├── README.md
├── dataset.py
├── get_the_metric.py
├── train_2d3d.py
├── labelflattenedp.csv
└── LICENSE


/README.md:
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 1 | # Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3DCoherent Layer Segmentation of Retina OCT Images
 2 | 
 3 | Code for paper "Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3DCoherent Layer Segmentation of Retina OCT Images."
 4 | 
 5 | The codes are implemented in PyTorch 1.4 and trained on NVIDIA 2080Ti GPUs.
 6 | 
 7 | Parts of codes are borrowed from https://github.com/Hui-Xie/DeepLearningSeg.
 8 | 
 9 | The preprocess code: https://github.com/YufanHe/oct_preprocess
10 | 
11 | ## datasets
12 | 
13 | 1. public A2A OCT dataset: https://people.duke.edu/~sf59/RPEDC_Ophth_2013_dataset.htm
14 | 2. public JHU OCT dataset: http://iacl.ece.jhu.edu/index.php/Resources
15 | 
16 | ## Contact
17 | 
18 | If you have any questions, please do not hesitate to contact liuhong@stu.xmu.edu.cn
19 | 


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/dataset.py:
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  1 | import os
  2 | import csv
  3 | import glob
  4 | import torch
  5 | import torch.nn.functional as F
  6 | import random
  7 | import torch.utils.data
  8 | import scipy.io as scio
  9 | import warnings
 10 | warnings.filterwarnings('ignore')
 11 | from numpy import *
 12 | import numpy as np
 13 | import scipy.ndimage
 14 | 
 15 | from sklearn import preprocessing
 16 | 
 17 | def getLayerLabels(surfaceLabels, height):
 18 | 
 19 |     if surfaceLabels is None:
 20 |         return None
 21 | 
 22 |     H = height
 23 |     device = surfaceLabels.device
 24 |     N, W, D = surfaceLabels.shape  # N is the number of surface
 25 |     layerLabels = torch.zeros((H, W, D), dtype=torch.long, device=device)
 26 |     surfaceLabels = (surfaceLabels + 0.5).long()  # let surface height match grid
 27 |     surfaceCodes = torch.tensor(range(1, N + 1), device=device).unsqueeze(dim=-1).unsqueeze(dim=-1).expand_as(surfaceLabels)
 28 |     layerLabels.scatter_(0, surfaceLabels, surfaceCodes)  #surface 0 in its exact location marks as 1, surface N-1 mark as N.
 29 | 
 30 |     for i in range(1,H):
 31 |         layerLabels[i,:, :] = torch.where(0 == layerLabels[i,:, :], layerLabels[i-1,:, :], layerLabels[i,:, :])
 32 | 
 33 |     return layerLabels
 34 | 
 35 |  
 36 | class sd_oct_flattenp_align(torch.utils.data.Dataset):
 37 |     """ 
 38 |         dataset for a2a_sd_oct.
 39 |         the train and valid sets are splited by the label.csv.
 40 |     """
 41 |     
 42 |     def __init__(self, label_dir, data_name = "a2a_oct", valid_fold = 0, usage = "train", transform=None, step=40):
 43 |         super(sd_oct_flattenp_align, self).__init__()
 44 | 
 45 |         self.data_path = []
 46 |         self.data = []
 47 |         self.seg = []
 48 |         self.name = []
 49 |         self.step = step
 50 |         self.usage = usage
 51 |         self.valid_fold = valid_fold
 52 | 
 53 |         # load file path and split train set and valid set.
 54 |         label_file = "labelflattenedp.csv"
 55 |         label_file_path = os.path.join(label_dir, label_file)
 56 |         label = open(label_file_path, "r")
 57 |         csv_reader = csv.reader(label)
 58 |     
 59 |         for path, fold in csv_reader:
 60 |             path = path.replace("afterfp2","flattened")
 61 |             if int(fold) == valid_fold and self.usage == "valid":
 62 |                 self.data_path.append(path)
 63 |             elif int(fold) != valid_fold and self.usage == "train":
 64 |                 self.data_path.append(path)
 65 |         print("load " + self.usage + " " + str(len(self.data_path)))
 66 |         
 67 |         # load data and seg
 68 |         for l in self.data_path:
 69 |             try:
 70 |                 cur_data = scio.loadmat(l)
 71 |             except:
 72 |                 print(l)
 73 |                 continue
 74 |                 
 75 |             cur_img = cur_data["data"][0,0]["flat_vol"][:, 300:700, 18:58]
 76 |             cur_seg = cur_data["data"][0,0]["bds"][300:700, 18:58, :]
 77 |             
 78 |             if cur_img.shape[0] != 320:
 79 |                 print(cur_img.shape[0])
 80 |                 continue
 81 |             if sum(np.isnan(cur_seg)) > 0:
 82 |                 continue
 83 |             if cur_seg.min() < 5 or cur_seg.max() > 315:
 84 |                 continue
 85 | 
 86 |             # normalization
 87 |             mu = np.mean(cur_img, axis=0)
 88 |             sigma = np.std(cur_img, axis=0)
 89 |             cur_img = (cur_img - mu) / sigma
 90 |             step = self.step
 91 | 
 92 |             for i in range((cur_img.shape[1] - 48) // step):
 93 |                 cur_idx = i*step
 94 |                 self.name.append(l.split("/")[-1]+str(i).zfill(3)+str(cur_idx).zfill(3))
