Simplify fluid api recognize digit (#10308)

* Save the base of the new test.

* Create the notest_recognize_digits_conv and notest_recognize_digits_mlp

* precommit check

* Change the function name from _network to _program.
Update several variable names to make them more consistant.
Update the Inferencer construction call.

* Fix the incorrect format.

Author: jetfuel

python/paddle/fluid/tests/book/notest_recognize_digits/notest_recognize_digits_conv.py

@@ -0,0 +1,118 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+import argparse
+import paddle.fluid as fluid
+import paddle
+import sys
+import numpy
+import unittest
+import math
+import sys
+import os
+import paddle.v2.dataset as dataset
+
+BATCH_SIZE = 64
+
+
+def inference_program():
+    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+
+    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        input=img,
+        filter_size=5,
+        num_filters=20,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+        input=conv_pool_1,
+        filter_size=5,
+        num_filters=50,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
+    return prediction
+
+
+def train_program():
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    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
+
+
+def train(use_cuda, save_dirname):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndIteration):
+            avg_cost, acc = event.values
+            print("avg_cost: %s" % avg_cost)
+            print("acc     : %s" % acc)
+
+            if (event.batch_id + 1) % 10 == 0:
+                test_metrics = trainer.test(reader=dataset.mnist.test())
+                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()
+                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.batch_id + 1, float(avg_cost), float(acc)))
+                    if math.isnan(float(avg_cost)):
+                        sys.exit("got NaN loss, training failed.")
+
+    trainer.train(
+        reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
+
+
+def infer(use_cuda, save_dirname=None):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    inferencer = fluid.Inferencer(
+        inference_program, param_path=save_dirname, place=place)
+
+    batch_size = 1
+    tensor_img = numpy.random.uniform(-1.0, 1.0,
+                                      [batch_size, 1, 28, 28]).astype("float32")
+
+    results = inferencer.infer({'img': tensor_img})
+
+    print("infer results: ", results[0])
+
+
+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)
+
+
+if __name__ == '__main__':
+    for use_cuda in (False, True):
+        main(use_cuda=use_cuda)

python/paddle/fluid/tests/book/notest_recognize_digits/notest_recognize_digits_mlp.py

@@ -0,0 +1,105 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+import argparse
+import paddle.fluid as fluid
+import paddle
+import sys
+import numpy
+import unittest
+import math
+import sys
+import os
+import paddle.v2.dataset as dataset
+
+BATCH_SIZE = 64
+
+
+def inference_program():
+    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+
+    hidden = fluid.layers.fc(input=img, size=200, act='tanh')
+    hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
+    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
+    return prediction
+
+
+def train_program():
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    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
+
+
+def train(use_cuda, save_dirname):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndIteration):
+            avg_cost, acc = event.values
+            print("avg_cost: %s" % avg_cost)
+            print("acc     : %s" % acc)
+
+            if (event.batch_id + 1) % 10 == 0:
+                test_metrics = trainer.test(reader=dataset.mnist.test())
+                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()
+                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.batch_id + 1, float(avg_cost), float(acc)))
+                    if math.isnan(float(avg_cost)):
+                        sys.exit("got NaN loss, training failed.")
+
+    trainer.train(
+        reader=dataset.mnist.train(), num_pass=100, event_handler=event_handler)
+
+
+def infer(use_cuda, save_dirname=None):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    inferencer = fluid.Inferencer(
+        inference_program, param_path=save_dirname, place=place)
+
+    batch_size = 1
+    tensor_img = numpy.random.uniform(-1.0, 1.0,
+                                      [batch_size, 1, 28, 28]).astype("float32")
+
+    results = inferencer.infer({'img': tensor_img})
+
+    print("infer results: ", results[0])
+
+
+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)
+
+
+if __name__ == '__main__':
+    for use_cuda in (False, True):
+        main(use_cuda=use_cuda)