Speed data reader in PyramidBox model. (#1016)

* Speed data processing by multi-threads/multi-process.
* Add profiling scripts.
* Use depthwise transposed conv2d.

fluid/face_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/face_detection/profile.py

+import os
+import shutil
+import numpy as np
+import time
+import argparse
+import functools
+
+import reader
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from pyramidbox import PyramidBox
+from utility import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+
+# yapf: disable
+add_arg('parallel',         bool,  True,            "parallel")
+add_arg('learning_rate',    float, 0.001,           "Learning rate.")
+add_arg('batch_size',       int,   20,              "Minibatch size.")
+add_arg('num_iteration',    int,   10,              "Epoch number.")
+add_arg('skip_reader',      bool,  False,            "Whether to skip data reader.")
+add_arg('use_gpu',          bool,  True,            "Whether use GPU.")
+add_arg('use_pyramidbox',   bool,  True,            "Whether use PyramidBox model.")
+add_arg('model_save_dir',   str,   'output',        "The path to save model.")
+add_arg('pretrained_model', str,   './pretrained/', "The init model path.")
+add_arg('resize_h',         int,   640,             "The resized image height.")
+add_arg('resize_w',         int,   640,             "The resized image height.")
+#yapf: enable
+
+
+def train(args, config, train_file_list, optimizer_method):
+    learning_rate = args.learning_rate
+    batch_size = args.batch_size
+    height = args.resize_h
+    width = args.resize_w
+    use_gpu = args.use_gpu
+    use_pyramidbox = args.use_pyramidbox
+    model_save_dir = args.model_save_dir
+    pretrained_model = args.pretrained_model
+    skip_reader = args.skip_reader
+    num_iterations = args.num_iteration
+    parallel = args.parallel
+
+    num_classes = 2
+    image_shape = [3, height, width]
+
+    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
+    devices_num = len(devices.split(","))
+
+    fetches = []
+    network = PyramidBox(image_shape, num_classes,
+                         sub_network=use_pyramidbox)
+    if use_pyramidbox:
+        face_loss, head_loss, loss = network.train()
+        fetches = [face_loss, head_loss]
+    else:
+        loss = network.vgg_ssd_loss()
+        fetches = [loss]
+
+    epocs = 12880 / 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
+    ]
+
+    if optimizer_method == "momentum":
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=boundaries, values=values),
+            momentum=0.9,
+            regularization=fluid.regularizer.L2Decay(0.0005),
+        )
+    else:
+        optimizer = fluid.optimizer.RMSProp(
+            learning_rate=fluid.layers.piecewise_decay(boundaries, values),
+            regularization=fluid.regularizer.L2Decay(0.0005),
+        )
+
+    optimizer.minimize(loss)
+    fluid.memory_optimize(fluid.default_main_program())
+
+    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    start_pass = 0
+    if pretrained_model:
+        if pretrained_model.isdigit():
+            start_pass = int(pretrained_model) + 1
+            pretrained_model = os.path.join(model_save_dir, pretrained_model)
+            print("Resume from %s " %(pretrained_model))
+
+        if not os.path.exists(pretrained_model):
+            raise ValueError("The pre-trained model path [%s] does not exist." %
+                             (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)
+
+    if parallel:
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=use_gpu, loss_name=loss.name)
+
+    train_reader = reader.train_batch_reader(config, train_file_list, batch_size=batch_size)
+
+    def tensor(data, place, lod=None):
+        t = fluid.core.LoDTensor()
+        t.set(data, place)
+        if lod:
+            t.set_lod(lod)
+        return t
+
+    im, face_box, head_box, labels, lod = next(train_reader)
+    im_t = tensor(im, place)
+    box1 = tensor(face_box, place, [lod])
+    box2 = tensor(head_box, place, [lod])
+    lbl_t = tensor(labels, place, [lod])
+    feed_data = {'image': im_t, 'face_box': box1,
+                 'head_box': box2, 'gt_label': lbl_t}
+
+    def run(iterations, feed_data):
+        # global feed_data
+        reader_time = []
+        run_time = []
+        for batch_id in range(iterations):
+            start_time = time.time()
+            if not skip_reader:
+                im, face_box, head_box, labels, lod = next(train_reader)
+                im_t = tensor(im, place)
+                box1 = tensor(face_box, place, [lod])
+                box2 = tensor(head_box, place, [lod])
+                lbl_t = tensor(labels, place, [lod])
+                feed_data = {'image': im_t, 'face_box': box1,
+                             'head_box': box2, 'gt_label': lbl_t}
+            end_time = time.time()
+            reader_time.append(end_time - start_time)
+
+            start_time = time.time()
+            if parallel:
+                fetch_vars = train_exe.run(fetch_list=[v.name for v in fetches],
+                                           feed=feed_data)
+            else:
+                fetch_vars = exe.run(fluid.default_main_program(),
+                                     feed=feed_data,
+                                     fetch_list=fetches)
+            end_time = time.time()
+            run_time.append(end_time - start_time)
+            fetch_vars = [np.mean(np.array(v)) for v in fetch_vars]
+            if not args.use_pyramidbox:
+                print("Batch {0}, loss {1}".format(batch_id, fetch_vars[0]))
+            else:
+                print("Batch {0}, face loss {1}, head loss {2}".format(
+                       batch_id, fetch_vars[0], fetch_vars[1]))
+
+        return reader_time, run_time
+
+    # start-up
+    run(2, feed_data)
+
+    # profiling
+    start = time.time()
+    if not parallel:
+        with profiler.profiler('All', 'total', '/tmp/profile_file'):
+            reader_time, run_time = run(num_iterations, feed_data)
+    else:
+        reader_time, run_time = run(num_iterations, feed_data)
+    end = time.time()
+    total_time = end - start
+    print("Total time: {0}, reader time: {1} s, run time: {2} s".format(
+        total_time, np.sum(reader_time), np.sum(run_time)))
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+
+    data_dir = 'data/WIDERFACE/WIDER_train/images/'
+    train_file_list = 'label/train_gt_widerface.res'
+
+    config = reader.Settings(
+        data_dir=data_dir,
+        resize_h=args.resize_h,
+        resize_w=args.resize_w,
+        apply_expand=False,
+        mean_value=[104., 117., 123.],
+        ap_version='11point')
+    train(args, config, train_file_list, optimizer_method="momentum")

