import torch import yaml import sys import copy import os sys.path.append("/home/ubuntu/Desktop/Domain_Adaptation_Project/repos/biastuning/") 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 = ['Grasper', 'L Hook Electrocautery', 'Liver', 'Fat', 'Gall Bladder','Abdominal Wall','Gastrointestinal Tract','Cystic Duct','Blood','Hepatic Vein', 'Liver Ligament', 'Connective Tissue'] # 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_dict2 = { 'Grasper':31, 'L Hook Electrocautery':32, 'Liver':21, 'Fat':12, 'Gall Bladder':22, 'Abdominal Wall':11, 'Gastrointestinal Tract':13, 'Cystic Duct':25, 'Blood':24, 'Hepatic Vein':33, 'Liver Ligament':5, 'Connective Tissue':23 } #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) if args.gt_path: 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='biastuning') 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 = Cholec_8k_Transform(config=data_config) #dice dices = [] ious=[] #load data for i,img_name in enumerate(sorted(os.listdir(args.data_folder))): # if i%5!=0: # continue img_path = (os.path.join(args.data_folder,img_name)) if args.gt_path: gt_path = (os.path.join(args.gt_path,img_name[:img_name.find('.')]+'_watershed_mask.png')) # 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 if args.gt_path: gold = np.array(Image.open(gt_path)) if len(gold.shape)==3: gold = gold[:,:,0] if gold.max()<2: gold = (gold*255).astype(int) mask = np.zeros((len(label_dict2),img.shape[1], img.shape[2])) for i,c in enumerate(list(label_dict2.keys())): mask[i,:,:] = (gold==label_dict2[c]) mask = torch.Tensor(mask+0) else: mask = torch.zeros((1,H,W)) img, mask = data_transform(img, mask, is_train=False, apply_norm=True) mask = (mask>=0.5)+0 #get image embeddings img = img.unsqueeze(0).to(args.device) #1XCXHXW masks = model(img,'') argmax_masks = torch.argmax(masks, dim=1).cpu().numpy() # print("argmax masks shape: ",argmax_masks.shape) classwise_dices = [] classwise_ious = [] for j,c1 in enumerate(label_dict): res = np.where(argmax_masks==j,1,0) # print("res shape: ",res.shape) plt.imshow(res[0], cmap='gray') save_dir = os.path.join(args.save_path, c1, 'rescaled_preds') os.makedirs(save_dir, exist_ok=True) plt.savefig(os.path.join(args.save_path, c1, 'rescaled_preds', img_name)) plt.close() if args.gt_path: plt.imshow((mask[j]), cmap='gray') save_dir = os.path.join(args.save_path, c1, 'rescaled_gt') os.makedirs(save_dir, exist_ok=True) plt.savefig(os.path.join(args.save_path, c1, 'rescaled_gt', img_name)) plt.close() classwise_dices.append(dice_coef(mask[j], torch.Tensor(res[0]))) classwise_ious.append(iou_coef(mask[j], torch.Tensor(res[0]))) # break dices.append(classwise_dices) ious.append(classwise_ious) # print("classwise_dices: ", classwise_dices) # print("classwise ious: ", classwise_ious) print(torch.mean(torch.Tensor(dices),dim=0)) print(torch.mean(torch.Tensor(ious),dim=0)) if __name__ == '__main__': main()