Source code for tensorpack.predict.base

# -*- coding: utf-8 -*-
# File:

from abc import ABCMeta, abstractmethod
import six
import tensorflow as tf

from ..input_source import PlaceholderInput
from ..tfutils.common import get_tensors_by_names
from ..tfutils.tower import PredictTowerContext

__all__ = ['PredictorBase', 'AsyncPredictorBase',
           'OnlinePredictor', 'OfflinePredictor',

[docs]@six.add_metaclass(ABCMeta) class PredictorBase(object): """ Base class for all predictors. Attributes: return_input (bool): whether the call will also return (inputs, outputs) or just outputs """
[docs] def __call__(self, *dp): """ Call the predictor on some inputs. Example: When you have a predictor defined with two inputs, call it with: .. code-block:: python predictor(e1, e2) """ output = self._do_call(dp) if self.return_input: return (dp, output) else: return output
@abstractmethod def _do_call(self, dp): """ Args: dp: input datapoint. must have the same length as input_names Returns: output as defined by the config """
[docs]class AsyncPredictorBase(PredictorBase): """ Base class for all async predictors. """
[docs] @abstractmethod def put_task(self, dp, callback=None): """ Args: dp (list): A datapoint as inputs. It could be either batched or not batched depending on the predictor implementation). callback: a thread-safe callback to get called with either outputs or (inputs, outputs). Returns: concurrent.futures.Future: a Future of results """
[docs] @abstractmethod def start(self): """ Start workers """
def _do_call(self, dp): assert six.PY3, "With Python2, sync methods not available for async predictor" fut = self.put_task(dp) # in Tornado, Future.result() doesn't wait return fut.result()
[docs]class OnlinePredictor(PredictorBase): """ A predictor which directly use an existing session and given tensors. """ ACCEPT_OPTIONS = False """ See Session.make_callable """ sess = None """ The tf.Session object associated with this predictor. """
[docs] def __init__(self, input_tensors, output_tensors, return_input=False, sess=None): """ Args: input_tensors (list): list of names. output_tensors (list): list of names. return_input (bool): same as :attr:`PredictorBase.return_input`. sess (tf.Session): the session this predictor runs in. If None, will use the default session at the first call. Note that in TensorFlow, default session is thread-local. """ self.return_input = return_input self.input_tensors = input_tensors self.output_tensors = output_tensors self.sess = sess if sess is not None: self._callable = sess.make_callable( fetches=output_tensors, feed_list=input_tensors, accept_options=self.ACCEPT_OPTIONS) else: self._callable = None
def _do_call(self, dp): assert len(dp) == len(self.input_tensors), \ "{} != {}".format(len(dp), len(self.input_tensors)) if self.sess is None: self.sess = tf.get_default_session() assert self.sess is not None, "Predictor isn't called under a default session!" if self._callable is None: self._callable = self.sess.make_callable( fetches=self.output_tensors, feed_list=self.input_tensors, accept_options=self.ACCEPT_OPTIONS) # run_metadata = tf.RunMetadata() # options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) return self._callable(*dp)
[docs]class OfflinePredictor(OnlinePredictor): """ A predictor built from a given config. A single-tower model will be built without any prefix. Example: .. code-block:: python config = PredictConfig(model=my_model, inputs_names=['image'], output_names=['linear/output', 'prediction']) predictor = OfflinePredictor(config) batch_image = np.random.rand(1, 100, 100, 3) batch_output, batch_prediction = predictor(batch_image) """
[docs] def __init__(self, config): """ Args: config (PredictConfig): the config to use. """ self.graph = config._maybe_create_graph() with self.graph.as_default(): input = PlaceholderInput() input.setup(config.input_signature) with PredictTowerContext(''): config.tower_func(*input.get_input_tensors()) input_tensors = get_tensors_by_names(config.input_names) output_tensors = get_tensors_by_names(config.output_names) config.session_init._setup_graph() sess = config.session_creator.create_session() config.session_init._run_init(sess) super(OfflinePredictor, self).__init__( input_tensors, output_tensors, config.return_input, sess)