FAA getBraTS_2

@@ -5,6 +5,7 @@ import collections
 import pickle as cp
 import pickle as cp
 import glob
 import glob
 import numpy as np
 import numpy as np
+import pandas as pd
 
 
 import torch
 import torch
 import torchvision
 import torchvision
@@ -31,7 +32,7 @@ current_epoch = 0
 def split_dataset(args, dataset, k):
 def split_dataset(args, dataset, k):
     # load dataset
     # load dataset
     X = list(range(len(dataset)))
     X = list(range(len(dataset)))
-    Y = dataset.targets
+    Y = dataset
 
 
     # split to k-fold
     # split to k-fold
     assert len(X) == len(Y)
     assert len(X) == len(Y)
@@ -162,9 +163,11 @@ class CustomDataset(Dataset):
       return self.len
       return self.len
 
 
   def __getitem__(self, idx): 
   def __getitem__(self, idx): 
-       if self.transforms is not None:
-            img = self.transforms(img)
-       return img
+       img, targets = self.img[idx], self.targets[idx]
+
+       if self.transform is not None:
+            img = self.transform(img)
+       return img, targets
 
 
 def get_dataset(args, transform, split='train'):
 def get_dataset(args, transform, split='train'):
     assert split in ['train', 'val', 'test', 'trainval']
     assert split in ['train', 'val', 'test', 'trainval']

@@ -15,6 +15,9 @@ from torchvision.transforms import transforms
 from sklearn.model_selection import StratifiedShuffleSplit
 from sklearn.model_selection import StratifiedShuffleSplit
 from theconf import Config as C
 from theconf import Config as C
 
 
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
 from FastAutoAugment.archive import arsaug_policy, autoaug_policy, autoaug_paper_cifar10, fa_reduced_cifar10, fa_reduced_svhn, fa_resnet50_rimagenet
 from FastAutoAugment.archive import arsaug_policy, autoaug_policy, autoaug_paper_cifar10, fa_reduced_cifar10, fa_reduced_svhn, fa_resnet50_rimagenet
 from FastAutoAugment.augmentations import *
 from FastAutoAugment.augmentations import *
 from FastAutoAugment.common import get_logger
 from FastAutoAugment.common import get_logger
@@ -79,6 +82,29 @@ def get_dataloaders(dataset, batch, dataroot, split=0.15, split_idx=0, multinode
             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         ])
         ])
 
 
+    elif 'BraTS' in dataset:
+        input_size = 240
+        sized_size = 256
+
+        if 'efficientnet' in C.get()['model']['type']:
+            input_size = EfficientNet.get_image_size(C.get()['model']['type'])
+            sized_size = input_size + 16    # TODO
+
+            logger.info('size changed to %d/%d.' % (input_size, sized_size))
+
+        transform_train = transforms.Compose([
+            EfficientNetRandomCrop(input_size),
+            transforms.Resize((input_size, input_size), interpolation=Image.BICUBIC),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+        ])
+
+        transform_test = transforms.Compose([
+            EfficientNetCenterCrop(input_size),
+            transforms.Resize((input_size, input_size), interpolation=Image.BICUBIC),
+            transforms.ToTensor(),
+        ])
+
     else:
     else:
         raise ValueError('dataset=%s' % dataset)
         raise ValueError('dataset=%s' % dataset)
 
 
@@ -111,7 +137,10 @@ def get_dataloaders(dataset, batch, dataroot, split=0.15, split_idx=0, multinode
     if C.get()['cutout'] > 0:
     if C.get()['cutout'] > 0:
         transform_train.transforms.append(CutoutDefault(C.get()['cutout']))
         transform_train.transforms.append(CutoutDefault(C.get()['cutout']))
 
 
-    if dataset == 'cifar10':
+    if dataset == 'BraTS':
+        total_trainset = 
+        testset = 
+    elif dataset == 'cifar10':
         total_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=transform_train)
         total_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=transform_train)
         testset = torchvision.datasets.CIFAR10(root=dataroot, train=False, download=True, transform=transform_test)
         testset = torchvision.datasets.CIFAR10(root=dataroot, train=False, download=True, transform=transform_test)
     elif dataset == 'reduced_cifar10':
     elif dataset == 'reduced_cifar10':

@@ -16,6 +16,9 @@ from ray.tune.suggest import HyperOptSearch
 from ray.tune import register_trainable, run_experiments
 from ray.tune import register_trainable, run_experiments
 from tqdm import tqdm
 from tqdm import tqdm
 
 
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
 from FastAutoAugment.archive import remove_deplicates, policy_decoder
 from FastAutoAugment.archive import remove_deplicates, policy_decoder
 from FastAutoAugment.augmentations import augment_list
 from FastAutoAugment.augmentations import augment_list
 from FastAutoAugment.common import get_logger, add_filehandler
 from FastAutoAugment.common import get_logger, add_filehandler

@@ -19,6 +19,9 @@ import torch.distributed as dist
 from tqdm import tqdm
 from tqdm import tqdm
 from theconf import Config as C, ConfigArgumentParser
 from theconf import Config as C, ConfigArgumentParser
 
 
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
 from FastAutoAugment.common import get_logger, EMA, add_filehandler
 from FastAutoAugment.common import get_logger, EMA, add_filehandler
 from FastAutoAugment.data import get_dataloaders
 from FastAutoAugment.data import get_dataloaders
 from FastAutoAugment.lr_scheduler import adjust_learning_rate_resnet
 from FastAutoAugment.lr_scheduler import adjust_learning_rate_resnet

@@ -32,6 +32,7 @@ for i = 1 : length(subFolders)
 
 
     % copy flair, segment flair data
     % copy flair, segment flair data
 
 
+    % seg의 검은 부분(정보 x)과 같은 인덱스 = 0
     cp_flair(seg == 0) = 0;
     cp_flair(seg == 0) = 0;
 
 
     % save a segmented data
     % save a segmented data

 inputheader = '..\data\MICCAI_BraTS_2019_Data_Training\HGG_seg_flair\';
 inputheader = '..\data\MICCAI_BraTS_2019_Data_Training\HGG_seg_flair\';
-outfolder = '..\data\MICCAI_BraTS_2019_Data_Training\frame\';
+outfolder = '..\data\MICCAI_BraTS_2019_Data_Training\total_frame\';
 
 
 files = dir(inputheader);
 files = dir(inputheader);
 id = {files.name};
 id = {files.name};
@@ -38,14 +38,14 @@ for i = 1 : length(files)
     c = 0;
     c = 0;
     step = round(((en) - (st))/11);
     step = round(((en) - (st))/11);
     for k = st + step : step : st + step*10
     for k = st + step : step : st + step*10
-        c = c+ 1; 
-
         type = '.png';
         type = '.png';
         filename = strcat(id, '_', int2str(c), type); % BraTS19_2013_2_1_seg_flair_c.png
         filename = strcat(id, '_', int2str(c), type); % BraTS19_2013_2_1_seg_flair_c.png
         outpath = strcat(outfolder, filename);
         outpath = strcat(outfolder, filename);
         % typecase int16 to double, range[0, 1], rotate 90 and filp updown
         % typecase int16 to double, range[0, 1], rotate 90 and filp updown
         cp_data = flipud(rot90(mat2gray(double(data(:,:,k)))));
         cp_data = flipud(rot90(mat2gray(double(data(:,:,k)))));
         imwrite(cp_data, outpath);
         imwrite(cp_data, outpath);
+        
+        c = c+ 1; 
     end
     end
     
     
 end
 end