Fix object detection reader (#1219)

* Accelerate reader in object_detection.

fluid/object_detection/data_util.py

+"""
+This code is based on https://github.com/fchollet/keras/blob/master/keras/utils/data_utils.py
+"""
+
+import time
+import numpy as np
+import threading
+import multiprocessing
+try:
+    import queue
+except ImportError:
+    import Queue as queue
+
+
+class GeneratorEnqueuer(object):
+    """
+    Builds a queue out of a data generator.
+
+    Args:
+        generator: a generator function which endlessly yields data
+        use_multiprocessing (bool): use multiprocessing if True,
+            otherwise use threading.
+        wait_time (float): time to sleep in-between calls to `put()`.
+        random_seed (int): Initial seed for workers,
+            will be incremented by one for each workers.
+    """
+
+    def __init__(self,
+                 generator,
+                 use_multiprocessing=False,
+                 wait_time=0.05,
+                 random_seed=None):
+        self.wait_time = wait_time
+        self._generator = generator
+        self._use_multiprocessing = use_multiprocessing
+        self._threads = []
+        self._stop_event = None
+        self.queue = None
+        self._manager = None
+        self.seed = random_seed
+
+    def start(self, workers=1, max_queue_size=10):
+        """
+        Start worker threads which add data from the generator into the queue.
+
+        Args:
+            workers (int): number of worker threads
+            max_queue_size (int): queue size
+                (when full, threads could block on `put()`)
+        """
+
+        def data_generator_task():
+            """
+            Data generator task.
+            """
+
+            def task():
+                if (self.queue is not None and
+                        self.queue.qsize() < max_queue_size):
+                    generator_output = next(self._generator)
+                    self.queue.put((generator_output))
+                else:
+                    time.sleep(self.wait_time)
+
+            if not self._use_multiprocessing:
+                while not self._stop_event.is_set():
+                    with self.genlock:
+                        try:
+                            task()
+                        except Exception:
+                            self._stop_event.set()
+                            break
+            else:
+                while not self._stop_event.is_set():
+                    try:
+                        task()
+                    except Exception:
+                        self._stop_event.set()
+                        break
+
+        try:
+            if self._use_multiprocessing:
+                self._manager = multiprocessing.Manager()
+                self.queue = self._manager.Queue(maxsize=max_queue_size)
+                self._stop_event = multiprocessing.Event()
+            else:
+                self.genlock = threading.Lock()
+                self.queue = queue.Queue()
+                self._stop_event = threading.Event()
+            for _ in range(workers):
+                if self._use_multiprocessing:
+                    # Reset random seed else all children processes
+                    # share the same seed
+                    np.random.seed(self.seed)
+                    thread = multiprocessing.Process(target=data_generator_task)
+                    thread.daemon = True
+                    if self.seed is not None:
+                        self.seed += 1
+                else:
+                    thread = threading.Thread(target=data_generator_task)
+                self._threads.append(thread)
+                thread.start()
+        except:
+            self.stop()
+            raise
+
+    def is_running(self):
+        """
+        Returns:
+            bool: Whether the worker theads are running.
+        """
+        return self._stop_event is not None and not self._stop_event.is_set()
+
+    def stop(self, timeout=None):
+        """
+        Stops running threads and wait for them to exit, if necessary.
+        Should be called by the same thread which called `start()`.
+
+        Args:
+            timeout(int|None): maximum time to wait on `thread.join()`.
+        """
+        if self.is_running():
+            self._stop_event.set()
+        for thread in self._threads:
+            if self._use_multiprocessing:
+                if thread.is_alive():
+                    thread.terminate()
+            else:
+                thread.join(timeout)
+        if self._manager:
+            self._manager.shutdown()
+
+        self._threads = []
+        self._stop_event = None
+        self.queue = None
+
+    def get(self):
+        """
+        Creates a generator to extract data from the queue.
+        Skip the data if it is `None`.
+
+        # Yields
+            tuple of data in the queue.
+        """
+        while self.is_running():
+            if not self.queue.empty():
+                inputs = self.queue.get()
+                if inputs is not None:
+                    yield inputs
+            else:
+                time.sleep(self.wait_time)

fluid/object_detection/eval.py

@@ -3,6 +3,7 @@ import time
 import numpy as np
 import argparse
 import functools
+import math
 
 import paddle
 import paddle.fluid as fluid
@@ -29,65 +30,75 @@ add_arg('mean_value_R',     float, 127.5,  "Mean value for R channel which will
 # yapf: enable
 
