Last active
August 27, 2021 18:55
-
-
Save 123epsilon/5ac4de0e43da895470ddca807acb5670 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| class Brain_MRI_Segmentation_Dataset(data.Dataset): | |
| def __init__(self, inputs, transform=None): | |
| self.inputs = inputs | |
| self.transform = transform | |
| self.input_dtype = torch.float32 | |
| self.target_dtype = torch.float32 | |
| def __len__(self): | |
| return len(self.inputs) | |
| def __getitem__(self, index): | |
| #for classification return only the image and the binary label | |
| img_path = self.inputs[index][0] | |
| mask_path = self.inputs[index][1] | |
| #mask_img = cv2.normalize(cv2.imread(mask_path), None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) | |
| mask_img = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED) | |
| x = torch.from_numpy(np.transpose(np.array(cv2.imread(img_path)), (2,0,1))).type(self.input_dtype) | |
| y = torch.from_numpy(np.resize(np.array(mask_img)/255., (1,256,256))).type(self.target_dtype) | |
| if self.transform is not None: | |
| x = self.transform(x) | |
| y = self.transform(y) | |
| return x,y | |
| #load data | |
| positive_diagnoses = [x for x in file_list if x[2] == 1] | |
| #print(positive_diagnoses[:5]) | |
| mri_dataset = Brain_MRI_Segmentation_Dataset(positive_diagnoses) | |
| validation_size = int(0.3 * len(mri_dataset)) | |
| train_set, val_set = data.random_split(mri_dataset, [len(mri_dataset)-validation_size, validation_size]) | |
| train_loader = data.DataLoader(dataset=train_set, batch_size=2, shuffle=True) | |
| val_loader = data.DataLoader(dataset=val_set, batch_size=2, shuffle=False) | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment