diff --git a/python/paddle/fluid/inferencer.py b/python/paddle/fluid/inferencer.py index 1b8b9c07622dc..56c008d1af70f 100644 --- a/python/paddle/fluid/inferencer.py +++ b/python/paddle/fluid/inferencer.py @@ -13,29 +13,35 @@ # limitations under the License. import core -import framework + import executor +import framework import io +import unique_name from trainer import check_and_get_place __all__ = ['Inferencer', ] class Inferencer(object): - def __init__(self, param_path, place=None): + def __init__(self, infer_func, param_path, place=None): """ - :param param_path: the path where the inference model is saved by fluid.io.save_inference_model + :param infer_func: a function that will return predict Variable + :param param_path: the path where the inference model is saved by fluid.io.save_params :param place: place to do the inference """ self.param_path = param_path self.scope = core.Scope() + self.inference_program = framework.Program() + with framework.program_guard(self.inference_program): + with unique_name.guard(): + self.predict_var = infer_func() + self.exe = executor.Executor(check_and_get_place(place)) with executor.scope_guard(self.scope): # load params from param_path into scope - [self.inference_program, _, - self.fetch_targets] = io.load_inference_model( - executor=self.exe, dirname=param_path) + io.load_params(self.exe, param_path, self.inference_program) def infer(self, inputs, return_numpy=True): """ @@ -51,7 +57,7 @@ def infer(self, inputs, return_numpy=True): with executor.scope_guard(self.scope): results = self.exe.run(self.inference_program, feed=inputs, - fetch_list=self.fetch_targets, + fetch_list=[self.predict_var], return_numpy=return_numpy) return results diff --git a/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py b/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py index 8c9bbb52d7692..fbcf2a282f642 100644 --- a/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py +++ b/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py @@ -48,12 +48,11 @@ def linear(): return avg_loss -def train(use_cuda, save_dirname): +def train(use_cuda, train_program, save_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() trainer = fluid.Trainer( - train_func=linear, - infer_func=inference_program, + train_func=train_program, place=place, optimizer=fluid.optimizer.SGD(learning_rate=0.001)) @@ -72,11 +71,7 @@ def event_handler(event): ''' if float(test_metrics[0]) < 20.0: if save_dirname is not None: - # NOT clear yet - # fluid.io.save_inference_model(save_dirname, ['x'], [y_predict]) - # trainer.save_params(save_dirname) - # https://github.com/PaddlePaddle/Paddle/pull/10445 - trainer.save_inference_model(save_dirname) + trainer.save_params(save_dirname) return trainer.train( @@ -87,12 +82,13 @@ def event_handler(event): # infer -def infer(use_cuda, save_dirname=None): +def infer(use_cuda, inference_program, save_dirname=None): if save_dirname is None: return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - inferencer = fluid.Inferencer(param_path=save_dirname, place=place) + inferencer = fluid.Inferencer( + infer_func=inference_program, param_path=save_dirname, place=place) batch_size = 10 tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32") @@ -108,8 +104,8 @@ def main(use_cuda): # Directory for saving the trained model save_dirname = "fit_a_line.inference.model" - train(use_cuda, save_dirname) - infer(use_cuda, save_dirname) + train(use_cuda, linear, save_dirname) + infer(use_cuda, inference_program, save_dirname) class TestFitALine(unittest.TestCase): diff --git a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py index 1f91f471f22f7..a19f9358c409e 100644 --- a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py +++ b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py @@ -53,48 +53,39 @@ def train_program(): predict = inference_program() cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) - # acc = fluid.layers.accuracy(input=predict, label=label) - # return avg_cost, acc - return avg_cost + acc = fluid.layers.accuracy(input=predict, label=label) + return [avg_cost, acc] -def train(use_cuda, save_dirname): +def train(use_cuda, train_program, save_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adam(learning_rate=0.001) trainer = fluid.Trainer( - train_func=train_program, - infer_func=inference_program, - place=place, - optimizer=optimizer) + train_func=train_program, place=place, optimizer=optimizer) def event_handler(event): if isinstance(event, fluid.EndEpochEvent): - # if (event.epoch + 1) % 10 == 0: - # trainer.save_params(save_dirname) - trainer.save_inference_model(save_dirname) - - # TODO: Uncomment this part once we are sure that .train is working - # test_reader = paddle.batch( - # paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) - # test_metrics = trainer.test(reader=test_reader) - # avg_cost_set = test_metrics[0] - # acc_set = test_metrics[1] - # - # # get test acc and loss - # acc = numpy.array(acc_set).mean() - # avg_cost = numpy.array(avg_cost_set).mean() - # - # print("avg_cost: %s" % avg_cost) - # print("acc : %s" % acc) - # - # if float(acc) > 0.2: # Smaller value to increase CI speed - # trainer.save_params(save_dirname) - # else: - # print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( - # event.epoch + 1, float(avg_cost), float(acc))) - # if math.isnan(float(avg_cost)): - # sys.exit("got NaN loss, training failed.") + test_reader = paddle.batch( + paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) + test_metrics = trainer.test(reader=test_reader) + avg_cost_set = test_metrics[0] + acc_set = test_metrics[1] + + # get test acc and loss + acc = numpy.array(acc_set).mean() + avg_cost = numpy.array(avg_cost_set).mean() + + print("avg_cost: %s" % avg_cost) + print("acc : %s" % acc) + + if float(acc) > 0.2: # Smaller value to increase CI speed + trainer.save_params(save_dirname) + else: + print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( + event.epoch + 1, float(avg_cost), float(acc))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") train_reader = paddle.batch( paddle.reader.shuffle( @@ -108,10 +99,11 @@ def event_handler(event): feed_order=['img', 'label']) -def infer(use_cuda, save_dirname=None): +def infer(use_cuda, inference_program, save_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - inferencer = fluid.Inferencer(param_path=save_dirname, place=place) + inferencer = fluid.Inferencer( + infer_func=inference_program, param_path=save_dirname, place=place) batch_size = 1 tensor_img = numpy.random.uniform(-1.0, 1.0, @@ -126,8 +118,14 @@ def main(use_cuda): save_dirname = "recognize_digits_conv.inference.model" # call train() with is_local argument to run distributed train - train(use_cuda=use_cuda, save_dirname=save_dirname) - infer(use_cuda=use_cuda, save_dirname=save_dirname) + train( + use_cuda=use_cuda, + train_program=train_program, + save_dirname=save_dirname) + infer( + use_cuda=use_cuda, + inference_program=inference_program, + save_dirname=save_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py index f072d70abdba5..9427a772f54fb 100644 --- a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py +++ b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py @@ -40,47 +40,40 @@ def train_program(): predict = inference_program() cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) - # acc = fluid.layers.accuracy(input=predict, label=label) - # return avg_cost, acc - return avg_cost + acc = fluid.layers.accuracy(input=predict, label=label) + return [avg_cost, acc] -def train(use_cuda, save_dirname): +def train(use_cuda, train_program, save_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adam(learning_rate=0.001) trainer = fluid.Trainer( - train_func=train_program, - infer_func=inference_program, - place=place, - optimizer=optimizer) + train_func=train_program, place=place, optimizer=optimizer) def event_handler(event): if isinstance(event, fluid.EndEpochEvent): - # if (event.epoch + 1) % 10 == 0: - trainer.save_inference_model(save_dirname) - - # TODO: Uncomment this part once we are sure that .train is working - # test_reader = paddle.batch( - # paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) - # test_metrics = trainer.test(reader=test_reader) - # avg_cost_set = test_metrics[0] - # acc_set = test_metrics[1] - # - # # get test acc and loss - # acc = numpy.array(acc_set).mean() - # avg_cost = numpy.array(avg_cost_set).mean() - # - # print("avg_cost: %s" % avg_cost) - # print("acc : %s" % acc) - # - # if float(acc) > 0.2: # Smaller value to increase CI speed - # trainer.save_params(save_dirname) - # else: - # print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( - # event.epoch + 1, float(avg_cost), float(acc))) - # if math.isnan(float(avg_cost)): - # sys.exit("got NaN loss, training failed.") + test_reader = paddle.batch( + paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) + test_metrics = trainer.test( + reader=test_reader, feed_order=['img', 'label']) + avg_cost_set = test_metrics[0] + acc_set = test_metrics[1] + + # get test acc