|
|
|
|
@ -1,5 +1,4 @@
|
|
|
|
|
# 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
|
|
|
|
|
@ -11,6 +10,7 @@
|
|
|
|
|
# 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 hashlib
|
|
|
|
|
import unittest
|
|
|
|
|
import os
|
|
|
|
|
import numpy as np
|
|
|
|
|
@ -21,16 +21,20 @@ import functools
|
|
|
|
|
import contextlib
|
|
|
|
|
from PIL import Image, ImageEnhance
|
|
|
|
|
import math
|
|
|
|
|
from paddle.dataset.common import download
|
|
|
|
|
from paddle.dataset.common import download, md5file
|
|
|
|
|
import tarfile
|
|
|
|
|
|
|
|
|
|
random.seed(0)
|
|
|
|
|
np.random.seed(0)
|
|
|
|
|
|
|
|
|
|
DATA_DIM = 224
|
|
|
|
|
|
|
|
|
|
SIZE_FLOAT32 = 4
|
|
|
|
|
SIZE_INT64 = 8
|
|
|
|
|
|
|
|
|
|
FULL_SIZE_BYTES = 30106000008
|
|
|
|
|
FULL_IMAGES = 50000
|
|
|
|
|
DATA_DIR_NAME = 'ILSVRC2012'
|
|
|
|
|
IMG_DIR_NAME = 'var'
|
|
|
|
|
TARGET_HASH = '8dc592db6dcc8d521e4d5ba9da5ca7d2'
|
|
|
|
|
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))
|
|
|
|
|
|
|
|
|
|
@ -70,19 +74,9 @@ def process_image(img_path, mode, color_jitter, rotate):
|
|
|
|
|
return img
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def download_unzip():
|
|
|
|
|
int8_download = 'int8/download'
|
|
|
|
|
|
|
|
|
|
target_name = 'data'
|
|
|
|
|
|
|
|
|
|
cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
|
|
|
|
|
int8_download)
|
|
|
|
|
|
|
|
|
|
target_folder = os.path.join(cache_folder, target_name)
|
|
|
|
|
|
|
|
|
|
def download_concat(cache_folder, zip_path):
|
|
|
|
|
data_urls = []
|
|
|
|
|
data_md5s = []
|
|
|
|
|
|
|
|
|
|
data_urls.append(
|
|
|
|
|
'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partaa'
|
|
|
|
|
)
|
|
|
|
|
@ -91,49 +85,80 @@ def download_unzip():
|
|
|
|
|
'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partab'
|
|
|
|
|
)
|
|
|
|
|
data_md5s.append('1e9f15f64e015e58d6f9ec3210ed18b5')
|
|
|
|
|
|
|
|
|
|
file_names = []
|
|
|
|
|
|
|
|
|
|
print("Downloading full ImageNet Validation dataset ...")
|
|
|
|
|
for i in range(0, len(data_urls)):
|
|
|
|
|
download(data_urls[i], cache_folder, data_md5s[i])
|
|
|
|
|
file_names.append(data_urls[i].split('/')[-1])
|
|
|
|
|
|
|
|
|
|
zip_path = os.path.join(cache_folder, 'full_imagenet_val.tar.gz')
|
|
|
|
|
|
|
|
|
|
file_name = os.path.join(cache_folder, data_urls[i].split('/')[-1])
|
|
|
|
|
file_names.append(file_name)
|
|
|
|
|
print("Downloaded part {0}\n".format(file_name))
|
|
|
|
|
if not os.path.exists(zip_path):
|
|
|
|
|
cat_command = 'cat'
|
|
|
|
|
for file_name in file_names:
|
|
|
|
|
cat_command += ' ' + os.path.join(cache_folder, file_name)
|
|
|
|
|
cat_command += ' > ' + zip_path
|
|
|
|
|
os.system(cat_command)
|
|
|
|
|
print('Data is downloaded at {0}\n').format(zip_path)
|
|
|
|
|
|
|
|
|
|
if not os.path.exists(target_folder):
|
|
|
|
|
cmd = 'mkdir {0} && tar xf {1} -C {0}'.format(target_folder, zip_path)
|
|
|
|
|
os.system(cmd)
|
|
|
|
|
print('Data is unzipped at {0}\n'.format(target_folder))
|
|
|
|
|
|
|
|
|
|
data_dir = os.path.join(target_folder, 'ILSVRC2012')
|
|
|
|
|
print('ILSVRC2012 full val set at {0}\n'.format(data_dir))
