Merge branch 'develop' of https://github.com/PaddlePaddle/models into fix_random

fluid/image_classification/.run_ce.sh

 #!/bin/bash
 
 # This file is only used for continuous evaluation.
+export FLAGS_cudnn_deterministic=True
 cudaid=${object_detection_cudaid:=0}
 export CUDA_VISIBLE_DEVICES=$cudaid
-python train.py --batch_size=64 --num_epochs=10 --total_images=6149 --enable_ce=True | python _ce.py
+python train.py --batch_size=64 --num_epochs=10 --enable_ce=True | python _ce.py
 
 cudaid=${object_detection_cudaid_m:=0, 1, 2, 3}
 export CUDA_VISIBLE_DEVICES=$cudaid
-python train.py --batch_size=64 --num_epochs=10 --total_images=6149 --enable_ce=True | python _ce.py
+python train.py --batch_size=64 --num_epochs=10 --enable_ce=True | python _ce.py

fluid/image_classification/_ce.py

 ####this file is only used for continuous evaluation test!
-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import sys
 sys.path.append(os.environ['ceroot'])
@@ -14,7 +16,7 @@ train_cost_kpi = CostKpi('train_cost', 0.5, 0, actived=True, desc='train cost')
 test_acc_top1_kpi = AccKpi('test_acc_top1', 0.05, 0, desc='TOP1 ACC')
 test_acc_top5_kpi = AccKpi(
     'test_acc_top5', 0.05, 0, actived=False, desc='TOP5 ACC')
-test_cost_kpi = CostKpi('test_cost', 1.0, 0, actived=True, desc='train cost')
+test_cost_kpi = CostKpi('test_cost', 0.5, 0, actived=True, desc='train cost')
 train_speed_kpi = AccKpi(
     'train_speed',
     0.5,
@@ -27,13 +29,13 @@ train_acc_top1_card4_kpi = AccKpi(
 train_acc_top5_card4_kpi = AccKpi(
     'train_acc_top5_card4', 0.05, 0, actived=False, desc='TOP5 ACC')
 train_cost_card4_kpi = CostKpi(
-    'train_cost_kpi', 0.3, 0, actived=True, desc='train cost')
+    'train_cost_card4', 0.5, 0, actived=True, desc='train cost')
 test_acc_top1_card4_kpi = AccKpi(
     'test_acc_top1_card4', 0.05, 0, desc='TOP1 ACC')
 test_acc_top5_card4_kpi = AccKpi(
     'test_acc_top5_card4', 0.05, 0, actived=False, desc='TOP5 ACC')
 test_cost_card4_kpi = CostKpi(
-    'test_cost_card4', 1.0, 0, actived=True, desc='train cost')
+    'test_cost_card4', 0.5, 0, actived=True, desc='train cost')
 train_speed_card4_kpi = AccKpi(
     'train_speed_card4',
     0.5,

fluid/image_classification/eval.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time

fluid/image_classification/infer.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
@@ -15,7 +18,6 @@ import math
 parser = argparse.ArgumentParser(description=__doc__)
 # yapf: disable
 add_arg = functools.partial(add_arguments, argparser=parser)
-add_arg('batch_size',       int,  256,                  "Minibatch size.")
 add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
 add_arg('class_dim',        int,  1000,                 "Class number.")
 add_arg('image_shape',      str,  "3,224,224",          "Input image size")

fluid/image_classification/models/alexnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math

fluid/image_classification/models/dpn.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
 import sys
-import paddle
-import paddle.fluid as fluid
-import paddle.fluid.layers.control_flow as control_flow
-import paddle.fluid.layers.nn as nn
-import paddle.fluid.layers.tensor as tensor
 import math
+import paddle.fluid as fluid
 
 __all__ = ["DPN", "DPN68", "DPN92", "DPN98", "DPN107", "DPN131"]
 
@@ -67,7 +66,7 @@ class DPN(object):
         for gc in range(4):
             bw = bws[gc]
             inc = inc_sec[gc]
-            R = (k_r * bw) / rs[gc]
+            R = (k_r * bw) // rs[gc]
             if gc == 0:
                 _type1 = 'proj'
                 _type2 = 'normal'
@@ -178,8 +177,8 @@ class DPN(object):
                           _type='normal'):
         kw = 3
         kh = 3
-        pw = (kw - 1) / 2
-        ph = (kh - 1) / 2
+        pw = (kw - 1) // 2
+        ph = (kh - 1) // 2
 
