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task.py
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task.py
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# -*- coding: utf-8 -*-
"""
@auth Jason Zhang <gsangeryeee@gmail.com>
@brief:
"""
import argparse
import random
import os
from os.path import join
import numpy as np
import torch
import torch.backends.cudnn
import config
from utils import unet_utils
# -------------------------------main-------------------------------------
def main():
parser = argparse.ArgumentParser(description='Kaggle TGS Competition')
parser.add_argument('-o', '--output_dir', default=None, help='output dir')
parser.add_argument('-b', '--batch_size', type=int, default=1, metavar='N',
help='input batch size for training')
parser.add_argument('--epochs', type=int, default=400, help='number of epochs to train')
parser.add_argument('-lr', '--lr', type=float, default=0.01, metavar='LR',
help='learning rate')
parser.add_argument('-reset_lr', '--reset_lr', action='store_true',
help='should reset lr cycles? If not count epochs from 0')
parser.add_argument('-opt', '--optimizer', default='sgd', choices=['sgd', 'adam', 'rmsprop'],
help='optimizer type')
parser.add_argument('--decay_step', type=float, default=100, metavar='EPOCHS',
help='learning rate decay step')
parser.add_argument('--decay_gamma', type=float, default=0.5,
help='learning rate decay coeeficient')
parser.add_argument('--cyclic_lr', type=int, default=None,
help='(int)Len of the cycle. If not None use cyclic lr with cycle_len) specified')
parser.add_argument('--cyclic_duration', type=float, default=1.0,
help='multiplier of the duration of segments in the cycle')
parser.add_argument('--weight_decay', type=float, default=0.0005,
help='L2 regularizer weight')
parser.add_argument('--seed', type=int, default=1993, help='random seed')
parser.add_argument('--log_aggr', type=int, default=None, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('-gacc', '--num_grad_acc_steps', type=int, default=1, metavar='N',
help='number of vatches to accumulate gradients')
parser.add_argument('-imsize', '--image_size', type=int, default=1024, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('-f', '--fold', type=int, default=0, metavar='N',
help='fold_id')
parser.add_argument('-nf', '--n_folds', type=int, default=0, metavar='N',
help='number of folds')
parser.add_argument('-fv', '--folds_version', type=int, default=1, choices=[1, 2],
help='version of folds (1) - random, (2) - stratified on mask area')
parser.add_argument('-group', '--group', type=parse_group, default='all',
help='group id')
parser.add_argument('-no_cudnn', '--no_cudnn', action='store_true',
help='dont use cudnn?')
parser.add_argument('-aug', '--aug', type=int, default=None,
help='use augmentations?')
parser.add_argument('-no_hq', '--no_hq', action='store_true',
help='do not use hq images?')
parser.add_argument('-dbg', '--dbg', action='store_true',
help='is debug?')
parser.add_argument('-is_log_dice', '--is_log_dice', action='store_true',
help='use -log(dice) in loss?')
parser.add_argument('-no_weight_loss', '--no_weight_loss', action='store_true',
help='do not weight border in loss?')
parser.add_argument('-suf', '--exp_suffix', default='', help='experiment suffix')
parser.add_argument('-net', '--network', default='Unet')
args = parser.parse_args()
print("aug:", args.aug)
# assert args.aug, 'Careful! No aug specified!'
if args.log_aggr is None:
args.log_aggr = 1
print('log_aggr', args.log_aggr)
# Set random seed
random.seed(42)
torch.manual_seed(args.seed)
print('CudNN:', torch.backends.cudnn.version())
print('Run on {} GPUs'.format(torch.cuda.device_count()))
torch.backends.cudnn.benchmark = not args.no_cudnn # Enable use of CudNN
experiment = "{}_s{}_im{}_gacc{}{}{}{}_{}fold{}.{}"\
.format(args.network, args.seed, args.image_size, args.num_grad_acc_steps,
'_aug{]'.format(args.aug) if args.aug is not None else '',
'_nohq' if args.no_hq else '',
'_g{}'.format(args.group) if args.group != 'all' else '',
'v2' if args.folds_version == 2 else '',
args.fold, args.n_folds)
if args.output_dir is None:
ckpt_dir = join(config.MODELS_DIR, experiment + args.exp_suffix)
if os.path.exists(join(ckpt_dir, 'checkpoint.pth.tar')):
args.output_dir = ckpt_dir
if args.output_dir is not None and os.path.exists(args.output_dir):
ckpt_path = join(args.output_dir, 'checkpoint.pth.tar')
if not os.path.isfile(ckpt_path):
print("=> no checkpoint found at '{}'\nUsing model_best.pth.tar".format(ckpt_path))
ckpt_path = join(args.output_dir, 'model_best.pth.tar')
if os.path.isfile(ckpt_path):
print("=> loading checkpoint '{}'".format(ckpt_path))
checkpoint = torch.load(ckpt_path)
if 'filters_sizes' in checkpoint:
filters_sizes = checkpoint['filters_sizes']
print("=> loaded checkpoint '{}' (epoch {})".format(ckpt_path, checkpoint['epoch']))
else:
raise IOError("=> no checkpoint found at '{}'".format(ckpt_path))
else:
checkpoint = None
if args.network == 'UNet':
filters_sizes = np.asarray([32, 64, 64, 128, 128, 256])
else:
raise ValueError('Unknown Net: {}'.format(args.network))
if args.network in ['vgg11v1','vgg11v2']:
pass
elif args.network in ['vgg11av1','vgg11va2']:
pass
else:
unet_class = getattr(unet_utils, args.network)
model = torch.nn.DataParallel(
unet_class(is_deconv=False, filters=filters_sizes)).cuda()
print(' + Number of params: {}'.format(sum([p.data.nelment() for p in model.parameters()])))
rescale_size = (args.image_size, args.image_size)
# Load train data
if __name__ == '__main__':
main()