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import torch
import yaml
import sys
import copy
import os
sys.path.append("/home/ubuntu/Desktop/Domain_Adaptation_Project/repos/SVDSAM/")
from data_utils import *
from model import *
from utils import *
from baselines import UNet, UNext, medt_net
from vit_seg_modeling import VisionTransformer
from vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg
from axialnet import MedT
label_names = ['Liver','Tumor']
# visualize_li = [[1,0,0],[0,1,0],[1,0,0], [0,0,1], [0,0,1]]
label_dict = {}
# visualize_dict = {}
for i,ln in enumerate(label_names):
label_dict[ln] = i
# visualize_dict[ln] = visualize_li[i]
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--data_folder', default='config_tmp.yml',
help='data folder file path')
parser.add_argument('--data_config', default='config_tmp.yml',
help='data config file path')
parser.add_argument('--model_config', default='model_baseline.yml',
help='model config file path')
parser.add_argument('--pretrained_path', default=None,
help='pretrained model path')
parser.add_argument('--save_path', default='checkpoints/temp.pth',
help='pretrained model path')
parser.add_argument('--gt_path', default='',
help='ground truth path')
parser.add_argument('--device', default='cuda:0', help='device to train on')
parser.add_argument('--codes', default='1,2,1,3,3', help='numeric label to save per instrument')
args = parser.parse_args()
return args
def main():
args = parse_args()
with open(args.data_config, 'r') as f:
data_config = yaml.load(f, Loader=yaml.FullLoader)
with open(args.model_config, 'r') as f:
model_config = yaml.load(f, Loader=yaml.FullLoader)
codes = args.codes.split(',')
codes = [int(c) for c in codes]
label_dict = {
'Liver': [[100,0,100]],
'Kidney': [[255,255,0]],
'Pancreas': [[0,0,255]],
'Vessels': [[255,0,0]],
'Adrenals': [[0,255,255]],
'Gall Bladder': [[0,255,0]],
'Bones': [[255,255,255]],
'Spleen': [[255,0,255]]
}
#make folder to save visualizations
os.makedirs(os.path.join(args.save_path,"preds"),exist_ok=True)
os.makedirs(os.path.join(args.save_path,"rescaled_preds"),exist_ok=True)
os.makedirs(os.path.join(args.save_path,"rescaled_gt"),exist_ok=True)
#load model
#change the img size in model config according to data config
in_channels = model_config['in_channels']
out_channels = model_config['num_classes']
img_size = model_config['img_size']
if model_config['arch']=='Prompt Adapted SAM':
model = Prompt_Adapted_SAM(model_config, label_dict, args.device, training_strategy='svdtuning')
elif model_config['arch']=='UNet':
model = UNet(in_channels=in_channels, out_channels=out_channels)
elif model_config['arch']=='UNext':
model = UNext(num_classes=out_channels, input_channels=in_channels, img_size=img_size)
elif model_config['arch']=='MedT':
#TODO
model = MedT(img_size=img_size, num_classes=out_channels)
elif model_config['arch']=='TransUNet':
config_vit = CONFIGS_ViT_seg['R50-ViT-B_16']
config_vit.n_classes = out_channels
config_vit.n_skip = 3
# if args.vit_name.find('R50') != -1:
# config_vit.patches.grid = (int(args.img_size / args.vit_patches_size), int(args.img_size / args.vit_patches_size))
model = VisionTransformer(config_vit, img_size=img_size, num_classes=config_vit.n_classes)
model.load_state_dict(torch.load(args.pretrained_path, map_location=args.device))
model = model.to(args.device)
model = model.eval()
#load data transform
data_transform = LiTS2_Transform(config=data_config)
#dice
tumor_dices = []
tumor_ious=[]
liver_dices = []
