update reader

fluid/PaddleCV/metric_learning/imgtool.py

@@ -13,15 +13,14 @@ import os
 
 is_ce = int(os.environ.get('is_ce', 0))
 
-#random.seed(0)
+if is_ce:
+    random.seed(0)
 
 def rotate_image(img):
     """ rotate_image """
     (h, w) = img.shape[:2]
     center = (w // 2, h // 2)
     angle = random.randint(-10, 10)
-    if is_ce:
-        aggle = 0
     M = cv2.getRotationMatrix2D(center, angle, 1.0)
     rotated = cv2.warpAffine(img, M, (w, h))
     return rotated
@@ -32,8 +31,6 @@ def random_crop(img, size, scale=None, ratio=None):
     ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
 
     aspect_ratio = math.sqrt(random.uniform(*ratio))
-    if is_ce:
-        aspect_ratio = math.sqrt(1.)
     w = 1. * aspect_ratio
     h = 1. / aspect_ratio
 
@@ -43,17 +40,12 @@ def random_crop(img, size, scale=None, ratio=None):
     scale_min = min(scale[0], bound)
 
     target_area = img.shape[0] * img.shape[1] * random.uniform(scale_min, scale_max)
-    if is_ce:
-        target_area = img.shape[0] * img.shape[1] * (scale_min + scale_max) / 2.
     target_size = math.sqrt(target_area)
     w = int(target_size * w)
     h = int(target_size * h)
 
     i = random.randint(0, img.shape[0] - h)
     j = random.randint(0, img.shape[1] - w)
-    if is_ce:
-        i = int(img.shape[0] - h) // 2
-        j = int(img.shape[1] - w) // 2
 
     img = img[i:i+h, j:j+w, :]
     resized = cv2.resize(img, (size, size), interpolation=cv2.INTER_LANCZOS4)
@@ -80,9 +72,6 @@ def crop_image(img, target_size, center):
     else:
         w_start = random.randint(0, width - size)
         h_start = random.randint(0, height - size)
-        if is_ce:
-            w_start = (width - size) // 2
-            h_start = (height - size) // 2
     w_end = w_start + size
     h_end = h_start + size
     img = img[h_start:h_end, w_start:w_end, :]
@@ -107,8 +96,6 @@ def process_image(sample, mode, color_jitter, rotate,
             img = distort_color(img)
         if random.randint(0, 1) == 1:
             img = img[:, ::-1, :]
-        if is_ce:
-            img = img[:, ::-1, :]
     else:
         if crop_size > 0:
             img = resize_short(img, crop_size)

fluid/PaddleCV/metric_learning/reader.py

@@ -34,8 +34,7 @@ def init_sop(mode):
             if label not in train_data:
                 train_data[label] = []
             train_data[label].append(path)
-        if not is_ce:
-            random.shuffle(train_image_list)
+        random.shuffle(train_image_list)
         print("{} dataset size: {}".format(mode, len(train_data)))
         return train_data, train_image_list
     else:
@@ -70,15 +69,13 @@ def common_iterator(data, settings):
         lab_num = len(labs)
         ind = list(range(0, lab_num))
         while True:
-            if not is_ce:
-                random.shuffle(ind)
+            random.shuffle(ind)
             ind_sample = ind[:class_num]
             for ind_i in ind_sample:
                 lab = labs[ind_i]
                 data_list = data[lab]
                 data_ind = list(range(0, len(data_list)))
-                if not is_ce:
-                    random.shuffle(data_ind)
+                random.shuffle(data_ind)
                 anchor_ind = data_ind[:samples_each_class]
 
                 for anchor_ind_i in anchor_ind:
@@ -95,21 +92,17 @@ def triplet_iterator(data, settings):
         lab_num = len(labs)
         ind = list(range(0, lab_num))
         while True:
-            if not is_ce:
-                random.shuffle(ind)
+            random.shuffle(ind)
             ind_pos, ind_neg = ind[:2]
             lab_pos = labs[ind_pos]
             pos_data_list = data[lab_pos]
             data_ind = list(range(0, len(pos_data_list)))
-            if not is_ce:
-                random.shuffle(data_ind)
+            random.shuffle(data_ind)
             anchor_ind, pos_ind = data_ind[:2]
 
             lab_neg = labs[ind_neg]
             neg_data_list = data[lab_neg]
             neg_ind = random.randint(0, len(neg_data_list) - 1)
-            if is_ce:
-                neg_ind = 1
             
             anchor_path = DATA_DIR + pos_data_list[anchor_ind]
             yield anchor_path, lab_pos