Python ops.default_session方法代码示例

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def run(self):
        self.dataflow.reset_state()

        with ops.default_session(self.sess):
            try:
                while True:
                    for dp in self.dataflow.get_data():
                        if self.coord.should_stop():
                            return
                        feed = dict(zip(self.input_vars, dp))
                        self.op.run(feed_dict=feed)
            except tf.errors.CancelledError as e:
                pass
            except Exception:
                logger.exception("Exception in EnqueueThread:")
            finally:
                try:
                    self.sess.run(self.close_op)
                except RuntimeError:    # session already closed
                    pass
                self.coord.request_stop()
                logger.info("Enqueue Thread Exited.") 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def as_default(self):
    return ops.default_session(self) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def _start_concurrency(self):
        """
        Run all threads before starting training
        """
        logger.info("Starting all threads & procs ...")
        tf.train.start_queue_runners(sess=self.sess.get(), coord=self.coord, daemon=True, start=True)

        #with self.sess.as_default():
        with ops.default_session(self.sess):
            # avoid sigint get handled by other processes
            start_proc_mask_signal(self._extra_threads_procs) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def as_default(self):
    """Returns a context manager that makes this object the default session.

    Use with the `with` keyword to specify that calls to
    @{tf.Operation.run} or @{tf.Tensor.eval} should be executed in
    this session.

    ```python
    c = tf.constant(..)
    sess = tf.Session()

    with sess.as_default():
      assert tf.get_default_session() is sess
      print(c.eval())
    ```

    To get the current default session, use @{tf.get_default_session}.

    *N.B.* The `as_default` context manager *does not* close the
    session when you exit the context, and you must close the session
    explicitly.

    ```python
    c = tf.constant(...)
    sess = tf.Session()
    with sess.as_default():
      print(c.eval())
    # ...
    with sess.as_default():
      print(c.eval())

    sess.close()
    ```

    Alternatively, you can use `with tf.Session():` to create a
    session that is automatically closed on exiting the context,
    including when an uncaught exception is raised.

    *N.B.* The default session is a property of the current thread. If you
    create a new thread, and wish to use the default session in that
    thread, you must explicitly add a `with sess.as_default():` in that
    thread's function.

    *N.B.* Entering a `with sess.as_default():` block does not affect
    the current default graph. If you are using multiple graphs, and
    `sess.graph` is different from the value of @{tf.get_default_graph},
    you must explicitly enter a `with sess.graph.as_default():` block
    to make `sess.graph` the default graph.

    Returns:
      A context manager using this session as the default session.
    """
    return ops.default_session(self) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def as_default(self):
    """Returns a context manager that makes this object the default session.

    Use with the `with` keyword to specify that calls to
    [`Operation.run()`](../../api_docs/python/framework.md#Operation.run) or
    [`Tensor.eval()`](../../api_docs/python/framework.md#Tensor.eval) should be
    executed in this session.

    ```python
    c = tf.constant(..)
    sess = tf.Session()

    with sess.as_default():
      assert tf.get_default_session() is sess
      print(c.eval())
    ```

    To get the current default session, use
    [`tf.get_default_session()`](#get_default_session).


    *N.B.* The `as_default` context manager *does not* close the
    session when you exit the context, and you must close the session
    explicitly.

    ```python
    c = tf.constant(...)
    sess = tf.Session()
    with sess.as_default():
      print(c.eval())
    # ...
    with sess.as_default():
      print(c.eval())

    sess.close()
    ```

    Alternatively, you can use `with tf.Session():` to create a
    session that is automatically closed on exiting the context,
    including when an uncaught exception is raised.

    *N.B.* The default graph is a property of the current thread. If you
    create a new thread, and wish to use the default session in that
    thread, you must explicitly add a `with sess.as_default():` in that
    thread's function.

    Returns:
      A context manager using this session as the default session.

    """
    return ops.default_session(self) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import default_session [as 别名]
def main_loop(self):
        # some final operations that might modify the graph
        logger.info("[{}] Initializing graph variables ...".format(os.environ['SLURMD_NODENAME']))

        #self.sess.run(tf.initialize_all_variables())

        self.config.session_init.init(self.sess)
#        tf.get_default_graph().finalize()
        callbacks = self.config.callbacks
        logger.info("[{}] Starting concurrency...".format(os.environ['SLURMD_NODENAME']))
        self._start_concurrency()
        #with self.sess.as_default():
        logger.info("[{}] Setting default session".format(os.environ['SLURMD_NODENAME']))
        with ops.default_session(self.sess):
            try:
                logger.info("[{}] Getting global step".format(os.environ['SLURMD_NODENAME']))
                self.global_step = get_global_step()
                logger.info("[{}] Start training with global_step={}".format(os.environ['SLURMD_NODENAME'], self.global_step))

                if self.config.extra_arg['is_chief']:
                    server = neptune_mp_server.Server(
                            self.config.extra_arg['n_workers'],
                            port=self.config.extra_arg['port'],
                            debug_charts=self.config.extra_arg['debug_charts'],
                            adam_debug=self.config.extra_arg['adam_debug'],
                            schedule_hyper=self.config.extra_arg['schedule_hyper'],
                            experiment_dir=self.config.extra_arg['experiment_dir'])
                    server.main_loop()

                callbacks.before_train()
                for epoch in range(self.config.starting_epoch, self.config.max_epoch+1):
                    with timed_operation(
                        'Epoch {}, global_step={}'.format(
                            epoch, self.global_step + self.config.step_per_epoch)):
                        for step in tqdm.trange(
                                self.config.step_per_epoch,
                                **get_tqdm_kwargs(leave=True)):
                            if self.coord.should_stop():
                                return
                            self.run_step()
                            callbacks.trigger_step()
                            try:
                                self.global_step += 1
                            except:
                                self.global_step = -1
                        self.trigger_epoch()
                        print 'EPOCH ENDS HERE'
            except (KeyboardInterrupt, Exception):
                raise
            finally:
                # Do I need to run queue.close?
                print('Handling finally block')
                callbacks.after_train()
                self.coord.request_stop()
                self.summary_writer.close()
                self.sess.close()