Add reader and train logic for PyramidBox. (#927)

* training code of pyramidbox

* Fix code format.

fluid/face_detction/image_util.py

+from PIL import Image, ImageEnhance, ImageDraw
+from PIL import ImageFile
+import numpy as np
+import random
+import math
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True  #otherwise IOError raised image file is truncated
+
+
+class sampler():
+    def __init__(self, max_sample, max_trial, min_scale, max_scale,
+                 min_aspect_ratio, max_aspect_ratio, min_jaccard_overlap,
+                 max_jaccard_overlap):
+        self.max_sample = max_sample
+        self.max_trial = max_trial
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.min_aspect_ratio = min_aspect_ratio
+        self.max_aspect_ratio = max_aspect_ratio
+        self.min_jaccard_overlap = min_jaccard_overlap
+        self.max_jaccard_overlap = max_jaccard_overlap
+
+
+class bbox():
+    def __init__(self, xmin, ymin, xmax, ymax):
+        self.xmin = xmin
+        self.ymin = ymin
+        self.xmax = xmax
+        self.ymax = ymax
+
+
+def bbox_area(src_bbox):
+    width = src_bbox.xmax - src_bbox.xmin
+    height = src_bbox.ymax - src_bbox.ymin
+    return width * height
+
+
+def generate_sample(sampler):
+    scale = random.uniform(sampler.min_scale, sampler.max_scale)
+    min_aspect_ratio = max(sampler.min_aspect_ratio, (scale**2.0))
+    max_aspect_ratio = min(sampler.max_aspect_ratio, 1 / (scale**2.0))
+    aspect_ratio = random.uniform(min_aspect_ratio, max_aspect_ratio)
+    bbox_width = scale * (aspect_ratio**0.5)
+    bbox_height = scale / (aspect_ratio**0.5)
+    xmin_bound = 1 - bbox_width
+    ymin_bound = 1 - bbox_height
+    xmin = random.uniform(0, xmin_bound)
+    ymin = random.uniform(0, ymin_bound)
+    xmax = xmin + bbox_width
+    ymax = ymin + bbox_height
+    sampled_bbox = bbox(xmin, ymin, xmax, ymax)
+    return sampled_bbox
+
+
+def jaccard_overlap(sample_bbox, object_bbox):
+    if sample_bbox.xmin >= object_bbox.xmax or \
+            sample_bbox.xmax <= object_bbox.xmin or \
+            sample_bbox.ymin >= object_bbox.ymax or \
+            sample_bbox.ymax <= object_bbox.ymin:
+        return 0
+    intersect_xmin = max(sample_bbox.xmin, object_bbox.xmin)
+    intersect_ymin = max(sample_bbox.ymin, object_bbox.ymin)
+    intersect_xmax = min(sample_bbox.xmax, object_bbox.xmax)
+    intersect_ymax = min(sample_bbox.ymax, object_bbox.ymax)
+    intersect_size = (intersect_xmax - intersect_xmin) * (
+        intersect_ymax - intersect_ymin)
+    sample_bbox_size = bbox_area(sample_bbox)
+    object_bbox_size = bbox_area(object_bbox)
+    overlap = intersect_size / (
+        sample_bbox_size + object_bbox_size - intersect_size)
+    return overlap
+
+
+def satisfy_sample_constraint(sampler, sample_bbox, bbox_labels):
+    if sampler.min_jaccard_overlap == 0 and sampler.max_jaccard_overlap == 0:
+        return True
+    for i in range(len(bbox_labels)):
+        object_bbox = bbox(
+            bbox_labels[i][0],
+            bbox_labels[i][1],  # tangxu @ 2018-05-17
+            bbox_labels[i][2],
+            bbox_labels[i][3])
+        overlap = jaccard_overlap(sample_bbox, object_bbox)
+        if sampler.min_jaccard_overlap != 0 and \
+                overlap < sampler.min_jaccard_overlap:
+            continue
+        if sampler.max_jaccard_overlap != 0 and \
+                overlap > sampler.max_jaccard_overlap:
+            continue
+        return True
+    return False
+
+
+def generate_batch_samples(batch_sampler, bbox_labels):
+    sampled_bbox = []
+    index = []
+    c = 0
+    for sampler in batch_sampler:
+        found = 0
+        for i in range(sampler.max_trial):
+            if found >= sampler.max_sample:
+                break
+            sample_bbox = generate_sample(sampler)
+            if satisfy_sample_constraint(sampler, sample_bbox, bbox_labels):
+                sampled_bbox.append(sample_bbox)
+                found = found + 1
+                index.append(c)
+        c = c + 1
+    return sampled_bbox
+
+
+def clip_bbox(src_bbox):
+    src_bbox.xmin = max(min(src_bbox.xmin, 1.0), 0.0)
+    src_bbox.ymin = max(min(src_bbox.ymin, 1.0), 0.0)
+    src_bbox.xmax = max(min(src_bbox.xmax, 1.0), 0.0)
+    src_bbox.ymax = max(min(src_bbox.ymax, 1.0), 0.0)
+    return src_bbox
+
+
+def meet_emit_constraint(src_bbox, sample_bbox):
+    center_x = (src_bbox.xmax + src_bbox.xmin) / 2
+    center_y = (src_bbox.ymax + src_bbox.ymin) / 2
+    if center_x >= sample_bbox.xmin and \
+        center_x <= sample_bbox.xmax and \
+        center_y >= sample_bbox.ymin and \
+        center_y <= sample_bbox.ymax:
+        return True
+    return False
+
+