 95 |                 cur_idx = i*step
 96 |                 self.data.append(cur_img.astype(np.float32)[:,cur_idx : cur_idx + 48,:])
 97 |                 self.seg.append(np.round(cur_seg).astype(np.float32)[cur_idx : cur_idx + 48,:,:])
 98 |             cur_idx = 400 - 48
 99 |             self.name.append(l.split("/")[-1]+str(i).zfill(3)+str(cur_idx).zfill(3))
100 |             self.data.append(cur_img.astype(np.float32)[:,cur_idx : cur_idx + 48,:])
101 |             self.seg.append(np.round(cur_seg).astype(np.float32)[cur_idx : cur_idx + 48,:,:])
102 |                 
103 |         print("load " + self.usage + " " + str(len(self.data)))
104 | 
105 |     def __getitem__(self, index):
106 |         patch = self.data[index]
107 |         name = self.name[index]
108 |         seg = self.seg[index]
109 |         
110 |         layer_gt = getLayerLabels(torch.tensor(seg).permute(2,0,1), 320)
111 |         return torch.tensor(patch).unsqueeze(0), torch.tensor(seg).permute(2,0,1), layer_gt, name
112 | 
113 |     def __len__(self):
114 |         return len(self.data)
115 |         
116 | 
117 | 
118 |         
119 | 
120 | 
121 | 


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/get_the_metric.py:
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  1 | import os
  2 | import argparse
  3 | import torch
  4 | import cv2
  5 | import sys 
  6 | import csv
  7 | import numpy as np
  8 | from numpy import *
  9 | import torchvision.transforms as transforms
 10 | import torch.nn as nn
 11 | from torch.utils.data.sampler import  WeightedRandomSampler
 12 | from model2d_3d import *
 13 | from sklearn.metrics import confusion_matrix,accuracy_score,f1_score,roc_auc_score,recall_score,precision_score
 14 | import scipy.ndimage
 15 | import scipy.io as io
 16 | 
 17 | from dataset import *
 18 | from losses import *
 19 | 
 20 | # for visualization
 21 | from torch.utils.tensorboard import SummaryWriter  
 22 | 
 23 | # Set arguments and hyper-parameters.
 24 | parser = argparse.ArgumentParser(description='baseline')
 25 | parser.add_argument('--data_dir', default='/data3/layer_segmentation/a2a_oct', type=str, help='Directory to load PCam data.')
 26 | parser.add_argument('--label_dir', default='./label', type=str, help='Directory to load label files.')
 27 | parser.add_argument('--ckpt_dir', default='./checkpoint', type=str, help='Directory to save checkpoint.')
 28 | 
 29 | parser.add_argument('--gpu', default='0', type=str, help='GPU Devices to use.')
 30 | parser.add_argument('--ck_name', default="2d3d_5_inter20_123_noncc_047_fold0_1.008%.t7", type=str, help='GPU Devices to use.')
 31 | parser.add_argument('--batch_size', default=7, type=int, help='Batch size.')
 32 | 
 33 | parser.add_argument('--lr', default=3e-4, type=float, help='Starting learning rate.')
 34 | parser.add_argument('--lr_decay', default=0.9, type=float, help='Learning rate decay.')
 35 | parser.add_argument('--lr_decay_step', default=1, type=int, help='Learning rate decay step.')
 36 | parser.add_argument('--weight_decay', default=1e-4, type=float, help='L2 penalty for regularization.')
 37 | parser.add_argument('--weight_g', default=0.3, type=float, help='L2 penalty for regularization.')
 38 | 
 39 | 
 40 | parser.add_argument('--start_epoch', default=1, type=int, help='Starting epoch.')
 41 | parser.add_argument('--epochs', default=20, type=int, help='Number of training epochs.')
 42 | parser.add_argument('--resume', '-r', action='store_true', help='Resume from checkpoint or not.')
 43 | parser.add_argument('--store_last', action='store_true', help='store the last model.')
 44 | parser.add_argument('--resume_last', action='store_true', help='resume the last model.')
 45 | parser.add_argument('--name', default='layers', type=str, help='The id of this train')
 46 | parser.add_argument('--valid_fold', default=0, type=int, help='Starting epoch.')
 47 | 
 48 | parser.add_argument('--norm', type=str, default='inorm', dest='norm')
 49 | parser.add_argument('--ny_in', type=int, default=320, dest='ny_in')
 50 | parser.add_argument('--nx_in', type=int, default=400, dest='nx_in')
 51 | parser.add_argument('--nch_in', type=int, default=40, dest='nch_in')
 52 | 
 53 | parser.add_argument('--ny_out', type=int, default=320, dest='ny_out')
 54 | parser.add_argument('--nx_out', type=int, default=400, dest='nx_out')
 55 | parser.add_argument('--nch_out', type=int, default=40, dest='nch_out')
 56 | 
 57 | parser.add_argument('--nch_ker', type=int, default=64, dest='nch_ker')
 58 | parser.add_argument('--image-loss', default='mse', help='image reconstruction loss - can be mse or ncc (default: mse)')
 59 | 
 60 | args = parser.parse_args()
 61 | 
 62 | 
 63 | if not os.path.exists(args.ckpt_dir): os.makedirs(args.ckpt_dir)
 64 | print('==> Arguments:')
 65 | print(args)
 66 | 
 67 | # Set GPU device.
