Fix the random difference between python2 and python3

fluid/image_classification/reader.py

@@ -7,6 +7,7 @@ import paddle
 from PIL import Image, ImageEnhance
 
 random.seed(0)
+np.random.seed(0)
 
 DATA_DIM = 224
 
@@ -36,8 +37,8 @@ def crop_image(img, target_size, center):
         w_start = (width - size) / 2
         h_start = (height - size) / 2
     else:
-        w_start = random.randint(0, width - size)
-        h_start = random.randint(0, height - size)
+        w_start = np.random.randint(0, width - size)
+        h_start = np.random.randint(0, height - size)
     w_end = w_start + size
     h_end = h_start + size
     img = img.crop((w_start, h_start, w_end, h_end))
@@ -45,7 +46,7 @@ def crop_image(img, target_size, center):
 
 
 def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
-    aspect_ratio = math.sqrt(random.uniform(*ratio))
+    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
     w = 1. * aspect_ratio
     h = 1. / aspect_ratio
 
@@ -54,14 +55,14 @@ def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
     scale_max = min(scale[1], bound)
     scale_min = min(scale[0], bound)
 
-    target_area = img.size[0] * img.size[1] * random.uniform(scale_min,
+    target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min,
                                                              scale_max)
     target_size = math.sqrt(target_area)
     w = int(target_size * w)
     h = int(target_size * h)
 
-    i = random.randint(0, img.size[0] - w)
-    j = random.randint(0, img.size[1] - h)
+    i = np.random.randint(0, img.size[0] - w)
+    j = np.random.randint(0, img.size[1] - h)
 
     img = img.crop((i, j, i + w, j + h))
     img = img.resize((size, size), Image.LANCZOS)
@@ -69,26 +70,26 @@ def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
 
 
 def rotate_image(img):
-    angle = random.randint(-10, 10)
+    angle = np.random.randint(-10, 10)
     img = img.rotate(angle)
     return img
 
 
 def distort_color(img):
     def random_brightness(img, lower=0.5, upper=1.5):
-        e = random.uniform(lower, upper)
+        e = np.random.uniform(lower, upper)
         return ImageEnhance.Brightness(img).enhance(e)
 
     def random_contrast(img, lower=0.5, upper=1.5):
-        e = random.uniform(lower, upper)
+        e = np.random.uniform(lower, upper)
         return ImageEnhance.Contrast(img).enhance(e)
 
     def random_color(img, lower=0.5, upper=1.5):
-        e = random.uniform(lower, upper)
+        e = np.random.uniform(lower, upper)
         return ImageEnhance.Color(img).enhance(e)
 
     ops = [random_brightness, random_contrast, random_color]
-    random.shuffle(ops)
+    np.random.shuffle(ops)
 
     img = ops[0](img)
     img = ops[1](img)
@@ -110,7 +111,7 @@ def process_image(sample, mode, color_jitter, rotate):
     if mode == 'train':
         if color_jitter:
             img = distort_color(img)
-        if random.randint(0, 1) == 1:
+        if np.random.randint(0, 1) == 1:
             img = img.transpose(Image.FLIP_LEFT_RIGHT)
 
     if img.mode != 'RGB':
@@ -135,7 +136,7 @@ def _reader_creator(file_list,
         with open(file_list) as flist:
             lines = [line.strip() for line in flist]
             if shuffle:
-                random.shuffle(lines)
+                np.random.shuffle(lines)
             for line in lines:
                 if mode == 'train' or mode == 'val':
                     img_path, label = line.split()

fluid/image_classification/train.py

@@ -172,6 +172,7 @@ def train(args):
         # but it is time consuming. For faster speed, need another dataset.
         import random
         random.seed(0)
+        np.random.seed(0)
         train_reader = paddle.batch(
             flowers.train(use_xmap=False), batch_size=train_batch_size)
         test_reader = paddle.batch(

fluid/object_detection/image_util.py

@@ -36,8 +36,8 @@ def bbox_area(src_bbox):
 