         # type
         if _type is 'proj':

fluid/image_classification/models/googlenet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 
@@ -36,7 +39,7 @@ class GoogleNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=act,
             param_attr=param_attr,

fluid/image_classification/models/inception_v4.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math

fluid/image_classification/models/learning_rate.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers.ops as ops

fluid/image_classification/models/mobilenet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr

fluid/image_classification/models/resnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math
@@ -75,7 +78,7 @@ class ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False)

fluid/image_classification/models/se_resnext.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math
@@ -11,7 +14,7 @@ train_parameters = {
     "input_size": [3, 224, 224],
     "input_mean": [0.485, 0.456, 0.406],
     "input_std": [0.229, 0.224, 0.225],
-    "dropout_seed": None,
+    "enable_ce": False,
     "learning_strategy": {
         "name": "piecewise_decay",
         "batch_size": 256,
@@ -102,9 +105,11 @@ class SE_ResNeXt():
 
         pool = fluid.layers.pool2d(
             input=conv, pool_size=7, pool_type='avg', global_pooling=True)
-        # do not set seed when traning, it is only used for debug
-        drop = fluid.layers.dropout(
-            x=pool, dropout_prob=0.5, seed=self.params["dropout_seed"])
+        # enable_ce is used for continuous evaluation to remove the randomness
+        if self.params["enable_ce"]:
+            drop = pool
+        else:
+            drop = fluid.layers.dropout(x=pool, dropout_prob=0.5)
         stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
         out = fluid.layers.fc(input=drop,
                               size=class_dim,
@@ -156,7 +161,7 @@ class SE_ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False)
@@ -167,7 +172,7 @@ class SE_ResNeXt():
             input=input, pool_size=0, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
         squeeze = fluid.layers.fc(input=pool,
-                                  size=num_channels / reduction_ratio,
+                                  size=num_channels // reduction_ratio,
                                   act='relu',
                                   param_attr=fluid.param_attr.ParamAttr(
                                       initializer=fluid.initializer.Uniform(

fluid/image_classification/models/vgg.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 

fluid/image_classification/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
 import sys
+import functools
+import math
 import paddle
 import paddle.fluid as fluid
 import paddle.dataset.flowers as flowers
 import models
 import reader
 import argparse
-import functools
 from models.learning_rate import cosine_decay
 from utility import add_arguments, print_arguments
-import math
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
@@ -104,8 +107,11 @@ def train(args):
 
     if args.enable_ce:
         assert model_name == "SE_ResNeXt50_32x4d"
+        fluid.default_startup_program().random_seed = 1000
+        model.params["enable_ce"] = True
+        class_dim = 102
 
-    if model_name is "GoogleNet":
+    if model_name == "GoogleNet":
         out0, out1, out2 = model.net(input=image, class_dim=class_dim)
         cost0 = fluid.layers.cross_entropy(input=out0, label=label)
         cost1 = fluid.layers.cross_entropy(input=out1, label=label)
@@ -134,8 +140,6 @@ def train(args):
     params["num_epochs"] = args.num_epochs
     params["learning_strategy"]["batch_size"] = args.batch_size
     params["learning_strategy"]["name"] = args.lr_strategy
-    if args.enable_ce:
-        params["dropout_seed"] = 10
 
     # initialize optimizer
     optimizer = optimizer_setting(params)
@@ -144,9 +148,6 @@ def train(args):
     if with_memory_optimization:
         fluid.memory_optimize(fluid.default_main_program())
 
