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config.py
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config.py
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import torch
import argparse
def get_config_tr(net_name):
# ---------------------------- #
# create ArgumentParser object
# ---------------------------- #
parser = argparse.ArgumentParser(description="PyTorch {} Training".format(net_name))
# ----------------------- #
# add network information
# ----------------------- #
parser.add_argument('--net', type=str, default='{}'.format(net_name),
choices=['DeeplabV3Plus', 'MFCNN', 'MSCFF', 'MUNet',
'TLNet', 'UNet', 'UNet-3', 'UNet-2', 'UNet-1',
'UNet-dilation', 'UNetS3', 'UNetS2', 'UNetS1'],
help='network name (default: ?)')
parser.add_argument('--in-channels', type=int, default=8,
help='number of input channels')
# DeeplabV3Plus
parser.add_argument('--backbone', type=str, default='resnet',
choices=['resnet', 'xception', 'drn', 'mobilenet'],
help='backbone name (default: resnet)')
parser.add_argument('--out-stride', type=int, default=16,
help='network output stride (default: 8)')
parser.add_argument('--sync-bn', type=bool, default=None,
help='whether to use sync bn (default: auto)')
parser.add_argument('--freeze-bn', type=bool, default=False,
help='whether to freeze bn parameters (default: False)')
parser.add_argument('--pretrained', action='store_true', default=False,
help='use pretrained model in backbone (resnet101)')
# MFCNN, RSNet
parser.add_argument('--dropout-p', type=float, default=0.2,
help='probability of dropout layer')
# UNet-dilation
parser.add_argument('--dilation', type=int, default=2,
help='the rate of dilation convolution in UNet')
# ----------------------- #
# cpu, cuda, gpu and seed
# ----------------------- #
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--num-proc', type=int, default=4,
metavar='N', help='metrics evaluation threads')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training') # compute unified device architecture
parser.add_argument('--gpu-ids', type=str, default='0,1',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--seed', type=int, default=3, metavar='S', # the same seed create the same random number
help='random seed (default: 1)') # to reduce the randomness of the results
# --------------------- #
# training hyper params
# --------------------- #
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 600)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--batch-size', type=int, default=24,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--test-batch-size', type=int, default=None,
metavar='N', help='input batch size for \
testing (default: auto)')
parser.add_argument('--use-balanced-weights', action='store_true', default=False,
help='whether to use balanced weights (default: False)')
# ---------------- #
# optimizer params
# ---------------- #
parser.add_argument('--loss-type', type=str, default='ce',
choices=['ce', 'focal', 'wb'],
help='loss func type (default: ce)')
parser.add_argument('--loss-interval', type=int, default=256,
help='print loss interval')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: auto)')
parser.add_argument('--lr-scheduler', type=str, default='step',
choices=['poly', 'step', 'cos'],
help='lr scheduler mode: (default: poly)')
parser.add_argument('--momentum', type=float, default=0.9,
metavar='M', help='momentum (default: 0.9)') # optimal method to speed up convergence
parser.add_argument('--weight-decay', type=float, default=5e-4,
metavar='M', help='w-decay (default: 5e-4)') # reduce over-fitting
parser.add_argument('--nesterov', action='store_true', default=False,
help='whether use nesterov (default: False)') # optimal method
# -------------- #
# checking point
# -------------- #
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
parser.add_argument('--checkname', type=str, default=None,
help='set the checkpoint name')
parser.add_argument('--save-epoch', action='store_true', default=True,
help='save checkpoint every epoch')
# finetuning pre-trained models
parser.add_argument('--ft', action='store_true', default=False,
help='finetuning on a different dataset') # finetuning on a different dataset
# ----------------- #
# evaluation option
# ----------------- #
parser.add_argument('--eval-interval', type=int, default=1,
help='evaluation interval (default: 1)')
parser.add_argument('--no-val', action='store_true', default=False,
help='skip validation during training')
# ---- #
# data
# ---- #
parser.add_argument('--dataset', type=str, default='RS',
choices=['RS'], help='dataset name (default: RS)')
parser.add_argument('--num-classes', type=int, default=2,
help='the number of classes (default:2)')
parser.add_argument('--h', type=int, default=256,
help='image height')
parser.add_argument('--w', type=int, default=256,
help='image width')
parser.add_argument('--train-root', type=str,
default='./example/train/Images',
help='image root of train set')
parser.add_argument('--train-list', type=str,
default='./example/train/train.txt',
help='image list of train set')
parser.add_argument('--val-root', type=str,
default='./example/val/Images',
help='image root of validation set')
parser.add_argument('--val-list', type=str,
default='./example/val/val.txt',
help='image list of validation set')
parser.add_argument('--mean', type=str,
default='0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051',
help='mean of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051)')