+def transform_labels(bbox_labels, sample_bbox):
+    proj_bbox = bbox(0, 0, 0, 0)
+    sample_labels = []
+    for i in range(len(bbox_labels)):
+        sample_label = []
+        object_bbox = bbox(bbox_labels[i][0], bbox_labels[i][1],
+                           bbox_labels[i][2], bbox_labels[i][3])
+        if not meet_emit_constraint(object_bbox, sample_bbox):
+            continue
+        sample_width = sample_bbox.xmax - sample_bbox.xmin
+        sample_height = sample_bbox.ymax - sample_bbox.ymin
+        proj_bbox.xmin = (object_bbox.xmin - sample_bbox.xmin) / sample_width
+        proj_bbox.ymin = (object_bbox.ymin - sample_bbox.ymin) / sample_height
+        proj_bbox.xmax = (object_bbox.xmax - sample_bbox.xmin) / sample_width
+        proj_bbox.ymax = (object_bbox.ymax - sample_bbox.ymin) / sample_height
+        proj_bbox = clip_bbox(proj_bbox)
+        if bbox_area(proj_bbox) > 0:
+            sample_label.append(bbox_labels[i][0])
+            sample_label.append(float(proj_bbox.xmin))
+            sample_label.append(float(proj_bbox.ymin))
+            sample_label.append(float(proj_bbox.xmax))
+            sample_label.append(float(proj_bbox.ymax))
+            #sample_label.append(bbox_labels[i][5])
+            sample_label = sample_label + bbox_labels[i][5:]
+            sample_labels.append(sample_label)
+    return sample_labels
+
+
+def crop_image(img, bbox_labels, sample_bbox, image_width, image_height):
+    sample_bbox = clip_bbox(sample_bbox)
+    xmin = int(sample_bbox.xmin * image_width)
+    xmax = int(sample_bbox.xmax * image_width)
+    ymin = int(sample_bbox.ymin * image_height)
+    ymax = int(sample_bbox.ymax * image_height)
+    sample_img = img[ymin:ymax, xmin:xmax]
+    sample_labels = transform_labels(bbox_labels, sample_bbox)
+    return sample_img, sample_labels
+
+
+def random_brightness(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._brightness_prob:
+        delta = random.uniform(-settings._brightness_delta,
+                               settings._brightness_delta) + 1
+        img = ImageEnhance.Brightness(img).enhance(delta)
+    return img
+
+
+def random_contrast(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._contrast_prob:
+        delta = random.uniform(-settings._contrast_delta,
+                               settings._contrast_delta) + 1
+        img = ImageEnhance.Contrast(img).enhance(delta)
+    return img
+
+
+def random_saturation(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._saturation_prob:
+        delta = random.uniform(-settings._saturation_delta,
+                               settings._saturation_delta) + 1
+        img = ImageEnhance.Color(img).enhance(delta)
+    return img
+
+
+def random_hue(img, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._hue_prob:
+        delta = random.uniform(-settings._hue_delta, settings._hue_delta)
+        img_hsv = np.array(img.convert('HSV'))
+        img_hsv[:, :, 0] = img_hsv[:, :, 0] + delta
+        img = Image.fromarray(img_hsv, mode='HSV').convert('RGB')
+    return img
+
+
+def distort_image(img, settings):
+    prob = random.uniform(0, 1)
+    # Apply different distort order
+    if prob > 0.5:
+        img = random_brightness(img, settings)
+        img = random_contrast(img, settings)
+        img = random_saturation(img, settings)
+        img = random_hue(img, settings)
+    else:
+        img = random_brightness(img, settings)
+        img = random_saturation(img, settings)
+        img = random_hue(img, settings)
+        img = random_contrast(img, settings)
+    return img
+
+
+def expand_image(img, bbox_labels, img_width, img_height, settings):
+    prob = random.uniform(0, 1)
+    if prob < settings._expand_prob:
+        if settings._expand_max_ratio - 1 >= 0.01:
+            expand_ratio = random.uniform(1, settings._expand_max_ratio)
+            height = int(img_height * expand_ratio)
+            width = int(img_width * expand_ratio)
+            h_off = math.floor(random.uniform(0, height - img_height))
+            w_off = math.floor(random.uniform(0, width - img_width))
+            expand_bbox = bbox(-w_off / img_width, -h_off / img_height,
+                               (width - w_off) / img_width,
+                               (height - h_off) / img_height)
+            expand_img = np.ones((height, width, 3))
+            expand_img = np.uint8(expand_img * np.squeeze(settings._img_mean))
+            expand_img = Image.fromarray(expand_img)
+            expand_img.paste(img, (int(w_off), int(h_off)))
+            bbox_labels = transform_labels(bbox_labels, expand_bbox)
+            return expand_img, bbox_labels, width, height
+    return img, bbox_labels, img_width, img_height