 
-def eval(args, data_args, test_list, batch_size, model_dir=None):
+def build_program(main_prog, startup_prog, args, data_args):
     image_shape = [3, data_args.resize_h, data_args.resize_w]
     if 'coco' in data_args.dataset:
         num_classes = 91
     elif 'pascalvoc' in data_args.dataset:
         num_classes = 21
 
-    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
-    gt_box = fluid.layers.data(
-        name='gt_box', shape=[4], dtype='float32', lod_level=1)
-    gt_label = fluid.layers.data(
-        name='gt_label', shape=[1], dtype='int32', lod_level=1)
-    difficult = fluid.layers.data(
-        name='gt_difficult', shape=[1], dtype='int32', lod_level=1)
+    with fluid.program_guard(main_prog, startup_prog):
+        py_reader = fluid.layers.py_reader(
+            capacity=64,
+            shapes=[[-1] + image_shape, [-1, 4], [-1, 1], [-1, 1]],
+            lod_levels=[0, 1, 1, 1],
+            dtypes=["float32", "float32", "int32", "int32"],
+            use_double_buffer=True)
+        with fluid.unique_name.guard():
+            image, gt_box, gt_label, difficult = fluid.layers.read_file(
+                py_reader)
+            locs, confs, box, box_var = mobile_net(num_classes, image,
+                                                   image_shape)
+            nmsed_out = fluid.layers.detection_output(
+                locs, confs, box, box_var, nms_threshold=args.nms_threshold)
+            with fluid.program_guard(main_prog):
+                map = fluid.evaluator.DetectionMAP(
+                    nmsed_out,
+                    gt_label,
+                    gt_box,
+                    difficult,
+                    num_classes,
+                    overlap_threshold=0.5,
+                    evaluate_difficult=False,
+                    ap_version=args.ap_version)
+    return py_reader, map
+
 
-    locs, confs, box, box_var = mobile_net(num_classes, image, image_shape)
-    nmsed_out = fluid.layers.detection_output(
-        locs, confs, box, box_var, nms_threshold=args.nms_threshold)
-    loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box, box_var)
-    loss = fluid.layers.reduce_sum(loss)
+def eval(args, data_args, test_list, batch_size, model_dir=None):
+    startup_prog = fluid.Program()
+    test_prog = fluid.Program()
 
+    test_py_reader, map_eval = build_program(
+        main_prog=test_prog,
+        startup_prog=startup_prog,
+        args=args,
+        data_args=data_args)
+    test_prog = test_prog.clone(for_test=True)
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
+    exe.run(startup_prog)
     # yapf: disable
     if model_dir:
         def if_exist(var):
             return os.path.exists(os.path.join(model_dir, var.name))
-        fluid.io.load_vars(exe, model_dir, predicate=if_exist)
+        fluid.io.load_vars(exe, model_dir, main_program=test_prog, predicate=if_exist)
     # yapf: enable
-    test_reader = paddle.batch(
-        reader.test(data_args, test_list), batch_size=batch_size)
-    feeder = fluid.DataFeeder(
-        place=place, feed_list=[image, gt_box, gt_label, difficult])
+    test_reader = reader.test(data_args, test_list, batch_size=batch_size)
+    test_py_reader.decorate_paddle_reader(test_reader)
 
-    def test():
-        # switch network to test mode (i.e. batch norm test mode)
-        test_program = fluid.default_main_program().clone(for_test=True)
-        with fluid.program_guard(test_program):
-            map_eval = fluid.evaluator.DetectionMAP(
-                nmsed_out,
-                gt_label,
-                gt_box,
-                difficult,
-                num_classes,
-                overlap_threshold=0.5,
-                evaluate_difficult=False,
-                ap_version=args.ap_version)
-
-        _, accum_map = map_eval.get_map_var()
-        map_eval.reset(exe)
-        for batch_id, data in enumerate(test_reader()):
-            test_map, = exe.run(test_program,
-                                feed=feeder.feed(data),
-                                fetch_list=[accum_map])
-            if batch_id % 20 == 0:
+    _, accum_map = map_eval.get_map_var()
+    map_eval.reset(exe)
+    test_py_reader.start()
+    try:
+        batch_id = 0
+        while True:
+            test_map, = exe.run(test_prog, fetch_list=[accum_map])
+            if batch_id % 10 == 0:
                 print("Batch {0}, map {1}".format(batch_id, test_map))
-        print("Test model {0}, map {1}".format(model_dir, test_map))
-
-    test()
+            batch_id += 1
+    except fluid.core.EOFException:
+        test_py_reader.reset()
+    print("Test model {0}, map {1}".format(model_dir, test_map))
 
