diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index d7eda619c34a20fa09a30afdcf90047d66a05cbf..28e54f5492e7b04a1406e319cecf977d4a55725e 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -160,6 +160,7 @@ class Variable(object): persistable=None, error_clip=None, stop_gradient=False, + is_data=False, **kwargs): self.block = block self.error_clip = error_clip @@ -238,6 +239,7 @@ class Variable(object): self.block.vars[name] = self self.op = None self.stop_gradient = stop_gradient + self.is_data = is_data def __str__(self): return self.to_string(True) @@ -475,7 +477,7 @@ class Operator(object): if isinstance(attrs[attr_name], Block): self.desc.set_block_attr(attr_name, attrs[attr_name].desc) elif isinstance(attrs[attr_name], core.BlockDesc) or \ - isinstance(attrs[attr_name], core.ProgramDesc): + isinstance(attrs[attr_name], core.ProgramDesc): self.desc.set_serialized_attr( attr_name, attrs[attr_name].serialize_to_string()) else: @@ -978,7 +980,8 @@ class Block(object): shape=var.shape, dtype=var.dtype, type=var.type, - persistable=True) + persistable=True, + is_data=var.is_data) else: ret_var = self.create_var( name=var.name, @@ -986,7 +989,8 @@ class Block(object): dtype=var.dtype, type=var.type, lod_level=var.lod_level, - persistable=True) + persistable=True, + is_data=var.is_data) return ret_var @@ -1051,6 +1055,7 @@ class Program(object): p.sync_with_cpp() p.copy_param_info_from(self) + p.copy_data_info_from(self) return p def prune(self, targets): @@ -1172,6 +1177,26 @@ class Program(object): "program, with represent the same topology") self.global_block().copy_param_info_from(other.global_block()) + def copy_data_info_from(self, other): + """ + Copy the information of data variables from other program. + Args: + other(Program): Other program + + Returns: + None + """ + if not isinstance(other, Program): + raise TypeError("copy_param_info_from should be invoked with " + "Program") + + if len(self.blocks) != len(other.blocks): + raise ValueError("copy_param_info_from should be invoked with two " + "program, with represent the same topology") + for var in other.global_block().vars.itervalues(): + if var.is_data: + self.global_block().var(var.name).is_data = True + def list_vars(self): for each_block in self.blocks: for each_var in each_block.vars.itervalues(): diff --git a/python/paddle/fluid/layers/io.py b/python/paddle/fluid/layers/io.py index 0a6befd1485a1f79d63873c47a9fd74ab4214f57..4d6ee3c51b7cccdaa3303b5a4cd8e7219b753ccb 100644 --- a/python/paddle/fluid/layers/io.py +++ b/python/paddle/fluid/layers/io.py @@ -78,8 +78,8 @@ def data(name, dtype=dtype, type=type, stop_gradient=stop_gradient, - lod_level=lod_level) - data_var.is_data = True + lod_level=lod_level, + is_data=True) return data_var diff --git a/python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py b/python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py index 35e163dc9df5a35ee5774b6b157366c4eabcb0f7..93f7757a66ea9b217c7831c7263936ece0aa8f18 100644 --- a/python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py +++ b/python/paddle/fluid/tests/book/high-level-api/word2vec/no_test_word2vec_new_api.py @@ -80,8 +80,11 @@ def inference_program(is_sparse): def train_program(is_sparse): - next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') + # The declaration of 'next_word' must be after the invoking of inference_program, + # or the data input order of train program would be [next_word, firstw, secondw, + # thirdw, forthw], which is not correct. predict_word = inference_program(is_sparse) + next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') cost = fluid.layers.cross_entropy(input=predict_word, label=next_word) avg_cost = fluid.layers.mean(cost) return avg_cost @@ -90,14 +93,17 @@ def train_program(is_sparse): def train(use_cuda, is_sparse, save_path): train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) + test_reader = paddle.batch( + paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() def event_handler(event): - print type(event) + # print type(event) if