Initial commit, added all files and initial progress

main.py

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+import torch
+
+# FOR DATA
+from torch import nn
+from torch.utils.data import DataLoader, Dataset
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+import os
+import pandas as pd
+from torchvision.io import read_image
+import nonechucks as nc     # Used to load data in pytorch even when images are corrupted / unavailable (skips them)
+
+
+# MAYBE??
+'''
+import sys
+sys.path.append('//data/data_wnx3/data_wnx1/rschuurs/CNN+RNN-2class-1cnn-CLEAN/utils')
+
+import os
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # use id from $ nvidia-smi
+'''
+
+
+
+
+# LOADING DATA
+# data & training properties:
+'''
+target_rows = 91
+target_cols = 109
+depth = 91
+axis = 1
+num_clinical = 2
+CNN_drop_rate = 0.3
+RNN_drop_rate = 0.1
+CNN_w_regularizer = regularizers.l2(2e-2)
+RNN_w_regularizer = regularizers.l2(1e-6)
+CNN_batch_size = 10
+RNN_batch_size = 5
+val_split = 0.2
+optimizer = Adam(lr=1e-5)
+final_layer_size = 5
+'''
+
+# Defines Dataset class, which is instantiated to create the dataset with the required images
+class CustomImageDataset(Dataset):
+    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
+        self.img_labels = pd.read_csv(annotations_file)
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
+        image = read_image(img_path)
+        label = self.img_labels.iloc[idx, 1]
+        if self.transform:
+            image = self.transform(image)
+        if self.target_transform:
+            label = self.target_transform(label)
+        return image, label
+
+
+# Might have to replace datapaths or separate between training and testing
+model_filepath = '//data/data_wnx1/rschuurs/pytorchCNN'
+mri_datapath = './ADNI_volumes_customtemplate_float32/'
+anotationsFileDataPath = './LP_ADNIMERGE.csv'
+
+training_data = CustomImageDataset(anotationsFileDataPath, mri_datapath)
+test_data = CustomImageDataset(anotationsFileDataPath, mri_datapath)
+
+# Skips corrupted or non-present files in CSV
+safeTrainingData = nc.SafeDataset(training_data)
+safeTestData = nc.SafeDataset(test_data)
+
+print("Initial Training data length: " + str(training_data.__len__()))
+print("Safe Training data length: " + str(safeTrainingData.__len__()))
+print("Test data length: " + str(training_data.__len__()))
+
+batch_size = 64
+
+# Create data loaders.
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+for X, y in test_dataloader:
+    print(f"Shape of X [N, C, H, W]: {X.shape}")
+    print(f"Shape of y: {y.shape} {y.dtype}")
+    break
+
+
+
+
+
+
+# EXTRA
+
+# will I need these params?
+'''
+params_dict = { 'CNN_w_regularizer': CNN_w_regularizer, 'RNN_w_regularizer': RNN_w_regularizer,
+               'CNN_batch_size': CNN_batch_size, 'RNN_batch_size': RNN_batch_size,
+               'CNN_drop_rate': CNN_drop_rate, 'epochs': 30,
+          'gpu': "/gpu:0", 'model_filepath': model_filepath,
+          'image_shape': (target_rows, target_cols, depth, axis),
+          'num_clinical': num_clinical,
+          'final_layer_size': final_layer_size,
+          'optimizer': optimizer, 'RNN_drop_rate': RNN_drop_rate,}
+
+params = Parameters(params_dict)
+
+# WHAT WAS THIS AGAIN?
+seeds = [np.random.randint(1, 5000) for _ in range(1)]
+
+# READ THIS TO UNDERSTAND TRAIN VS VALIDATION DATA
+def evaluate_net (seed):
+    n_classes = 2
+    data_loader = DataLoader((target_rows, target_cols, depth, axis), seed = seed)
+    train_data, val_data, test_data,rnn_HdataT1,rnn_HdataT2,rnn_HdataT3,rnn_AdataT1,rnn_AdataT2,rnn_AdataT3, test_mri_nonorm = data_loader.get_train_val_test(val_split, mri_datapath)
+
+    print('Length Val Data[0]: ',len(val_data[0]))
+'''