fluid/face_detection/pyramidbox.py

@@ -81,10 +81,7 @@ class PyramidBox(object):
         if self.is_infer:
             return [self.image]
         else:
-            return [
-                self.image, self.face_box, self.head_box, self.gt_label,
-                self.difficult
-            ]
+            return [self.image, self.face_box, self.head_box, self.gt_label]
 
     def _input(self):
         self.image = fluid.layers.data(
@@ -96,8 +93,6 @@ class PyramidBox(object):
                 name='head_box', shape=[4], dtype='float32', lod_level=1)
             self.gt_label = fluid.layers.data(
                 name='gt_label', shape=[1], dtype='int32', lod_level=1)
-            self.difficult = fluid.layers.data(
-                name='gt_difficult', shape=[1], dtype='int32', lod_level=1)
 
     def _vgg(self):
         self.conv1, self.pool1 = conv_block(self.image, 2, [64] * 2, [3] * 2)
@@ -144,7 +139,8 @@ class PyramidBox(object):
                     stride=2,
                     groups=ch,
                     param_attr=w_attr,
-                    bias_attr=False)
+                    bias_attr=False,
+                    use_cudnn=True)
             else:
                 upsampling = fluid.layers.resize_bilinear(
                     conv1, out_shape=up_to.shape[2:])

fluid/face_detection/reader.py

@@ -24,6 +24,7 @@ import time
 import copy
 import random
 import cv2
+from data_util import GeneratorEnqueuer
 
 
 class Settings(object):
@@ -184,20 +185,20 @@ def preprocess(img, bbox_labels, mode, settings, image_path):
     return img, sampled_labels
 
 
-def put_txt_in_dict(input_txt):
+def load_file_list(input_txt):
     with open(input_txt, 'r') as f_dir:
         lines_input_txt = f_dir.readlines()
 