parser.add_argument('--std', type=str,
default='0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412',
help='standard deviation of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412)')
args = parser.parse_args() # analyze parameters
args.cuda = not args.no_cuda and torch.cuda.is_available() # CUDA can work
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1: # multiple gpu
args.sync_bn = True
else:
args.sync_bn = False
# default settings for epochs, batch_size, lr and data info
if args.epochs is None:
epoches = {
'rs': 100
}
args.epochs = epoches[args.dataset.lower()] # .lower() convert uppercase to lowercase
if args.batch_size is None:
args.batch_size = 4 * len(args.gpu_ids) # relate to the time and accuracy of gradient descent
if args.test_batch_size is None:
args.test_batch_size = args.batch_size
if args.lr is None:
lrs = {
'rs': 0.1,
}
args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size # learning rate
args.mean = [float(s) for s in args.mean.split(',')][0:args.in_channels]
args.std = [float(s) for s in args.std.split(',')][0:args.in_channels]
# args.in_channels = len(args.mean)
if args.checkname is None:
if args.net == 'DeeplabV3Plus':
args.checkname = 'deeplab-' + str(args.backbone) + \
'-outputstride' + str(args.out_stride) + \
'-in_channels' + str(args.in_channels) + \
'-lr' + str(args.lr) + \
'-batch' + str(args.batch_size) + \
'-seed' + str(args.seed)
elif args.net == 'UNet-dilation':
args.checkname = args.net + \
'-UNetD' + str(args.dilation) + \
'-in_channels' + str(args.in_channels) + \
'-lr' + str(args.lr) + \
'-batch' + str(args.batch_size) + \
'-seed' + str(args.seed)
else:
args.checkname = args.net + \
'-in_channels' + str(args.in_channels) + \
'-lr' + str(args.lr) + \
'-batch' + str(args.batch_size) + \
'-seed' + str(args.seed)
return args
def get_config_test(net_name):
# ---------------------------- #
# create ArgumentParser object
# ---------------------------- #
parser = argparse.ArgumentParser(description="PyTorch {} Inference".format(net_name))
# ----------------------- #
# add network information
# ----------------------- #
parser.add_argument('--net', type=str, default='{}'.format(net_name),
choices=['DeeplabV3Plus', 'MFCNN', 'MSCFF', 'MUNet',
'TLNet', 'UNet', 'UNet-3', 'UNet-2', 'UNet-1',
'UNet-dilation', 'UNetS3', 'UNetS2', 'UNetS1'],
help='network name (default: ?)')
parser.add_argument('--in-channels', type=int, default=8,
help='number of input channels')
# DeeplabV3Plus
parser.add_argument('--backbone', type=str, default='resnet',
choices=['resnet', 'xception', 'drn', 'mobilenet'],
help='backbone name (default: resnet)')
parser.add_argument('--out-stride', type=int, default=8,
help='network output stride (default: 8)')
parser.add_argument('--sync-bn', type=bool, default=None,
help='whether to use sync bn (default: auto)')
parser.add_argument('--freeze-bn', type=bool, default=False,
help='whether to freeze bn parameters (default: False)')
parser.add_argument('--pretrained', action='store_true', default=False,
help='use pretrained model in backbone (resnet101)')
# MFCNN, RSNet
parser.add_argument('--dropout-p', type=float, default=0.2,
help='probability of dropout layer')
# UNet-dilation
parser.add_argument('--dilation', type=int, default=1,
help='the rate of dilation convolution in UNet')
# ----------------------- #
# cpu, cuda, gpu and seed
# ----------------------- #
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--num-proc', type=int, default=2,
metavar='N', help='metrics evaluation threads')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training') # compute unified device architecture
parser.add_argument('--gpu-ids', type=str, default='0,1',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--seed', type=int, default=1, metavar='S', # the same seed create the same random number
help='random seed (default: 1)') # to reduce the randomness of the results
# --------------------- #
# inference hyper params
# --------------------- #
parser.add_argument('--batch-size', type=int, default=64,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--loss-type', type=str, default='ce',
choices=['ce', 'focal', 'wb'],
help='loss func type (default: ce)')
parser.add_argument('--use-balanced-weights', action='store_true', default=False,
help='whether to use balanced weights (default: False)')
# -------------- #
# checking point
# -------------- #
parser.add_argument('--load-paths', type=str, default=None,
help='put the model path, must be a comma-separated list')
# ---- #
# data
# ---- #
parser.add_argument('--dataset', type=str, default='RS',
choices=['RS'], help='dataset name (default: RS)')
parser.add_argument('--num-classes', type=int, default=2,
help='the number of classes (default:2)')
parser.add_argument('--h', type=int, default=256,
help='image height')
parser.add_argument('--w', type=int, default=256,
help='image width')
parser.add_argument('--test-root', type=str,
default='./example/test/Images',
help='image root of test set')
parser.add_argument('--test-list', type=str,
default='./example/test/test.txt',
help='image list of test set')
parser.add_argument('--no-gt', action='store_true', default=False,
help='no available ground truth')
parser.add_argument('--mean', type=str,
default='0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051',
help='mean of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051)')
parser.add_argument('--std', type=str,
