preprocess with PIL the full val dataset and save binary

test=develop

paddle/fluid/inference/tests/api/preprocess.py

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+#   copyright (c) 2019 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 unittest
+import os
+import numpy as np
+import time
+import sys
+import random
+import functools
+import contextlib
+from PIL import Image, ImageEnhance
+import math
+
+random.seed(0)
+np.random.seed(0)
+
+DATA_DIM = 224
+
+SIZE_FLOAT32 = 4
+SIZE_INT64 = 8
+
+DATA_DIR = '/data/ILSVRC2012'
+
+img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+
+
+def resize_short(img, target_size):
+    percent = float(target_size) / min(img.size[0], img.size[1])
+    resized_width = int(round(img.size[0] * percent))
+    resized_height = int(round(img.size[1] * percent))
+    img = img.resize((resized_width, resized_height), Image.LANCZOS)
+    return img
+
+
+def crop_image(img, target_size, center):
+    width, height = img.size
+    size = target_size
+    if center == True:
+        w_start = (width - size) / 2
+        h_start = (height - size) / 2
+    else:
+        w_start = np.random.randint(0, width - size + 1)
+        h_start = np.random.randint(0, height - size + 1)
+    w_end = w_start + size
+    h_end = h_start + size
+    img = img.crop((w_start, h_start, w_end, h_end))
+    return img
+
+
+def process_image(img_path, mode, color_jitter, rotate):
+    img = Image.open(img_path)
+    img = resize_short(img, target_size=256)
+    img = crop_image(img, target_size=DATA_DIM, center=True)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
+    img -= img_mean
+    img /= img_std
+    return img
+
+
+def reader():
+    data_dir = DATA_DIR
+    file_list = os.path.join(data_dir, 'val_list.txt')
+    bin_file = os.path.join(data_dir, 'data.bin')
+    with open(file_list) as flist:
+        lines = [line.strip() for line in flist]
+        num_images = len(lines)
+
+        with open(bin_file, "w+b") as of:
+            of.seek(0)
+            num = np.array(int(num_images)).astype('int64')
+            of.write(num.tobytes())
+            for idx, line in enumerate(lines):
+                img_path, label = line.split()
+                img_path = os.path.join(data_dir, img_path)
+                if not os.path.exists(img_path):
+                    continue
+
+                #save image(float32) to file
+                img = process_image(
+                    img_path, 'val', color_jitter=False, rotate=False)
+                np_img = np.array(img)
+                of.seek(SIZE_INT64 + SIZE_FLOAT32 * DATA_DIM * DATA_DIM * 3 *
+                        idx)
+                of.write(np_img.astype('float32').tobytes())
+
+                #save label(int64_t) to file
+                label_int = (int)(label)
+                np_label = np.array(label_int)
+                of.seek(SIZE_INT64 + SIZE_FLOAT32 * DATA_DIM * DATA_DIM * 3 *
+                        num_images + idx * SIZE_INT64)
+                of.write(np_label.astype('int64').tobytes())
+
+
+if __name__ == '__main__':
+    reader()