and loss + acc = numpy.array(acc_set).mean() + avg_cost = numpy.array(avg_cost_set).mean() + + print("avg_cost: %s" % avg_cost) + print("acc : %s" % acc) + + if float(acc) > 0.2: # Smaller value to increase CI speed + trainer.save_params(save_dirname) + else: + print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( + event.epoch + 1, float(avg_cost), float(acc))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") train_reader = paddle.batch( paddle.reader.shuffle( @@ -94,10 +87,11 @@ def event_handler(event): feed_order=['img', 'label']) -def infer(use_cuda, save_dirname=None): +def infer(use_cuda, inference_program, save_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - inferencer = fluid.Inferencer(param_path=save_dirname, place=place) + inferencer = fluid.Inferencer( + infer_func=inference_program, param_path=save_dirname, place=place) batch_size = 1 tensor_img = numpy.random.uniform(-1.0, 1.0, @@ -112,8 +106,14 @@ def main(use_cuda): save_dirname = "recognize_digits_mlp.inference.model" # call train() with is_local argument to run distributed train - train(use_cuda=use_cuda, save_dirname=save_dirname) - infer(use_cuda=use_cuda, save_dirname=save_dirname) + train( + use_cuda=use_cuda, + train_program=train_program, + save_dirname=save_dirname) + infer( + use_cuda=use_cuda, + inference_program=inference_program, + save_dirname=save_dirname) if __name__ == '__main__': 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 00ba4acf88b1b..4f861e5aaeca7 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 @@ -90,7 +90,7 @@ def train_program(is_sparse): return avg_cost -def train(use_cuda, is_sparse, save_path): +def train(use_cuda, train_program, save_path): train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) test_reader = paddle.batch( @@ -105,23 +105,21 @@ def event_handler(event): print("loss= ", avg_cost) if avg_cost < 5.0: - trainer.save_inference_model(save_path) + trainer.save_params(save_path) return if math.isnan(avg_cost): sys.exit("got NaN loss, training failed.") trainer = fluid.Trainer( - partial(train_program, is_sparse), - partial(inference_program, is_sparse), - fluid.optimizer.SGD(learning_rate=0.001), - place=place) + train_program, fluid.optimizer.SGD(learning_rate=0.001), place=place) trainer.train( reader=train_reader, num_epochs=1, event_handler=event_handler) -def infer(use_cuda, is_sparse, save_path): +def infer(use_cuda, inference_program, save_path): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() - inferencer = fluid.Inferencer(param_path=save_path, place=place) + inferencer = fluid.Inferencer( + infer_func=inference_program, param_path=save_path, place=place) lod = [0, 1] first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1) @@ -144,9 +142,9 @@ def main(use_cuda, is_sparse): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "word2vec.inference.model" - train(use_cuda, is_sparse, save_path) - infer(use_cuda, is_sparse, save_path) + save_path = "word2vec.params" + train(use_cuda, partial(train_program, is_sparse), save_path) + infer(use_cuda, partial(inference_program, is_sparse), save_path) if __name__ == '__main__': diff --git a/python/paddle/fluid/trainer.py b/python/paddle/fluid/trainer.py index 67d8be82d5fa8..2f1e70724fbdb 100644 --- a/python/paddle/fluid/trainer.py +++ b/python/paddle/fluid/trainer.py @@ -92,19 +92,13 @@ class Trainer(object): place: The device place of this trainer. """ - def __init__(self, - train_func, - infer_func, - optimizer, - param_path=None, - place=None): + def __init__(self, train_func, optimizer, param_path=None, place=None): # 1. we need to generate a framework.Program by calling # program_func. Reference: fluid.program_guard in # test_word2vec.py if not isinstance(optimizer, opt_module.Optimizer): raise TypeError("The optimizer should be an instance of Optimizer") - self.infer_func = infer_func self.scope = core.Scope() self.startup_program = framework.Program() @@ -226,15 +220,6 @@ def save_params(self, param_path): exe = executor.Executor(self.place) io.save_persistables(exe, dirname=param_path) - def save_inference_model(self, model_path): - inference_program = framework.Program() - with framework.program_guard(inference_program): - with unique_name.guard(): - predict_var = self.infer_func() - predict_var = self.train_program.block(0).var(predict_var.name) - exe = executor.Executor(self.place) - io.save_inference_model(model_path, [], [predict_var], exe) - @contextlib.contextmanager def _prog_and_scope_guard(self): with framework.program_guard(