default='0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412',
help='standard deviation of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412)')
# ------ #
# output
# ------ #
parser.add_argument('--out-path', type=str, default=None,
help='result output path')
parser.add_argument('--save-img', action='store_true', default=True,
help='save predicted image. If no_gt is true, \
the option fails, i.e. always true')
args = parser.parse_args() # analyze parameters
args.cuda = not args.no_cuda and torch.cuda.is_available() # CUDA can work
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1: # multiple gpu
args.sync_bn = True
else:
args.sync_bn = False
if args.load_paths:
args.load_paths = args.load_paths.split(sep=',')
# default settings for batch_size and data info
if args.batch_size is None:
args.batch_size = 4 * len(args.gpu_ids) # relate to the time and accuracy of gradient descent
args.mean = [float(s) for s in args.mean.split(',')][0:args.in_channels]
args.std = [float(s) for s in args.std.split(',')][0:args.in_channels]
# args.in_channels = len(args.mean)
return args
def get_config_erf(net_name):
# ---------------------------- #
# create ArgumentParser object
# ---------------------------- #
parser = argparse.ArgumentParser(description="PyTorch {} (Calculate ERF)".format(net_name))
# ----------------------- #
# add network information
# ----------------------- #
parser.add_argument('--net', type=str, default='{}'.format(net_name),
choices=['DeeplabV3Plus', 'MFCNN', 'MSCFF', 'MUNet',
'TLNet', 'UNet', 'UNet-3', 'UNet-2', 'UNet-1',
'UNet-dilation', 'UNetS3', 'UNetS2', 'UNetS1'],
help='network name (default: ?)')
parser.add_argument('--in-channels', type=int, default=8,
help='number of input channels')
# DeeplabV3Plus
parser.add_argument('--backbone', type=str, default='resnet',
choices=['resnet', 'xception', 'drn', 'mobilenet'],
help='backbone name (default: resnet)')
parser.add_argument('--out-stride', type=int, default=8,
help='network output stride (default: 8)')
parser.add_argument('--sync-bn', type=bool, default=None,
help='whether to use sync bn (default: auto)')
parser.add_argument('--freeze-bn', type=bool, default=False,
help='whether to freeze bn parameters (default: False)')
parser.add_argument('--pretrained', action='store_true', default=False,
help='use pretrained model in backbone (resnet101)')
# MFCNN, RSNet
parser.add_argument('--dropout-p', type=float, default=0.2,
help='probability of dropout layer')
# UNet-dilation
parser.add_argument('--dilation', type=int, default=1,
help='the rate of dilation convolution in UNet')
# ----------------------- #
# cpu, cuda, gpu and seed
# ----------------------- #
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training') # compute unified device architecture
parser.add_argument('--gpu-ids', type=str, default='0,1',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--seed', type=int, default=1, metavar='S', # the same seed create the same random number
help='random seed (default: 1)') # to reduce the randomness of the results
# --------------------- #
# erf hyper params
# --------------------- #
parser.add_argument('--batch-size', type=int, default=32,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--max-pro', action='store_true', default=False,
help='whether to use the max class probability')
# -------------- #
# checking point
# -------------- #
parser.add_argument('--load-paths', type=str, default=None,
help='put the model path, must be a comma-separated list')
# ---- #
# data
# ---- #
parser.add_argument('--dataset', type=str, default='RS',
choices=['RS'], help='dataset name (default: RS)')
parser.add_argument('--num-classes', type=int, default=2,
help='the number of classes (default:2)')
parser.add_argument('--img-root', type=str,
default='./example/test/Images',
help='image root of train set')
parser.add_argument('--pixel-list', type=str,
default='./erf/selected_pixel_all.json',
help='image list of train set')
parser.add_argument('--mean', type=str,
default='0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051',
help='mean of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.432, 0.398, 0.411, 0.479, 0.240, 0.192, 0.037, 268.051)')
parser.add_argument('--std', type=str,
default='0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412',
help='standard deviation of each channel (used in data normalization), \
must be a comma-separated list of floats only \
(default: 0.313, 0.295, 0.311, 0.285, 0.162, 0.132, 0.079, 25.412)')
# ------ #
# output
# ------ #
parser.add_argument('--out-path', type=str, default=None,
help='result output path')
parser.add_argument('--save-img', action='store_true', default=False,
help='save predicted image. If no_gt is true, \
the option fails, i.e. always true')
args = parser.parse_args() # analyze parameters
args.cuda = not args.no_cuda and torch.cuda.is_available() # CUDA can work
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1: # multiple gpu
args.sync_bn = True
else:
args.sync_bn = False
if args.load_paths:
args.load_paths = args.load_paths.split(sep=',')
# default settings for batch_size and data info
if args.batch_size is None:
args.batch_size = 4 * len(args.gpu_ids) # relate to the time and accuracy of gradient descent
args.mean = [float(s) for s in args.mean.split(',')][0:args.in_channels]
args.std = [float(s) for s in args.std.split(',')][0:args.in_channels]
# args.in_channels = len(args.mean)
return args