 68 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 69 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 70 | 
 71 | ck_name = args.ck_name
 72 | 
 73 | # load checkpoint
 74 | checkpoint = torch.load("./checkpoint/"+ ck_name)
 75 | 
 76 | model = checkpoint["model"].module
 77 | model = model.to(device)
 78 | 
 79 | if len(args.gpu) > 1:
 80 |     model = nn.DataParallel(model)
 81 |     
 82 | 
 83 | 
 84 | def load_data(data_dir, label_dir):
 85 |     valid_set = sd_oct_flattenp_align(data_dir, label_dir, usage = 'valid', valid_fold = args.valid_fold, step = 20)
 86 |     
 87 |     valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)
 88 |     return valid_loader
 89 | 
 90 | 
 91 | def valid(epoch, valid_loader):
 92 |     print("valid:")
 93 |     global best_loss
 94 | 
 95 |     model.eval()
 96 |     surface_ces = []
 97 |     loss_sl1s = []
 98 |     loss_grads = []
 99 |     loss_gds = []
100 |     loss_nccs = []
101 | 
102 |     flows = []
103 |     result = []
104 |     target = []
105 |     metric = [] # mean_dis
106 |     
107 |     store_path = ""
108 |     if store_path != "" and not os.path.exists(store_path):
109 |         os.mkdir(store_path)
110 |     
111 |     names = []
112 |     with torch.no_grad():
113 |         for idx, (data, mask, layer_gt, name) in enumerate(valid_loader):
114 |             data = data.to(device)
115 |             mask = mask.float()
116 |             layer_gt = layer_gt.float().to(device)
117 |             s, logits, layerProb, mu, x, flow = model(data)
118 |             
119 |             
120 |             #print(flow[:, 1].abs().mean(), mean_dis)
121 |             result.append(s.cpu()[:,:,:,:] + flow.detach().cpu()[:,:,:,:])
122 |             target.append(mask[:,:,:,:])
123 |             flows.append(flow.detach().cpu()[:,:,:,:])
124 |             names.extend(name) 
125 |             
126 |             
127 |     print()
128 |     result = torch.cat(result).numpy()
129 |     target = torch.cat(target).numpy()
130 |     flows = torch.cat(flows).numpy()
131 |     cat_metric(names, result, target, flows)
132 |     print(mean(metric), mean(loss_nccs), mean(surface_ces), mean(loss_sl1s), np.mean(loss_grads, 0), mean(loss_gds))
133 |     
134 |  
135 | def cat_metric(name, result, target, flows):
136 |     
137 |     # to concat the patches to original volume to calculate the final metric, each A-scan will have more than one segmentation, depend on the step of patch(dataset)
138 |     
139 |     name_list = list(set([l[:-6] for l in name]))
140 | 
141 |     n, nc, w, h = result.shape
142 |     AMD_list = []
143 |     NOR_list = []
144 |     
145 |     for idx,l in enumerate(name_list):
146 |         if "AMD" in l:
147 |             AMD_list.append(idx)
148 |         else:
149 |             NOR_list.append(idx)
150 |             
151 |     deno = np.zeros((len(name_list), nc, 400, 40))  # denominator
152 |     mulo = np.zeros((len(name_list), nc, 400, 40))  # molecule
153 |     
154 |     denot = np.zeros((len(name_list), nc, 400, 40))   # denominator
155 |     mulot = np.zeros((len(name_list), nc, 400, 40))   # molecule
156 |     
157 |     ft = np.zeros((len(name_list), 1, 1, 40))   # denominator
158 |     mft = np.zeros((len(name_list),1,1,1))   # molecule
159 |     
160 |     for idx, l in enumerate(name):
161 | 
162 |         cname = l[:-6]
163 |         widx = int(l[-3:])
164 | 
165 |         nameidx = name_list.index(cname)
166 | 
167 |         deno[nameidx, :, widx: widx + 48, :] = deno[nameidx, :, widx: widx + 48, :] + result[idx,:,:,:]
168 |         denot[nameidx, :, widx: widx + 48, :] = denot[nameidx, :, widx: widx + 48, :] + target[idx,:,:,:]
169 |         
170 |         mulo[nameidx, :, widx: widx + 48, :] += 1
171 |         mulot[nameidx, :, widx: widx + 48, :] += 1
172 |         
173 |         ft[nameidx, :, :, :] += flows[idx, :,:,:]
174 |         mft[nameidx, :,:,:] += 1
175 |     
176 |     deno = deno / mulo
177 |     
178 |     # this target is the same as the target in origin volume.
179 |     denot = denot / mulot
180 |     
181 |     ft = ft / mft
182 |     
183 |     rmean = np.abs(np.round(deno)-np.round(denot))
184 |     print(rmean.mean() * 3.24)
185 |     print(np.mean(rmean, (0,2,3)) * 3.24)
186 |     AMD_mean = rmean[AMD_list]
187 |     NOR_mean = rmean[NOR_list]
188 |     print("AMD:")
189 |     print(np.mean(AMD_mean, (0,2,3)) * 3.24)
190 |     
191 |     print("NOR:")
192 |     print(np.mean(NOR_mean, (0,2,3)) * 3.24)
193 |     
194 |     means = np.mean(rmean, (2,3))
195 | 
196 |     
197 | 
198 | 
199 |  
200 | if __name__ == '__main__':
201 |     valid_loader= load_data(args.data_dir, args.label_dir)
202 |     valid(0, valid_loader)
203 | 


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/train_2d3d.py:
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  1 | import os
  2 | import argparse
  3 | import torch
  4 | import cv2
  5 | import sys 
  6 | import csv
  7 | import numpy as np
  8 | from numpy import *
  9 | import torchvision.transforms as transforms
 10 | import torch.nn as nn
 11 | from torch.utils.data.sampler import  WeightedRandomSampler
 12 | from model2d_3d import *
 13 | from sklearn.metrics import confusion_matrix,accuracy_score,f1_score,roc_auc_score,recall_score,precision_score
 14 | import scipy.ndimage
 15 | import scipy.io as io
 16 | 
 17 | from dataset import *
 18 | from losses import *
 19 | 
 20 | # for visualization
 21 | from torch.utils.tensorboard import SummaryWriter   
 22 | 
 23 | 
 24 | # Set arguments and hyper-parameters.
 25 | parser = argparse.ArgumentParser(description='baseline')
 26 | parser.add_argument('--label_dir', default='./label', type=str, help='Directory to load label files.')