-    dict_input_txt = {}
+    file_dict = {}
     num_class = 0
     for i in range(len(lines_input_txt)):
         tmp_line_txt = lines_input_txt[i].strip('\n\t\r')
         if '--' in tmp_line_txt:
             if i != 0:
                 num_class += 1
-            dict_input_txt[num_class] = []
+            file_dict[num_class] = []
             dict_name = tmp_line_txt
-            dict_input_txt[num_class].append(tmp_line_txt)
+            file_dict[num_class].append(tmp_line_txt)
         if '--' not in tmp_line_txt:
             if len(tmp_line_txt) > 6:
                 split_str = tmp_line_txt.split(' ')
@@ -207,11 +208,11 @@ def put_txt_in_dict(input_txt):
                 y2_max = float(split_str[3])
                 tmp_line_txt = str(x1_min) + ' ' + str(y1_min) + ' ' + str(
                     x2_max) + ' ' + str(y2_max)
-                dict_input_txt[num_class].append(tmp_line_txt)
+                file_dict[num_class].append(tmp_line_txt)
             else:
-                dict_input_txt[num_class].append(tmp_line_txt)
+                file_dict[num_class].append(tmp_line_txt)
 
-    return dict_input_txt
+    return file_dict
 
 
 def expand_bboxes(bboxes,
@@ -238,68 +239,106 @@ def expand_bboxes(bboxes,
     return expand_boxes
 
 
-def pyramidbox(settings, file_list, mode, shuffle):
+def train_generator(settings, file_list, batch_size, shuffle=True):
+    file_dict = load_file_list(file_list)
+    while True:
+        if shuffle:
+            random.shuffle(file_dict)
+        images, face_boxes, head_boxes, label_ids = [], [], [], []
+        label_offs = [0]
 
-    dict_input_txt = {}
-    dict_input_txt = put_txt_in_dict(file_list)
-
-    def reader():
-        if mode == 'train' and shuffle:
-            random.shuffle(dict_input_txt)
-        for index_image in range(len(dict_input_txt)):
-
-            image_name = dict_input_txt[index_image][0] + '.jpg'
+        for index_image in file_dict.keys():
+            image_name = file_dict[index_image][0] + '.jpg'
             image_path = os.path.join(settings.data_dir, image_name)
-
             im = Image.open(image_path)
             if im.mode == 'L':
                 im = im.convert('RGB')
             im_width, im_height = im.size
 
             # layout: label | xmin | ymin | xmax | ymax
-            if mode == 'train':
-                bbox_labels = []
-                for index_box in range(len(dict_input_txt[index_image])):
-                    if index_box >= 2:
-                        bbox_sample = []
-                        temp_info_box = dict_input_txt[index_image][
-                            index_box].split(' ')
-                        xmin = float(temp_info_box[0])
-                        ymin = float(temp_info_box[1])
-                        w = float(temp_info_box[2])
-                        h = float(temp_info_box[3])
-                        xmax = xmin + w
-                        ymax = ymin + h
-
-                        bbox_sample.append(1)
-                        bbox_sample.append(float(xmin) / im_width)
-                        bbox_sample.append(float(ymin) / im_height)
-                        bbox_sample.append(float(xmax) / im_width)
-                        bbox_sample.append(float(ymax) / im_height)
-                        bbox_labels.append(bbox_sample)
-
-                im, sample_labels = preprocess(im, bbox_labels, mode, settings,
-                                               image_path)
-                sample_labels = np.array(sample_labels)
-                if len(sample_labels) == 0: continue
-                im = im.astype('float32')
-                boxes = sample_labels[:, 1:5]
-                lbls = [1] * len(boxes)
-                difficults = [1] * len(boxes)
-                yield im, boxes, expand_bboxes(boxes), lbls, difficults
-
-            if mode == 'test':
-                yield im, image_path
-
-    return reader
+            bbox_labels = []
+            for index_box in range(len(file_dict[index_image])):
+                if index_box >= 2:
+                    bbox_sample = []
+                    temp_info_box = file_dict[index_image][index_box].split(' ')
+                    xmin = float(temp_info_box[0])
+                    ymin = float(temp_info_box[1])
+                    w = float(temp_info_box[2])
+                    h = float(temp_info_box[3])
+                    xmax = xmin + w
+                    ymax = ymin + h
+
+                    bbox_sample.append(1)
+                    bbox_sample.append(float(xmin) / im_width)
+                    bbox_sample.append(float(ymin) / im_height)
+                    bbox_sample.append(float(xmax) / im_width)
+                    bbox_sample.append(float(ymax) / im_height)
+                    bbox_labels.append(bbox_sample)
+
+            im, sample_labels = preprocess(im, bbox_labels, "train", settings,
+                                           image_path)
+            sample_labels = np.array(sample_labels)
+            if len(sample_labels) == 0: continue
+
+            im = im.astype('float32')
+            face_box = sample_labels[:, 1:5]
+            head_box = expand_bboxes(face_box)
+            label = [1] * len(face_box)
+
+            images.append(im)
+            face_boxes.extend(face_box)
+            head_boxes.extend(head_box)
+            label_ids.extend(label)
+            label_offs.append(label_offs[-1] + len(face_box))
+
+            if len(images) == batch_size:
+                images = np.array(images).astype('float32')
+                face_boxes = np.array(face_boxes).astype('float32')
+                head_boxes = np.array(head_boxes).astype('float32')
+                label_ids = np.array(label_ids).astype('int32')
+                yield images, face_boxes, head_boxes, label_ids, label_offs
+                images, face_boxes, head_boxes = [], [], []
+                label_ids, label_offs = [], [0]
+
+
+def train_batch_reader(settings,
+                       file_list,
+                       batch_size,
+                       shuffle=True,
+                       num_workers=8):
+    try:
+        enqueuer = GeneratorEnqueuer(
+            train_generator(settings, file_list, 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()
 