fluid/face_detction/pyramidbox.py

@@ -45,7 +45,7 @@ def conv_block(input, groups, filters, ksizes, strides=None, with_pool=True):
 
 
 class PyramidBox(object):
-    def __init__(self, data_shape, is_infer=False):
+    def __init__(self, data_shape, is_infer=False, sub_network=False):
         self.data_shape = data_shape
         self.min_sizes = [16., 32., 64., 128., 256., 512.]
         self.steps = [4., 8., 16., 32., 64., 128.]
@@ -54,9 +54,10 @@ class PyramidBox(object):
         # the base network is VGG with atrus layers
         self._input()
         self._vgg()
-        self._low_level_fpn()
-        self._cpm_module()
-        self._pyramidbox()
+        if sub_network:
+            self._low_level_fpn()
+            self._cpm_module()
+            self._pyramidbox()
 
     def _input(self):
         self.image = fluid.layers.data(
@@ -66,6 +67,8 @@ class PyramidBox(object):
                 name='gt_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 = conv_block(self.image, 2, [64] * 2, [3] * 2)
@@ -232,6 +235,39 @@ class PyramidBox(object):
         self.prior_boxes = fluid.layers.concat(boxes)
         self.box_vars = fluid.layers.concat(vars)
 
+    def vgg_ssd(self, num_classes, image_shape):  # tangxu
+
+        self.conv3_norm = self._l2_norm_scale(self.conv3)
+        self.conv4_norm = self._l2_norm_scale(self.conv4)
+        self.conv5_norm = self._l2_norm_scale(self.conv5)
+
+        mbox_locs, mbox_confs, box, box_var = fluid.layers.multi_box_head(
+            inputs=[
+                self.conv3_norm, self.conv4_norm, self.conv5_norm, self.conv6,
+                self.conv7, self.conv8
+            ],
+            image=self.image,
+            num_classes=num_classes,
+            # min_ratio=20,
+            # max_ratio=90,
+            min_sizes=[16.0, 32.0, 64.0, 128.0, 256.0, 512.0],
+            max_sizes=[[], [], [], [], [], []],
+            # max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0],
+            aspect_ratios=[[1.], [1.], [1.], [1.], [1.], [1.]],
+            steps=[4.0, 8.0, 16.0, 32.0, 64.0, 128.0],
+            base_size=image_shape[2],
+            offset=0.5,
+            flip=False)
+
+        # locs, confs, box, box_var = vgg_extra_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(mbox_locs, mbox_confs, self.gt_box,
+                                     self.gt_label, box, box_var)
+        loss = fluid.layers.reduce_sum(loss)
+
+        return loss
+
     def train(self):
         face_loss = fluid.layers.ssd_loss(
             self.face_mbox_loc, self.face_mbox_conf, self.gt_box, self.gt_label,