-    if args.enable_ce:
-        fluid.default_startup_program().random_seed = 1000
-
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
@@ -257,7 +258,7 @@ def train(args):
         # This is for continuous evaluation only
         if args.enable_ce and pass_id == args.num_epochs - 1:
             if gpu_nums == 1:
-                # Use the last cost/acc for training
+                # Use the mean cost/acc for training
                 print("kpis	train_cost	%s" % train_loss)
                 print("kpis	train_acc_top1	%s" % train_acc1)
                 print("kpis	train_acc_top5	%s" % train_acc5)
@@ -267,7 +268,7 @@ def train(args):
                 print("kpis	test_acc_top5	%s" % test_acc5)
                 print("kpis	train_speed	%s" % train_speed)
             else:
-                # Use the last cost/acc for training
+                # Use the mean cost/acc for training
                 print("kpis	train_cost_card%s	%s" %
                       (gpu_nums, train_loss))
                 print("kpis	train_acc_top1_card%s	%s" %

fluid/image_classification/utility.py

@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 import distutils.util
 import numpy as np
+import six
 from paddle.fluid import core
 
 
@@ -37,7 +38,7 @@ def print_arguments(args):
     :type args: argparse.Namespace
     """
     print("-----------  Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).iteritems()):
+    for arg, value in sorted(six.iteritems(vars(args))):
         print("%s: %s" % (arg, value))
     print("------------------------------------------------")
 

fluid/neural_machine_translation/transformer/config.py

@@ -190,6 +190,3 @@ fast_decoder_data_input_fields = (
     "trg_word",
     "init_score",
     "trg_src_attn_bias", )
-# fast_decoder_util_input_fields = (
-#     "trg_slf_attn_pre_softmax_shape_delta",
-#     "trg_slf_attn_post_softmax_shape_delta", )

fluid/neural_machine_translation/transformer/train.py

@@ -258,7 +258,7 @@ def split_data(data, num_part):
 
 
 def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
-                 util_input_names, sum_cost, token_num):
+                 sum_cost, token_num):
     # Context to do validation.
     test_program = train_progm.clone()
     with fluid.program_guard(test_program):
@@ -299,9 +299,9 @@ def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
                     split_data(
                         data, num_part=dev_count)):
                 data_input_dict, _ = prepare_batch_input(
-                    data_buffer, data_input_names, util_input_names,
-                    ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
-                    ModelHyperParams.n_head, ModelHyperParams.d_model)
+                    data_buffer, data_input_names, ModelHyperParams.eos_idx,
+                    ModelHyperParams.eos_idx, ModelHyperParams.n_head,
+                    ModelHyperParams.d_model)
                 feed_list.append(data_input_dict)
 
             outs = exe.run(feed=feed_list,
@@ -363,8 +363,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
 
     if args.val_file_pattern is not None:
         test = test_context(train_progm, avg_cost, train_exe, dev_count,
-                            data_input_names, util_input_names, sum_cost,
-                            token_num)
+                            data_input_names, sum_cost, token_num)
 
     # the best cross-entropy value with label smoothing
     loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(

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/reader.py

@@ -22,6 +22,7 @@ import os
 import time
 import copy
 import six
+from data_util import GeneratorEnqueuer
 
 
 class Settings(object):
@@ -167,7 +168,7 @@ 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
@@ -182,9 +183,10 @@ def coco(settings, file_list, mode, shuffle):
         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:
+    while True:
+        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)
@@ -221,25 +223,28 @@ def coco(settings, file_list, mode, shuffle):
             boxes = sample_labels[:, 1:5]
             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 = []
 
-    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:
+    while True:
+        if mode == "train" and shuffle:
             np.random.shuffle(images)
+        batch_out = []
         for image in images:
             image_path, label_path = image.split()
             image_path = os.path.join(settings.data_dir, image_path)
@@ -273,7 +278,51 @@ 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
+                batch_out = []
+
+
+def batch_reader(settings,
+                 file_list,
+                 batch_size,
+                 mode,
+                 shuffle=True,
+                 num_workers=8):
+    file_list = os.path.join(settings.data_dir, file_list)
+    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)
+
+    def reader():
+        try:
+            if 'coco' in settings.dataset:
+                enqueuer = GeneratorEnqueuer(
+                    coco(train_settings, file_list, mode, batch_size, shuffle),
+                    use_multiprocessing=False)
+            else:
+                enqueuer = GeneratorEnqueuer(
+                    pascalvoc(settings, file_list, mode, batch_size, shuffle),
+                    use_multiprocessing=False)
+            enqueuer.start(max_queue_size=24, 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(0.01)
+                yield generator_output
+                generator_output = None
+        finally:
+            if enqueuer is not None:
+                enqueuer.stop()
 
     return reader
 
@@ -292,7 +341,7 @@ def train(settings, file_list, shuffle=True):
         return pascalvoc(settings, file_list, 'train', shuffle)
 
