mirror of
https://github.com/vale981/ray
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120 lines
4 KiB
Python
120 lines
4 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import numpy as np
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import threading
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from ray.rllib.policy.sample_batch import MultiAgentBatch
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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logger = logging.getLogger(__name__)
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@PublicAPI
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class InputReader(object):
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"""Input object for loading experiences in policy evaluation."""
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@PublicAPI
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def next(self):
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"""Return the next batch of experiences read.
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Returns:
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SampleBatch or MultiAgentBatch read.
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"""
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raise NotImplementedError
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@PublicAPI
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def tf_input_ops(self, queue_size=1):
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"""Returns TensorFlow queue ops for reading inputs from this reader.
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The main use of these ops is for integration into custom model losses.
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For example, you can use tf_input_ops() to read from files of external
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experiences to add an imitation learning loss to your model.
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This method creates a queue runner thread that will call next() on this
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reader repeatedly to feed the TensorFlow queue.
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Arguments:
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queue_size (int): Max elements to allow in the TF queue.
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Example:
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>>> class MyModel(rllib.model.Model):
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... def custom_loss(self, policy_loss, loss_inputs):
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... reader = JsonReader(...)
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... input_ops = reader.tf_input_ops()
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... logits, _ = self._build_layers_v2(
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... {"obs": input_ops["obs"]},
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... self.num_outputs, self.options)
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... il_loss = imitation_loss(logits, input_ops["action"])
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... return policy_loss + il_loss
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You can find a runnable version of this in examples/custom_loss.py.
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Returns:
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dict of Tensors, one for each column of the read SampleBatch.
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"""
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if hasattr(self, "_queue_runner"):
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raise ValueError(
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"A queue runner already exists for this input reader. "
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"You can only call tf_input_ops() once per reader.")
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logger.info("Reading initial batch of data from input reader.")
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batch = self.next()
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if isinstance(batch, MultiAgentBatch):
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raise NotImplementedError(
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"tf_input_ops() is not implemented for multi agent batches")
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keys = [
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k for k in sorted(batch.keys())
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if np.issubdtype(batch[k].dtype, np.number)
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]
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dtypes = [batch[k].dtype for k in keys]
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shapes = {
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k: (-1, ) + s[1:]
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for (k, s) in [(k, batch[k].shape) for k in keys]
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}
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queue = tf.FIFOQueue(capacity=queue_size, dtypes=dtypes, names=keys)
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tensors = queue.dequeue()
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logger.info("Creating TF queue runner for {}".format(self))
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self._queue_runner = _QueueRunner(self, queue, keys, dtypes)
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self._queue_runner.enqueue(batch)
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self._queue_runner.start()
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out = {k: tf.reshape(t, shapes[k]) for k, t in tensors.items()}
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return out
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class _QueueRunner(threading.Thread):
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"""Thread that feeds a TF queue from a InputReader."""
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def __init__(self, input_reader, queue, keys, dtypes):
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threading.Thread.__init__(self)
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self.sess = tf.get_default_session()
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self.daemon = True
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self.input_reader = input_reader
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self.keys = keys
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self.queue = queue
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self.placeholders = [tf.placeholder(dtype) for dtype in dtypes]
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self.enqueue_op = queue.enqueue(dict(zip(keys, self.placeholders)))
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def enqueue(self, batch):
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data = {
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self.placeholders[i]: batch[key]
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for i, key in enumerate(self.keys)
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}
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self.sess.run(self.enqueue_op, feed_dict=data)
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def run(self):
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while True:
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try:
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batch = self.input_reader.next()
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self.enqueue(batch)
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except Exception:
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logger.exception("Error reading from input")
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