|
|
|
|
|
return data_dir
|
|
|
|
|
with open(zip_path, "w+") as outfile:
|
|
|
|
|
for fname in file_names:
|
|
|
|
|
with open(fname) as infile:
|
|
|
|
|
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
|
|
|
|
|
sys.stdout.write("\r[%s%s]%d%%" %
|
|
|
|
|
(done_filled, empty_filled, percentage_done))
|
|
|
|
|
sys.stdout.flush()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
with open(filename) as ifs:
|
|
|
|
|
while True:
|
|
|
|
|
buf = ifs.read(8192)
|
|
|
|
|
if count % onepart == 0:
|
|
|
|
|
done = count / onepart
|
|
|
|
|
print_processbar(done, total_parts)
|
|
|
|
|
count = count + 1
|
|
|
|
|
if not buf:
|
|
|
|
|
break
|
|
|
|
|
md.update(buf)
|
|
|
|
|
hash1 = md.hexdigest()
|
|
|
|
|
if hash1 == target_hash:
|
|
|
|
|
return True
|
|
|
|
|
else:
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def reader():
|
|
|
|
|
data_dir = download_unzip()
|
|
|
|
|
file_list = os.path.join(data_dir, 'val_list.txt')
|
|
|
|
|
output_file = os.path.join(data_dir, 'int8_full_val.bin')
|
|
|
|
|
def convert(file_list, data_dir, 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)
|
|
|
|
|
if not os.path.exists(output_file):
|
|
|
|
|
print(
|
|
|
|
|
'Preprocessing to binary file...<num_images><all images><all labels>...\n'
|
|
|
|
|
)
|
|
|
|
|
with open(output_file, "w+b") as of:
|
|
|
|
|
with open(output_file, "w+b") as ofs:
|
|
|
|
|
#save num_images(int64_t) to file
|
|
|
|
|
of.seek(0)
|
|
|
|
|
ofs.seek(0)
|
|
|
|
|
num = np.array(int(num_images)).astype('int64')
|
|
|
|
|
of.write(num.tobytes())
|
|
|
|
|
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)
|
|
|
|
|
@ -144,19 +169,54 @@ def reader():
|
|
|
|
|
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())
|
|
|
|
|
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)
|
|
|
|
|
of.seek(SIZE_INT64 + SIZE_FLOAT32 * DATA_DIM * DATA_DIM * 3
|
|
|
|
|
* num_images + idx * SIZE_INT64)
|
|
|
|
|
of.write(np_label.astype('int64').tobytes())
|
|
|
|
|
|
|
|
|
|
print('The preprocessed binary file path {}\n'.format(output_file))
|
|
|
|
|
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)
|
|
|
|
|
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')
|
|
|
|
|
output_file = os.path.join(cache_folder, 'int8_full_val.bin')
|
|
|
|
|
retry = 0
|
|
|
|
|
try_limit = 3
|
|
|
|
|
|
|
|
|
|
while not (os.path.exists(output_file) and
|
|
|
|
|
os.path.getsize(output_file) == FULL_SIZE_BYTES and
|
|
|
|
|
check_integrity(output_file, TARGET_HASH)):
|
|
|
|
|
if os.path.exists(output_file):
|
|
|
|
|
sys.stderr.write(
|
|
|
|
|
"\n\nThe existing binary file is broken. Start to generate new one...\n\n".
|
|
|
|
|
format(output_file))
|
|
|
|
|
os.remove(output_file)
|
|
|
|
|
if retry < try_limit:
|
|
|
|
|
retry = retry + 1
|
|
|
|
|
else:
|
|
|
|
|
raise RuntimeError(
|
|
|
|
|
"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)
|
|
|
|
|
print("\nSuccess! The binary file can be found at {0}".format(output_file))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
reader()
|
|
|
|
|
run_convert()
|
|
|
|
|
|
Tao Luo
6 years ago
committed by
GitHub