Huawei_Technology
/
mindspore

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""eval deeplabv3."""

import os
import argparse
import numpy as np
import cv2
from mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.nets import net_factory
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
                    device_id=int(os.getenv('DEVICE_ID')))


def parse_args():
    parser = argparse.ArgumentParser('mindspore deeplabv3 eval')

    # val data
    parser.add_argument('--data_root', type=str, default='', help='root path of val data')
    parser.add_argument('--data_lst', type=str, default='', help='list of val data')
    parser.add_argument('--batch_size', type=int, default=16, help='batch size')
    parser.add_argument('--crop_size', type=int, default=513, help='crop size')
    parser.add_argument('--image_mean', type=list, default=[103.53, 116.28, 123.675], help='image mean')
    parser.add_argument('--image_std', type=list, default=[57.375, 57.120, 58.395], help='image std')
    parser.add_argument('--scales', type=float, action='append', help='scales of evaluation')
    parser.add_argument('--flip', action='store_true', help='perform left-right flip')
    parser.add_argument('--ignore_label', type=int, default=255, help='ignore label')
    parser.add_argument('--num_classes', type=int, default=21, help='number of classes')

    # model
    parser.add_argument('--model', type=str, default='deeplab_v3_s16', help='select model')
    parser.add_argument('--freeze_bn', action='store_true', default=False, help='freeze bn')
    parser.add_argument('--ckpt_path', type=str, default='', help='model to evaluate')

    args, _ = parser.parse_known_args()
    return args


def cal_hist(a, b, n):
    k = (a >= 0) & (a < n)
    return np.bincount(n * a[k].astype(np.int32) + b[k], minlength=n ** 2).reshape(n, n)


def resize_long(img, long_size=513):
    h, w, _ = img.shape
    if h > w:
        new_h = long_size
        new_w = int(1.0 * long_size * w / h)
    else:
        new_w = long_size
        new_h = int(1.0 * long_size * h / w)
    imo = cv2.resize(img, (new_w, new_h))
    return imo


class BuildEvalNetwork(nn.Cell):
    def __init__(self, network):
        super(BuildEvalNetwork, self).__init__()
        self.network = network
        self.softmax = nn.Softmax(axis=1)

    def construct(self, input_data):
        output = self.network(input_data)
        output = self.softmax(output)
        return output


def pre_process(args, img_, crop_size=513):
    # resize
    img_ = resize_long(img_, crop_size)
    resize_h, resize_w, _ = img_.shape

    # mean, std
    image_mean = np.array(args.image_mean)
    image_std = np.array(args.image_std)
    img_ = (img_ - image_mean) / image_std

    # pad to crop_size
    pad_h = crop_size - img_.shape[0]
    pad_w = crop_size - img_.shape[1]
    if pad_h > 0 or pad_w > 0:
        img_ = cv2.copyMakeBorder(img_, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)

    # hwc to chw
    img_ = img_.transpose((2, 0, 1))
    return img_, resize_h, resize_w


def eval_batch(args, eval_net, img_lst, crop_size=513, flip=True):
    result_lst = []
    batch_size = len(img_lst)
    batch_img = np.zeros((args.batch_size, 3, crop_size, crop_size), dtype=np.float32)
    resize_hw = []
    for l in range(batch_size):
        img_ = img_lst[l]
        img_, resize_h, resize_w = pre_process(args, img_, crop_size)
        batch_img[l] = img_
        resize_hw.append([resize_h, resize_w])

    batch_img = np.ascontiguousarray(batch_img)
    net_out = eval_net(Tensor(batch_img, mstype.float32))
    net_out = net_out.asnumpy()

    if flip:
        batch_img = batch_img[:, :, :, ::-1]
        net_out_flip = eval_net(Tensor(batch_img, mstype.float32))
        net_out += net_out_flip.asnumpy()[:, :, :, ::-1]

    for bs in range(batch_size):
        probs_ = net_out[bs][:, :resize_hw[bs][0], :resize_hw[bs][1]].transpose((1, 2, 0))
        ori_h, ori_w = img_lst[bs].shape[0], img_lst[bs].shape[1]
        probs_ = cv2.resize(probs_, (ori_w, ori_h))
        result_lst.append(probs_)

    return result_lst


def eval_batch_scales(args, eval_net, img_lst, scales,
                      base_crop_size=513, flip=True):
    sizes_ = [int((base_crop_size - 1) * sc) + 1 for sc in scales]
    probs_lst = eval_batch(args, eval_net, img_lst, crop_size=sizes_[0], flip=flip)
    print(sizes_)
    for crop_size_ in sizes_[1:]:
        probs_lst_tmp = eval_batch(args, eval_net, img_lst, crop_size=crop_size_, flip=flip)
        for pl, _ in enumerate(probs_lst):
            probs_lst[pl] += probs_lst_tmp[pl]

    result_msk = []
    for i in probs_lst:
        result_msk.append(i.argmax(axis=2))
    return result_msk


def net_eval():
    args = parse_args()

    # data list
    with open(args.data_lst) as f:
        img_lst = f.readlines()

    # network
    if args.model == 'deeplab_v3_s16':
        network = net_factory.nets_map[args.model]('eval', args.num_classes, 16, args.freeze_bn)
    elif args.model == 'deeplab_v3_s8':
        network = net_factory.nets_map[args.model]('eval', args.num_classes, 8, args.freeze_bn)
    else:
        raise NotImplementedError('model [{:s}] not recognized'.format(args.model))

    eval_net = BuildEvalNetwork(network)

    # load model
    param_dict = load_checkpoint(args.ckpt_path)
    load_param_into_net(eval_net, param_dict)
    eval_net.set_train(False)

    # evaluate
    hist = np.zeros((args.num_classes, args.num_classes))
    batch_img_lst = []
    batch_msk_lst = []
    bi = 0
    image_num = 0
    for i, line in enumerate(img_lst):
        img_path, msk_path = line.strip().split(' ')
        img_path = os.path.join(args.data_root, img_path)
        msk_path = os.path.join(args.data_root, msk_path)
        img_ = cv2.imread(img_path)
        msk_ = cv2.imread(msk_path, cv2.IMREAD_GRAYSCALE)
        batch_img_lst.append(img_)
        batch_msk_lst.append(msk_)
        bi += 1
        if bi == args.batch_size:
            batch_res = eval_batch_scales(args, eval_net, batch_img_lst, scales=args.scales,
                                          base_crop_size=args.crop_size, flip=args.flip)
            for mi in range(args.batch_size):
                hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes)

            bi = 0
            batch_img_lst = []
            batch_msk_lst = []
            print('processed {} images'.format(i+1))
        image_num = i

    if bi > 0:
        batch_res = eval_batch_scales(args, eval_net, batch_img_lst, scales=args.scales,
                                      base_crop_size=args.crop_size, flip=args.flip)
        for mi in range(bi):
            hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes)
        print('processed {} images'.format(image_num + 1))

    print(hist)
    iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
    print('per-class IoU', iu)
    print('mean IoU', np.nanmean(iu))


if __name__ == '__main__':
    net_eval()