add smoke model for superiving under CE

fluid/mnist/.run.sh

+#!/bin/bash
+
+rm -rf *_factor.txt
+model_file='model.py'
+python $model_file --batch_size 256 --pass_num 2 --device CPU

fluid/mnist/model.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+
+SEED = 90
+DTYPE = "float32"
+
+# random seed must set before configuring the network.
+fluid.default_startup_program().random_seed = SEED
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("mnist model benchmark.")
+    parser.add_argument(
+        '--batch_size', type=int, default=128, help='The minibatch size.')
+    parser.add_argument(
+        '--iterations',
+        type=int,
+        default=35,
+        help='The number of minibatches.')
+    parser.add_argument(
+        '--pass_num', type=int, default=5, help='The number of passes.')
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='GPU',
+        choices=['CPU', 'GPU'],
+        help='The device type.')
+    parser.add_argument(
+        '--infer_only', action='store_true', help='If set, run forward only.')
+    parser.add_argument(
+        '--use_cprof', action='store_true', help='If set, use cProfile.')
+    parser.add_argument(
+        '--use_nvprof',
+        action='store_true',
+        help='If set, use nvprof for CUDA.')
+    args = parser.parse_args()
+    return args
+
+
+def print_arguments(args):
+    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
+                                vars(args)['device'] == 'GPU')
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def cnn_model(data):
+    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        input=data,
+        filter_size=5,
+        num_filters=20,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    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")
+
+    # TODO(dzhwinter) : refine the initializer and random seed settting
+    SIZE = 10
+    input_shape = conv_pool_2.shape
+    param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
+    scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
+
+    predict = fluid.layers.fc(
+        input=conv_pool_2,
+        size=SIZE,
+        act="softmax",
+        param_attr=fluid.param_attr.ParamAttr(
+            initializer=fluid.initializer.NormalInitializer(
+                loc=0.0, scale=scale)))
+    return predict
+
+
+def eval_test(exe, batch_acc, batch_size_tensor, inference_program):
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=args.batch_size)
+    test_pass_acc = fluid.average.WeightedAverage()
+    for batch_id, data in enumerate(test_reader()):
+        img_data = np.array(map(lambda x: x[0].reshape([1, 28, 28]),
+                                data)).astype(DTYPE)
+        y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+        y_data = y_data.reshape([len(y_data), 1])
+
+        acc, weight = exe.run(inference_program,
+                              feed={"pixel": img_data,
+                                    "label": y_data},
+                              fetch_list=[batch_acc, batch_size_tensor])
+        test_pass_acc.add(value=acc, weight=weight)
+        pass_acc = test_pass_acc.eval()
+    return pass_acc
+
+
+def run_benchmark(model, args):
+    if args.use_cprof:
+        pr = cProfile.Profile()
+        pr.enable()
+    start_time = time.time()
+    # Input data
+    images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    # Train program
+    predict = model(images)
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    # Evaluator
+    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(
+        input=predict, label=label, total=batch_size_tensor)
+
+    # inference program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(
+            target_vars=[batch_acc, batch_size_tensor])
+
+    # Optimization
+    opt = fluid.optimizer.AdamOptimizer(
+        learning_rate=0.001, beta1=0.9, beta2=0.999)
+    opt.minimize(avg_cost)
+
+    fluid.memory_optimize(fluid.default_main_program())
+
+    # Initialize executor
+    place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+
+    # Parameter initialization
+    exe.run(fluid.default_startup_program())
+
+    # Reader
+    train_reader = paddle.batch(
+        paddle.dataset.mnist.train(), batch_size=args.batch_size)
+
+    accuracy = fluid.average.WeightedAverage()
+    for pass_id in range(args.pass_num):
+        accuracy.reset()
+        pass_start = time.time()
+        every_pass_loss = []
+        for batch_id, data in enumerate(train_reader()):
+            img_data = np.array(
+                map(lambda x: x[0].reshape([1, 28, 28]), data)).astype(DTYPE)
+            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+            y_data = y_data.reshape([len(y_data), 1])
+
+            start = time.time()
+            loss, acc, weight = exe.run(
+                fluid.default_main_program(),
+                feed={"pixel": img_data,
+                      "label": y_data},
+                fetch_list=[avg_cost, batch_acc, batch_size_tensor]
+            )  # The accuracy is the accumulation of batches, but not the current batch.
+            end = time.time()
+            accuracy.add(value=acc, weight=weight)
+            every_pass_loss.append(loss)
+            print ("Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
+                    (pass_id, batch_id, loss, acc))
+
+        pass_end = time.time()
+
+        train_avg_acc = accuracy.eval()
+        train_avg_loss = np.mean(every_pass_loss)
+        test_avg_acc = eval_test(exe, batch_acc, batch_size_tensor,
+                                 inference_program)
+
+        print("pass=%d, train_avg_acc=%f,train_avg_loss=%f, test_avg_acc=%f, elapse=%f" %
+              (pass_id, train_avg_acc, train_avg_loss, test_avg_acc, (pass_end - pass_start)))
+
+        print (train_avg_acc)
+        with open("train_acc_factor.txt", 'a+') as f:
+            f.write("%s\n" % train_avg_acc)
+        with open("train_cost_factor.txt", 'a+') as f:
+            print ([train_avg_loss])
+            f.write('%s\n' % [train_avg_loss])
+        with open("test_acc_factor.txt", 'a+') as f:
+            f.write("%s\n" % test_avg_acc)
+        with open("train_duration_factor.txt", 'a+') as f:
+            print ([pass_end - pass_start])
+            f.write('%s\n' % [pass_end - pass_start])
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    print_arguments(args)
+    if args.use_nvprof and args.device == 'GPU':
+        with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
+            run_benchmark(cnn_model, args)
+    else:
+        run_benchmark(cnn_model, args)