,
_max_seq_len=max_seq_len,
)
def concat_key(*values):
return concat_aligned(values, time_major)
def concat_samples(
samples: Union[List["SampleBatch"], List["MultiAgentBatch"]],
) -> Union["SampleBatch", "MultiAgentBatch"]:
"""Concatenates n SampleBatches or MultiAgentBatches.
Args:
samples (Union[List[SampleBatch], List[MultiAgentBatch]]): List of
SampleBatches or MultiAgentBatches to be concatenated.
Returns:
Union[SampleBatch, MultiAgentBatch]: A new (concatenated)
SampleBatch or MultiAgentBatch.
Examples:
>>> b1 = SampleBatch({"a": np.array([1, 2]),
... "b": np.array([10, 11])})
>>> b2 = SampleBatch({"a": np.array([3]),
... "b": np.array([12])})
>>> print(SampleBatch.concat_samples([b1, b2]))
{"a": np.array([1, 2, 3]), "b": np.array([10, 11, 12])}
"""
if isinstance(samples[0], MultiAgentBatch):
return MultiAgentBatch.concat_samples(samples)
concatd_seq_lens = []
concat_samples = []
zero_padded = samples[0].zero_padded
max_seq_len = samples[0].max_seq_len
time_major = samples[0].time_major
for s in samples:
if s.count > 0:
assert s.zero_padded == zero_padded
assert s.time_major == time_major
if zero_padded:
assert s.max_seq_len == max_seq_len
concat_samples.append(s)
if s.get(SampleBatch.SEQ_LENS) is not None:
concatd_seq_lens.extend(s[SampleBatch.SEQ_LENS])
# If we don't have any samples (0 or only empty SampleBatches),
# return an empty SampleBatch here.
if len(concat_samples) == 0:
return SampleBatch()
# Collect the concat'd data.
concatd_data = {}
def concat_key(*values):
return concat_aligned(values, time_major)
try:
for k in concat_samples[0].keys():
if k == "infos":
concatd_data[k] = concat_aligned(
[s[k] for s in concat_samples], time_major=time_major)
else:
concatd_data[k] = tree.map_structure(
concat_key, *[c[k] for c in concat_samples])
except Exception:
raise ValueError(f"Cannot concat data under key '{k}', b/c "
"sub-structures under that key don't match. "
f"`samples`={samples}")
# Return a new (concat'd) SampleBatch.
return SampleBatch(
concatd_data,
seq_lens=concatd_seq_lens,
_time_major=time_major,
_zero_padded=zero_padded,
_max_seq_len=max_seq_len,
)