 
-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)
@@ -301,9 +350,9 @@ def test(settings, file_list):
         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(test_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

@@ -15,7 +15,7 @@ parser = argparse.ArgumentParser(description=__doc__)
 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('batch_size',       int,   16,        "Minibatch size.")
 add_arg('num_passes',       int,   120,       "Epoch number.")
 add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
 add_arg('parallel',         bool,  True,      "Parallel.")
@@ -36,45 +36,41 @@ add_arg('data_dir',         str,   'data/pascalvoc', "data directory")
 add_arg('enable_ce',     bool,  False, "Whether use CE to evaluate the model")
 #yapf: enable
 
-
-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
-
+def build_program(is_train, main_prog, startup_prog, args, data_args,
+                  boundaries=None, values=None, train_file_list=None):
     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
 
-    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
-    devices_num = len(devices.split(","))
+    def get_optimizer():
+        optimizer = fluid.optimizer.RMSProp(
+            learning_rate=fluid.layers.piecewise_decay(boundaries, values),
+            regularization=fluid.regularizer.L2Decay(0.00005), )
+        return optimizer
 
-    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)
+            if is_train:
                 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(
+                optimizer = get_optimizer()
+                optimizer.minimize(loss)
+            else:
+                nmsed_out = fluid.layers.detection_output(
+                   locs, confs, box, box_var, nms_threshold=args.nms_threshold)
+                with fluid.program_guard(main_prog):
+                    loss = fluid.evaluator.DetectionMAP(
                         nmsed_out,
                         gt_label,
                         gt_box,
@@ -83,82 +79,121 @@ def train(args,
                         overlap_threshold=0.5,
                         evaluate_difficult=False,
                         ap_version=args.ap_version)
+    if not is_train:
+        main_prog = main_prog.clone(for_test=True)
+    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):
+
+    startup_prog = fluid.Program()
+    train_prog = fluid.Program()
+    test_prog = fluid.Program()
+
+    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
+    devices_num = len(devices.split(","))
     if 'coco' in data_args.dataset:
         # learning rate decay in 12, 19 pass, respectively
         if '2014' in train_file_list:
-            epocs = 82783 // batch_size
+            epocs = 82783 // batch_size // devices_num
+            test_epocs = 40504 // batch_size
             boundaries = [epocs * 12, epocs * 19]
         elif '2017' in train_file_list:
-            epocs = 118287 // batch_size
+            epocs = 118287 // batch_size // devices_num
+            test_epocs = 5000 // batch_size
             boundaries = [epocs * 12, epocs * 19]
-        values = [
-            learning_rate, learning_rate * 0.5, learning_rate * 0.25
-        ]
+        values = [learning_rate, learning_rate * 0.5,
+            learning_rate * 0.25]
     elif 'pascalvoc' in data_args.dataset:
-        epocs = 19200 // batch_size
+        epocs = 19200 // batch_size // devices_num
+        test_epocs = 4952 // 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), )
+            learning_rate * 0.1, learning_rate * 0.01]
 
-    optimizer.minimize(loss)
+    if args.enable_ce:
+        startup_prog.random_seed = 111
+        train_prog.random_seed = 111
+        test_prog.random_seed = 111
 