 27 | parser.add_argument('--ckpt_dir', default='./checkpoint', type=str, help='Directory to save checkpoint.')
 28 | 
 29 | parser.add_argument('--gpu', default='0', type=str, help='GPU Devices to use.')
 30 | parser.add_argument('--batch_size', default=9, type=int, help='Batch size.')
 31 | 
 32 | parser.add_argument('--lr', default=0.001, type=float, help='Starting learning rate.')
 33 | parser.add_argument('--lr_decay', default=0.9, type=float, help='Learning rate decay.')
 34 | parser.add_argument('--lr_decay_step', default=1, type=int, help='Learning rate decay step.')
 35 | parser.add_argument('--weight_decay', default=1e-4, type=float, help='L2 penalty for regularization.')
 36 | parser.add_argument('--weight_g', default=0.3, type=float, help='L2 penalty for regularization.')
 37 | 
 38 | 
 39 | parser.add_argument('--start_epoch', default=1, type=int, help='Starting epoch.')
 40 | parser.add_argument('--epochs', default=90, type=int, help='Number of training epochs.')
 41 | parser.add_argument('--resume', '-r', action='store_true', help='Resume from checkpoint or not.')
 42 | parser.add_argument('--store_last', action='store_true', help='store the last model.')
 43 | parser.add_argument('--resume_last', action='store_true', help='resume the last model.')
 44 | parser.add_argument('--test', action='store_true', help='resume the last model.')
 45 | 
 46 | parser.add_argument('--name', default='layers', type=str, help='The id of this train')
 47 | parser.add_argument('--valid_fold', default=0, type=int, help='Starting epoch.')
 48 | 
 49 | args = parser.parse_args()
 50 | 
 51 | if not os.path.exists(args.ckpt_dir): os.makedirs(args.ckpt_dir)
 52 | print('==> Arguments:')
 53 | print(args)
 54 | 
 55 | # Set GPU device.
 56 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 57 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 58 | 
 59 | if args.resume_last:
 60 |     check = torch.load("")
 61 |     model = check["model"].module
 62 |     
 63 | else:
 64 |     model = UNet3D_dual_up(n_layer = 3)
 65 | 
 66 | # set optimizer
 67 | optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
 68 | model = model.to(device)
 69 | model = nn.DataParallel(model)
 70 | 
 71 | # prepare image loss
 72 | image_loss_func = MultiSurfaceCrossEntropyLoss()
 73 | grad_loss = Grad_2d(weight = torch.tensor(np.array([0, 0.8, 1.5]).astype(np.float32)).to(device))
 74 | smoothl1loss = nn.SmoothL1Loss()
 75 | meandis = nn.L1Loss()
 76 | 
 77 | # loss for segmentation branch
 78 | Gloss = GeneralizedDiceLoss()
 79 | mlloss = MultiLayerCrossEntropyLoss()
 80 | 
 81 | # loss for alignment
 82 | bce_Bscan = MSE_bscan()
 83 | ncc = NCC_oct().loss
 84 | 
 85 | # set best loss
 86 | best_loss = 10000
 87 | 
 88 | # set visualization writer
 89 | logpt = "./log/" + args.name
 90 | if not os.path.exists(logpt):
 91 |     os.mkdir(logpt)
 92 | writer = SummaryWriter(logpt)
 93 | 
 94 | def load_data(label_dir):
 95 |     valid_set = sd_oct_flattenp_align(label_dir, usage = 'valid', valid_fold = args.valid_fold)
 96 |     valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)
 97 | 
 98 |     if args.test:
 99 |         return valid_loader, valid_loader
100 |     train_set = sd_oct_flattenp_align(label_dir, usage = 'train', valid_fold = args.valid_fold)
101 |     train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle = True, pin_memory=True, num_workers=4)
102 |     return train_loader, valid_loader
103 | 
104 | def train(epoch, train_loader):
105 |     print("train:")
106 |     model.train()
107 |     surface_ces = []
108 |     loss_sl1s = []
109 |     loss_grads = []
110 |     loss_gds = []
111 | 
112 |     loss_nccs = []
113 |     loss_mses = []
114 | 
115 |     metric = [] # mean_dis
116 |     
117 |     for idx, (data, mask, _, _) in enumerate(train_loader):
118 |         data = data.to(device)
119 |         mask = mask.float().to(device)
120 |         optimizer.zero_grad()
121 |         
122 |         s, logits, layerProb, mu, x, flow = model(data)
123 | 
124 |         # for the branch 2, the layer_label need to be align
125 |         new_label = (mask - flow.detach())
126 |         new_label[new_label > 319] = 319
127 |         new_label[new_label < 0] = 0
128 |         layer_gt = []
129 |         for l in new_label:
130 |             layer_gt.append(getLayerLabels(l, 320).unsqueeze(0))
131 |         layer_gt = torch.cat(layer_gt, dim = 0)
132 |         
133 |         lossgd = Gloss(layerProb, layer_gt.float())  # diceloss
134 |         lossgd += mlloss(layerProb, layer_gt.float())   # layer_cross_entropy
135 |         surface_ce = image_loss_func(logits[:,:,:,:], new_label)  # surface_cross_entropy
136 |         loss_grad = grad_loss.loss(mask, mu[:,:3,:,:]) # grad_loss
137 |         lossmse = bce_Bscan.loss(mask - flow)
138 | 
139 |         ncc_loss = ncc(x)
140 |         sl1loss = smoothl1loss(mu, new_label)   # smooth_l1_loss # why use smooth l1