 
 def generate_sample(sampler):
-    scale = random.uniform(sampler.min_scale, sampler.max_scale)
-    aspect_ratio = random.uniform(sampler.min_aspect_ratio,
+    scale = np.random.uniform(sampler.min_scale, sampler.max_scale)
+    aspect_ratio = np.random.uniform(sampler.min_aspect_ratio,
                                   sampler.max_aspect_ratio)
     aspect_ratio = max(aspect_ratio, (scale**2.0))
     aspect_ratio = min(aspect_ratio, 1 / (scale**2.0))
@@ -46,8 +46,8 @@ def generate_sample(sampler):
     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)
+    xmin = np.random.uniform(0, xmin_bound)
+    ymin = np.random.uniform(0, ymin_bound)
     xmax = xmin + bbox_width
     ymax = ymin + bbox_height
     sampled_bbox = bbox(xmin, ymin, xmax, ymax)
@@ -167,36 +167,36 @@ def crop_image(img, bbox_labels, sample_bbox, image_width, image_height):
 
 
 def random_brightness(img, settings):
-    prob = random.uniform(0, 1)
+    prob = np.random.uniform(0, 1)
     if prob < settings._brightness_prob:
-        delta = random.uniform(-settings._brightness_delta,
+        delta = np.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)
+    prob = np.random.uniform(0, 1)
     if prob < settings._contrast_prob:
-        delta = random.uniform(-settings._contrast_delta,
+        delta = np.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)
+    prob = np.random.uniform(0, 1)
     if prob < settings._saturation_prob:
-        delta = random.uniform(-settings._saturation_delta,
+        delta = np.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)
+    prob = np.random.uniform(0, 1)
     if prob < settings._hue_prob:
-        delta = random.uniform(-settings._hue_delta, settings._hue_delta)
+        delta = np.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')
@@ -204,30 +204,30 @@ def random_hue(img, settings):
 
 
 def distort_image(img, settings):
-    prob = random.uniform(0, 1)
+    prob = np.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)
+        img = np.random_brightness(img, settings)
+        img = np.random_contrast(img, settings)
+        img = np.random_saturation(img, settings)
+        img = np.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)
+        img = np.random_brightness(img, settings)
+        img = np.random_saturation(img, settings)
+        img = np.random_hue(img, settings)
+        img = np.random_contrast(img, settings)
     return img
 
 
 def expand_image(img, bbox_labels, img_width, img_height, settings):
-    prob = random.uniform(0, 1)
+    prob = np.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)
+            expand_ratio = np.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))
+            h_off = math.floor(np.random.uniform(0, height - img_height))
+            w_off = math.floor(np.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)

fluid/object_detection/reader.py

@@ -14,7 +14,6 @@
 
 import image_util
 from paddle.utils.image_util import *
-import random
 from PIL import Image
 from PIL import ImageDraw
 import numpy as np
@@ -140,7 +139,7 @@ def preprocess(img, bbox_labels, mode, settings):
 
         img = np.array(img)
         if len(sampled_bbox) > 0:
-            idx = int(random.uniform(0, len(sampled_bbox)))
+            idx = int(np.random.uniform(0, len(sampled_bbox)))
             img, sampled_labels = image_util.crop_image(
                 img, bbox_labels, sampled_bbox[idx], img_width, img_height)
 
@@ -149,7 +148,7 @@ def preprocess(img, bbox_labels, mode, settings):
     img = np.array(img)
 
     if mode == 'train':
-        mirror = int(random.uniform(0, 2))
+        mirror = int(np.random.uniform(0, 2))
         if mirror == 1:
             img = img[:, ::-1, :]
             for i in six.moves.xrange(len(sampled_labels)):
@@ -185,7 +184,7 @@ def coco(settings, file_list, mode, shuffle):
 
     def reader():
         if mode == 'train' and shuffle:
-            random.shuffle(images)
+            np.random.shuffle(images)
         for image in images:
             image_name = image['file_name']
             image_path = os.path.join(settings.data_dir, image_name)
@@ -240,7 +239,7 @@ def pascalvoc(settings, file_list, mode, shuffle):
 
     def reader():
         if mode == 'train' and shuffle:
-            random.shuffle(images)
+            np.random.shuffle(images)
         for image in images:
             image_path, label_path = image.split()
             image_path = os.path.join(settings.data_dir, image_path)