 
 if __name__ == '__main__':
@@ -117,8 +128,7 @@ if __name__ == '__main__':
         mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
         apply_distort=False,
         apply_expand=False,
-        ap_version=args.ap_version,
-        toy=0)
+        ap_version=args.ap_version)
     eval(
         args,
         data_args=data_args,

fluid/object_detection/infer.py

@@ -127,8 +127,7 @@ if __name__ == '__main__':
         mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
         apply_distort=False,
         apply_expand=False,
-        ap_version='',
-        toy=0)
+        ap_version='')
     infer(
         args,
         data_args=data_args,

fluid/object_detection/reader.py

@@ -22,6 +22,7 @@ import os
 import time
 import copy
 import six
+from data_util import GeneratorEnqueuer
 
 
 class Settings(object):
@@ -34,11 +35,9 @@ class Settings(object):
                  mean_value=[127.5, 127.5, 127.5],
                  apply_distort=True,
                  apply_expand=True,
-                 ap_version='11point',
-                 toy=0):
+                 ap_version='11point'):
         self._dataset = dataset
         self._ap_version = ap_version
-        self._toy = toy
         self._data_dir = data_dir
         if 'pascalvoc' in dataset:
             self._label_list = []
@@ -71,10 +70,6 @@ class Settings(object):
     def ap_version(self):
         return self._ap_version
 
-    @property
-    def toy(self):
-        return self._toy
-
     @property
     def apply_distort(self):
         return self._apply_expand
@@ -167,7 +162,7 @@ def preprocess(img, bbox_labels, mode, settings):
     return img, sampled_labels
 
 
-def coco(settings, file_list, mode, shuffle):
+def coco(settings, file_list, mode, batch_size, shuffle):
     # cocoapi
     from pycocotools.coco import COCO
     from pycocotools.cocoeval import COCOeval
@@ -175,16 +170,12 @@ def coco(settings, file_list, mode, shuffle):
     coco = COCO(file_list)
     image_ids = coco.getImgIds()
     images = coco.loadImgs(image_ids)
-    category_ids = coco.getCatIds()
-    category_names = [item['name'] for item in coco.loadCats(category_ids)]
-
-    if not settings.toy == 0:
-        images = images[:settings.toy] if len(images) > settings.toy else images
     print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
 
     def reader():
         if mode == 'train' and shuffle:
             np.random.shuffle(images)
+        batch_out = []
         for image in images:
             image_name = image['file_name']
             image_path = os.path.join(settings.data_dir, image_name)
@@ -203,7 +194,6 @@ def coco(settings, file_list, mode, shuffle):
                 bbox_sample = []
                 # start from 1, leave 0 to background
                 bbox_sample.append(float(ann['category_id']))
-                #float(category_ids.index(ann['category_id'])) + 1)
                 bbox = ann['bbox']
                 xmin, ymin, w, h = bbox
                 xmax = xmin + w
@@ -222,24 +212,32 @@ def coco(settings, file_list, mode, shuffle):
             lbls = sample_labels[:, 0].astype('int32')
             iscrowd = sample_labels[:, -1].astype('int32')
             if 'cocoMAP' in settings.ap_version:
-                yield im, boxes, lbls, iscrowd, \
-                    [im_id, im_width, im_height]
+                batch_out.append((im, boxes, lbls, iscrowd,
+                                  [im_id, im_width, im_height]))
             else:
-                yield im, boxes, lbls, iscrowd
+                batch_out.append((im, boxes, lbls, iscrowd))
+
+            if len(batch_out) == batch_size:
+                yield batch_out
+                batch_out = []
+
+        if mode == 'test' and len(batch_out) > 1:
+            yield batch_out
+            batch_out = []
 
     return reader
 
 
-def pascalvoc(settings, file_list, mode, shuffle):
+def pascalvoc(settings, file_list, mode, batch_size, shuffle):
     flist = open(file_list)
     images = [line.strip() for line in flist]
-    if not settings.toy == 0:
-        images = images[:settings.toy] if len(images) > settings.toy else images
     print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
 
