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 * label_names = ['Liver', 'Kidney', 'Pancreas', 'Vessels', 'Adrenals', 'Gall Bladder', 'Bones', 'Spleen'] # 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('--labels_of_interest', default='Left Prograsp Forceps,Maryland Bipolar Forceps,Right Prograsp Forceps,Left Large Needle Driver,Right Large Needle Driver', help='labels of interest') 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) labels_of_interest = args.labels_of_interest.split(',') 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) if args.gt_path: os.makedirs(os.path.join(args.save_path,"rescaled_gt"),exist_ok=True) #load model model = Prompt_Adapted_SAM(config=model_config, label_text_dict=label_dict, device=args.device, training_strategy='svdtuning') # model = Prompt_Adapted_SAM(config=model_config, label_text_dict=label_dict, device=args.device, training_strategy='lora') #legacy model support sdict = torch.load(args.pretrained_path, map_location=args.device) # for key in list(sdict.keys()): # if 'sam_encoder.neck' in key: # if '0' in key: # new_key = key.replace('0','conv1') # if '1' in key: # new_key = key.replace('1','ln1') # if '2' in key: # new_key = key.replace('2','conv2') # if '3' in key: # new_key = key.replace('3','ln2') # sdict[new_key] = sdict[key] # _ = sdict.pop(key) # if 'mask_decoder' in key: # if 'trainable' in key: # _ = sdict.pop(key) model.load_state_dict(sdict,strict=True) model = model.to(args.device) model = model.eval() #load data transform data_transform = Ultrasound_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)) if not os.path.exists(gt_path): gt_path = (os.path.join(args.gt_path,img_name[:-4]+'.png')) if not os.path.exists(gt_path): continue # 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: 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) 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 img_embeds = model.get_image_embeddings(img) # generate masks for all labels of interest img_embeds_repeated = img_embeds.repeat(len(labels_of_interest),1,1,1) x_text = [t for t in labels_of_interest] masks = model.get_masks_for_multiple_labels(img_embeds_repeated, x_text).cpu() argmax_masks = torch.argmax(masks, dim=0) final_mask = torch.zeros(masks[0].shape) final_mask_rescaled = torch.zeros(masks[0].shape).unsqueeze(-1).repeat(1,1,3) #save masks for i in range(final_mask.shape[0]): for j in range(final_mask.shape[1]): final_mask[i,j] = codes[argmax_masks[i,j]] if masks[argmax_masks[i,j],i,j]>=0.5 else 0 # final_mask_rescaled[i,j] = torch.Tensor(visualize_dict[(labels_of_interest[argmax_masks[i,j]])] if masks[argmax_masks[i,j],i,j]>=0.5 else [0,0,0]) # save_im = Image.fromarray(final_mask.numpy()) # save_im.save(os.path.join(args.save_path,'preds', img_name)) # plt.imshow(final_mask_rescaled,cmap='gray') # plt.savefig(os.path.join(args.save_path,'rescaled_preds', img_name)) # plt.close() # print("label shape: ", label.shape) # plt.imshow(label[0], cmap='gray') # plt.show() plt.imshow((masks[0]>=0.5), cmap='gray') plt.savefig(os.path.join(args.save_path,'rescaled_preds', img_name)) plt.close() if args.gt_path: plt.imshow((mask[0]), cmap='gray') plt.savefig(os.path.join(args.save_path,'rescaled_gt', img_name)) plt.close() # print("dice: ",dice_coef(label, (masks>0.5)+0)) dices.append(dice_coef(mask, (masks>=0.5)+0)) ious.append(iou_coef(mask, (masks>=0.5)+0)) # break print(torch.mean(torch.Tensor(dices))) print(torch.mean(torch.Tensor(ious))) if __name__ == '__main__': main()