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- # import mindspore
- # import mindspore.ops as ops
- # from mindspore import Tensor
- # import numpy as np
- # from mindspore import nn as nn
- # from mindspore.dataset import context
-
- # from loss import JointEdgeSegLoss
- # from network.mynn import initialize_weights, Norm2d
- # import cv2
- # import argparse
- # from datasetsnew import setup_data
- # from network.gscnn import GSCNN
-
- # parser = argparse.ArgumentParser(description='GSCNN')
- # parser.add_argument('--lr', type=float, default=0.01)
- # parser.add_argument('--arch', type=str, default='network.gscnn.GSCNN')
- # parser.add_argument('--dataset', type=str, default='cityscapes')
- # parser.add_argument('--cv', type=int, default=0,
- # help='cross validation split')
- # parser.add_argument('--joint_edgeseg_loss', action='store_true', default=True,
- # help='joint loss')
- # parser.add_argument('--img_wt_loss', action='store_true', default=False,
- # help='per-image class-weighted loss')
- # parser.add_argument('--batch_weighting', action='store_true', default=False,
- # help='Batch weighting for class')
- # parser.add_argument('--eval_thresholds', type=str, default='0.0005,0.001875,0.00375,0.005',
- # help='Thresholds for boundary evaluation')
- # parser.add_argument('--rescale', type=float, default=1.0,
- # help='Rescaled LR Rate')
- # parser.add_argument('--repoly', type=float, default=1.5,
- # help='Rescaled Poly')
-
- # parser.add_argument('--edge_weight', type=float, default=1.0,
- # help='Edge loss weight for joint loss')
- # parser.add_argument('--seg_weight', type=float, default=1.0,
- # help='Segmentation loss weight for joint loss')
- # parser.add_argument('--att_weight', type=float, default=1.0,
- # help='Attention loss weight for joint loss')
- # parser.add_argument('--dual_weight', type=float, default=1.0,
- # help='Dual loss weight for joint loss')
-
- # parser.add_argument('--evaluate', action='store_true', default=False)
-
- # parser.add_argument("--local_rank", default=0, type=int)
-
- # parser.add_argument('--sgd', action='store_true', default=True)
- # parser.add_argument('--sgd_finetuned',action='store_true',default=False)
- # parser.add_argument('--adam', action='store_true', default=False)
- # parser.add_argument('--amsgrad', action='store_true', default=False)
-
- # parser.add_argument('--trunk', type=str, default='resnet101',
- # help='trunk model, can be: resnet101 (default), resnet50')
- # parser.add_argument('--max_epoch', type=int, default=175)
- # parser.add_argument('--start_epoch', type=int, default=0)
- # parser.add_argument('--color_aug', type=float,
- # default=0.25, help='level of color augmentation')
- # parser.add_argument('--rotate', type=float,
- # default=0, help='rotation')
- # parser.add_argument('--gblur', action='store_true', default=True)
- # parser.add_argument('--bblur', action='store_true', default=False)
- # parser.add_argument('--lr_schedule', type=str, default='poly',
- # help='name of lr schedule: poly')
- # parser.add_argument('--poly_exp', type=float, default=1.0,
- # help='polynomial LR exponent')
- # parser.add_argument('--bs_mult', type=int, default=1)
- # parser.add_argument('--bs_mult_val', type=int, default=2)
- # parser.add_argument('--crop_size', type=int, default=720,
- # help='training crop size')
- # parser.add_argument('--pre_size', type=int, default=None,
- # help='resize image shorter edge to this before augmentation')
- # parser.add_argument('--scale_min', type=float, default=0.5,
- # help='dynamically scale training images down to this size')
- # parser.add_argument('--scale_max', type=float, default=2.0,
- # help='dynamically scale training images up to this size')
- # parser.add_argument('--weight_decay', type=float, default=1e-4)
- # parser.add_argument('--momentum', type=float, default=0.9)
- # parser.add_argument('--snapshot', type=str, default=None)
- # parser.add_argument('--restore_optimizer', action='store_true', default=False)
- # parser.add_argument('--exp', type=str, default='default',
- # help='experiment directory name')
- # parser.add_argument('--tb_tag', type=str, default='',
- # help='add tag to tb dir')
- # parser.add_argument('--ckpt', type=str, default='logs/ckpt')
- # parser.add_argument('--tb_path', type=str, default='logs/tb')
- # parser.add_argument('--syncbn', action='store_true', default=True,
- # help='Synchronized BN')
- # parser.add_argument('--dump_augmentation_images', action='store_true', default=False,
- # help='Synchronized BN')
- # parser.add_argument('--test_mode', action='store_true', default=False,
- # help='minimum testing (1 epoch run ) to verify nothing failed')
- # parser.add_argument('-wb', '--wt_bound', type=float, default=1.0)
- # parser.add_argument('--maxSkip', type=int, default=0)
- # args = parser.parse_args()
- # args.best_record = {'epoch': -1, 'iter': 0, 'val_loss': 1e10, 'acc': 0,
- # 'acc_cls': 0, 'mean_iu': 0, 'fwavacc': 0}
-
- # if __name__ == '__main__':
- # context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=1)
- # dataset, valset = setup_data.setup_loaders(args)
- # # data = trainset.__getitem__(0)
- # # print(len(trainset.__getitem__(0)))
- # # dataset, val, train_data = setup_data.setup_loaders(args)
- # # data = train_data.__getitem__(36)
- # # print(data[0])
- # # print(data[1])
- # # print(data[2])
- # # print(data[3])
- # i = 0
- # for data in dataset.create_dict_iterator():
- # #print(data["img"], data["canny"])
- # inputs = data["img"]
- # canny = data["canny"]
- # mask = data["mask"]
- # edgemap = data["edgemap"]
- # # print(inputs)
- # # print(canny)
- # # print(mask)
- # # edge = np.load("edgenew.npy")
- # # edge = mindspore.Tensor(edge)
- # # masknew = np.load("masknew.npy")
- # # masknew = mindspore.Tensor(masknew)
- # # sk = (edge == edgemap)
- # # print(edge == edgemap)
- # # print("下一个")
- # # print(mask == masknew)
- # model = GSCNN(19).set_train(True)
- # #model = GSCNN(19)
- # output1 = model(inputs, mask, edgemap, canny)
- # # output1, output2 = model(inputs, canny)
- # # output1 = output1.asnumpy()
- # # output2 = output2.asnumpy()
- # # mask = mask.asnumpy()
- # # edgemap = edgemap.asnumpy()
- # # np.save("output1.npy", output1)
- # # np.save("output2.npy", output2)
- # # np.save("mask.npy", mask)
- # # np.save("edgemap.npy", edgemap)
- # i = i + 1
- # print("-------------------------------开始----------------------------------------------")
- # print("----------------------------------输出loss-----------------------------------------")
- # print(i)
- # print(output1)
-
- # print("----------------------------------------------------------------------------")
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