Python tensorflow.QueueBase() Examples
The following are 11
code examples of tensorflow.QueueBase().
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Example #1
Source File: trainer.py From VDAIC2017 with MIT License | 6 votes |
def __init__(self, config, input_queue=None, predict_tower=None): """ :param config: a `TrainConfig` instance :param input_queue: a `tf.QueueBase` instance to be used to buffer datapoints. Defaults to a FIFO queue of size 100. :param predict_tower: list of gpu relative idx to run prediction. default to be [0]. Use -1 for cpu. """ super(QueueInputTrainer, self).__init__(config) self.input_vars = self.model.get_input_vars() # use a smaller queue size for now, to avoid https://github.com/tensorflow/tensorflow/issues/2942 if input_queue is None: self.input_queue = tf.FIFOQueue( 50, [x.dtype for x in self.input_vars], name='input_queue') else: self.input_queue = input_queue # by default, use the first training gpu for prediction self.predict_tower = predict_tower or [0] self.dequed_inputs = None
Example #2
Source File: trainer.py From ternarynet with Apache License 2.0 | 6 votes |
def __init__(self, config, input_queue=None, predict_tower=None): """ :param config: a `TrainConfig` instance :param input_queue: a `tf.QueueBase` instance to be used to buffer datapoints. Defaults to a FIFO queue of size 100. :param predict_tower: list of gpu relative idx to run prediction. default to be [0]. Use -1 for cpu. """ super(QueueInputTrainer, self).__init__(config) self.input_vars = self.model.get_input_vars() # use a smaller queue size for now, to avoid https://github.com/tensorflow/tensorflow/issues/2942 if input_queue is None: self.input_queue = tf.FIFOQueue( 50, [x.dtype for x in self.input_vars], name='input_queue') else: self.input_queue = input_queue # by default, use the first training gpu for prediction self.predict_tower = predict_tower or [0] self.dequed_inputs = None
Example #3
Source File: trainer.py From DDRL with Apache License 2.0 | 5 votes |
def __init__(self, config, input_queue=None, predict_tower=None): """ :param config: a `TrainConfig` instance :param input_queue: a `tf.QueueBase` instance to be used to buffer datapoints. Defaults to a FIFO queue of size 100. :param predict_tower: list of gpu relative idx to run prediction. default to be [0]. Use -1 for cpu. """ super(QueueInputTrainer, self).__init__(config) self.input_vars = self.model.get_input_vars() # use a smaller queue size for now, to avoid https://github.com/tensorflow/tensorflow/issues/2942 queue_size = config.extra_arg['queue_size'] self.dummy_predictor = config.extra_arg['dummy_predictor'] print 'DUMMY PREDICTOR', self.dummy_predictor if input_queue is None: self.input_queue = tf.FIFOQueue( queue_size, [x.dtype for x in self.input_vars], name='input_queue') else: self.input_queue = input_queue # by default, use the first training gpu for prediction self.predict_tower = predict_tower or [0] self.dequed_inputs = None self.queue_size_op = self.input_queue.size()
Example #4
Source File: input_source.py From petridishnn with MIT License | 5 votes |
def __init__(self, ds, queue=None): """ Args: ds(DataFlow): the input DataFlow. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 50. """ if not isinstance(ds, DataFlow): raise ValueError("QueueInput takes a DataFlow! Got {}".format(ds)) self.queue = queue self.ds = ds self._inf_ds = RepeatedData(ds, -1) self._started = False
Example #5
Source File: input_source.py From petridishnn with MIT License | 5 votes |
def __init__(self, ds, batch_size, queue=None): """ Args: ds(DataFlow): the input DataFlow. batch_size(int): the batch size. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 3000. """ super(BatchQueueInput, self).__init__(ds, queue) self.batch_size = int(batch_size)
Example #6
Source File: input_source.py From ADL with MIT License | 5 votes |
