improve preprocess script and read from tar

test=develop

paddle/fluid/inference/tests/api/full_ILSVRC2012_val_preprocess.py

@@ -19,10 +19,11 @@ import sys
 import random
 import functools
 import contextlib
-from PIL import Image, ImageEnhance
+from PIL import Image
 import math
-from paddle.dataset.common import download, md5file
+from paddle.dataset.common import download
 import tarfile
+import StringIO
 
 random.seed(0)
 np.random.seed(0)
@@ -32,9 +33,11 @@ SIZE_FLOAT32 = 4
 SIZE_INT64 = 8
 FULL_SIZE_BYTES = 30106000008
 FULL_IMAGES = 50000
-DATA_DIR_NAME = 'ILSVRC2012'
-IMG_DIR_NAME = 'var'
-TARGET_HASH = '8dc592db6dcc8d521e4d5ba9da5ca7d2'
+TARGET_HASH = '22d2e0008dca693916d9595a5ea3ded8'
+FOLDER_NAME = "ILSVRC2012/"
+VALLIST_TAR_NAME = "ILSVRC2012/val_list.txt"
+CHUNK_SIZE = 8192
+
 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))
 
@@ -62,8 +65,7 @@ def crop_image(img, target_size, center):
     return img
 
 
-def process_image(img_path, mode, color_jitter, rotate):
-    img = Image.open(img_path)
+def process_image(img):
     img = resize_short(img, target_size=256)
     img = crop_image(img, target_size=DATA_DIM, center=True)
     if img.mode != 'RGB':
@@ -99,26 +101,11 @@ def download_concat(cache_folder, zip_path):
                     outfile.write(infile.read())
 
 
-def extract(zip_path, extract_folder):
-    data_dir = os.path.join(extract_folder, DATA_DIR_NAME)
-    img_dir = os.path.join(data_dir, IMG_DIR_NAME)
-    print("Extracting...\n")
-
-    if not (os.path.exists(img_dir) and
-            len(os.listdir(img_dir)) == FULL_IMAGES):
-        tar = tarfile.open(zip_path)
-        tar.extractall(path=extract_folder)
-        tar.close()
-    print('Extracted. Full Imagenet Validation dataset is located at {0}\n'.
-          format(data_dir))
-
-
-def print_processbar(done, total):
-    done_filled = done * '='
-    empty_filled = (total - done) * ' '
-    percentage_done = done * 100 / total
+def print_processbar(done_percentage):
+    done_filled = done_percentage * '='
+    empty_filled = (100 - done_percentage) * ' '
     sys.stdout.write("\r[%s%s]%d%%" %
-                     (done_filled, empty_filled, percentage_done))
+                     (done_filled, empty_filled, done_percentage))
     sys.stdout.flush()
 
 
@@ -126,15 +113,13 @@ def check_integrity(filename, target_hash):
     print('\nThe binary file exists. Checking file integrity...\n')
     md = hashlib.md5()
     count = 0
-    total_parts = 50
-    chunk_size = 8192
-    onepart = FULL_SIZE_BYTES / chunk_size / total_parts
+    onepart = FULL_SIZE_BYTES / CHUNK_SIZE / 100
     with open(filename) as ifs:
         while True:
-            buf = ifs.read(8192)
+            buf = ifs.read(CHUNK_SIZE)
             if count % onepart == 0:
                 done = count / onepart
-                print_processbar(done, total_parts)
+                print_processbar(done)
             count = count + 1
             if not buf:
                 break
@@ -146,54 +131,61 @@ def check_integrity(filename, target_hash):
         return False
 
