getRDAugmented_saveimg.py
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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)