+    train_py_reader, loss = build_program(
+        is_train=True,
+        main_prog=train_prog,
+        startup_prog=startup_prog,
+        args=args,
+        data_args=data_args,
+        values = values,
+        boundaries = boundaries,
+        train_file_list=train_file_list)
+    test_py_reader, map_eval = build_program(
+        is_train=False,
+        main_prog=test_prog,
+        startup_prog=startup_prog,
+        args=args,
+        data_args=data_args)
     place = fluid.CUDAPlace(0) if args.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(
+        train_exe = fluid.ParallelExecutor(main_program=train_prog,
             use_cuda=args.use_gpu, loss_name=loss.name)
-
+        test_exe = fluid.ParallelExecutor(main_program=test_prog,
+            use_cuda=args.use_gpu, share_vars_from=train_exe)
     if not args.enable_ce:
-        train_reader = paddle.batch(
-            reader.train(data_args, train_file_list), batch_size=batch_size)
+        train_reader = reader.batch_reader(data_args, train_file_list, batch_size, "train")
     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.batch_reader(data_args, train_file_list, batch_size, "train", shuffle=False)
+    test_reader = reader.batch_reader(data_args, val_file_list, batch_size, "test")
+    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):
         _, 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),
+        test_py_reader.start()
+        batch_id = 0
+        try:
+            while True:
+                test_map, = exe.run(test_prog,
                                    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))
+                batch_id += 1
+                if batch_id > test_epocs:
+                    break
+        except fluid.core.EOFException:
+            test_py_reader.reset()
         mean_map = np.mean(every_pass_map)
         if test_map[0] > best_map:
             best_map = test_map[0]
-            save_model('best_model')
+            save_model('best_model', test_prog)
         print("Pass {0}, test map {1}".format(pass_id, test_map))
         return best_map, mean_map
 
@@ -166,30 +201,33 @@ def train(args,
     for pass_id in range(num_passes):
         epoch_idx = pass_id + 1
         start_time = time.time()
+        train_py_reader.start()
         prev_start_time = start_time
         every_pass_loss = []
-        for batch_id, data in enumerate(train_reader()):
+        batch_id = 0
+        try:
+            while True:
                 prev_start_time = start_time
                 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))
+                    loss_v, = train_exe.run(fetch_list=[loss.name])
                 else:
-                loss_v, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[loss])
+                    loss_v, = exe.run(train_prog, 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))
+                batch_id += 1
+                if batch_id > epocs:
+                    break
+        except fluid.core.EOFException:
+            train_py_reader.reset()
 
         end_time = time.time()
         best_map, mean_map = test(pass_id, best_map)
-        if args.enable_ce and pass_id == 1:
+        if args.enable_ce and pass_id == num_passes - 1:
             total_time += end_time - start_time
             train_avg_loss = np.mean(every_pass_loss)
             if devices_num == 1:
@@ -204,9 +242,8 @@ def train(args,
                 print("kpis	train_speed_card%s	%f" %
                        (devices_num, total_time / epoch_idx))
 
-
         if pass_id % 10 == 0 or pass_id == num_passes - 1:
-            save_model(str(pass_id))
+            save_model(str(pass_id), train_prog)
     print("Best test map {0}".format(best_map))
 
 if __name__ == '__main__':

fluid/ocr_recognition/.run_ce.sh

 export ce_mode=1
-python train.py --batch_size=32 --total_step=1 --eval_period=1 --log_period=1 --use_gpu=True 1> ./tmp.log
+rm -f *_factor.txt
+python train.py --batch_size=32 --total_step=100 --eval_period=100 --log_period=100 --use_gpu=True 1> ./tmp.log
 cat tmp.log | python _ce.py
 rm tmp.log

fluid/ocr_recognition/_ce.py

@@ -8,14 +8,10 @@ from kpi import CostKpi, DurationKpi, AccKpi
 # NOTE kpi.py should shared in models in some way!!!!
 
 train_cost_kpi = CostKpi('train_cost', 0.05, 0, actived=True)
-test_acc_kpi = AccKpi('test_acc', 0.005, 0, actived=True)
 train_duration_kpi = DurationKpi('train_duration', 0.06, 0, actived=True)
-train_acc_kpi = AccKpi('train_acc', 0.005, 0, actived=True)
 
 tracking_kpis = [
-    train_acc_kpi,
     train_cost_kpi,
-    test_acc_kpi,
     train_duration_kpi,
 ]