141 |         main_loss =  surface_ce + sl1loss + ncc_loss + lossgd + loss_grad * args.weight_g + lossmse
142 |  
143 |         main_loss.backward()
144 | 
145 |         optimizer.step()
146 |         metric.append(meandis(s[:,:,:,:], mask[:,:3,:,:] - flow.detach()).detach().cpu().numpy())
147 |         loss_mses.append(lossmse.detach().cpu().numpy())
148 |         loss_sl1s.append(sl1loss.detach().cpu().numpy())
149 |         loss_grads.append(loss_grad.detach().cpu().numpy())
150 |         loss_gds.append(lossgd.detach().cpu().numpy())
151 |         surface_ces.append(surface_ce.detach().cpu().numpy())
152 |         loss_nccs.append(ncc_loss.detach().cpu().numpy())
153 |         smooth_loss = sum(loss_sl1s[-100:]) / min(len(loss_sl1s), 100)
154 |         print(idx, sl1loss.item(), metric[-1], mean(surface_ces), surface_ce.item(), loss_grad.item(), ncc_loss.item(), end = "\r")
155 |         
156 |     loss = mean(loss_sl1s)
157 |     print(mean(metric), mean(surface_ces), mean(loss_sl1s))
158 |     writer.add_scalar('loss_sl1s/train', mean(loss_sl1s), epoch)
159 |     writer.add_scalar('loss_grads/train', mean(loss_grads), epoch)
160 |     writer.add_scalar('loss_mses/train', mean(loss_mses), epoch)
161 |     writer.add_scalar('mean_dis/train', mean(metric), epoch)
162 |     writer.add_scalar('surface_ce/train', mean(surface_ces), epoch)
163 |     writer.add_scalar('loss_gds/train', mean(loss_gds), epoch)
164 |     writer.add_scalar('ncc/train', mean(loss_nccs), epoch)
165 | 
166 | 
167 | def valid(epoch, valid_loader, test = False):
168 |     print("valid:")
169 |     global best_loss
170 | 
171 |     model.eval()
172 |     surface_ces = []
173 |     loss_sl1s = []
174 |     loss_grads = []
175 |     loss_gds = []
176 |     loss_nccs = []
177 |     loss_mses = []
178 |     if test:
179 |         store_path = "/data3/layer_segmentation/final"
180 |         if not os.path.exists(store_path):
181 |             os.mkdir(store_path)
182 |     metric = [] # mean_dis
183 |     
184 |     names = []
185 |     with torch.no_grad():
186 |         for idx, (data, mask, layer_gt, name) in enumerate(valid_loader):
187 |             data = data.to(device)
188 |             mask = mask.float()
189 |             s, logits, layerProb, mu, x, flow = model(data)
190 |             
191 |             new_label = (mask - flow.detach().cpu())
192 |             new_label[new_label > 319] = 319
193 |             new_label[new_label < 0] = 0
194 |             
195 |             surface_ce = image_loss_func(logits.detach().cpu(), new_label)  # surface_cross_entropy
196 |             loss_grad = grad_loss.loss(mask, mu[:,:3,:,:].detach()) # grad_loss
197 |             lossmse = bce_Bscan.loss(mask - flow.detach().cpu())
198 | 
199 |             ncc_loss = ncc(x)
200 |             sl1loss = smoothl1loss(mu[:,:3,:,:].detach().cpu(), new_label)   # smooth_l1_loss # why use smooth l1
201 | 
202 |             mean_dis = np.abs(s.detach().cpu().numpy()[:,:,:,:] - new_label.numpy()).mean()
203 |             metric.append(mean_dis)
204 |             
205 |             if test:
206 |                 res = x.detach().cpu().numpy()
207 |                 out = flow.detach().cpu().numpy()
208 |                 cpath = os.path.join(store_path, name[0])
209 |                 io.savemat(cpath, {"img":res.squeeze(), "flow" : out, "imgr": data.cpu().numpy()})
210 |             
211 |             loss_sl1s.append(sl1loss.detach().numpy())
212 |             loss_grads.append(loss_grad.detach().cpu().numpy())
213 |             loss_nccs.append(ncc_loss.detach().cpu().numpy())
214 |             loss_mses.append(lossmse.detach().cpu().numpy())
215 |             surface_ces.append(surface_ce.detach().cpu().numpy())
216 |             names.append(name) 
217 |             
218 |             smooth_loss = sum(metric[-100:]) / min(len(metric), 100)
219 |             print(idx, smooth_loss, end = "\r")
220 |     
221 |     print(mean(metric), mean(surface_ces), mean(loss_sl1s), mean(loss_grads), mean(loss_nccs))
222 |     
223 |     writer.add_scalar('loss_sl1s/valid', mean(loss_sl1s), epoch)
224 |     writer.add_scalar('loss_grads/valid', mean(loss_grads), epoch)
225 |     writer.add_scalar('loss_mses/valid', mean(loss_mses), epoch)
226 |     writer.add_scalar('mean_dis/valid', mean(metric), epoch)
227 |     writer.add_scalar('surface_ce/valid', mean(surface_ces), epoch)
228 |     writer.add_scalar('ncc/valid', mean(loss_nccs), epoch)
229 |     metric = mean(metric)
230 |     if metric < best_loss:
231 |         best_loss = metric
232 |         save_checkpoint(epoch)
233 |     return metric
234 | 
235 | def save_checkpoint(epoch, name = None):
236 |     ''' Save checkpoint if accuracy is higher than before.
237 |     
238 |     # Arguments
239 |         epoch (int): Current epoch.
240 |     '''
241 |     # Save model and global variables into checkpoint.
242 |     global best_loss
243 |     print('==> Saving checkpoint...')