     def reader():
         if mode == 'train' and shuffle:
             np.random.shuffle(images)
+        batch_out = []
+        cnt = 0
         for image in images:
             image_path, label_path = image.split()
             image_path = os.path.join(settings.data_dir, image_path)
@@ -273,37 +271,69 @@ def pascalvoc(settings, file_list, mode, shuffle):
             boxes = sample_labels[:, 1:5]
             lbls = sample_labels[:, 0].astype('int32')
             difficults = sample_labels[:, -1].astype('int32')
-            yield im, boxes, lbls, difficults
+
+            batch_out.append((im, boxes, lbls, difficults))
+            if len(batch_out) == batch_size:
+                yield batch_out
+                cnt += len(batch_out)
+                batch_out = []
+
+        if mode == 'test' and len(batch_out) > 1:
+            yield batch_out
+            cnt += len(batch_out)
+            batch_out = []
 
     return reader
 
 
-def train(settings, file_list, shuffle=True):
+def train(settings,
+          file_list,
+          batch_size,
+          shuffle=True,
+          use_multiprocessing=True,
+          num_workers=8,
+          max_queue=24):
     file_list = os.path.join(settings.data_dir, file_list)
+
+    def infinite_reader(gen):
+        while True:
+            for data in gen():
+                yield data
+
     if 'coco' in settings.dataset:
-        train_settings = copy.copy(settings)
-        if '2014' in file_list:
-            sub_dir = "train2014"
-        elif '2017' in file_list:
-            sub_dir = "train2017"
-        train_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
-        return coco(train_settings, file_list, 'train', shuffle)
+        generator = coco(settings, file_list, "train", batch_size, shuffle)
     else:
-        return pascalvoc(settings, file_list, 'train', shuffle)
+        generator = pascalvoc(settings, file_list, "train", batch_size, shuffle)
+
+    def reader():
+        try:
+            enqueuer = GeneratorEnqueuer(
+                infinite_reader(generator),
+                use_multiprocessing=use_multiprocessing)
+            enqueuer.start(max_queue_size=max_queue, workers=num_workers)
+            generator_output = None
+            while True:
+                while enqueuer.is_running():
+                    if not enqueuer.queue.empty():
+                        generator_output = enqueuer.queue.get()
+                        break
+                    else:
+                        time.sleep(0.02)
+                yield generator_output
+                generator_output = None
+        finally:
+            if enqueuer is not None:
+                enqueuer.stop()
+
+    return reader
 
 
-def test(settings, file_list):
+def test(settings, file_list, batch_size):
     file_list = os.path.join(settings.data_dir, file_list)
     if 'coco' in settings.dataset:
-        test_settings = copy.copy(settings)
-        if '2014' in file_list:
-            sub_dir = "val2014"
-        elif '2017' in file_list:
-            sub_dir = "val2017"
-        test_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
-        return coco(test_settings, file_list, 'test', False)
+        return coco(settings, file_list, 'test', batch_size, False)
     else:
-        return pascalvoc(settings, file_list, 'test', False)
+        return pascalvoc(settings, file_list, 'test', batch_size, False)
 
 
 def infer(settings, image_path):

fluid/object_detection/train.py

@@ -4,6 +4,7 @@ import numpy as np
 import argparse
 import functools
 import shutil
+import math
 
 import paddle
 import paddle.fluid as fluid
@@ -16,233 +17,290 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('learning_rate',    float, 0.001,     "Learning rate.")
 add_arg('batch_size',       int,   64,        "Minibatch size.")
-add_arg('num_passes',       int,   120,       "Epoch number.")
+add_arg('epoc_num',         int,   120,       "Epoch number.")
 add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
 add_arg('parallel',         bool,  True,      "Parallel.")
 add_arg('dataset',          str,   'pascalvoc', "coco2014, coco2017, and pascalvoc.")
 add_arg('model_save_dir',   str,   'model',     "The path to save model.")
 add_arg('pretrained_model', str,   'pretrained/ssd_mobilenet_v1_coco/', "The init model path.")
-add_arg('apply_distort',    bool,  True,   "Whether apply distort.")
-add_arg('apply_expand',     bool,  True,   "Whether apply expand.")
-add_arg('nms_threshold',    float, 0.45,   "NMS threshold.")
-add_arg('ap_version',       str,   '11point',   "integral, 11point.")
-add_arg('resize_h',         int,   300,    "The resized image height.")
-add_arg('resize_w',         int,   300,    "The resized image height.")
-add_arg('mean_value_B',     float, 127.5,  "Mean value for B channel which will be subtracted.")  #123.68
-add_arg('mean_value_G',     float, 127.5,  "Mean value for G channel which will be subtracted.")  #116.78
-add_arg('mean_value_R',     float, 127.5,  "Mean value for R channel which will be subtracted.")  #103.94
-add_arg('is_toy',           int,   0, "Toy for quick debug, 0 means using all data, while n means using only n sample.")
+add_arg('ap_version',       str,   '11point',           "Integral, 11point.")
+add_arg('image_shape',      str,   '3,300,300',         "Input image shape.")
+add_arg('mean_BGR',   str,   '127.5,127.5,127.5', "Mean value for B,G,R channel which will be subtracted.")
 add_arg('data_dir',         str,   'data/pascalvoc', "data directory")
 add_arg('enable_ce',     bool,  False, "Whether use CE to evaluate the model")
 #yapf: enable
 