isinstance(event, fluid.EndEpochEvent): - avg_cost = trainer.test(reader=paddle.dataset.imikolov.test( - word_dict, N)) + outs = trainer.test(reader=test_reader) + avg_cost = outs[0] + print("loss= ", avg_cost) if avg_cost < 5.0: trainer.save_params(save_path) diff --git a/python/paddle/fluid/trainer.py b/python/paddle/fluid/trainer.py index d44cb16bfb1545fc840d1a38155ec407afd4473d..30b58b465ef2a7945ed87ce69397a050fc337623 100644 --- a/python/paddle/fluid/trainer.py +++ b/python/paddle/fluid/trainer.py @@ -75,11 +75,15 @@ class Trainer(object): self.train_program = framework.Program() with framework.program_guard(self.train_program, self.startup_program): - loss = program_func() + program_func_outs = program_func() + self.test_outputs = program_func_outs if isinstance( + program_func_outs, list) else [program_func_outs] + self.test_program = self.train_program.clone() if not isinstance(optimizer, opt_module.Optimizer): raise TypeError( "The optimizer should be an instance of Optimizer") - + # The fisrt element of program_func_outs is loss. + loss = self.test_outputs[0] optimize_ops, params_grads = optimizer.minimize(loss) self.place = Trainer._check_and_get_place(place) @@ -168,8 +172,17 @@ class Trainer(object): self._train_by_executor(num_epochs, event_handler, reader, feed_order) - def test(self, reader): - pass + def test(self, reader, feed_order=None): + """ + Test the model on given test data + + Args: + reader: The reader that yields test data. + feed_order: Feeding order of reader. None will following the defining + order in program + """ + + return self._test_by_executor(reader, feed_order, self.test_outputs) def save_params(self, param_path): # reference: save_persistables in io.py @@ -225,22 +238,10 @@ class Trainer(object): """ with self._prog_and_scope_guard(): - exe = executor.Executor(self.place) - if feed_order is None: - feed_var_list = [ - var - for var in self.train_program.global_block( - ).vars.itervalues() - if hasattr(var, 'is_data') and var.is_data - ] - else: - feed_var_list = [ - self.train_program.global_block().var(var_name) - for var_name in feed_order - ] - + feed_var_list = build_feed_var_list(self.train_program, feed_order) feeder = data_feeder.DataFeeder( feed_list=feed_var_list, place=self.place) + exe = executor.Executor(self.place) for epoch_id in range(num_epochs): event_handler(BeginEpochEvent(epoch_id)) for step_id, data in enumerate(reader()): @@ -248,3 +249,48 @@ class Trainer(object): exe.run(feed=feeder.feed(data), fetch_list=[]) event_handler(EndStepEvent(epoch_id, step_id)) event_handler(EndEpochEvent(epoch_id)) + + def _test_by_executor(self, reader, feed_order, fetch_list): + with executor.scope_guard(self.scope): + feed_var_list = build_feed_var_list(self.test_program, feed_order) + feeder = data_feeder.DataFeeder( + feed_list=feed_var_list, place=self.place) + exe = executor.Executor(self.place) + accumulated = len(fetch_list) * [0] + count = 0 + for data in reader(): + outs = exe.run(program=self.test_program, + feed=feeder.feed(data), + fetch_list=fetch_list) + accumulated = [x[0] + x[1][0] for x in zip(accumulated, outs)] + count += 1 + + return [x / count for x in accumulated] + + +def build_feed_var_list(program, feed_order): + if not isinstance(program, framework.Program): + raise TypeError("The 'program' should be an object of Program") + + if feed_order is None: + feed_var_list = [ + var for var in program.global_block().vars.itervalues() + if var.is_data + ] + elif isinstance(feed_order, list): + feed_var_list = [ + program.global_block().var(var_name) for var_name in feed_order + ] + else: + if not isinstance(feed_order, dict): + raise TypeError( + "The 'feed_order' should be either None, list or dict.") + if not sorted(feed_order.values()) == range(len(feed_order)): + raise ValueError( + "The values of 'feed_order' should be a permutation of [0, len(feed_order))" + ) + sorted_pair_list = sorted(feed_order.items(), key=lambda item: item[1]) + feed_var_list = [ + program.global_block().var(pair[0]) for pair in sorted_pair_list + ] + return feed_var_list