clean code of test_async_ssa_graph_executor_mnist

python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py

+#   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 os
+from PIL import Image
+import numpy
+import paddle
+import paddle.fluid as fluid
+
+BATCH_SIZE = 64
+PASS_NUM = 5
+
+
+def loss_net(hidden, label):
+    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_loss = fluid.layers.mean(loss)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+    return prediction, avg_loss, acc
+
+
+def convolutional_neural_network(img, label):
+    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")
+    return loss_net(conv_pool_2, label)
+
+
+def train(use_cuda,
+          save_dirname=None,
+          model_filename=None,
+          params_filename=None):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    prediction, avg_loss, acc = convolutional_neural_network(img, label)
+
+    test_program = fluid.default_main_program().clone(for_test=True)
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    optimizer.minimize(avg_loss)
+
+    def train_test(train_test_program, train_test_feed, train_test_reader):
+        acc_set = []
+        avg_loss_set = []
+        for test_data in train_test_reader():
+            acc_np, avg_loss_np = exe.run(program=train_test_program,
+                                          feed=train_test_feed.feed(test_data),
+                                          fetch_list=[acc, avg_loss])
+            acc_set.append(float(acc_np))
+            avg_loss_set.append(float(avg_loss_np))
+        # get test acc and loss
+        acc_val_mean = numpy.array(acc_set).mean()
+        avg_loss_val_mean = numpy.array(avg_loss_set).mean()
+        return avg_loss_val_mean, acc_val_mean
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    exe = fluid.Executor(place)
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.mnist.train(), buf_size=500),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
+    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
+
+    exe.run(fluid.default_startup_program())
+    main_program = fluid.default_main_program()
+
+    exec_strategy = fluid.ExecutionStrategy()
+    build_strategy = fluid.BuildStrategy()
+
+    cpu_num = int(os.environ.get('CPU_NUM'))
+    thread_num = int(os.getenv("NUM_THREADS"))
+
+    print("cpu_num:" + str(cpu_num))
+    print("thread_num:" + str(thread_num))
+
+    build_strategy.async_mode = True
+
+    exec_strategy.num_threads = thread_num
+    exec_strategy.num_iteration_per_drop_scope = 1
+    exec_strategy.num_iteration_per_run = 10
+
+    pe = fluid.ParallelExecutor(
+        use_cuda=False,
+        loss_name=avg_loss.name,
+        main_program=main_program,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy)
+
+    lists = []
+    step = 0
+    for epoch_id in range(PASS_NUM):
+        for step_id, data in enumerate(train_reader()):
+            loss_val, acc_val = pe.run(feed=feeder.feed(data),
+                                       fetch_list=[avg_loss.name, acc.name])
+            loss_val = numpy.mean(loss_val)
+            acc_val = numpy.mean(acc_val)
+            if step % 100 == 0:
+                print("Pass %d, Batch %d, Cost %f" % (epoch_id, step, loss_val))
+            step += 1
+        # test for epoch
+        avg_loss_val, acc_val = train_test(
+            train_test_program=test_program,
+            train_test_reader=test_reader,
+            train_test_feed=feeder)
+
+        print("Test with Epoch %d, avg_cost: %s, acc: %s" %
+              (epoch_id, avg_loss_val, acc_val))
+        lists.append((epoch_id, avg_loss_val, acc_val))
+        if save_dirname is not None:
+            fluid.io.save_inference_model(
+                save_dirname, ["img"], [prediction],
+                exe,
+                model_filename=model_filename,
+                params_filename=params_filename)
+
+    # find the best pass
+    best = sorted(lists, key=lambda list: float(list[1]))[0]
+    print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1]))
+    print('The classification accuracy is %.2f%%' % (float(best[2]) * 100))
+
+
+def infer(use_cuda,
+          save_dirname=None,
+          model_filename=None,
+          params_filename=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    def load_image(file):
+        im = Image.open(file).convert('L')
+        im = im.resize((28, 28), Image.ANTIALIAS)
+        im = numpy.array(im).reshape(1, 1, 28, 28).astype(numpy.float32)
+        im = im / 255.0 * 2.0 - 1.0
+        return im
+
+    cur_dir = os.path.dirname(os.path.realpath(__file__))
+    tensor_img = load_image(cur_dir + '/image/infer_3.png')
+
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
+        # Use fluid.io.load_inference_model to obtain the inference program desc,
+        # the feed_target_names (the names of variables that will be feeded
+        # data using feed operators), and the fetch_targets (variables that
+        # we want to obtain data from using fetch operators).
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(
+             save_dirname, exe, model_filename, params_filename)
+
+        # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+        # and results will contain a list of data corresponding to fetch_targets.
+        results = exe.run(inference_program,
+                          feed={feed_target_names[0]: tensor_img},
+                          fetch_list=fetch_targets)
+        lab = numpy.argsort(results)
+        print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
+
+
+def main(use_cuda):
+    model_filename = None
+    params_filename = None
+    save_dirname = "recognize_digits" + ".inference.model"
+
+    # call train() with is_local argument to run distributed train
+    train(
+        use_cuda=use_cuda,
+        save_dirname=save_dirname,
+        model_filename=model_filename,
+        params_filename=params_filename)
+    infer(
+        use_cuda=use_cuda,
+        save_dirname=save_dirname,
+        model_filename=model_filename,
+        params_filename=params_filename)
+
+
+if __name__ == '__main__':
+    use_cuda = False
+    main(use_cuda=use_cuda)