def __init__(self):
gs = globalsetting()
self.cuda_id = [0, 0]
self.save_route = os.path.join(gs.ckpt_path, 'temp')
self.flow_feat_scales = 5 # total scales of features
self.flow_scale_times = 2 # output scale for flow map, 0 for non-scale, [0, flow_feat_scales-1]
self.flow_base_filters = 128
self.flow_max_filters = 128
self.flow_downsample = 'pool'
self.learing_rate = 1e-3
self.rand_offset = 5
self.totalvar = 2e-5 # 2e-3 for L2sq
self.ssim = 1e-5
self.photometric = 1e-3
self.smoothness_type = 'L1' # 'L2sq'
self.rot_inv_type = 'rot' # 'rot': rotation of 180 degree / 'neg': negative image
self.debug = False # if True, save training results every epoch
self.interval_test = 100
self.interval_save_ckpt = 100
self.total_epoch = int(5e4)
self.checkpoint_recovery = None
self.grid_size = 40 # int(np.ceil(768./5))
self.style_target = os.path.join(gs.data_path, 'HRF/manual1/12_h.tif')
self.dataset_path = os.path.join(
gs.data_path,
'Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients'
)
self.csv_path = os.path.join(gs.proj_path, 'FFAPCFIDP_affine.csv')
self.save_codes = ''
self.rand_offset_folder = None # not for training
self.mode = 'train' # 'eval' on specified data / 'test' on outside data
self.save_im = False # save image during 'train' or 'eval'
import numpy as np
import os, time
import scipy.io as sio
from RetSegReg import options, RetSegReg
from globalsetting import globalsetting
torch.manual_seed(2019)
torch.cuda.manual_seed_all(2019)
np.random.seed(2019)
os.environ['OMP_NUM_THREADS'] = '1'
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
gs = globalsetting()
opt = options()
opt.cuda_id = 0
opt.save_route = ''
opt.flow_feat_scales = 5
opt.flow_scale_times = 2
opt.flow_base_filters = 128
opt.flow_max_filters = 128
opt.flow_downsample = 'pool'
opt.rand_offset_folder = os.path.join(
gs.ckpt_path, 'FFAPCFIDP_random_offset') # not for training
opt.mode = 'eval'
opt.save_im = False