paddlepaddle--paddle/test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py

#   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 argparse
import io
import os
import shutil
import sys
import tarfile

import numpy as np
from PIL import Image

from paddle.dataset.common import download

np.random.seed(0)

DATA_DIM = 224
SIZE_FLOAT32 = 4
SIZE_INT64 = 8
FULL_SIZE_BYTES = 30106000008
FULL_IMAGES = 50000
FOLDER_NAME = "ILSVRC2012/"
VALLIST_TAR_NAME = "ILSVRC2012/val_list.txt"

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:
        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(f"Downloaded part {file_name}\n")
    with open(zip_path, "wb") as outfile:
        for fname in file_names:
            shutil.copyfileobj(open(fname, 'rb'), outfile)


def print_processbar(done_percentage):
    done_filled = done_percentage * '='
    empty_filled = (100 - done_percentage) * ' '
    sys.stdout.write(f"\r[{done_filled}{empty_filled}]{done_percentage}%")
    sys.stdout.flush()


def convert_Imagenet_tar2bin(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

        val_info = tar.getmember(VALLIST_TAR_NAME)
        val_list = tar.extractfile(val_info).read().decode("utf-8")
        lines = val_list.splitlines()
        idx = 0
        for imagedata in dataset.values():
            img = Image.open(io.BytesIO(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_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
    ):
        if os.path.exists(output_file):
            sys.stderr.write(
                f"\n\nThe existing binary file[{output_file}] is broken. Start to generate new one...\n\n"
            )
            os.remove(output_file)
        if retry < try_limit:
            retry = retry + 1
        else:
            raise RuntimeError(
                f"Can not convert the dataset to binary file with try limit {try_limit}"
            )
        download_concat(cache_folder, zip_path)
        convert_Imagenet_tar2bin(zip_path, output_file)
    print(f"\nSuccess! The binary file can be found at {output_file}")


def convert_Imagenet_local2bin(args):
    data_dir = args.data_dir
    label_list_path = os.path.join(args.data_dir, args.label_list)
    bin_file_path = os.path.join(args.data_dir, args.output_file)
    assert data_dir, 'Once set --local, user need to provide the --data_dir'
    with open(label_list_path) as flist:
        lines = [line.strip() for line in flist]
        num_images = len(lines)

        with open(bin_file_path, "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 = Image.open(img_path)
                img = process_image(img)
                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())

        # The bin file should contain
        # number of images + all images data + all corresponding labels
        # so the file target_size should be as follows
        target_size = (
            SIZE_INT64
            + num_images * 3 * args.data_dim * args.data_dim * SIZE_FLOAT32
            + num_images * SIZE_INT64
        )
        if os.path.getsize(bin_file_path) == target_size:
            print(
                f"Success! The user data output binary file can be found at: {bin_file_path}"
            )
        else:
            print("Conversion failed!")


def main_preprocess_Imagenet(args):
    parser = argparse.ArgumentParser(
        description="Convert the full Imagenet val set or local data to binary file.",
        usage=None,
        add_help=True,
    )
    parser.add_argument(
        '--local',
        action="store_true",
        help="If used, user need to set --data_dir and then convert file",
    )
    parser.add_argument(
        "--data_dir", default="", type=str, help="Dataset root directory"
    )
    parser.add_argument(
        "--label_list",
        type=str,
        default="val_list.txt",
        help="List of object labels with same sequence as denoted in the annotation file",
    )
    parser.add_argument(
        "--output_file",
        type=str,
        default="imagenet_small.bin",
        help="File path of the output binary file",
    )
    parser.add_argument(
        "--data_dim",
        type=int,
        default=DATA_DIM,
        help="Image preprocess with data_dim width and height",
    )

    args = parser.parse_args()
    if args.local:
        convert_Imagenet_local2bin(args)
    else:
        run_convert()


if __name__ == '__main__':
    main_preprocess_Imagenet(sys.argv)