Update trainer api

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

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):

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__':

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__':

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__':