eval_quant.py

# coding: utf8
# 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 sys
import time
import argparse
import functools
import pprint
import cv2
import numpy as np
import paddle
import paddle.fluid as fluid

from utils.config import cfg
from utils.timer import Timer, calculate_eta
from models.model_builder import build_model
from models.model_builder import ModelPhase
from reader import SegDataset
from metrics import ConfusionMatrix
from paddleslim.quant import quant_aware, convert


def parse_args():
    parser = argparse.ArgumentParser(description='PaddleSeg model evalution')
    parser.add_argument(
        '--cfg',
        dest='cfg_file',
        help='Config file for training (and optionally testing)',
        default=None,
        type=str)
    parser.add_argument(
        '--use_gpu',
        dest='use_gpu',
        help='Use gpu or cpu',
        action='store_true',
        default=False)
    parser.add_argument(
        '--use_mpio',
        dest='use_mpio',
        help='Use multiprocess IO or not',
        action='store_true',
        default=False)
    parser.add_argument(
        'opts',
        help='See utils/config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)
    parser.add_argument(
        '--convert',
        dest='convert',
        help='Convert or not',
        action='store_true',
        default=False)
    parser.add_argument(
        "--not_quant_pattern",
        nargs='+',
        type=str,
        help=
        "Layers which name_scope contains string in not_quant_pattern will not be quantized"
    )

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)
    return parser.parse_args()


def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs):
    np.set_printoptions(precision=5, suppress=True)

    startup_prog = fluid.Program()
    test_prog = fluid.Program()
    dataset = SegDataset(
        file_list=cfg.DATASET.VAL_FILE_LIST,
        mode=ModelPhase.EVAL,
        data_dir=cfg.DATASET.DATA_DIR)

    def data_generator():
        #TODO: check is batch reader compatitable with Windows
        if use_mpio:
            data_gen = dataset.multiprocess_generator(
                num_processes=cfg.DATALOADER.NUM_WORKERS,
                max_queue_size=cfg.DATALOADER.BUF_SIZE)
        else:
            data_gen = dataset.generator()

        for b in data_gen:
            yield b[0], b[1], b[2]

    py_reader, avg_loss, pred, grts, masks = build_model(
        test_prog, startup_prog, phase=ModelPhase.EVAL)

    py_reader.decorate_sample_generator(
        data_generator, drop_last=False, batch_size=cfg.BATCH_SIZE)

    # Get device environment
    places = fluid.cuda_places() if use_gpu else fluid.cpu_places()
    place = places[0]
    dev_count = len(places)
    print("#Device count: {}".format(dev_count))

    exe = fluid.Executor(place)
    exe.run(startup_prog)

    test_prog = test_prog.clone(for_test=True)
    not_quant_pattern_list = []
    if kwargs['not_quant_pattern'] is not None:
        not_quant_pattern_list = kwargs['not_quant_pattern']
    config = {
        'weight_quantize_type': 'channel_wise_abs_max',
        'activation_quantize_type': 'moving_average_abs_max',
        'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
        'not_quant_pattern': not_quant_pattern_list
    }
    test_prog = quant_aware(test_prog, place, config, for_test=True)

    ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir

    if not os.path.exists(ckpt_dir):
        raise ValueError('The TEST.TEST_MODEL {} is not found'.format(ckpt_dir))

    if ckpt_dir is not None:
        print('load test model:', ckpt_dir)
        fluid.io.load_persistables(exe, ckpt_dir, main_program=test_prog)
    if kwargs['convert']:
        test_prog = convert(test_prog, place, config)
    # Use streaming confusion matrix to calculate mean_iou
    np.set_printoptions(
        precision=4, suppress=True, linewidth=160, floatmode="fixed")
    conf_mat = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
    fetch_list = [avg_loss.name, pred.name, grts.name, masks.name]
    num_images = 0
    step = 0
    all_step = cfg.DATASET.TEST_TOTAL_IMAGES // cfg.BATCH_SIZE + 1
    timer = Timer()
    timer.start()
    py_reader.start()
    while True:
        try:
            step += 1
            loss, pred, grts, masks = exe.run(
                test_prog, fetch_list=fetch_list, return_numpy=True)

            loss = np.mean(np.array(loss))

            num_images += pred.shape[0]
            conf_mat.calculate(pred, grts, masks)
            _, iou = conf_mat.mean_iou()
            _, acc = conf_mat.accuracy()

            speed = 1.0 / timer.elapsed_time()

            print(
                "[EVAL]step={} loss={:.5f} acc={:.4f} IoU={:.4f} step/sec={:.2f} | ETA {}"
                .format(step, loss, acc, iou, speed,
                        calculate_eta(all_step - step, speed)))
            timer.restart()
            sys.stdout.flush()
        except fluid.core.EOFException:
            break

    category_iou, avg_iou = conf_mat.mean_iou()
    category_acc, avg_acc = conf_mat.accuracy()
    print("[EVAL]#image={} acc={:.4f} IoU={:.4f}".format(
        num_images, avg_acc, avg_iou))
    print("[EVAL]Category IoU:", category_iou)
    print("[EVAL]Category Acc:", category_acc)
    print("[EVAL]Kappa:{:.4f}".format(conf_mat.kappa()))

    return category_iou, avg_iou, category_acc, avg_acc


def main():
    args = parse_args()
    if args.cfg_file is not None:
        cfg.update_from_file(args.cfg_file)
    if args.opts:
        cfg.update_from_list(args.opts)
    cfg.check_and_infer()
    print(pprint.pformat(cfg))
    evaluate(cfg, **args.__dict__)


if __name__ == '__main__':
    main()