liver_ious=[]
#load data
root_path = "/media/ubuntu/New Volume/jay/LiTS2/archive"
imgs_path = os.path.join(root_path, 'dataset_6/dataset_6')
test_csv = pd.read_csv(os.path.join(root_path, 'lits_test.csv'))
for i in range(len(test_csv)):
if i%10!=0:
continue
img_path = (os.path.join(root_path,'dataset_6',test_csv['filepath'].iloc[i][18:]))
image_name = test_csv['filepath'].iloc[i][28:]
liver_mask_path = os.path.join(root_path,'dataset_6',test_csv['liver_maskpath'].iloc[i][18:])
tumor_mask_path = os.path.join(root_path,'dataset_6',test_csv['tumor_maskpath'].iloc[i][18:])
# print(img_path)
img = torch.as_tensor(np.array(Image.open(img_path).convert("RGB")))
img = img.permute(2,0,1)
C,H,W = img.shape
#make a dummy mask of shape 1XHXW
try:
liver_label = torch.Tensor(np.array(Image.open(liver_mask_path)))[:,:,0]
tumor_label = torch.Tensor(np.array(Image.open(tumor_mask_path)))[:,:,0]
except:
liver_label = torch.zeros(H, W)
tumor_label = torch.zeros(H, W)
# label = np.array(Image.open(gt_path).convert("RGB"))
# temp = np.zeros((H,W)).astype('uint8')
# selected_color_list = label_dict[args.labels_of_interest]
# for c in selected_color_list:
# temp = temp | (np.all(np.where(label==c,1,0),axis=2))
# # plt.imshow(gold)
# # plt.show()
# mask = torch.Tensor(temp).unsqueeze(0)
liver_label = liver_label.unsqueeze(0)
liver_label = (liver_label>0)+0
tumor_label = tumor_label.unsqueeze(0)
tumor_label = (tumor_label>0)+0
#convert all grayscale pixels due to resizing back to 0, 1
img1, liver_label = data_transform(img, liver_label, is_train=False, apply_norm=True)
liver_label = (liver_label>=0.5)+0
# liver_label = liver_label[0]
#convert all grayscale pixels due to resizing back to 0, 1
_, tumor_label = data_transform(img, tumor_label, is_train=False, apply_norm=True)
tumor_label = (tumor_label>=0.5)+0
# tumor_label = tumor_label[0]
#get image embeddings
img = img1.unsqueeze(0).to(args.device) #1XCXHXW
final_label = torch.cat([liver_label,tumor_label], dim=0)
masks,_ = model(img,'')
masks_liver = masks[:,0,:,:].cpu()
masks_tumor = masks[:,1,:,:].cpu()
plt.imshow(((masks_liver>=0.5)[0]), cmap='gray')
plt.savefig(os.path.join(args.save_path,'rescaled_preds', image_name[:-4] +'_liver.png'))
plt.close()
# plt.show()
plt.imshow(((masks_tumor>=0.5)[0]), cmap='gray')
plt.savefig(os.path.join(args.save_path,'rescaled_preds', image_name[:-4] +'_tumor.png'))
plt.close()
# plt.show()
plt.imshow((liver_label[0]), cmap='gray')
plt.savefig(os.path.join(args.save_path,'rescaled_gt', image_name[:-4] +'_liver.png'))
plt.close()
# plt.show()
plt.imshow((tumor_label[0]), cmap='gray')
plt.savefig(os.path.join(args.save_path,'rescaled_gt', image_name[:-4] +'_tumor.png'))
plt.close()
# plt.show()
# print("dice: ",dice_coef(label, (masks>0.5)+0))
# print(liver_label.shape)
# print((((masks[0]>=0.5)+0).unsqueeze(0)).shape)
liver_dices.append(dice_coef(liver_label, ((masks_liver[0]>=0.5)+0).unsqueeze(0)))
tumor_dices.append(dice_coef(tumor_label, ((masks_tumor[0]>=0.5)+0).unsqueeze(0)))
liver_ious.append(iou_coef(liver_label, ((masks_liver[0]>=0.5)+0).unsqueeze(0)))
tumor_ious.append(iou_coef(tumor_label, ((masks_tumor[0]>=0.5)+0).unsqueeze(0)))
# 1/0
# break
print("Liver DICE: ",torch.mean(torch.Tensor(liver_dices)))
print("Liver IoU", torch.mean(torch.Tensor(liver_ious)))
print("Tumor DICE", torch.mean(torch.Tensor(tumor_dices)))
print("Tumor IoU", torch.mean(torch.Tensor(tumor_ious)))
if __name__ == '__main__':
main()
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