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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 = ['liver', 'tumor']
# 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='tumor', 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': 1,
'tumor': 2,
}
#change the img size in model config according to data config
model_config['sam']['img_size'] = data_config['data_transforms']['img_size']
model_config['sam']['num_classes'] = len(data_config['data']['label_list'])
#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')
#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()
data_transform = Slice_Transforms(config=data_config)
label_text = args.labels_of_interest
#load data
for i, file_name in enumerate(sorted(os.listdir(args.data_folder))):
print(i)
file_path = os.path.join(args.data_folder, file_name)
im_nib = nib.load(file_path)
# for 2d mode
#image loading and conversion to rgb by replicating channels
if data_config['data']['volume_channel']==2: #data originally is HXWXC
im = (torch.Tensor(np.asanyarray(im_nib.dataobj)).permute(2,0,1).unsqueeze(1).repeat(1,3,1,1))
else: #data originally is CXHXW
im = (torch.Tensor(np.asanyarray(im_nib.dataobj)).unsqueeze(1).repeat(1,3,1,1))
num_slices = im.shape[0]
preds = []
for i in range(num_slices):
slice_im = im[i]
slice_im = data_transform(slice_im)
slice_im = torch.Tensor(slice_im).to(args.device)
with torch.set_grad_enabled(False):
outputs, reg_loss = model(slice_im, [label_text], [i])
slice_pred = (outputs>=0.5) +0
preds.append(slice_pred)
# print(len(preds))
# print(preds[0].shape)
preds = (torch.cat(preds, dim=0).permute(1,2,0)).cpu().numpy().astype('uint8')
# print(preds.shape)
ni_img = nib.Nifti1Image(preds, im_nib.affine)
nib.save(ni_img, os.path.join(args.save_path,'preds',file_name))
if __name__ == '__main__':
main()
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