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run_sample.py
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run_sample.py
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import argparse
import os
import numpy as np
import os.path as osp
from misc import pyutils
import random
import torch
# def seed_torch(seed=1):
# random.seed(seed)
# os.environ['PYTHONHASHSEED'] = str(seed)
# np.random.seed(seed)
# torch.manual_seed(seed)
# torch.cuda.manual_seed(seed)
# torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
# torch.backends.cudnn.benchmark = False
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.enabled = False
if __name__ == '__main__':
# seed_torch(seed=1)
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser()
# Environment
# parser.add_argument("--num_workers", default=os.cpu_count()//2, type=int)
parser.add_argument("--num_workers", default=12, type=int)
parser.add_argument("--voc12_root", default='/data1/xjheng/dataset/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_aug.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)
# Class Activation Map
parser.add_argument("--cam_network", default="net.resnet50_cam", type=str)
parser.add_argument("--feature_dim", default=2048, type=int)
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")
parser.add_argument("--num_cores_eval", default=8, type=int)
# CLIMS
parser.add_argument("--clims_network", default="net.resnet50_clims", type=str)
parser.add_argument("--clims_num_epoches", default=15, type=int)
parser.add_argument("--clims_learning_rate", default=0.00025, type=float)
parser.add_argument('--hyper', default='10,24,1,0.2', type=str)
parser.add_argument('--clip', default='ViT-B/32', type=str)
# Mining Inter-pixel Relations
parser.add_argument("--conf_fg_thres", default=0.3, type=float)
parser.add_argument("--conf_bg_thres", default=0.1, 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("--sem_seg_bg_thres", default=0.2)
# Output Path
parser.add_argument("--work_space", default="result_default5", type=str) # set your path
parser.add_argument("--log_name", default="sample_train_eval", type=str)
parser.add_argument("--cam_weights_name", default="res50_cam.pth", type=str)
parser.add_argument("--irn_weights_name", default="res50_irn.pth", type=str)
parser.add_argument("--cam_out_dir", default="cam_mask", type=str)
parser.add_argument("--ir_label_out_dir", default="ir_label", type=str)
parser.add_argument("--sem_seg_out_dir", default="sem_seg", type=str)
parser.add_argument("--ins_seg_out_dir", default="ins_seg", type=str)
parser.add_argument("--clims_weights_name", default="res50_clims", type=str)
# Step
parser.add_argument("--train_cam_pass", type=str2bool, default=False)
parser.add_argument("--train_clims_pass", type=str2bool, default=False)
parser.add_argument("--make_cam_pass", type=str2bool, default=False)
parser.add_argument("--make_clims_pass", type=str2bool, default=False)
parser.add_argument("--eval_cam_pass", type=str2bool, default=False)
parser.add_argument("--cam_to_ir_label_pass", type=str2bool, default=False)
parser.add_argument("--train_irn_pass", type=str2bool, default=False)
parser.add_argument("--make_ins_seg_pass", type=str2bool, default=False)
parser.add_argument("--eval_ins_seg_pass", type=str2bool, default=False)
parser.add_argument("--make_sem_seg_pass", type=str2bool, default=False)
parser.add_argument("--eval_sem_seg_pass", type=str2bool, default=False)
args = parser.parse_args()
args.log_name = osp.join(args.work_space,args.log_name)
args.cam_weights_name = osp.join(args.work_space,args.cam_weights_name)
args.irn_weights_name = osp.join(args.work_space,args.irn_weights_name)
args.cam_out_dir = osp.join(args.work_space,args.cam_out_dir)
args.ir_label_out_dir = osp.join(args.work_space,args.ir_label_out_dir)
args.sem_seg_out_dir = osp.join(args.work_space,args.sem_seg_out_dir)
args.ins_seg_out_dir = osp.join(args.work_space,args.ins_seg_out_dir)
args.clims_weights_name = osp.join(args.work_space, args.clims_weights_name)
os.makedirs(args.work_space, 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.train_clims_pass is True:
import step.train_clims
timer = pyutils.Timer('step.train_clims:')
step.train_clims.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.make_clims_pass is True:
import step.make_clims
timer = pyutils.Timer('step.make_clims:')
step.make_clims.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_sem_seg_pass is True:
import step.make_sem_seg_labels
args.sem_seg_bg_thres = float(args.sem_seg_bg_thres)
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)