infer.py

#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time
import sys
import math
import numpy as np
import argparse
import functools
import re
import logging

import paddle
import paddle.fluid as fluid
import reader
import models
import json
from utils import *

parser = argparse.ArgumentParser(description=__doc__)
# yapf: disable
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg('data_dir',         str,  "./data/ILSVRC2012/val/", "The ImageNet data")
add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
add_arg('class_dim',        int,  1000,                 "Class number.")
parser.add_argument("--pretrained_model", default=None, required=True, type=str, help="The path to load pretrained model")
add_arg('model',            str,  "ResNet50",           "Set the network to use.")
add_arg('save_inference',   bool, False,                "Whether to save inference model or not")
add_arg('resize_short_size',int,  256,                  "Set resize short size")
add_arg('reader_thread',    int,  1,                    "The number of multi thread reader")
add_arg('reader_buf_size',  int,  2048,                 "The buf size of multi thread reader")
parser.add_argument('--image_mean', nargs='+', type=float, default=[0.485, 0.456, 0.406], help="The mean of input image data")
parser.add_argument('--image_std', nargs='+', type=float, default=[0.229, 0.224, 0.225], help="The std of input image data")
parser.add_argument('--image_shape', nargs='+', type=int, default=[3, 224, 224], help="the shape of image")
add_arg('topk',             int,  1,                    "topk")
add_arg('class_map_path',   str,  "./utils/tools/readable_label.txt", "readable label filepath")
add_arg('interpolation',    int,  None,                 "The interpolation mode")
add_arg('padding_type',     str,  "SAME",               "Padding type of convolution")
add_arg('use_se',           bool, True,                 "Whether to use Squeeze-and-Excitation module for EfficientNet.")
add_arg('image_path',       str,  None,                 "single image path")
add_arg('batch_size',       int,  8,                    "batch_size on all the devices")
add_arg('save_json_path',        str,  "test_res.json",            "save output to a json file")
# yapf: enable

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def infer(args):
    model_list = [m for m in dir(models) if "__" not in m]
    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                     model_list)
    assert os.path.isdir(args.pretrained_model
                         ), "please load right pretrained model path for infer"

    assert args.image_shape[
        1] <= args.resize_short_size, "Please check the args:image_shape and args:resize_short_size, The croped size(image_shape[1]) must smaller than or equal to the resized length(resize_short_size) "

    if args.image_path:
        assert os.path.isfile(
            args.image_path
        ), "Please check the args:image_path, it should be a path to single image."
        if args.use_gpu:
            assert fluid.core.get_cuda_device_count(
            ) == 1, "please set \"export CUDA_VISIBLE_DEVICES=\" available single card"
        else:
            assert int(os.environ.get('CPU_NUM',
                                      1)) == 1, "please set CPU_NUM as 1"

    image = fluid.data(
        name='image', shape=[None] + args.image_shape, dtype='float32')

    if args.model.startswith('EfficientNet'):
        model = models.__dict__[args.model](is_test=True,
                                            padding_type=args.padding_type,
                                            use_se=args.use_se)
    else:
        model = models.__dict__[args.model]()

    if args.model == "GoogLeNet":
        out, _, _ = model.net(input=image, class_dim=args.class_dim)
    else:
        out = model.net(input=image, class_dim=args.class_dim)
        out = fluid.layers.softmax(out)

    test_program = fluid.default_main_program().clone(for_test=True)

    fetch_list = [out.name]
    gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
    place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    if args.use_gpu:
        places = fluid.framework.cuda_places()
    else:
        places = fluid.framework.cpu_places()
    compiled_program = fluid.compiler.CompiledProgram(
        test_program).with_data_parallel(places=places)

    fluid.io.load_persistables(exe, args.pretrained_model)
    if args.save_inference:
        fluid.io.save_inference_model(
            dirname=args.model,
            feeded_var_names=['image'],
            main_program=test_program,
            target_vars=out,
            executor=exe,
            model_filename='model',
            params_filename='params')
        logger.info("model: {0} is already saved".format(args.model))
        exit(0)

    imagenet_reader = reader.ImageNetReader()
    test_reader = imagenet_reader.test(settings=args)
    feeder = fluid.DataFeeder(place=places, feed_list=[image])

    TOPK = args.topk
    if os.path.exists(args.class_map_path):
        logger.info(
            "The map of readable label and numerical label has been found!")
        with open(args.class_map_path) as f:
            label_dict = {}
            strinfo = re.compile(r"\d+ ")
            for item in f.readlines():
                key = item.split(" ")[0]
                value = [
                    strinfo.sub("", l).replace("\n", "")
                    for l in item.split(", ")
                ]
                label_dict[key] = value

    info = {}
    parallel_data = []
    parallel_id = []
    place_num = paddle.fluid.core.get_cuda_device_count(
    ) if args.use_gpu else int(os.environ.get('CPU_NUM', 1))
    if os.path.exists(args.save_json_path):
        logger.warning("path: {} Already exists! will recover it\n".format(
            args.save_json_path))
    with open(args.save_json_path, "w") as fout:
        for batch_id, data in enumerate(test_reader()):
            image_data = [[items[0]] for items in data]
            image_id = [items[1] for items in data]

            parallel_id.append(image_id)
            parallel_data.append(image_data)

            if place_num == len(parallel_data):
                result = exe.run(
                    compiled_program,
                    fetch_list=fetch_list,
                    feed=list(feeder.feed_parallel(parallel_data, place_num)))
                for i, res in enumerate(result[0]):
                    pred_label = np.argsort(res)[::-1][:TOPK]
                    real_id = str(np.array(parallel_id).flatten()[i])
                    _, real_id = os.path.split(real_id)

                    if os.path.exists(args.class_map_path):
                        readable_pred_label = []
                        for label in pred_label:
                            readable_pred_label.append(label_dict[str(label)])

                        info[real_id] = {}
                        info[real_id]['score'], info[real_id]['class'], info[
                            real_id]['class_name'] = str(res[pred_label]), str(
                                pred_label), readable_pred_label
                    else:
                        info[real_id] = {}
                        info[real_id]['score'], info[real_id]['class'] = str(
                            res[pred_label]), str(pred_label)

                    logger.info("{}, {}".format(real_id, info[real_id]))
                    sys.stdout.flush()
                    fout.write(real_id + "\t" + json.dumps(info[real_id]) +
                               "\n")

                parallel_data = []
                parallel_id = []

    os.remove(".tmp.txt")


def main():
    args = parser.parse_args()
    print_arguments(args)
    check_gpu()
    check_version()
    infer(args)


if __name__ == '__main__':
    main()