Python tensorflow.python.framework.ops.get_from_proto_function() Examples

The following are 2 code examples of tensorflow.python.framework.ops.get_from_proto_function(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.framework.ops , or try the search function .
Example #1
Source File: in_graph_parallel.py    From parallax with Apache License 2.0 4 votes vote down vote up 
def _handle_collection_def(multi_gpu_meta_graph_def, op_names_to_replicate,
                           num_replicas):
    allow_bytes_list_keys = [tf.GraphKeys.QUEUE_RUNNERS,
                             tf.GraphKeys.GLOBAL_VARIABLES,
                             tf.GraphKeys.TRAINABLE_VARIABLES,
                             tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
                             tf.GraphKeys.LOCAL_VARIABLES,
                             tf.GraphKeys.MODEL_VARIABLES,
                             tf.GraphKeys.GRADIENTS_INFO,
                             tf.GraphKeys.GLOBAL_STEP]
    keys_to_remove = []
    for key, col_def in multi_gpu_meta_graph_def.collection_def.items():
        kind = col_def.WhichOneof("kind")
        # Update node_list collections (e.g., GLOBAL_STEP, TRAIN_OP, UPDATE_OP,
        # LOSSES, ...)
        if kind == 'node_list':
            new_col_def = get_new_col_def_of_node_list(
                            col_def, op_names_to_replicate, num_replicas)
            multi_gpu_meta_graph_def.collection_def[key].Clear()
            multi_gpu_meta_graph_def.collection_def[key].CopyFrom(new_col_def)
        elif kind == 'bytes_list':
            if ops.get_from_proto_function(key):
                # Collections in allow_bytes_list_keys will be handled
                # explicitly below
                # (e.g., QUEUE_RUNNERS, LOCAL_VARIABLES, ...)
                if key in allow_bytes_list_keys:
                    continue
                # Remove unhandled collections (e.g., COND_CONTEXT)
                # TODO: Handle all protos in tf.GraphKeys
                else:
                    keys_to_remove.append(key)
            # Keep collections without proto function
            # (e.g., user defined string)
            else:
                continue
        else:
            raise RuntimeError("Should not reach here")
    for key in keys_to_remove:
        del multi_gpu_meta_graph_def.collection_def[key]

    # Update QUEUE_RUNNERS and LOCAL_VARIABLES collection
    update_queue_runners(multi_gpu_meta_graph_def, op_names_to_replicate,
                          num_replicas)
    update_local_variables(multi_gpu_meta_graph_def, op_names_to_replicate,
                            num_replicas)
    update_shard_info_for_in_graph(multi_gpu_meta_graph_def, num_replicas)
Example #2
Source File: in_graph_parallel.py    From parallax with Apache License 2.0 4 votes vote down vote up 
def _handle_collection_def(multi_gpu_meta_graph_def, op_names_to_replicate,
                           num_replicas):
    allow_bytes_list_keys = [tf.GraphKeys.QUEUE_RUNNERS,
                             tf.GraphKeys.GLOBAL_VARIABLES,
                             tf.GraphKeys.TRAINABLE_VARIABLES,
                             tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
                             tf.GraphKeys.LOCAL_VARIABLES,
                             tf.GraphKeys.MODEL_VARIABLES,
                             tf.GraphKeys.GRADIENTS_INFO,
                             tf.GraphKeys.GLOBAL_STEP]
    keys_to_remove = []
    for key, col_def in multi_gpu_meta_graph_def.collection_def.items():
        kind = col_def.WhichOneof("kind")
        # Update node_list collections (e.g., GLOBAL_STEP, TRAIN_OP, UPDATE_OP,
        # LOSSES, ...)
        if kind == 'node_list':
            new_col_def = get_new_col_def_of_node_list(
                            col_def, op_names_to_replicate, num_replicas)
            multi_gpu_meta_graph_def.collection_def[key].Clear()
            multi_gpu_meta_graph_def.collection_def[key].CopyFrom(new_col_def)
        elif kind == 'bytes_list':
            if ops.get_from_proto_function(key):
                # Collections in allow_bytes_list_keys will be handled
                # explicitly below
                # (e.g., QUEUE_RUNNERS, LOCAL_VARIABLES, ...)
                if key in allow_bytes_list_keys:
                    continue
                # Remove unhandled collections (e.g., COND_CONTEXT)
                # TODO: Handle all protos in tf.GraphKeys
                else:
                    keys_to_remove.append(key)
            # Keep collections without proto function
            # (e.g., user defined string)
            else:
                continue
        else:
            raise RuntimeError("Should not reach here")
    for key in keys_to_remove:
        del multi_gpu_meta_graph_def.collection_def[key]

    # Update QUEUE_RUNNERS and LOCAL_VARIABLES collection
    update_queue_runners(multi_gpu_meta_graph_def, op_names_to_replicate,
                          num_replicas)
    update_local_variables(multi_gpu_meta_graph_def, op_names_to_replicate,
                            num_replicas)
    update_shard_info_for_in_graph(multi_gpu_meta_graph_def, num_replicas)