File size: 6,454 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 |
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 = ['Left Prograsp Forceps', 'Maryland Bipolar Forceps', 'Right Prograsp Forceps', 'Left Large Needle Driver', 'Right Large Needle Driver', 'Left Grasping Retractor', 'Right Grasping Retractor', 'Vessel Sealer', 'Monopolar Curved Scissors']
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]
#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)
model = Prompt_Adapted_SAM(config=model_config, label_text_dict=label_dict, device=args.device, training_strategy='lora')
if args.pretrained_path:
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 = ENDOVIS_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:
label_name = labels_of_interest[0].replace(' ','_')+'_labels'
#for test data, the labels are arranged differently so uncomment the line below
gt_path = (os.path.join(args.gt_path,img_name))
# gt_path = (os.path.join(args.gt_path,label_name,img_name))
# 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.as_tensor(np.array(Image.open(gt_path))).unsqueeze(0)
#for test data, the labels are arranged differently so uncomment th line below
label = (label==codes[0])+0
label = (label>0)+0
else:
label = torch.zeros((1,H,W))
img, label = data_transform(img, label, is_train=False, apply_norm=True)
label = (label>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((label[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(label, (masks>0.5)+0))
ious.append(iou_coef(label, (masks>0.5)+0))
# break
print("Dice: ",torch.mean(torch.Tensor(dices)))
print("IoU: ",torch.mean(torch.Tensor(ious)))
if __name__ == '__main__':
main()
# {
# "Bipolar Forceps": 1,
# "Prograsp Forceps": 2,
# "Large Needle Driver": 3,
# "Vessel Sealer": 4,
# "Grasping Retractor": 5,
# "Monopolar Curved Scissors": 6,
# "Other": 7
# }
|