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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 *
label_names = ['Effusion', 'Nodule', 'Cardiomegaly', 'Fibrosis', 'Consolidation', 'Emphysema', 'Mass', 'Fracture', 'Calcification', 'Pleural Thickening', 'Pneumothorax', 'Atelectasis', 'Diffuse Nodule']
# 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 = {
'Effusion': 1,
'Nodule': 2,
'Cardiomegaly': 3,
'Fibrosis': 4,
'Consolidation': 5,
'Emphysema': 6,
'Mass': 7,
'Fracture': 8,
'Calcification': 9,
'Pleural Thickening': 10,
'Pneumothorax': 11,
'Atelectasis': 12,
'Diffuse Nodule': 13
}
#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')
# print(model)
# model.load_state_dict(torch.load(args.pretrained_path, map_location=args.device), strict=False)
# temp = torch.load(args.pretrained_path, map_location=args.device)
# print(list(temp.keys()))
#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 = ChestXDet_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))
c = label_dict[args.labels_of_interest]
temp = (label==c)
# plt.imshow(gold)
# plt.show()
mask = torch.Tensor(temp).unsqueeze(0)
mask = 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
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()
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