244 |     state = {
245 |         'model': model,
246 |         'epoch': epoch,
247 |         'acc': best_loss,
248 |     }
249 |     if name == None:
250 |         checkpoint_name = args.name + "_" +  str(epoch).zfill(3) + '_fold' + str(args.valid_fold) + "_"+str(round(best_loss, 3)) + '%.t7'
251 |     else:
252 |         checkpoint_name = args.name + "_" + name + ".t7"
253 |     torch.save(state, os.path.join(args.ckpt_dir, checkpoint_name))    
254 | 
255 | if __name__ == '__main__':
256 | 
257 |     train_loader, valid_loader= load_data(args.label_dir)
258 |     
259 |     if args.test:
260 |         valid(0, valid_loader, True)
261 |         exit(0)
262 |         
263 |     for epoch in range(args.start_epoch, args.epochs + 1):
264 |         print('\n************** Epoch: %d **************' % epoch)
265 |         train(epoch, train_loader)
266 |         print()
267 |         valid_loss = valid(epoch, valid_loader)
268 | 


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280 | Farsiu_Ophthalmology_2013_AMD_Subject_1099.mat,4
281 | Farsiu_Ophthalmology_2013_AMD_Subject_1024.mat,0
282 | Farsiu_Ophthalmology_2013_AMD_Subject_1090.mat,1
283 | Farsiu_Ophthalmology_2013_AMD_Subject_1119.mat,2
284 | Farsiu_Ophthalmology_2013_AMD_Subject_1044.mat,3
285 | Farsiu_Ophthalmology_2013_AMD_Subject_1261.mat,4
286 | Farsiu_Ophthalmology_2013_Control_Subject_1112.mat,0
287 | Farsiu_Ophthalmology_2013_AMD_Subject_1122.mat,1
288 | Farsiu_Ophthalmology_2013_AMD_Subject_1161.mat,2
289 | Farsiu_Ophthalmology_2013_AMD_Subject_1215.mat,3
290 | Farsiu_Ophthalmology_2013_AMD_Subject_1236.mat,4
291 | Farsiu_Ophthalmology_2013_AMD_Subject_1190.mat,0
292 | Farsiu_Ophthalmology_2013_AMD_Subject_1244.mat,1
293 | Farsiu_Ophthalmology_2013_Control_Subject_1064.mat,2
294 | Farsiu_Ophthalmology_2013_AMD_Subject_1014.mat,3
295 | Farsiu_Ophthalmology_2013_AMD_Subject_1169.mat,4
296 | Farsiu_Ophthalmology_2013_AMD_Subject_1039.mat,0
297 | Farsiu_Ophthalmology_2013_AMD_Subject_1146.mat,1
298 | Farsiu_Ophthalmology_2013_AMD_Subject_1030.mat,2
299 | Farsiu_Ophthalmology_2013_AMD_Subject_1082.mat,3
300 | Farsiu_Ophthalmology_2013_Control_Subject_1018.mat,4
301 | Farsiu_Ophthalmology_2013_AMD_Subject_1153.mat,0
302 | Farsiu_Ophthalmology_2013_AMD_Subject_1128.mat,1
303 | Farsiu_Ophthalmology_2013_AMD_Subject_1212.mat,2
304 | Farsiu_Ophthalmology_2013_AMD_Subject_1217.mat,3
305 | Farsiu_Ophthalmology_2013_AMD_Subject_1223.mat,4
306 | Farsiu_Ophthalmology_2013_AMD_Subject_1051.mat,0
307 | Farsiu_Ophthalmology_2013_AMD_Subject_1239.mat,1
308 | Farsiu_Ophthalmology_2013_Control_Subject_1085.mat,2
309 | Farsiu_Ophthalmology_2013_AMD_Subject_1205.mat,3
310 | Farsiu_Ophthalmology_2013_AMD_Subject_1123.mat,4
311 | Farsiu_Ophthalmology_2013_AMD_Subject_1157.mat,0
312 | Farsiu_Ophthalmology_2013_AMD_Subject_1095.mat,1
313 | Farsiu_Ophthalmology_2013_AMD_Subject_1053.mat,2
314 | Farsiu_Ophthalmology_2013_Control_Subject_1009.mat,3
315 | Farsiu_Ophthalmology_2013_Control_Subject_1012.mat,4
316 | Farsiu_Ophthalmology_2013_AMD_Subject_1229.mat,0
317 | Farsiu_Ophthalmology_2013_AMD_Subject_1100.mat,1
318 | Farsiu_Ophthalmology_2013_AMD_Subject_1134.mat,2
319 | Farsiu_Ophthalmology_2013_Control_Subject_1094.mat,3
320 | Farsiu_Ophthalmology_2013_Control_Subject_1027.mat,4
321 | Farsiu_Ophthalmology_2013_Control_Subject_1090.mat,0
322 | Farsiu_Ophthalmology_2013_Control_Subject_1030.mat,1
323 | Farsiu_Ophthalmology_2013_Control_Subject_1107.mat,2
324 | Farsiu_Ophthalmology_2013_AMD_Subject_1268.mat,3
325 | Farsiu_Ophthalmology_2013_AMD_Subject_1043.mat,4
326 | Farsiu_Ophthalmology_2013_Control_Subject_1071.mat,0
327 | Farsiu_Ophthalmology_2013_AMD_Subject_1248.mat,1
328 | Farsiu_Ophthalmology_2013_AMD_Subject_1035.mat,2
329 | Farsiu_Ophthalmology_2013_AMD_Subject_1017.mat,3
330 | Farsiu_Ophthalmology_2013_AMD_Subject_1098.mat,4
331 | Farsiu_Ophthalmology_2013_Control_Subject_1075.mat,0
332 | Farsiu_Ophthalmology_2013_AMD_Subject_1150.mat,1
333 | Farsiu_Ophthalmology_2013_AMD_Subject_1106.mat,2
334 | Farsiu_Ophthalmology_2013_AMD_Subject_1136.mat,3
335 | Farsiu_Ophthalmology_2013_AMD_Subject_1055.mat,4
336 | Farsiu_Ophthalmology_2013_AMD_Subject_1231.mat,0
337 | Farsiu_Ophthalmology_2013_Control_Subject_1011.mat,1
338 | Farsiu_Ophthalmology_2013_AMD_Subject_1151.mat,2
339 | Farsiu_Ophthalmology_2013_AMD_Subject_1147.mat,3
340 | Farsiu_Ophthalmology_2013_AMD_Subject_1154.mat,4
341 | Farsiu_Ophthalmology_2013_Control_Subject_1059.mat,0
342 | Farsiu_Ophthalmology_2013_AMD_Subject_1269.mat,1
343 | Farsiu_Ophthalmology_2013_AMD_Subject_1175.mat,2
344 | Farsiu_Ophthalmology_2013_AMD_Subject_1228.mat,3
345 | Farsiu_Ophthalmology_2013_Control_Subject_1006.mat,4
346 | Farsiu_Ophthalmology_2013_Control_Subject_1035.mat,0
347 | Farsiu_Ophthalmology_2013_AMD_Subject_1216.mat,1
348 | Farsiu_Ophthalmology_2013_Control_Subject_1049.mat,2
349 | Farsiu_Ophthalmology_2013_AMD_Subject_1249.mat,3
350 | Farsiu_Ophthalmology_2013_Control_Subject_1024.mat,4
351 | Farsiu_Ophthalmology_2013_AMD_Subject_1138.mat,0
352 | Farsiu_Ophthalmology_2013_AMD_Subject_1065.mat,1
353 | Farsiu_Ophthalmology_2013_AMD_Subject_1033.mat,2
354 | Farsiu_Ophthalmology_2013_AMD_Subject_1220.mat,3