+train_parameters = {
+    "pascalvoc": {
+        "train_images": 19200,
+        "image_shape": [3, 300, 300],
+        "class_num": 21,
+        "batch_size": 64,
+        "lr": 0.001,
+        "lr_epochs": [40, 60, 80, 100],
+        "lr_decay": [1, 0.5, 0.25, 0.1, 0.01]
+    },
+    "coco2014": {
+        "train_images": 82783,
+        "image_shape": [3, 300, 300],
+        "class_num": 91,
+        "batch_size": 64,
+        "lr": 0.001,
+        "lr_epochs": [12, 19],
+        "lr_decay": [1, 0.5, 0.25]
+    },
+    "coco2017": {
+        "train_images": 118287,
+        "image_shape": [3, 300, 300],
+        "class_num": 91,
+        "batch_size": 64,
+        "lr": 0.001,
+        "lr_epochs": [12, 19],
+        "lr_decay": [1, 0.5, 0.25]
+    }
+}
+
+def optimizer_setting(train_params):
+    batch_size = train_params["batch_size"]
+    iters = train_params["train_images"] / batch_size
+    lr = train_params["lr"]
+    boundaries = [i * iters  for i in train_params["lr_epochs"]]
+    values = [ i * lr for i in train_params["lr_decay"]]
+
+    optimizer = fluid.optimizer.RMSProp(
+        learning_rate=fluid.layers.piecewise_decay(boundaries, values),
+        regularization=fluid.regularizer.L2Decay(0.00005), )
+
+    return optimizer
+
+
+def build_program(main_prog, startup_prog, train_params, is_train):
+    image_shape = train_params['image_shape']
+    class_num = train_params['class_num']
+    with fluid.program_guard(main_prog, startup_prog):
+        py_reader = fluid.layers.py_reader(
+            capacity=64,
+            shapes=[[-1] + image_shape, [-1, 4], [-1, 1], [-1, 1]],
+            lod_levels=[0, 1, 1, 1],
+            dtypes=["float32", "float32", "int32", "int32"],
+            use_double_buffer=True)
+        with fluid.unique_name.guard():
+            image, gt_box, gt_label, difficult = fluid.layers.read_file(py_reader)
+            locs, confs, box, box_var = mobile_net(class_num, image, image_shape)
+            if is_train:
+                loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box,
+                    box_var)
+                loss = fluid.layers.reduce_sum(loss)
+                optimizer = optimizer_setting(train_params)
+                optimizer.minimize(loss)
+            else:
+
+                nmsed_out = fluid.layers.detection_output(
+                    locs, confs, box, box_var, nms_threshold=0.45)
+                with fluid.program_guard(main_prog):
+                    loss = fluid.evaluator.DetectionMAP(
+                        nmsed_out,
+                        gt_label,
+                        gt_box,
+                        difficult,
+                        class_num,
+                        overlap_threshold=0.5,
+                        evaluate_difficult=False,
+                        ap_version=args.ap_version)
+    return py_reader, loss
+
+
 def train(args,
-          train_file_list,
-          val_file_list,
           data_args,
-          learning_rate,
-          batch_size,
-          num_passes,
-          model_save_dir,
-          pretrained_model=None):
-    if args.enable_ce:
-        fluid.framework.default_startup_program().random_seed = 111
-
-    image_shape = [3, data_args.resize_h, data_args.resize_w]
-    if 'coco' in data_args.dataset:
-        num_classes = 91
-    elif 'pascalvoc' in data_args.dataset:
-        num_classes = 21
+          train_params,
+          train_file_list,
+          val_file_list):
+
+    model_save_dir = args.model_save_dir
+    pretrained_model = args.pretrained_model
+    epoc_num = args.epoc_num
+    use_gpu = args.use_gpu
+    parallel = args.parallel
+    enable_ce = args.enable_ce
+    is_shuffle = True
 
     devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
     devices_num = len(devices.split(","))
+    batch_size = train_params['batch_size']
+    batch_size_per_device = batch_size // devices_num
+    iters_per_epoc = train_params["train_images"] // batch_size
+    num_workers = 8
 
