mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
import numpy as np
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import os
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import pprint
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import random
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from typing import Any, Mapping, Optional
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from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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_printer = pprint.PrettyPrinter(indent=2, width=60)
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def summarize(obj: Any) -> Any:
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"""Return a pretty-formatted string for an object.
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This has special handling for pretty-formatting of commonly used data types
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in RLlib, such as SampleBatch, numpy arrays, etc.
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Args:
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obj: The object to format.
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Returns:
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The summarized object.
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"""
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return _printer.pformat(_summarize(obj))
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def _summarize(obj):
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if isinstance(obj, Mapping):
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return {k: _summarize(v) for k, v in obj.items()}
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elif hasattr(obj, "_asdict"):
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return {
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"type": obj.__class__.__name__,
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"data": _summarize(obj._asdict()),
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}
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elif isinstance(obj, list):
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return [_summarize(x) for x in obj]
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elif isinstance(obj, tuple):
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return tuple(_summarize(x) for x in obj)
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elif isinstance(obj, np.ndarray):
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if obj.size == 0:
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return _StringValue("np.ndarray({}, dtype={})".format(
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obj.shape, obj.dtype))
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elif obj.dtype == object or obj.dtype.type is np.str_:
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return _StringValue("np.ndarray({}, dtype={}, head={})".format(
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obj.shape, obj.dtype, _summarize(obj[0])))
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else:
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return _StringValue(
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"np.ndarray({}, dtype={}, min={}, max={}, mean={})".format(
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obj.shape, obj.dtype, round(float(np.min(obj)), 3),
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round(float(np.max(obj)), 3), round(
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float(np.mean(obj)), 3)))
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elif isinstance(obj, MultiAgentBatch):
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return {
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"type": "MultiAgentBatch",
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"policy_batches": _summarize(obj.policy_batches),
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"count": obj.count,
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}
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elif isinstance(obj, SampleBatch):
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return {
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"type": "SampleBatch",
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"data": {k: _summarize(v)
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for k, v in obj.items()},
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}
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else:
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return obj
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class _StringValue:
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def __init__(self, value):
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self.value = value
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def __repr__(self):
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return self.value
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def update_global_seed_if_necessary(framework: Optional[str] = None,
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seed: Optional[int] = None) -> None:
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"""Seed global modules such as random, numpy, torch, or tf.
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This is useful for debugging and testing.
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Args:
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framework: The framework specifier (may be None).
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seed: An optional int seed. If None, will not do
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anything.
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"""
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if seed is None:
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return
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# Python random module.
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random.seed(seed)
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# Numpy.
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np.random.seed(seed)
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# Torch.
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if framework == "torch":
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torch, _ = try_import_torch()
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torch.manual_seed(seed)
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# See https://github.com/pytorch/pytorch/issues/47672.
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cuda_version = torch.version.cuda
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if cuda_version is not None and float(torch.version.cuda) >= 10.2:
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os.environ["CUBLAS_WORKSPACE_CONFIG"] = "4096:8"
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else:
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from distutils.version import LooseVersion
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if LooseVersion(torch.__version__) >= LooseVersion("1.8.0"):
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# Not all Operations support this.
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torch.use_deterministic_algorithms(True)
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else:
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torch.set_deterministic(True)
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# This is only for Convolution no problem.
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torch.backends.cudnn.deterministic = True
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elif framework == "tf2" or framework == "tfe":
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tf1, tf, _ = try_import_tf()
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# Tf2.x.
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if framework == "tf2":
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tf.random.set_seed(seed)
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# Tf-eager.
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elif framework == "tfe":
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tf1.set_random_seed(seed)
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