import paddle.v2.fluid.core as core from paddle.v2.fluid.framework import Block, Program, g_main_program g_scope = core.Scope() class Executor(object): def __init__(self, places): if not isinstance(places, list) and not isinstance(places, tuple): places = [places] act_places = [] for each in places: p = core.Place() p.set_place(each) act_places.append(p) self.executor = core.Executor(act_places) def run(self, program=None, feed=None, fetch_list=None, feed_var_name='feed', fetch_var_name='fetch', scope=None): if feed is None: feed = {} if fetch_list is None: fetch_list = [] if program is None: program = g_main_program if not isinstance(program, Program): raise TypeError() if scope is None: scope = g_scope program = program.clone() global_block = program.global_block() feed_var = global_block.create_var( name=feed_var_name, type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True) for i, name in enumerate(feed): out = global_block.var(name) global_block.prepend_op( 'feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, attrs={'col': i}) core.set_feed_variable(scope, feed[name], feed_var.name, i) fetch_var = global_block.create_var( name=fetch_var_name, type=core.VarDesc.VarType.FETCH_LIST, persistable=True) for i, var in enumerate(fetch_list): global_block.append_op( type='fetch', inputs={'X': [var]}, outputs={'Out': [fetch_var]}, attrs={'col': i}) self.executor.run(program.desc, scope, 0, True) return [ core.get_fetch_variable(scope, fetch_var_name, i) for i in xrange(len(fetch_list)) ]