def __init__(self, ds, queue=None): """ Args: ds(DataFlow): the input DataFlow. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 50. """ if not isinstance(ds, DataFlow): raise ValueError("QueueInput takes a DataFlow! Got {}".format(ds)) self.queue = queue self.ds = ds self._inf_ds = RepeatedData(ds, -1) self._started = False
Example #7
Source File: input_source.py From ADL with MIT License | 5 votes |
def __init__(self, ds, batch_size, queue=None): """ Args: ds(DataFlow): the input DataFlow. batch_size(int): the batch size. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 3000. """ super(BatchQueueInput, self).__init__(ds, queue) self.batch_size = int(batch_size)
Example #8
Source File: plan_test.py From fold with Apache License 2.0 | 5 votes |
def test_init_loom(self): p = plan.TrainPlan() p.compiler = block_compiler.Compiler().compile(blocks.Scalar()) p.batch_size = 3 p.task = 13 p.num_dequeuers = 7 self.assertRaisesWithLiteralMatch( ValueError, 'must have at least one PS task; 0', p.init_loom) p.ps_tasks = 5 self.assertRaisesWithLiteralMatch( ValueError, 'worker_replicas must be at least num_queues + ' 'num_dequeuers; 0 vs. 5 + 7 = 12', p.init_loom) p.worker_replicas = 14 # Would be best to actually create a queue and inspect it, but # tf.QueueBase doesn't currently expose these properties. self.assertEqual(p._create_queue(3, ctor=dict)['capacity'], 12) p.queue_capacity = 42 q_dict = p._create_queue(3, ctor=dict) self.assertEqual(q_dict['capacity'], 42) self.assertEqual(q_dict['shared_name'], 'tensorflow_fold_plan_queue3') self.assertEqual(p.init_loom(), (True, False)) p.compiler = block_compiler.Compiler().compile(blocks.Scalar()) p.task = 3 self.assertEqual(p.init_loom(), (False, True)) p.compiler = block_compiler.Compiler().compile(blocks.Scalar()) p.num_dequeuers = 0 self.assertRaisesWithLiteralMatch( ValueError, 'cannot specify queue_capacity without also ' 'specifying num_dequeuers', p.init_loom) p.compiler = block_compiler.Compiler().compile(blocks.Scalar()) p.queue_capacity = 0 self.assertEqual(p.init_loom(), (True, True))
Example #9
Source File: trainer.py From Distributed-BA3C with Apache License 2.0 | 5 votes |
def __init__(self, config, input_queue=None, predict_tower=None): """ :param config: a `TrainConfig` instance :param input_queue: a `tf.QueueBase` instance to be used to buffer datapoints. Defaults to a FIFO queue of size 100. :param predict_tower: list of gpu relative idx to run prediction. default to be [0]. Use -1 for cpu. """ super(QueueInputTrainer, self).__init__(config) self.input_vars = self.model.get_input_vars() # use a smaller queue size for now, to avoid https://github.com/tensorflow/tensorflow/issues/2942 queue_size = config.extra_arg['queue_size'] self.dummy_predictor = config.extra_arg['dummy_predictor'] print 'DUMMY PREDICTOR', self.dummy_predictor if input_queue is None: self.input_queue = tf.FIFOQueue( queue_size, [x.dtype for x in self.input_vars], name='input_queue') else: self.input_queue = input_queue # by default, use the first training gpu for prediction self.predict_tower = predict_tower or [0] self.dequed_inputs = None self.queue_size_op = self.input_queue.size()
Example #10
Source File: input_source.py From tensorpack with Apache License 2.0 | 5 votes |
def __init__(self, ds, queue=None): """ Args: ds(DataFlow): the input DataFlow. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 50. """ if not isinstance(ds, DataFlow): raise ValueError("QueueInput takes a DataFlow! Got {}".format(ds)) self.queue = queue self.ds = ds self._inf_ds = RepeatedData(ds, -1) self._started = False
Example #11
Source File: input_source.py From tensorpack with Apache License 2.0 | 5 votes |
def __init__(self, ds, batch_size, queue=None): """ Args: ds(DataFlow): the input DataFlow. batch_size(int): the batch size. queue (tf.QueueBase): A :class:`tf.QueueBase` whose type should match the corresponding input signature of the model. Defaults to a FIFO queue of size 3000. """ super(BatchQueueInput, self).__init__(ds, queue) self.batch_size = int(batch_size)