-    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
-    gt_box = fluid.layers.data(
-        name='gt_box', shape=[4], dtype='float32', lod_level=1)
-    gt_label = fluid.layers.data(
-        name='gt_label', shape=[1], dtype='int32', lod_level=1)
-    difficult = fluid.layers.data(
-        name='gt_difficult', shape=[1], dtype='int32', lod_level=1)
-    locs, confs, box, box_var = mobile_net(num_classes, image, image_shape)
-    nmsed_out = fluid.layers.detection_output(
-        locs, confs, box, box_var, nms_threshold=args.nms_threshold)
-    loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box,
-                                 box_var)
-    loss = fluid.layers.reduce_sum(loss)
-
-    test_program = fluid.default_main_program().clone(for_test=True)
-    with fluid.program_guard(test_program):
-        map_eval = fluid.evaluator.DetectionMAP(
-            nmsed_out,
-            gt_label,
-            gt_box,
-            difficult,
-            num_classes,
-            overlap_threshold=0.5,
-            evaluate_difficult=False,
-            ap_version=args.ap_version)
-
-    if 'coco' in data_args.dataset:
-        # learning rate decay in 12, 19 pass, respectively
-        if '2014' in train_file_list:
-            epocs = 82783 // batch_size
-            boundaries = [epocs * 12, epocs * 19]
-        elif '2017' in train_file_list:
-            epocs = 118287 // batch_size
-            boundaries = [epocs * 12, epocs * 19]
-        values = [
-            learning_rate, learning_rate * 0.5, learning_rate * 0.25
-        ]
-    elif 'pascalvoc' in data_args.dataset:
-        epocs = 19200 // batch_size
-        boundaries = [epocs * 40, epocs * 60, epocs * 80, epocs * 100]
-        values = [
-            learning_rate, learning_rate * 0.5, learning_rate * 0.25,
-            learning_rate * 0.1, learning_rate * 0.01
-        ]
-    optimizer = fluid.optimizer.RMSProp(
-        learning_rate=fluid.layers.piecewise_decay(boundaries, values),
-        regularization=fluid.regularizer.L2Decay(0.00005), )
+    startup_prog = fluid.Program()
+    train_prog = fluid.Program()
+    test_prog = fluid.Program()
+
+    if enable_ce:
+        import random
+        random.seed(0)
+        np.random.seed(0)
+        is_shuffle = False
+        startup_prog.random_seed = 111
+        train_prog.random_seed = 111
+        test_prog.random_seed = 111
+        num_workers = 1
 
-    optimizer.minimize(loss)
+    train_py_reader, loss = build_program(
+        main_prog=train_prog,
+        startup_prog=startup_prog,
+        train_params=train_params,
+        is_train=True)
+    test_py_reader, map_eval = build_program(
+        main_prog=test_prog,
+        startup_prog=startup_prog,
+        train_params=train_params,
+        is_train=False)
 
-    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    test_prog = test_prog.clone(for_test=True)
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
+    exe.run(startup_prog)
 
     if pretrained_model:
         def if_exist(var):
             return os.path.exists(os.path.join(pretrained_model, var.name))
-        fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
+        fluid.io.load_vars(exe, pretrained_model, main_program=train_prog,
+                           predicate=if_exist)
 
-    if args.parallel:
-        train_exe = fluid.ParallelExecutor(
-            use_cuda=args.use_gpu, loss_name=loss.name)
+    if parallel:
+        train_exe = fluid.ParallelExecutor(main_program=train_prog,
+            use_cuda=use_gpu, loss_name=loss.name)
 
