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utils.py
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##load data
from torch.utils.data import Dataset
import numpy as np
import os
import torch
import yaml
import random
import configparser
# config setting
def is_int(val_str):
start_digit = 0
if(val_str[0] =='-'):
start_digit = 1
flag = True
for i in range(start_digit, len(val_str)):
if(str(val_str[i]) < '0' or str(val_str[i]) > '9'):
flag = False
break
return flag
def is_float(val_str):
flag = False
if('.' in val_str and len(val_str.split('.'))==2 and not('./' in val_str)):
if(is_int(val_str.split('.')[0]) and is_int(val_str.split('.')[1])):
flag = True
else:
flag = False
elif('e' in val_str and val_str[0] != 'e' and len(val_str.split('e'))==2):
if(is_int(val_str.split('e')[0]) and is_int(val_str.split('e')[1])):
flag = True
else:
flag = False
else:
flag = False
return flag
def is_bool(var_str):
if( var_str.lower() =='true' or var_str.lower() == 'false'):
return True
else:
return False
def parse_bool(var_str):
if(var_str.lower() =='true'):
return True
else:
return False
def is_list(val_str):
if(val_str[0] == '[' and val_str[-1] == ']'):
return True
else:
return False
def parse_list(val_str):
sub_str = val_str[1:-1]
splits = sub_str.split(',')
output = []
for item in splits:
item = item.strip()
if(is_int(item)):
output.append(int(item))
elif(is_float(item)):
output.append(float(item))
elif(is_bool(item)):
output.append(parse_bool(item))
elif(item.lower() == 'none'):
output.append(None)
else:
output.append(item)
return output
def parse_value_from_string(val_str):
# val_str = val_str.encode('ascii','ignore')
if(is_int(val_str)):
val = int(val_str)
elif(is_float(val_str)):
val = float(val_str)
elif(is_list(val_str)):
val = parse_list(val_str)
elif(is_bool(val_str)):
val = parse_bool(val_str)
elif(val_str.lower() == 'none'):
val = None
else:
val = val_str
return val
def parse_config(filename):
config = configparser.ConfigParser()
config.read(filename)
output = {}
for section in config.sections():
output[section] = {}
for key in config[section]:
val_str = str(config[section][key])
if(len(val_str)>0):
val = parse_value_from_string(val_str)
output[section][key] = val
else:
val = None
print(section, key, val_str, val)
return output
def load_npz(path):
img = np.load(path)['arr_0']
gt = np.load(path)['arr_1']
return img, gt
def get_config(config):
with open(config, 'r') as stream:
return yaml.load(stream,Loader=yaml.FullLoader)
def set_random(seed_id=1234):
np.random.seed(seed_id)
torch.manual_seed(seed_id) #for cpu
torch.cuda.manual_seed_all(seed_id) #for GPU
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
# config setting
def is_int(val_str):
start_digit = 0
if(val_str[0] =='-'):
start_digit = 1
flag = True
for i in range(start_digit, len(val_str)):
if(str(val_str[i]) < '0' or str(val_str[i]) > '9'):
flag = False
break
return flag
def is_float(val_str):
flag = False
if('.' in val_str and len(val_str.split('.'))==2 and not('./' in val_str)):
if(is_int(val_str.split('.')[0]) and is_int(val_str.split('.')[1])):
flag = True
else:
flag = False
elif('e' in val_str and val_str[0] != 'e' and len(val_str.split('e'))==2):
if(is_int(val_str.split('e')[0]) and is_int(val_str.split('e')[1])):
flag = True
else:
flag = False
else:
flag = False
return flag
def is_bool(var_str):
if( var_str.lower() =='true' or var_str.lower() == 'false'):
return True
else:
return False
def parse_bool(var_str):
if(var_str.lower() =='true'):
return True
else:
return False
def is_list(val_str):
if(val_str[0] == '[' and val_str[-1] == ']'):
return True
else:
return False
def parse_list(val_str):
sub_str = val_str[1:-1]
splits = sub_str.split(',')
output = []
for item in splits:
item = item.strip()
if(is_int(item)):
output.append(int(item))
elif(is_float(item)):
output.append(float(item))
elif(is_bool(item)):
output.append(parse_bool(item))
elif(item.lower() == 'none'):
output.append(None)
else:
output.append(item)
return output
def parse_value_from_string(val_str):
# val_str = val_str.encode('ascii','ignore')
if(is_int(val_str)):
val = int(val_str)
elif(is_float(val_str)):
val = float(val_str)
elif(is_list(val_str)):
val = parse_list(val_str)
elif(is_bool(val_str)):
val = parse_bool(val_str)
elif(val_str.lower() == 'none'):
val = None
else:
val = val_str
return val
def parse_config(filename):
config = configparser.ConfigParser()
config.read(filename)
output = {}
for section in config.sections():
output[section] = {}
for key in config[section]:
val_str = str(config[section][key])
if(len(val_str)>0):
val = parse_value_from_string(val_str)
output[section][key] = val
else:
val = None
print(section, key, val_str, val)
return output
class UnpairedDataset(Dataset):
#get unpaired dataset, such as MR-CT dataset
def __init__(self,A_path,B_path):
listA = os.listdir(A_path)
listB = os.listdir(B_path)
self.listA = [os.path.join(A_path,k) for k in listA]
self.listB = [os.path.join(B_path,k) for k in listB]
self.Asize = len(self.listA)
self.Bsize = len(self.listB)
self.dataset_size = max(self.Asize,self.Bsize)
def __getitem__(self,index):
if self.Asize == self.dataset_size:
A,A_gt = load_npz(self.listA[index])
B,B_gt = load_npz(self.listB[random.randint(0, self.Bsize - 1)])
else :
B,B_gt = load_npz(self.listB[index])
A,A_gt = load_npz(self.listA[random.randint(0, self.Asize - 1)])
A = torch.from_numpy(A.copy()).unsqueeze(0).float()
A_gt = torch.from_numpy(A_gt.copy()).unsqueeze(0).float()
B = torch.from_numpy(B.copy()).unsqueeze(0).float()
B_gt = torch.from_numpy(B_gt.copy()).unsqueeze(0).float()
return A,A_gt,B,B_gt
def __len__(self):
return self.dataset_size
class SingleDataset(Dataset):
def __init__(self,test_path):
test_list = os.listdir(test_path)
self.test = [os.path.join(test_path,k) for k in test_list]
def __getitem__(self,index):
img,gt = load_npz(self.test[index])
img = torch.from_numpy(img.copy()).unsqueeze(0).float()
gt = torch.from_numpy(gt.copy()).unsqueeze(0).float()
return img, gt
def __len__(self):
return len(self.test)