add ssd latest data augmentation

fluid/object_detection/image_util.py

-from PIL import Image
+from PIL import Image, ImageEnhance
 import numpy as np
 import random
 import math
@@ -159,3 +159,77 @@ def crop_image(img, bbox_labels, sample_bbox, image_width, 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._hue_prob:
+        expand_ratio = random.uniform(1, settings._expand_max_ratio)
+        if expand_ratio - 1 >= 0.01:
+            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
+    return img, bbox_labels

fluid/object_detection/reader.py

@@ -22,17 +22,38 @@ import os
 
 
 class Settings(object):
-    def __init__(self, data_dir, label_file, resize_h, resize_w, mean_value):
+    def __init__(self, data_dir, label_file, resize_h, resize_w, mean_value,
+                 apply_distort, apply_expand):
         self._data_dir = data_dir
         self._label_list = []
         label_fpath = os.path.join(data_dir, label_file)
         for line in open(label_fpath):
             self._label_list.append(line.strip())
 
+        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 apply_distort(self):
+        return self._apply_expand
+
+    @property
+    def apply_distort(self):
+        return self._apply_distort
 
     @property
     def data_dir(self):
@@ -71,7 +92,6 @@ def _reader_creator(settings, file_list, mode, shuffle):
 
                 img = Image.open(img_path)
                 img_width, img_height = img.size
-                img = np.array(img)
 
                 # layout: label | xmin | ymin | xmax | ymax | difficult
                 if mode == 'train' or mode == 'test':
@@ -99,6 +119,12 @@ def _reader_creator(settings, file_list, mode, shuffle):
 
                     sample_labels = bbox_labels
                     if mode == 'train':
+                        if settings._apply_distort:
+                            img = image_util.distort_image(img, settings)
+                        if settings._apply_expand:
+                            img, bbox_labels = image_util.expand_image(
+                                img, bbox_labels, img_width, img_height,
+                                settings)
                         batch_sampler = []
                         # hard-code here
                         batch_sampler.append(
@@ -126,13 +152,14 @@ def _reader_creator(settings, file_list, mode, shuffle):
                         sampled_bbox = image_util.generate_batch_samples(
                             batch_sampler, bbox_labels, img_width, img_height)
 
+                        img = np.array(img)
                         if len(sampled_bbox) > 0:
                             idx = int(random.uniform(0, len(sampled_bbox)))
                             img, sample_labels = image_util.crop_image(
                                 img, bbox_labels, sampled_bbox[idx], img_width,
                                 img_height)
 
-                img = Image.fromarray(img)
+                        img = Image.fromarray(img)
                 img = img.resize((settings.resize_w, settings.resize_h),
                                  Image.ANTIALIAS)
                 img = np.array(img)

fluid/object_detection/train.py

@@ -45,13 +45,10 @@ def train(train_file_list,
             evaluate_difficult=False,
             ap_version='11point')
 
-    optimizer = fluid.optimizer.Momentum(
-        learning_rate=fluid.layers.exponential_decay(
-            learning_rate=learning_rate,
-            decay_steps=40000,
-            decay_rate=0.1,
-            staircase=True),
-        momentum=0.9,
+    boundaries = [40000, 60000]
+    values = [0.001, 0.0005, 0.00025]
+    optimizer = fluid.optimizer.RMSProp(
+        learning_rate=fluid.layers.piecewise_decay(boundaries, values),
         regularization=fluid.regularizer.L2Decay(0.00005), )
 
     optimizer.minimize(loss)
@@ -60,7 +57,8 @@ def train(train_file_list,
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    load_model.load_paddlev1_vars(place)
+    load_model.load_and_set_vars(place)
+    #load_model.load_paddlev1_vars(place)
     train_reader = paddle.batch(
         reader.train(data_args, train_file_list), batch_size=batch_size)
     test_reader = paddle.batch(
@@ -85,8 +83,9 @@ def train(train_file_list,
             loss_v = exe.run(fluid.default_main_program(),
                              feed=feeder.feed(data),
                              fetch_list=[loss])
-            print("Pass {0}, batch {1}, loss {2}"
-                  .format(pass_id, batch_id, loss_v[0]))
+            if batch_id % 20 == 0:
+                print("Pass {0}, batch {1}, loss {2}"
+                      .format(pass_id, batch_id, loss_v[0]))
         test(pass_id)
 
         if pass_id % 10 == 0:
@@ -100,6 +99,8 @@ if __name__ == '__main__':
     data_args = reader.Settings(
         data_dir='./data',
         label_file='label_list',
+        apply_distort=True,
+        apply_expand=True,
         resize_h=300,
         resize_w=300,
         mean_value=[127.5, 127.5, 127.5])