-    if not args.enable_ce:
-        train_reader = paddle.batch(
-            reader.train(data_args, train_file_list), batch_size=batch_size)
-    else:
-        import random
-        random.seed(0)
-        np.random.seed(0)
-        train_reader = paddle.batch(
-            reader.train(data_args, train_file_list, False), batch_size=batch_size)
-    test_reader = paddle.batch(
-        reader.test(data_args, val_file_list), batch_size=batch_size)
-    feeder = fluid.DataFeeder(
-        place=place, feed_list=[image, gt_box, gt_label, difficult])
-
-    def save_model(postfix):
+    train_reader = reader.train(data_args,
+                                train_file_list,
+                                batch_size_per_device,
+                                shuffle=is_shuffle,
+                                use_multiprocessing=True,
+                                num_workers=num_workers,
+                                max_queue=24)
+    test_reader = reader.test(data_args, val_file_list, batch_size)
+    train_py_reader.decorate_paddle_reader(train_reader)
+    test_py_reader.decorate_paddle_reader(test_reader)
+
+    def save_model(postfix, main_prog):
         model_path = os.path.join(model_save_dir, postfix)
         if os.path.isdir(model_path):
             shutil.rmtree(model_path)
         print('save models to %s' % (model_path))
-        fluid.io.save_persistables(exe, model_path)
+        fluid.io.save_persistables(exe, model_path, main_program=main_prog)
 
     best_map = 0.
-
-    def test(pass_id, best_map):
+    def test(epoc_id, best_map):
         _, accum_map = map_eval.get_map_var()
         map_eval.reset(exe)
-        every_pass_map=[]
-        for batch_id, data in enumerate(test_reader()):
-            test_map, = exe.run(test_program,
-                               feed=feeder.feed(data),
-                               fetch_list=[accum_map])
-            if batch_id % 20 == 0:
-                every_pass_map.append(test_map)
-                print("Batch {0}, map {1}".format(batch_id, test_map))
-        mean_map = np.mean(every_pass_map)
+        every_epoc_map=[]
+        test_py_reader.start()
+        try:
+            batch_id = 0
+            while True:
+                test_map, = exe.run(test_prog, fetch_list=[accum_map])
+                if batch_id % 10 == 0:
+                    every_epoc_map.append(test_map)
+                    print("Batch {0}, map {1}".format(batch_id, test_map))
+                batch_id += 1
+        except fluid.core.EOFException:
+            test_py_reader.reset()
+        mean_map = np.mean(every_epoc_map)
+        print("Epoc {0}, test map {1}".format(epoc_id, test_map))
         if test_map[0] > best_map:
             best_map = test_map[0]
-            save_model('best_model')
-        print("Pass {0}, test map {1}".format(pass_id, test_map))
+            save_model('best_model', test_prog)
         return best_map, mean_map
 
-    for pass_id in range(num_passes):
-        batch_begin = time.time()
-        start_time = time.time()
-        prev_start_time = start_time
-        every_pass_loss = []
-        for batch_id, data in enumerate(train_reader()):
-            prev_start_time = start_time
+
+    train_py_reader.start()
+    total_time = 0.0
+    try:
+        for epoc_id in range(epoc_num):
+            epoch_idx = epoc_id + 1
             start_time = time.time()
-            if len(data) < (devices_num * 2):
-                print("There are too few data to train on all devices.")
-                continue
-            if args.parallel:
-                loss_v, = train_exe.run(fetch_list=[loss.name],
-                                        feed=feeder.feed(data))
-            else:
-                loss_v, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[loss])
-            loss_v = np.mean(np.array(loss_v))
-            every_pass_loss.append(loss_v)
-            if batch_id % 20 == 0:
-                print("Pass {0}, batch {1}, loss {2}, time {3}".format(
-                    pass_id, batch_id, loss_v, start_time - prev_start_time))
-
-        end_time = time.time()
-        best_map, mean_map = test(pass_id, best_map)
-        if args.enable_ce and pass_id == args.num_passes - 1:
-            total_time = end_time - start_time
-            train_avg_loss = np.mean(every_pass_loss)
-            if devices_num == 1:
-                print("kpis	train_cost	%s" % train_avg_loss)
-                print("kpis	test_acc	%s" % mean_map)
-                print("kpis	train_speed	%s" % (epocs / total_time))
-            else:
-                print("kpis	train_cost_card%s	%s" %
-                       (devices_num, train_avg_loss))
-                print("kpis	test_acc_card%s	%s" %
-                       (devices_num, mean_map))
-                print("kpis	train_speed_card%s	%f" %
-                       (devices_num, epocs / total_time))
+            prev_start_time = start_time
+            every_epoc_loss = []
+            for batch_id in range(iters_per_epoc):
+                prev_start_time = start_time
+                start_time = time.time()
+                if parallel:
+                    loss_v, = train_exe.run(fetch_list=[loss.name])
+                else:
+                    loss_v, = exe.run(train_prog, fetch_list=[loss])
+                loss_v = np.mean(np.array(loss_v))
+                every_epoc_loss.append(loss_v)
+                if batch_id % 20 == 0:
+                    print("Epoc {0}, batch {1}, loss {2}, time {3}".format(
+                        epoc_id, batch_id, loss_v, start_time - prev_start_time))
+            end_time = time.time()
+            total_time += end_time - start_time
+
+            best_map, mean_map = test(epoc_id, best_map)
+            print("Best test map {0}".format(best_map))
+            if epoc_id % 10 == 0 or epoc_id == epoc_num - 1:
+                save_model(str(epoc_id), train_prog)
+
+            if enable_ce and epoc_id == epoc_num - 1:
+                train_avg_loss = np.mean(every_epoc_loss)
+                if devices_num == 1:
+                    print("kpis	train_cost	%s" % train_avg_loss)
+                    print("kpis	test_acc	%s" % mean_map)
+                    print("kpis	train_speed	%s" % (total_time / epoch_idx))
+                else:
+                    print("kpis	train_cost_card%s	%s" %
+                           (devices_num, train_avg_loss))
+                    print("kpis	test_acc_card%s	%s" %
+                           (devices_num, mean_map))
+                    print("kpis	train_speed_card%s	%f" %
+                           (devices_num, total_time / epoch_idx))
 