 
-def train(settings, file_list, shuffle=True):
-    return pyramidbox(settings, file_list, 'train', shuffle)
+def test(settings, file_list):
+    file_dict = load_file_list(file_list)
 
+    def reader():
+        for index_image in file_dict.keys():
+            image_name = file_dict[index_image][0] + '.jpg'
+            image_path = os.path.join(settings.data_dir, image_name)
+            im = Image.open(image_path)
+            if im.mode == 'L':
+                im = im.convert('RGB')
+            yield im, image_path
 
-def test(settings, file_list):
-    return pyramidbox(settings, file_list, 'test', False)
+    return reader
 
 
 def infer(settings, image_path):

fluid/face_detection/train.py

@@ -58,8 +58,9 @@ def train(args, config, train_file_list, optimizer_method):
         loss = network.vgg_ssd_loss()
         fetches = [loss]
 
-    epocs = 12880 / batch_size
-    boundaries = [epocs * 50, epocs * 80, epocs * 120, epocs * 140]
+    steps_per_pass = 12880 / batch_size
+    boundaries = [steps_per_pass * 50, steps_per_pass * 80,
+                  steps_per_pass * 120, steps_per_pass * 140]
     values = [
         learning_rate, learning_rate * 0.5, learning_rate * 0.25,
         learning_rate * 0.1, learning_rate * 0.01
@@ -104,9 +105,7 @@ def train(args, config, train_file_list, optimizer_method):
         train_exe = fluid.ParallelExecutor(
             use_cuda=use_gpu, loss_name=loss.name)
 
-    train_reader = paddle.batch(
-        reader.train(config, train_file_list), batch_size=batch_size)
-    feeder = fluid.DataFeeder(place=place, feed_list=network.feeds())
+    train_reader = reader.train_batch_reader(config, train_file_list, batch_size=batch_size)
 
     def save_model(postfix):
         model_path = os.path.join(model_save_dir, postfix)
@@ -115,20 +114,34 @@ def train(args, config, train_file_list, optimizer_method):
         print 'save models to %s' % (model_path)
         fluid.io.save_persistables(exe, model_path)
 
+    def tensor(data, place, lod=None):
+        t = fluid.core.LoDTensor()
+        t.set(data, place)
+        if lod:
+            t.set_lod(lod)
+        return t
+
     for pass_id in range(start_pass, num_passes):
         start_time = time.time()
         prev_start_time = start_time
         end_time = 0
-        for batch_id, data in enumerate(train_reader()):
+        for batch_id in range(steps_per_pass):
+            im, face_box, head_box, labels, lod = next(train_reader)
+            im_t = tensor(im, place)
+            box1 = tensor(face_box, place, [lod])
+            box2 = tensor(head_box, place, [lod])
+            lbl_t = tensor(labels, place, [lod])
+            feeding = {'image': im_t, 'face_box': box1,
+                       'head_box': box2, 'gt_label': lbl_t}
+
             prev_start_time = start_time
             start_time = time.time()
-            if len(data) < 2 * devices_num: continue
             if args.parallel:
                 fetch_vars = train_exe.run(fetch_list=[v.name for v in fetches],
-                                           feed=feeder.feed(data))
+                                           feed=feeding)
             else:
                 fetch_vars = exe.run(fluid.default_main_program(),
-                                     feed=feeder.feed(data),
+                                     feed=feeding,
                                      fetch_list=fetches)
             end_time = time.time()
             fetch_vars = [np.mean(np.array(v)) for v in fetch_vars]