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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 data_transforms.glas_transform import GLAS_Transform
label_names = ['Glands']
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(',')
#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
if args.pretrained_path:
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 = GLAS_Transform(config=data_config)
#dice
dices = []
ious=[]
#load data
for i,img_name in enumerate(sorted(os.listdir(args.data_folder))):
if (('png' not in img_name) and ('jpg' not in img_name) and ('jpeg' not in img_name) and ('bmp' not in img_name)):
continue
if 'anno' in img_name:
continue
# 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.data_folder,img_name[:-4]+'_anno.bmp'))
# 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 = torch.Tensor(np.array(Image.open(gt_path)))
if len(label.shape)==3:
label = label[:,:,0]
label = label.unsqueeze(0)
mask = (label>0)+0
# plt.imshow(gold)
# plt.show()
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 positive point prompts
_,y,x = torch.where(mask==1)
pos_prompts = torch.cat([x.unsqueeze(1),y.unsqueeze(1)],dim=1)
#get negative point prompts
_,y_neg,x_neg = torch.where(mask==0)
neg_prompts = (torch.cat([x_neg.unsqueeze(1),y_neg.unsqueeze(1)],dim=1))
if len(y)>0:
pos_point_idx = random.randint(0,y.shape[0]-1)
neg_point_idx = random.randint(0,y_neg.shape[0]-1)
# points = (torch.cat([pos_prompts[pos_point_idx].unsqueeze(0), neg_prompts[neg_point_idx].unsqueeze(0)],dim=0).unsqueeze(0).to(args.device), torch.Tensor([1,-1]).unsqueeze(0).to(args.device))
points = (pos_prompts[pos_point_idx].unsqueeze(0).unsqueeze(0).to(args.device), torch.Tensor([1]).unsqueeze(0).to(args.device))
else:
neg_point_idx1 = random.randint(0,y_neg.shape[0]-1)
neg_point_idx2 = random.randint(0,y_neg.shape[0]-1)
# points = (torch.cat([neg_prompts[neg_point_idx1].unsqueeze(0), neg_prompts[neg_point_idx2].unsqueeze(0)],dim=0).unsqueeze(0).to(args.device), torch.Tensor([-1,-1]).unsqueeze(0).to(args.device))
points = (neg_prompts[neg_point_idx1].unsqueeze(0).unsqueeze(0).to(args.device), torch.Tensor([-1]).unsqueeze(0).to(args.device))
#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()
masks = model.get_masks_with_manual_prompts(img_embeds_repeated, points=points).cpu()
plt.imshow((masks[0]>=0.5), cmap='gray')
if len(y)>0:
plt.scatter(x[pos_point_idx], y[pos_point_idx], c='green')
# plt.scatter(x_neg[neg_point_idx], y_neg[neg_point_idx], c='red')
else:
plt.scatter(x_neg[neg_point_idx1], y_neg[neg_point_idx1], c='red')
# plt.scatter(x_neg[neg_point_idx2], y_neg[neg_point_idx2], c='red')
plt.savefig(os.path.join(args.save_path,'rescaled_preds', img_name[:-4]+'.png'))
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()
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