+    except fluid.core.EOFException:
+        train_py_reader.reset()
+    except StopIteration:
+        train_py_reader.reset()
+    train_py_reader.reset()
 
-        if pass_id % 10 == 0 or pass_id == num_passes - 1:
-            save_model(str(pass_id))
-    print("Best test map {0}".format(best_map))
 
 if __name__ == '__main__':
     args = parser.parse_args()
     print_arguments(args)
 
     data_dir = args.data_dir
+    dataset = args.dataset
+    assert dataset in ['pascalvoc', 'coco2014', 'coco2017']
+
+    # for pascalvoc
     label_file = 'label_list'
-    model_save_dir = args.model_save_dir
     train_file_list = 'trainval.txt'
     val_file_list = 'test.txt'
-    if 'coco' in args.dataset:
-        data_dir = 'data/coco'
-        if '2014' in args.dataset:
-            train_file_list = 'annotations/instances_train2014.json'
-            val_file_list = 'annotations/instances_val2014.json'
-        elif '2017' in args.dataset:
-            train_file_list = 'annotations/instances_train2017.json'
-            val_file_list = 'annotations/instances_val2017.json'
+
+    if dataset == 'coco2014':
+        train_file_list = 'annotations/instances_train2014.json'
+        val_file_list = 'annotations/instances_val2014.json'
+    elif dataset == 'coco2017':
+        train_file_list = 'annotations/instances_train2017.json'
+        val_file_list = 'annotations/instances_val2017.json'
+
+    mean_BGR = [float(m) for m in args.mean_BGR.split(",")]
+    image_shape = [int(m) for m in args.image_shape.split(",")]
+    train_parameters[dataset]['image_shape'] = image_shape
+    train_parameters[dataset]['batch_size'] = args.batch_size
+    train_parameters[dataset]['lr'] = args.learning_rate
+    train_parameters[dataset]['epoc_num'] = args.epoc_num
 
     data_args = reader.Settings(
         dataset=args.dataset,
         data_dir=data_dir,
         label_file=label_file,
-        resize_h=args.resize_h,
-        resize_w=args.resize_w,
-        mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
-        apply_distort=args.apply_distort,
-        apply_expand=args.apply_expand,
-        ap_version = args.ap_version,
-        toy=args.is_toy)
-    train(
-        args,
-        train_file_list=train_file_list,
-        val_file_list=val_file_list,
-        data_args=data_args,
-        learning_rate=args.learning_rate,
-        batch_size=args.batch_size,
-        num_passes=args.num_passes,
-        model_save_dir=model_save_dir,
-        pretrained_model=args.pretrained_model)
+        resize_h=image_shape[1],
+        resize_w=image_shape[2],
+        mean_value=mean_BGR,
+        apply_distort=True,
+        apply_expand=True,
+        ap_version = args.ap_version)
+    train(args,
+          data_args,
+          train_parameters[dataset],
+          train_file_list=train_file_list,
+          val_file_list=val_file_list)