355 | Farsiu_Ophthalmology_2013_Control_Subject_1077.mat,4
356 | Farsiu_Ophthalmology_2013_Control_Subject_1111.mat,0
357 | 


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444 | work results from an entity transaction, each party to that
445 | transaction who receives a copy of the work also receives whatever
446 | licenses to the work the party's predecessor in interest had or could
447 | give under the previous paragraph, plus a right to possession of the
448 | Corresponding Source of the work from the predecessor in interest, if
449 | the predecessor has it or can get it with reasonable efforts.
450 | 
451 |   You may not impose any further restrictions on the exercise of the
452 | rights granted or affirmed under this License.  For example, you may
453 | not impose a license fee, royalty, or other charge for exercise of
454 | rights granted under this License, and you may not initiate litigation
455 | (including a cross-claim or counterclaim in a lawsuit) alleging that
456 | any patent claim is infringed by making, using, selling, offering for
457 | sale, or importing the Program or any portion of it.
458 | 
459 |   11. Patents.
460 | 
461 |   A "contributor" is a copyright holder who authorizes use under this
462 | License of the Program or a work on which the Program is based.  The
463 | work thus licensed is called the contributor's "contributor version".
464 | 
465 |   A contributor's "essential patent claims" are all patent claims
466 | owned or controlled by the contributor, whether already acquired or
467 | hereafter acquired, that would be infringed by some manner, permitted
468 | by this License, of making, using, or selling its contributor version,
469 | but do not include claims that would be infringed only as a
470 | consequence of further modification of the contributor version.  For
471 | purposes of this definition, "control" includes the right to grant
472 | patent sublicenses in a manner consistent with the requirements of
473 | this License.
474 | 
475 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
476 | patent license under the contributor's essential patent claims, to
477 | make, use, sell, offer for sale, import and otherwise run, modify and
478 | propagate the contents of its contributor version.
479 | 
480 |   In the following three paragraphs, a "patent license" is any express
481 | agreement or commitment, however denominated, not to enforce a patent
482 | (such as an express permission to practice a patent or covenant not to
483 | sue for patent infringement).  To "grant" such a patent license to a
484 | party means to make such an agreement or commitment not to enforce a
485 | patent against the party.
486 | 
487 |   If you convey a covered work, knowingly relying on a patent license,
488 | and the Corresponding Source of the work is not available for anyone
489 | to copy, free of charge and under the terms of this License, through a
490 | publicly available network server or other readily accessible means,
491 | then you must either (1) cause the Corresponding Source to be so
492 | available, or (2) arrange to deprive yourself of the benefit of the
493 | patent license for this particular work, or (3) arrange, in a manner
494 | consistent with the requirements of this License, to extend the patent
495 | license to downstream recipients.  "Knowingly relying" means you have
496 | actual knowledge that, but for the patent license, your conveying the
497 | covered work in a country, or your recipient's use of the covered work
498 | in a country, would infringe one or more identifiable patents in that
499 | country that you have reason to believe are valid.
500 | 
501 |   If, pursuant to or in connection with a single transaction or
502 | arrangement, you convey, or propagate by procuring conveyance of, a
503 | covered work, and grant a patent license to some of the parties
504 | receiving the covered work authorizing them to use, propagate, modify
505 | or convey a specific copy of the covered work, then the patent license
506 | you grant is automatically extended to all recipients of the covered
507 | work and works based on it.
508 | 
509 |   A patent license is "discriminatory" if it does not include within
510 | the scope of its coverage, prohibits the exercise of, or is
511 | conditioned on the non-exercise of one or more of the rights that are
512 | specifically granted under this License.  You may not convey a covered
513 | work if you are a party to an arrangement with a third party that is
514 | in the business of distributing software, under which you make payment
515 | to the third party based on the extent of your activity of conveying
516 | the work, and under which the third party grants, to any of the
517 | parties who would receive the covered work from you, a discriminatory
518 | patent license (a) in connection with copies of the covered work
519 | conveyed by you (or copies made from those copies), or (b) primarily
520 | for and in connection with specific products or compilations that
521 | contain the covered work, unless you entered into that arrangement,
522 | or that patent license was granted, prior to 28 March 2007.
