infer.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.
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 paddle
import paddle.fluid as fluid
import reader
import models
import utils
from utils.utility import add_arguments,print_arguments


parser = argparse.ArgumentParser(description=__doc__)
# yapf: disable
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
add_arg('class_dim',        int,  5000,                 "Class number.")
add_arg('image_shape',      str,  "3,224,224",          "Input image size")
add_arg('pretrained_model', str,  None,                 "Whether to use pretrained model.")
add_arg('model',            str,  "ResNeXt101_32x4d",   "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('img_list',          str, None,                 "list of valset")
add_arg('img_path',          str, None,                 "path of valset")
# yapf: enable

def infer(args):
    # parameters from arguments
    class_dim = args.class_dim
    model_name = args.model
    save_inference = args.save_inference
    pretrained_model = args.pretrained_model
    image_shape = [int(m) for m in args.image_shape.split(",")]
    model_list = [m for m in dir(models) if "__" not in m]
    assert model_name in model_list, "{} is not in lists: {}".format(args.model,
                                                                     model_list)

    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')

    # model definition
    model = models.__dict__[model_name]()
    if model_name == "GoogleNet":
        out, _, _ = model.net(input=image, class_dim=class_dim)
    else:
        out = model.net(input=image, class_dim=class_dim)

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

    fetch_list = [out.name]

    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    fluid.io.load_persistables(exe, pretrained_model)
    if save_inference:
        fluid.io.save_inference_model(
                dirname=model_name,
                feeded_var_names=['image'],
                main_program=test_program,
                target_vars=out,
                executor=exe,
                model_filename='model',
                params_filename='params')
        print("model: ",model_name," is already saved")
        exit(0)
    test_batch_size = 1
    img_size = image_shape[1]
    test_reader = paddle.batch(reader.test(args, img_size), batch_size=test_batch_size)
    feeder = fluid.DataFeeder(place=place, feed_list=[image])

    TOPK = 1
    for batch_id, data in enumerate(test_reader()):
        result = exe.run(test_program,
                         fetch_list=fetch_list,
                         feed=feeder.feed(data))

        result = result[0][0]
        pred_label = np.argsort(result)[::-1][:TOPK]
        print("Test-{0}-score: {1}, class {2}"
              .format(batch_id, result[pred_label], pred_label))
        sys.stdout.flush()


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


if __name__ == '__main__':
    main()