File size: 9,002 Bytes
4a1f918 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
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 = ['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('--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_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
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 = 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%10!=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:
label_of_interest = args.labels_of_interest
gold = np.array(Image.open(gt_path))
if len(gold.shape)==3:
gold = gold[:,:,0]
if gold.max()<2:
gold = (gold*255).astype(int)
# plt.imshow(gold)
# plt.show()
mask = (gold==label_dict2[label_of_interest])
mask = torch.Tensor(mask+0)
mask = torch.Tensor(mask).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 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)
masks = model.get_masks_with_manual_prompts(img_embeds_repeated, points=points).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')
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))
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()
|