About every time the results are unstable

[Elaineok]

在run_sample.py中，加入seed，具体代码如下： import argparse import os os.environ["CUDA_VISIBLE_DEVICES"] = "2,3" from misc import pyutils import torch import numpy as np import random

def setup_seed(seed): print("random seed is set to", seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True

if name == 'main':

parser = argparse.ArgumentParser()

# Environment
parser.add_argument("--num_workers", default=os.cpu_count()//2, type=int)
parser.add_argument("--voc12_root", default="/disk4/xxx/2022-02-08-wang-peak/irn-master/VOC2012", type=str,
                    help="Path to VOC 2012 Devkit, must contain ./JPEGImages as subdirectory.")

# Dataset
parser.add_argument("--train_list", default="voc12/train_aug.txt", type=str)
parser.add_argument("--val_list", default="voc12/val.txt", type=str)
parser.add_argument("--infer_list", default="voc12/train.txt", type=str,
                    help="voc12/train_aug.txt to train a fully supervised model, "
                         "voc12/train.txt or voc12/val.txt to quickly check the quality of the labels.")
parser.add_argument("--chainer_eval_set", default="train", type=str)
parser.add_argument("--seed", default=15, type=int)

# Class Activation Map
parser.add_argument("--cam_network", default="net.resnet50_cam", type=str)
parser.add_argument("--cam_crop_size", default=512, type=int)
parser.add_argument("--cam_batch_size", default=16, type=int)
parser.add_argument("--cam_num_epoches", default=5, type=int)
parser.add_argument("--cam_learning_rate", default=0.1, type=float)
parser.add_argument("--cam_weight_decay", default=1e-4, type=float)
parser.add_argument("--cam_eval_thres", default=0.15, type=float)
parser.add_argument("--cam_scales", default=(1.0, 0.5, 1.5, 2.0),
                    help="Multi-scale inferences")

# Mining Inter-pixel Relations
parser.add_argument("--conf_fg_thres", default=0.30, type=float)
parser.add_argument("--conf_bg_thres", default=0.05, type=float)

# Inter-pixel Relation Network (IRNet)
parser.add_argument("--irn_network", default="net.resnet50_irn", type=str)
parser.add_argument("--irn_crop_size", default=512, type=int)
parser.add_argument("--irn_batch_size", default=32, type=int)
parser.add_argument("--irn_num_epoches", default=3, type=int)
parser.add_argument("--irn_learning_rate", default=0.1, type=float)
parser.add_argument("--irn_weight_decay", default=1e-4, type=float)

# Random Walk Params
parser.add_argument("--beta", default=10)
parser.add_argument("--exp_times", default=8,
                    help="Hyper-parameter that controls the number of random walk iterations,"
                         "The random walk is performed 2^{exp_times}.")
parser.add_argument("--ins_seg_bg_thres", default=0.25)
parser.add_argument("--sem_seg_bg_thres", default=0.25)

# Output Path
parser.add_argument("--log_name", default="sample_train_eval", type=str)
parser.add_argument("--cam_weights_name", default="sess/res50_cam.pth", type=str)
parser.add_argument("--irn_weights_name", default="sess/res50_irn.pth", type=str)
parser.add_argument("--cam_out_dir", default="result/cam", type=str)
parser.add_argument("--ir_label_out_dir", default="result/ir_label", type=str)
parser.add_argument("--sem_seg_out_dir", default="result/sem_seg", type=str)
parser.add_argument("--ins_seg_out_dir", default="result/ins_seg", type=str)

# Step
parser.add_argument("--train_cam_pass", default=True)
parser.add_argument("--make_cam_pass", default=True)
parser.add_argument("--eval_cam_pass", default=True)
parser.add_argument("--cam_to_ir_label_pass", default=False)
parser.add_argument("--train_irn_pass", default=False)
parser.add_argument("--make_ins_seg_pass", default=False)
parser.add_argument("--eval_ins_seg_pass", default=False)
parser.add_argument("--make_sem_seg_pass", default=False)
parser.add_argument("--eval_sem_seg_pass", default=False)

args = parser.parse_args()
setup_seed(args.seed)
os.makedirs("sess", exist_ok=True)
os.makedirs(args.cam_out_dir, exist_ok=True)
os.makedirs(args.ir_label_out_dir, exist_ok=True)
os.makedirs(args.sem_seg_out_dir, exist_ok=True)
os.makedirs(args.ins_seg_out_dir, exist_ok=True)

pyutils.Logger(args.log_name + '.log')
print(vars(args))

if args.train_cam_pass is True:
    import step.train_cam

    timer = pyutils.Timer('step.train_cam:')
    step.train_cam.run(args)

if args.make_cam_pass is True:
    import step.make_cam

    timer = pyutils.Timer('step.make_cam:')
    step.make_cam.run(args)

if args.eval_cam_pass is True:
    import step.eval_cam

    timer = pyutils.Timer('step.eval_cam:')
    step.eval_cam.run(args)

if args.cam_to_ir_label_pass is True:
    import step.cam_to_ir_label

    timer = pyutils.Timer('step.cam_to_ir_label:')
    step.cam_to_ir_label.run(args)

if args.train_irn_pass is True:
    import step.train_irn

    timer = pyutils.Timer('step.train_irn:')
    step.train_irn.run(args)

if args.make_ins_seg_pass is True:
    import step.make_ins_seg_labels

    timer = pyutils.Timer('step.make_ins_seg_labels:')
    step.make_ins_seg_labels.run(args)

if args.eval_ins_seg_pass is True:
    import step.eval_ins_seg

    timer = pyutils.Timer('step.eval_ins_seg:')
    step.eval_ins_seg.run(args)

if args.make_sem_seg_pass is True:
    import step.make_sem_seg_labels

    timer = pyutils.Timer('step.make_sem_seg_labels:')
    step.make_sem_seg_labels.run(args)

if args.eval_sem_seg_pass is True:
    import step.eval_sem_seg

    timer = pyutils.Timer('step.eval_sem_seg:')
    step.eval_sem_seg.run(args)