fluid/face_detction/reader.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import image_util
+from paddle.utils.image_util import *
+import random
+from PIL import Image
+from PIL import ImageDraw
+import numpy as np
+import xml.etree.ElementTree
+import os
+import time
+import copy
+
+
+class Settings(object):
+    def __init__(self,
+                 dataset=None,
+                 data_dir=None,
+                 label_file=None,
+                 resize_h=300,
+                 resize_w=300,
+                 mean_value=[127.5, 127.5, 127.5],
+                 apply_distort=True,
+                 apply_expand=True,
+                 ap_version='11point',
+                 toy=0):
+        self._dataset = dataset
+        self._ap_version = ap_version
+        self._toy = toy
+        self._data_dir = data_dir
+        self._apply_distort = apply_distort
+        self._apply_expand = apply_expand
+        self._resize_height = resize_h
+        self._resize_width = resize_w
+        self._img_mean = np.array(mean_value)[:, np.newaxis, np.newaxis].astype(
+            'float32')
+        self._expand_prob = 0.5
+        self._expand_max_ratio = 4
+        self._hue_prob = 0.5
+        self._hue_delta = 18
+        self._contrast_prob = 0.5
+        self._contrast_delta = 0.5
+        self._saturation_prob = 0.5
+        self._saturation_delta = 0.5
+        self._brightness_prob = 0.5
+        self._brightness_delta = 0.125
+
+    @property
+    def dataset(self):
+        return self._dataset
+
+    @property
+    def ap_version(self):
+        return self._ap_version
+
+    @property
+    def toy(self):
+        return self._toy
+
+    @property
+    def apply_distort(self):
+        return self._apply_expand
+
+    @property
+    def apply_distort(self):
+        return self._apply_distort
+
+    @property
+    def data_dir(self):
+        return self._data_dir
+
+    @data_dir.setter
+    def data_dir(self, data_dir):
+        self._data_dir = data_dir
+
+    @property
+    def label_list(self):
+        return self._label_list
+
+    @property
+    def resize_h(self):
+        return self._resize_height
+
+    @property
+    def resize_w(self):
+        return self._resize_width
+
+    @property
+    def img_mean(self):
+        return self._img_mean
+
+
+def preprocess(img, bbox_labels, mode, settings):
+    img_width, img_height = img.size
+    sampled_labels = bbox_labels
+    if mode == 'train':
+        if settings._apply_distort:
+            img = image_util.distort_image(img, settings)
+        if settings._apply_expand:
+            img, bbox_labels, img_width, img_height = image_util.expand_image(
+                img, bbox_labels, img_width, img_height, settings)
+        # sampling
+        batch_sampler = []
+        # hard-code here
+        batch_sampler.append(
+            image_util.sampler(1, 50, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0))
+        batch_sampler.append(
+            image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 1.0, 1.0))
+        batch_sampler.append(
+            image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 1.0, 1.0))
+        batch_sampler.append(
+            image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 1.0, 1.0))
+        batch_sampler.append(
+            image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 1.0, 1.0))
+        sampled_bbox = image_util.generate_batch_samples(batch_sampler,
+                                                         bbox_labels)
+
+        img = np.array(img)
+        if len(sampled_bbox) > 0:
+            idx = int(random.uniform(0, len(sampled_bbox)))
+            img, sampled_labels = image_util.crop_image(
