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import torch
import requests
from tqdm import tqdm
import zipfile
import shutil
from pathlib import Path
import os
from functools import partial
from Model import Unet_TS
def convert_torchScript_full(model_name: str, model: torch.nn.Module, type: int, url: str) -> None:
state_dict = download(url)
tmp = {}
with open("Destination_Unet_{}.txt".format(type)) as f2:
it = iter(state_dict.keys())
for l1 in f2:
key = next(it)
while "decoder.seg_layers" in key:
if type == 1:
if "decoder.seg_layers.4" in key :
break
if type == 2:
if "decoder.seg_layers.3" in key:
break
if type == 3:
if "decoder.seg_layers.2" in key:
break
key = next(it)
while "all_modules" in key or "decoder.encoder" in key:
key = next(it)
tmp[l1.replace("\n", "")] = state_dict[key]
model.load_state_dict(tmp)
torch.save({"Model" : {"Unet_TS" : tmp}}, f"{model_name}.pt")
def download(url: str) -> dict[str, torch.Tensor]:
with open(url.split("/")[-1], 'wb') as f:
with requests.get(url, stream=True) as r:
r.raise_for_status()
total_size = int(r.headers.get('content-length', 0))
progress_bar = tqdm(total=total_size, unit='B', unit_scale=True, desc="Downloading")
for chunk in r.iter_content(chunk_size=8192 * 16):
progress_bar.update(len(chunk))
f.write(chunk)
progress_bar.close()
with zipfile.ZipFile(url.split("/")[-1], 'r') as zip_f:
zip_f.extractall(url.split("/")[-1].replace(".zip", ""))
os.remove(url.split("/")[-1])
state_dict = torch.load(next(Path(url.split("/")[-1].replace(".zip", "")).rglob("checkpoint_final.pth"), None), weights_only=False)["network_weights"]
shutil.rmtree(url.split("/")[-1].replace(".zip", ""))
return state_dict
url = "https://github.com/wasserth/TotalSegmentator/releases/download/"
UnetCPP_1 = partial(Unet_TS, channels = [1,32,64,128,256,320,320])
UnetCPP_2 = partial(Unet_TS, channels = [1,32,64,128,256,320])
UnetCPP_3 = partial(Unet_TS, channels = [1,32,64,128,256])
models = {
"M291" : (UnetCPP_1(nb_class=25), 1, url+"v2.0.0-weights/Dataset291_TotalSegmentator_part1_organs_1559subj.zip"),
"M292" : (UnetCPP_1(nb_class=27), 1, url+"v2.0.0-weights/Dataset292_TotalSegmentator_part2_vertebrae_1532subj.zip"),
"M293" : (UnetCPP_1(nb_class=19), 1, url+"v2.0.0-weights/Dataset293_TotalSegmentator_part3_cardiac_1559subj.zip"),
"M294" : (UnetCPP_1(nb_class=24), 1, url+"v2.0.0-weights/Dataset294_TotalSegmentator_part4_muscles_1559subj.zip"),
"M295" : (UnetCPP_1(nb_class=27), 1, url+"v2.0.0-weights/Dataset295_TotalSegmentator_part5_ribs_1559subj.zip"),
"M297" : (UnetCPP_2(nb_class=118), 2, url+"v2.0.4-weights/Dataset297_TotalSegmentator_total_3mm_1559subj_v204.zip"),
"M298" : (UnetCPP_2(nb_class=118), 2, url+"v2.0.0-weights/Dataset298_TotalSegmentator_total_6mm_1559subj.zip"),
"M730" : (UnetCPP_1(nb_class=30, mri = True), 1, url+"v2.2.0-weights/Dataset730_TotalSegmentatorMRI_part1_organs_495subj.zip"),
"M731" : (UnetCPP_1(nb_class=28, mri = True), 1, url+"v2.2.0-weights/Dataset731_TotalSegmentatorMRI_part2_muscles_495subj.zip"),
"M732" : (UnetCPP_2(nb_class=57), 2, url+"v2.2.0-weights/Dataset732_TotalSegmentatorMRI_total_3mm_495subj.zip"),
"M733" : (UnetCPP_3(nb_class=57), 3, url+"v2.2.0-weights/Dataset733_TotalSegmentatorMRI_total_6mm_495subj.zip"),
"M850" : (UnetCPP_1(nb_class=30, mri = True), 1, url+"v2.5.0-weights/Dataset850_TotalSegMRI_part1_organs_1088subj.zip"),
"M851" : (UnetCPP_1(nb_class=22, mri = True), 1, url+"v2.5.0-weights/Dataset851_TotalSegMRI_part2_muscles_1088subj.zip"),
"M852" : (UnetCPP_2(nb_class=51), 2, url+"v2.5.0-weights/Dataset852_TotalSegMRI_total_3mm_1088subj.zip"),
"M853" : (UnetCPP_3(nb_class=51), 3, url+"v2.5.0-weights/Dataset853_TotalSegMRI_total_6mm_1088subj.zip")}
if __name__ == "__main__":
for name, model in models.items():
convert_torchScript_full(name, model[0], model[1], model[2]) |