infer.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import time
import sys
import paddle
import paddle.fluid as fluid
import reader
import argparse
import functools
import models
import utils
from utils.utility import add_arguments,print_arguments
import math

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,  1000,                 "Class number.")
add_arg('image_shape',      str,  "3,224,224",          "Input image size")
add_arg('with_mem_opt',     bool, True,                 "Whether to use memory optimization or not.")
add_arg('pretrained_model', str,  None,                 "Whether to use pretrained model.")
add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
add_arg('save_inference',   bool, False,                 "Whether to save inference model or not")
# 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
    with_memory_optimization = args.with_mem_opt
    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]
    if with_memory_optimization and not save_inference:
        fluid.memory_optimize(
            fluid.default_main_program(), skip_opt_set=set(fetch_list))

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

    if pretrained_model:

        def if_exist(var):
            return os.path.exists(os.path.join(pretrained_model, var.name))

        fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
    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
    test_reader = paddle.batch(reader.test(), 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()