523 | 
524 |   Nothing in this License shall be construed as excluding or limiting
525 | any implied license or other defenses to infringement that may
526 | otherwise be available to you under applicable patent law.
527 | 
528 |   12. No Surrender of Others' Freedom.
529 | 
530 |   If conditions are imposed on you (whether by court order, agreement or
531 | otherwise) that contradict the conditions of this License, they do not
532 | excuse you from the conditions of this License.  If you cannot convey a
533 | covered work so as to satisfy simultaneously your obligations under this
534 | License and any other pertinent obligations, then as a consequence you may
535 | not convey it at all.  For example, if you agree to terms that obligate you
536 | to collect a royalty for further conveying from those to whom you convey
537 | the Program, the only way you could satisfy both those terms and this
538 | License would be to refrain entirely from conveying the Program.
539 | 
540 |   13. Remote Network Interaction; Use with the GNU General Public License.
541 | 
542 |   Notwithstanding any other provision of this License, if you modify the
543 | Program, your modified version must prominently offer all users
544 | interacting with it remotely through a computer network (if your version
545 | supports such interaction) an opportunity to receive the Corresponding
546 | Source of your version by providing access to the Corresponding Source
547 | from a network server at no charge, through some standard or customary
548 | means of facilitating copying of software.  This Corresponding Source
549 | shall include the Corresponding Source for any work covered by version 3
550 | of the GNU General Public License that is incorporated pursuant to the
551 | following paragraph.
552 | 
553 |   Notwithstanding any other provision of this License, you have
554 | permission to link or combine any covered work with a work licensed
555 | under version 3 of the GNU General Public License into a single
556 | combined work, and to convey the resulting work.  The terms of this
557 | License will continue to apply to the part which is the covered work,
558 | but the work with which it is combined will remain governed by version
559 | 3 of the GNU General Public License.
560 | 
561 |   14. Revised Versions of this License.
562 | 
563 |   The Free Software Foundation may publish revised and/or new versions of
564 | the GNU Affero General Public License from time to time.  Such new versions
565 | will be similar in spirit to the present version, but may differ in detail to
566 | address new problems or concerns.
567 | 
568 |   Each version is given a distinguishing version number.  If the
569 | Program specifies that a certain numbered version of the GNU Affero General
570 | Public License "or any later version" applies to it, you have the
571 | option of following the terms and conditions either of that numbered
572 | version or of any later version published by the Free Software
573 | Foundation.  If the Program does not specify a version number of the
574 | GNU Affero General Public License, you may choose any version ever published
575 | by the Free Software Foundation.
576 | 
577 |   If the Program specifies that a proxy can decide which future
578 | versions of the GNU Affero General Public License can be used, that proxy's
579 | public statement of acceptance of a version permanently authorizes you
580 | to choose that version for the Program.
581 | 
582 |   Later license versions may give you additional or different
583 | permissions.  However, no additional obligations are imposed on any
584 | author or copyright holder as a result of your choosing to follow a
585 | later version.
586 | 
587 |   15. Disclaimer of Warranty.
588 | 
589 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
590 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
591 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
592 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
593 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
594 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
595 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
596 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
597 | 
598 |   16. Limitation of Liability.
599 | 
600 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
601 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
602 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
603 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
604 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
605 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
606 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
607 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
608 | SUCH DAMAGES.
609 | 
610 |   17. Interpretation of Sections 15 and 16.
611 | 
612 |   If the disclaimer of warranty and limitation of liability provided
613 | above cannot be given local legal effect according to their terms,
614 | reviewing courts shall apply local law that most closely approximates
615 | an absolute waiver of all civil liability in connection with the
616 | Program, unless a warranty or assumption of liability accompanies a
617 | copy of the Program in return for a fee.
618 | 
619 |                      END OF TERMS AND CONDITIONS
620 | 
621 |             How to Apply These Terms to Your New Programs
622 | 
623 |   If you develop a new program, and you want it to be of the greatest
624 | possible use to the public, the best way to achieve this is to make it
625 | free software which everyone can redistribute and change under these terms.
626 | 
627 |   To do so, attach the following notices to the program.  It is safest
628 | to attach them to the start of each source file to most effectively
629 | state the exclusion of warranty; and each file should have at least
630 | the "copyright" line and a pointer to where the full notice is found.
631 | 
632 |     <one line to give the program's name and a brief idea of what it does.>
633 |     Copyright (C) <year>  <name of author>
634 | 
635 |     This program is free software: you can redistribute it and/or modify
636 |     it under the terms of the GNU Affero General Public License as published
637 |     by the Free Software Foundation, either version 3 of the License, or
638 |     (at your option) any later version.
639 | 
640 |     This program is distributed in the hope that it will be useful,
641 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
642 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
643 |     GNU Affero General Public License for more details.
644 | 
645 |     You should have received a copy of the GNU Affero General Public License
646 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
647 | 
648 | Also add information on how to contact you by electronic and paper mail.
649 | 
650 |   If your software can interact with users remotely through a computer
651 | network, you should also make sure that it provides a way for users to
652 | get its source.  For example, if your program is a web application, its
653 | interface could display a "Source" link that leads users to an archive
654 | of the code.  There are many ways you could offer source, and different
655 | solutions will be better for different programs; see section 13 for the
656 | specific requirements.
657 | 
658 |   You should also get your employer (if you work as a programmer) or school,
659 | if any, to sign a "copyright disclaimer" for the program, if necessary.
660 | For more information on this, and how to apply and follow the GNU AGPL, see
661 | <https://www.gnu.org/licenses/>.
662 | 


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