 
-def convert(file_list, data_dir, output_file):
+def convert(tar_file, output_file):
     print('Converting 50000 images to binary file ...\n')
-    with open(file_list) as flist:
-        lines = [line.strip() for line in flist]
-        num_images = len(lines)
-        with open(output_file, "w+b") as ofs:
-            #save num_images(int64_t) to file
-            ofs.seek(0)
-            num = np.array(int(num_images)).astype('int64')
-            ofs.write(num.tobytes())
-            per_parts = 1000
-            full_parts = FULL_IMAGES / per_parts
-            print_processbar(0, full_parts)
-            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)
-                ofs.seek(SIZE_INT64 + SIZE_FLOAT32 * DATA_DIM * DATA_DIM * 3 *
-                         idx)
-                ofs.write(np_img.astype('float32').tobytes())
-                ofs.flush()
-
-                #save label(int64_t) to file
-                label_int = (int)(label)
-                np_label = np.array(label_int)
-                ofs.seek(SIZE_INT64 + SIZE_FLOAT32 * DATA_DIM * DATA_DIM * 3 *
-                         num_images + idx * SIZE_INT64)
-                ofs.write(np_label.astype('int64').tobytes())
-                ofs.flush()
-                if (idx + 1) % per_parts == 0:
-                    done = (idx + 1) / per_parts
-                    print_processbar(done, full_parts)
+    tar = tarfile.open(name=tar_file, mode='r:gz')
+
+    print_processbar(0)
+
+    dataset = {}
+    for tarInfo in tar:
+        if tarInfo.isfile() and tarInfo.name != VALLIST_TAR_NAME:
+            dataset[tarInfo.name] = tar.extractfile(tarInfo).read()
+
+    with open(output_file, "w+b") as ofs:
+        ofs.seek(0)
+        num = np.array(int(FULL_IMAGES)).astype('int64')
+        ofs.write(num.tobytes())
+
+        per_percentage = FULL_IMAGES / 100
+
+        idx = 0
+        for imagedata in dataset.values():
+            img = Image.open(StringIO.StringIO(imagedata))
+            img = process_image(img)
+            np_img = np.array(img)
+            ofs.write(np_img.astype('float32').tobytes())
+            if idx % per_percentage == 0:
+                print_processbar(idx / per_percentage)
+            idx = idx + 1
+
+        val_info = tar.getmember(VALLIST_TAR_NAME)
+        val_list = tar.extractfile(val_info).read()
+
+        lines = val_list.split('\n')
+        val_dict = {}
+        for line_idx, line in enumerate(lines):
+            if line_idx == FULL_IMAGES:
+                break
+            name, label = line.split()
+            val_dict[name] = label
+
+        for img_name in dataset.keys():
+            remove_len = (len(FOLDER_NAME))
+            img_name_prim = img_name[remove_len:]
+            label = val_dict[img_name_prim]
+            label_int = (int)(label)
+            np_label = np.array(label_int)
+            ofs.write(np_label.astype('int64').tobytes())
+        print_processbar(100)
+    tar.close()
     print("Conversion finished.")
 
 
 def run_convert():
     print('Start to download and convert 50000 images to binary file...')
     cache_folder = os.path.expanduser('~/.cache/paddle/dataset/int8/download')
-    extract_folder = os.path.join(cache_folder, 'full_data')
-    data_dir = os.path.join(extract_folder, DATA_DIR_NAME)
-    file_list = os.path.join(data_dir, 'val_list.txt')
-    zip_path = os.path.join(cache_folder, 'full_imagenet_val.tar.gz')
+    zip_path = os.path.join(cache_folder, 'full_imagenet_val.tar.gz.partaa')
     output_file = os.path.join(cache_folder, 'int8_full_val.bin')
     retry = 0
     try_limit = 3
@@ -213,8 +205,7 @@ def run_convert():
                 "Can not convert the dataset to binary file with try limit {0}".
                 format(try_limit))
         download_concat(cache_folder, zip_path)
-        extract(zip_path, extract_folder)
-        convert(file_list, data_dir, output_file)
+        convert(zip_path, output_file)
     print("\nSuccess! The binary file can be found at {0}".format(output_file))