eval.py

import os
import numpy as np
import time
import sys
import paddle
import paddle.fluid as fluid
from resnet import TSN_ResNet
import reader

import argparse
import functools
from paddle.fluid.framework import Parameter
from utility import add_arguments, print_arguments

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size',       int,    128,            "Minibatch size.")
add_arg('num_layers',       int,    50,             "How many layers for ResNet model.")
add_arg('with_mem_opt',     bool,   True,           "Whether to use memory optimization or not.")
add_arg('class_dim',        int,    101,            "Number of class.")
add_arg('seg_num',          int,    7,              "Number of segments.")
add_arg('image_shape',      str,    "3,224,224",    "Input image size.")
add_arg('test_model',       str,    None,           "Test model path.")
# yapf: enable


def eval(args):
    # parameters from arguments
    seg_num = args.seg_num
    class_dim = args.class_dim
    num_layers = args.num_layers
    batch_size = args.batch_size
    test_model = args.test_model

    if test_model == None:
        print('Please specify the test model ...')
        return

    image_shape = [int(m) for m in args.image_shape.split(",")]
    image_shape = [seg_num] + image_shape

    # model definition
    model = TSN_ResNet(layers=num_layers, seg_num=seg_num)
    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    out = model.net(input=image, class_dim=class_dim)
    cost = fluid.layers.cross_entropy(input=out, label=label)

    avg_cost = fluid.layers.mean(x=cost)
    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
    acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)

    # for test
    inference_program = fluid.default_main_program().clone(for_test=True)

    if args.with_mem_opt:
        fluid.memory_optimize(fluid.default_main_program())

    place = fluid.CUDAPlace(0)
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    def is_parameter(var):
        if isinstance(var, Parameter):
            return isinstance(var, Parameter)

    if test_model is not None:
        vars = filter(is_parameter, inference_program.list_vars())
        fluid.io.load_vars(exe, test_model, vars=vars)

    # reader
    test_reader = paddle.batch(reader.test(seg_num), batch_size=batch_size / 16)
    feeder = fluid.DataFeeder(place=place, feed_list=[image, label])

    fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]

    # test
    cnt = 0
    pass_id = 0
    test_info = [[], [], []]
    for batch_id, data in enumerate(test_reader()):
        t1 = time.time()
        loss, acc1, acc5 = exe.run(inference_program,
                                   fetch_list=fetch_list,
                                   feed=feeder.feed(data))
        t2 = time.time()
        period = t2 - t1
        loss = np.mean(loss)
        acc1 = np.mean(acc1)
        acc5 = np.mean(acc5)
        test_info[0].append(loss * len(data))
        test_info[1].append(acc1 * len(data))
        test_info[2].append(acc5 * len(data))
        cnt += len(data)
        if batch_id % 10 == 0:
            print(
                "[TEST] Pass: {0}\ttestbatch: {1}\tloss: {2}\tacc1: {3}\tacc5: {4}\ttime: {5}"
                .format(pass_id, batch_id, '%.6f' % loss, acc1, acc5,
                        "%2.2f sec" % period))
            sys.stdout.flush()

    test_loss = np.sum(test_info[0]) / cnt
    test_acc1 = np.sum(test_info[1]) / cnt
    test_acc5 = np.sum(test_info[2]) / cnt

    print("+ End pass: {0}, test_loss: {1}, test_acc1: {2}, test_acc5: {3}"
          .format(pass_id, '%.3f' % test_loss, '%.3f' % test_acc1, '%.3f' %
                  test_acc5))
    sys.stdout.flush()


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


if __name__ == '__main__':
    main()