random policy

+
+import os
+import fire
+import json
+from pprint import pprint
+import pickle
+import random
+import numpy as np
+import cv2
+
+import torch
+import torch.nn as nn
+from torchvision.utils import save_image
+import torchvision.transforms as transforms
+
+from transforms import *
+from utils import *
+
+# command
+# python getRDAugmented_saveimg.py --model_path='logs/April_26_17:36:17_NL_resnet50__None'
+
+
+DEFALUT_CANDIDATES = [
+    ShearXY,
+    TranslateXY,
+    # Rotate,
+    AutoContrast,
+    # Invert,
+    # Equalize, # Histogram Equalize --> white tumor
+    # Solarize,
+    Posterize,
+    Contrast,
+    # Color,
+    Brightness,
+    Sharpness,
+    Cutout
+]
+
+def get_next_subpolicy(transform_candidates = DEFALUT_CANDIDATES, op_per_subpolicy=2):
+    if not transform_candidates:
+        transform_candidates = DEFALUT_CANDIDATES
+
+    n_candidates = len(transform_candidates)
+    subpolicy = []
+
+    for i in range(op_per_subpolicy):
+        indx = random.randrange(n_candidates)
+        prob = random.random()
+        mag = random.random()
+        subpolicy.append(transform_candidates[indx](prob, mag))
+
+    subpolicy = transforms.Compose([
+            transforms.Pad(4),
+            transforms.RandomHorizontalFlip(),
+            *subpolicy,
+            transforms.Resize([240, 240]),
+            transforms.ToTensor()
+        ])
+
+    return subpolicy
+
+def eval(model_path):
+    print('\n[+] Parse arguments')
+    kwargs_path = os.path.join(model_path, 'kwargs.json')
+    kwargs = json.loads(open(kwargs_path).read())
+    args, kwargs = parse_args(kwargs)
+    pprint(args)
+    device = torch.device('cuda' if args.use_cuda else 'cpu')
+
+    cp_path = os.path.join(model_path, 'augmentation.cp')
+
+
+    print('\n[+] Load transform')
+    # list to tensor
+
+    aug_transform_list = []
+
+    for i in range (16):
+        aug_transform_list.append(get_next_subpolicy(DEFALUT_CANDIDATES))
+
+    
+    transform = transforms.RandomChoice(aug_transform_list)
+    print(transform)
+"""
+    print('\n[+] Load dataset')
+
+    dataset = get_dataset(args, transform, 'train')
+    loader = iter(get_aug_dataloader(args, dataset))
+
+
+    print('\n[+] Save 1 random policy')   
+
+    save_dir = os.path.join(model_path, 'RD_aug_synthesized')
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    
+    # non lesion
+    normal_dir = '/root/volume/2016104167/data/MICCAI_BraTS_2019_Data_Training/NonLesion_flair_frame_all'
+
+    for i, (image, target) in enumerate(loader):
+        image = image.view(240, 240)
+
+        # get random normal brain img
+        nor_file = random.choice(os.listdir(normal_dir))
+        nor_img = cv2.imread(os.path.join(normal_dir, nor_file), cv2.IMREAD_GRAYSCALE)
+        # print(nor_img.shape) # (256, 224)
+        nor_img = cv2.resize(nor_img, (240, 240))
+
+        # synthesize
+        image = np.asarray(image)    
+        image_255 = image * 255
+        image_255[image_255 < 10] = 0
+        nor_img[image_255 > 10] = 0
+        syn_image = nor_img + image_255
+
+        # save synthesized img
+        cv2.imwrite(os.path.join(save_dir, 'aug_'+ str(i) + '.png'), syn_image)
+
+        if((i+1) % 1000 == 0):
+            print("\n saved images: ", i)
+            break
+
+    print('\n[+] Finished to save')
+    """
+
+if __name__ == '__main__':
+    fire.Fire(eval)
+
+    
+    
+

@@ -25,12 +25,12 @@ from networks import basenet, grayResNet2
 
 DATASET_PATH = '/content/drive/My Drive/CD2 Project/'
 
-TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/nonaug+Normal_train/'
-TRAIN_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/train_nonaug_classify_target.csv'
-VAL_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/nonaug+Normal_val/'
-VAL_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/val_nonaug_classify_target.csv'
-TEST_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/nonaug+Normal_test/'
-TEST_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/test_nonaug_classify_target.csv'
+TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/aug&HGG+NL_train/'
+TRAIN_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/train_augNL_classify_target.csv'
+VAL_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/NL+HGG_val/'
+VAL_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/val_nonaugNL_classify_target.csv'
+TEST_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/NL+HGG_test/'
+TEST_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/classification data/test_nonaugNL_classify_target.csv'
 
 current_epoch = 0