+                img, bbox_labels, sampled_bbox[idx], img_width, img_height)
+
+        img = Image.fromarray(img)
+    img = img.resize((settings.resize_w, settings.resize_h), Image.ANTIALIAS)
+    img = np.array(img)
+
+    if mode == 'train':
+        mirror = int(random.uniform(0, 2))
+        if mirror == 1:
+            img = img[:, ::-1, :]
+            for i in xrange(len(sampled_labels)):
+                tmp = sampled_labels[i][1]
+                sampled_labels[i][1] = 1 - sampled_labels[i][3]
+                sampled_labels[i][3] = 1 - tmp
+    # HWC to CHW
+    if len(img.shape) == 3:
+        img = np.swapaxes(img, 1, 2)
+        img = np.swapaxes(img, 1, 0)
+    # RBG to BGR
+    img = img[[2, 1, 0], :, :]
+    img = img.astype('float32')
+    img -= settings.img_mean
+    img = img * 0.007843
+    return img, sampled_labels
+
+
+def put_txt_in_dict(input_txt):
+    with open(input_txt, 'r') as f_dir:
+        lines_input_txt = f_dir.readlines()
+
+    dict_input_txt = {}
+    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] = []
+            dict_name = tmp_line_txt
+            dict_input_txt[num_class].append(tmp_line_txt)
+        if '--' not in tmp_line_txt:
+            if len(tmp_line_txt) > 6:
+                # tmp_line_txt = tmp_line_txt[:-2]
+                split_str = tmp_line_txt.split(' ')
+                x1_min = float(split_str[0])
+                y1_min = float(split_str[1])
+                x2_max = float(split_str[2])
+                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)
+            else:
+                dict_input_txt[num_class].append(tmp_line_txt)
+
+    return dict_input_txt
+
+
+def pyramidbox(settings, file_list, mode, shuffle):
+
+    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'
+            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: category_id | xmin | ymin | xmax | ymax | iscrowd
+            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(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)
+            sample_labels = np.array(sample_labels)
+            if len(sample_labels) == 0: continue
+            im = im.astype('float32')
+            boxes = sample_labels[:, 0:4]
+            lbls = [1] * len(boxes)
+            difficults = [1] * len(boxes)
+
+            yield im, boxes, lbls, difficults
+
+    return reader
+
+
+def train(settings, file_list, shuffle=True):
+    return pyramidbox(settings, file_list, 'train', shuffle)
+
+
+def test(settings, file_list):
+    return pyramidbox(settings, file_list, 'test', False)

fluid/face_detction/train.py

 import os
 import numpy as np
+import time
 import argparse
 import functools
 
+import reader
 import paddle
 import paddle.fluid as fluid
 from pyramidbox import PyramidBox
@@ -10,53 +12,127 @@ from utility import add_arguments, print_arguments
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
+
 # yapf: disable
-add_arg('batch_size',       int,   32,        "Minibatch size.")
-add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
-add_arg('parallel',         bool,  True,      "Parallel.")
-add_arg('pretrained_model', str,   "./vgg_model/", "The init model path.")
+add_arg('parallel', bool, True, "parallel")
+add_arg('learning_rate', float, 0.0001, "Learning rate.")
+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('dataset', str, 'WIDERFACE', "coco2014, coco2017, and pascalvoc.")
+add_arg('model_save_dir', str, 'model', "The path to save model.")
+add_arg('pretrained_model', str, './vgg_model/', "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,
-          learning_rate,
-          batch_size,
-          pretrained_model):
 
-    network = PyramidBox([3, 640, 640])
-    face_loss, head_loss = network.train()
-    loss = face_loss + head_loss
+def train(args, data_args, learning_rate, batch_size, pretrained_model,
+          num_passes):
+
+    num_classes = 2
+
+    devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
+    devices_num = len(devices.split(","))
+
+    image_shape = [3, data_args.resize_h, data_args.resize_w]
 
-    test_program, face_map_eval, head_map_eval = network.test()
+    network = PyramidBox(image_shape)
+    loss = network.vgg_ssd(num_classes, image_shape)
+
+    epocs = 12880 / batch_size
+    boundaries = [epocs * 100, epocs * 125, epocs * 150]
+    values = [
+        learning_rate, learning_rate * 0.1, learning_rate * 0.01,
+        learning_rate * 0.001
+    ]
 
-    epocs = 19200 / batch_size
-    boundaries = [epocs * 40, epocs * 60, epocs * 80, epocs * 100]
-    lr = learning_rate
-    values = [lr, lr * 0.5, lr * 0.25, lr * 0.1, lr * 0.01]
     optimizer = fluid.optimizer.RMSProp(
         learning_rate=fluid.layers.piecewise_decay(boundaries, values),
-        regularization=fluid.regularizer.L2Decay(0.00005), )
+        regularization=fluid.regularizer.L2Decay(0.0005),
+    )
 
     optimizer.minimize(loss)
+
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
     # fluid.io.save_inference_model('./vgg_model/', ['image'], [loss], exe)
     if pretrained_model:
+
         def if_exist(var):
             return os.path.exists(os.path.join(pretrained_model, var.name))
+
+        print('Load pre-trained model.')
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    # print(fluid.default_main_program())
-    # print(test_program)
-    # fluid.io.save_persistables(exe, "model")
+    if args.parallel:
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=args.use_gpu, loss_name=loss.name)
+
+    train_reader = paddle.batch(
+        reader.train(data_args, train_file_list), batch_size=batch_size)
+    feeder = fluid.DataFeeder(
+        place=place,
+        feed_list=[
+            network.image, network.gt_box, network.gt_label, network.difficult
+        ])
+
+    def save_model(postfix):
+        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)
+
+    best_map = 0.
+
+    for pass_id in range(num_passes):
+        start_time = time.time()
+        prev_start_time = start_time
+        end_time = 0
+        for batch_id, data in enumerate(train_reader()):
+            prev_start_time = start_time
+            start_time = time.time()
+            if len(data) < devices_num: 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])
+            end_time = time.time()
+            loss_v = np.mean(np.array(loss_v))
+            if batch_id % 1 == 0:
+                print("Pass {0}, batch {1}, loss {2}, time {3}".format(
+                    pass_id, batch_id, loss_v, start_time - prev_start_time))
+        test(pass_id, best_map)
+        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)
 
-    train(args,
-          learning_rate=0.01,
-          batch_size=args.batch_size,
-          pretrained_model=args.pretrained_model)
+    data_dir = 'data/WIDERFACE/WIDER_train/images/'
+    train_file_list = 'label/train_gt_widerface.res'
+    val_file_list = 'label/val_gt_widerface.res'
+    model_save_dir = args.model_save_dir
+
+    data_args = reader.Settings(
+        dataset=args.dataset,
+        data_dir=data_dir,
+        resize_h=args.resize_h,
+        resize_w=args.resize_w,
+        ap_version='11point')
+    train(
+        args,
+        data_args=data_args,
+        learning_rate=0.01,
+        batch_size=args.batch_size,
+        pretrained_model=args.pretrained_model,
+        num_passes=args.num_passes)