# 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 hashlib import unittest import os import numpy as np import time import sys import random import functools import contextlib from PIL import Image import math from paddle.dataset.common import download import tarfile import StringIO random.seed(0) np.random.seed(0) DATA_DIM = 224 SIZE_FLOAT32 = 4 SIZE_INT64 = 8 FULL_SIZE_BYTES = 30106000008 FULL_IMAGES = 50000 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)) 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): 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 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' ) data_md5s.append('60f6525b0e1d127f345641d75d41f0a8') data_urls.append( '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_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): with open(zip_path, "w+") as outfile: for fname in file_names: with open(fname) as infile: outfile.write(infile.read()) 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, done_percentage)) sys.stdout.flush() def check_integrity(filename, target_hash): print('\nThe binary file exists. Checking file integrity...\n') md = hashlib.md5() count = 0 onepart = FULL_SIZE_BYTES / CHUNK_SIZE / 100 with open(filename) as ifs: while True: buf = ifs.read(CHUNK_SIZE) if count % onepart == 0: done = count / onepart print_processbar(done) count = count + 1 if not buf: break md.update(buf) hash1 = md.hexdigest() if hash1 == target_hash: return True else: return False def convert(tar_file, output_file): print('Converting 50000 images to binary file ...\n') 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') 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 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) convert(zip_path, output_file) print("\nSuccess! The binary file can be found at {0}".format(output_file)) if __name__ == '__main__': run_convert()