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- import numpy as np
- import os
- import pprint
- import random
- from typing import Any, Mapping, Optional
- from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
- from ray.rllib.utils.framework import try_import_tf, try_import_torch
- _printer = pprint.PrettyPrinter(indent=2, width=60)
- def summarize(obj: Any) -> Any:
- """Return a pretty-formatted string for an object.
- This has special handling for pretty-formatting of commonly used data types
- in RLlib, such as SampleBatch, numpy arrays, etc.
- Args:
- obj: The object to format.
- Returns:
- The summarized object.
- """
- return _printer.pformat(_summarize(obj))
- def _summarize(obj):
- if isinstance(obj, Mapping):
- return {k: _summarize(v) for k, v in obj.items()}
- elif hasattr(obj, "_asdict"):
- return {
- "type": obj.__class__.__name__,
- "data": _summarize(obj._asdict()),
- }
- elif isinstance(obj, list):
- return [_summarize(x) for x in obj]
- elif isinstance(obj, tuple):
- return tuple(_summarize(x) for x in obj)
- elif isinstance(obj, np.ndarray):
- if obj.size == 0:
- return _StringValue("np.ndarray({}, dtype={})".format(
- obj.shape, obj.dtype))
- elif obj.dtype == object or obj.dtype.type is np.str_:
- return _StringValue("np.ndarray({}, dtype={}, head={})".format(
- obj.shape, obj.dtype, _summarize(obj[0])))
- else:
- return _StringValue(
- "np.ndarray({}, dtype={}, min={}, max={}, mean={})".format(
- obj.shape, obj.dtype, round(float(np.min(obj)), 3),
- round(float(np.max(obj)), 3), round(
- float(np.mean(obj)), 3)))
- elif isinstance(obj, MultiAgentBatch):
- return {
- "type": "MultiAgentBatch",
- "policy_batches": _summarize(obj.policy_batches),
- "count": obj.count,
- }
- elif isinstance(obj, SampleBatch):
- return {
- "type": "SampleBatch",
- "data": {k: _summarize(v)
- for k, v in obj.items()},
- }
- else:
- return obj
- class _StringValue:
- def __init__(self, value):
- self.value = value
- def __repr__(self):
- return self.value
- def update_global_seed_if_necessary(framework: Optional[str] = None,
- seed: Optional[int] = None) -> None:
- """Seed global modules such as random, numpy, torch, or tf.
- This is useful for debugging and testing.
- Args:
- framework: The framework specifier (may be None).
- seed: An optional int seed. If None, will not do
- anything.
- """
- if seed is None:
- return
- # Python random module.
- random.seed(seed)
- # Numpy.
- np.random.seed(seed)
- # Torch.
- if framework == "torch":
- torch, _ = try_import_torch()
- torch.manual_seed(seed)
- # See https://github.com/pytorch/pytorch/issues/47672.
- cuda_version = torch.version.cuda
- if cuda_version is not None and float(torch.version.cuda) >= 10.2:
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = "4096:8"
- else:
- from distutils.version import LooseVersion
- if LooseVersion(torch.__version__) >= LooseVersion("1.8.0"):
- # Not all Operations support this.
- torch.use_deterministic_algorithms(True)
- else:
- torch.set_deterministic(True)
- # This is only for Convolution no problem.
- torch.backends.cudnn.deterministic = True
- elif framework == "tf2" or framework == "tfe":
- tf1, tf, _ = try_import_tf()
- # Tf2.x.
- if framework == "tf2":
- tf.random.set_seed(seed)
- # Tf-eager.
- elif framework == "tfe":
- tf1.set_random_seed(seed)
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