Valentin Boussot commited on Nov 7

Commit

6ec9956

1 Parent(s): a1cf612

Add TotalSegmentator KonfAI weights

Files changed (22) hide show

Build.py +82 -0
Destination_Unet_1.txt +100 -0
Destination_Unet_2.txt +82 -0
Destination_Unet_3.txt +64 -0
M291.pt +3 -0
M292.pt +3 -0
M293.pt +3 -0
M294.pt +3 -0
M295.pt +3 -0
M297.pt +3 -0
M298.pt +3 -0
M730.pt +3 -0
M731.pt +3 -0
M732.pt +3 -0
M733.pt +3 -0
M850.pt +3 -0
M851.pt +3 -0
M852.pt +3 -0
M853.pt +3 -0
Model.py +53 -0
Prediction_CT.yml +99 -0
Prediction_MR.yml +92 -0

Build.py ADDED Viewed

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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])

Destination_Unet_1.txt ADDED Viewed

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Destination_Unet_2.txt ADDED Viewed

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Destination_Unet_3.txt ADDED Viewed

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Model.py ADDED Viewed

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+import torch
+from konfai.network import network, blocks
+class ConvBlock(network.ModuleArgsDict):
+    def __init__(self, in_channels : int, out_channels : int, stride: int = 1 ) -> None:
+        super().__init__()
+        self.add_module("Conv_0", torch.nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1, bias=True))
+        self.add_module("Norm_0", torch.nn.InstanceNorm3d(num_features=out_channels, affine=True))
+        self.add_module("Activation_0", torch.nn.LeakyReLU(negative_slope=0.01, inplace=True))
+        self.add_module("Conv_1", torch.nn.Conv3d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=True))
+        self.add_module("Norm_1", torch.nn.InstanceNorm3d(num_features=out_channels, affine=True))
+        self.add_module("Activation_1", torch.nn.LeakyReLU(negative_slope=0.01, inplace=True))
+class UNetHead(network.ModuleArgsDict):
+    def __init__(self, in_channels: int, nb_class: int) -> None:
+        super().__init__()
+        self.add_module("Conv", torch.nn.Conv3d(in_channels = in_channels, out_channels = nb_class, kernel_size = 1, stride = 1, padding = 0))
+class UNetBlock(network.ModuleArgsDict):
+    def __init__(self, channels, nb_class: int, mri: bool, i : int = 0) -> None:
+        super().__init__()
+        self.add_module("DownConvBlock", ConvBlock(in_channels=channels[0], out_channels=channels[1], stride= ((1,2,2) if mri and i > 4 else 2) if i>0 else 1))
+        if len(channels) > 2:
+            self.add_module("UNetBlock", UNetBlock(channels[1:], nb_class, mri, i+1))
+            self.add_module("UpConvBlock", ConvBlock(in_channels=channels[1]*2, out_channels=channels[1]))
+        if i > 0:
+            self.add_module("CONV_TRANSPOSE", torch.nn.ConvTranspose3d(in_channels = channels[1], out_channels = channels[0], kernel_size =  (1,2,2) if mri and i > 4 else 2, stride = (1,2,2) if mri and i > 4 else 2, padding = 0))
+            self.add_module("SkipConnection", blocks.Concat(), in_branch=[0, 1])
+class Unet_TS(network.Network):
+    def __init__(self,
+        optimizer: network.OptimizerLoader = network.OptimizerLoader(),
+        schedulers: dict[str, network.LRSchedulersLoader] = {
+            "default:ReduceLROnPlateau": network.LRSchedulersLoader(0)
+        },
+        outputs_criterions: dict[str, network.TargetCriterionsLoader] = {"default": network.TargetCriterionsLoader()},
+        channels = [1, 32, 64, 128, 320, 320],
+        nb_class: int = 41,
+        mri: bool = False) -> None:
+        super().__init__(
+            in_channels=channels[0],
+            optimizer=optimizer,
+            schedulers=schedulers,
+            outputs_criterions=outputs_criterions,
+            patch=None,
+            dim=3,
+        )
+        self.add_module("UNetBlock", UNetBlock(channels, nb_class, mri))
+        self.add_module("Head", UNetHead(channels[1], nb_class))

Prediction_CT.yml ADDED Viewed

	@@ -0,0 +1,99 @@

+Predictor:
+  Model:
+    classpath: Model:Unet_TS
+    Unet_TS:
+      outputs_criterions: None
+      channels:
+      - 1
+      - 32
+      - 64
+      - 128
+      - 256
+      - 320
+      - 320
+      nb_class: 25
+      mri: false
+  Dataset:
+    groups_src:
+      Volume:
+        groups_dest:
+          Volume:
+            transforms:
+              TensorCast:
+                dtype: float32
+                inverse: false
+              Canonical:
+                inverse: true
+              Clip:
+                min_value: -1024
+                max_value: 276
+                save_clip_min: false
+                save_clip_max: false
+                mask: None
+              Standardize:
+                lazy: false
+                mean: -370.00039267657144
+                std: 436.5998675471528
+                mask: None
+                inverse: true
+              ResampleToResolution:
+                spacing:
+                - 1.5
+                - 1.5
+                - 1.5
+                inverse: true
+              Padding:
+                padding:
+                - 32
+                - 32
+                - 32
+                - 32
+                - 32
+                - 32
+                mode: constant
+                inverse: true
+            patch_transforms: None
+            is_input: true
+    augmentations: None
+    Patch:
+      patch_size:
+      - 96
+      - 128
+      - 160
+      overlap: 32
+      mask: None
+      pad_value: 0
+      extend_slice: 0
+    subset: None
+    filter: None
+    dataset_filenames:
+    - ./Dataset/:nii.gz
+    use_cache: false
+    batch_size: 1
+  outputs_dataset:
+    Head:Conv:
+      OutputDataset:
+        name_class: OutSameAsGroupDataset
+        before_reduction_transforms: None
+        after_reduction_transforms: None
+        final_transforms:
+          Softmax:
+            dim: 0
+          Argmax:
+            dim: 0
+          TensorCast:
+            dtype: uint8
+            inverse: true
+        dataset_filename: Dataset:mha
+        group: MASK
+        same_as_group: Volume:Volume
+        patch_combine: Cosinus
+        inverse_transform: true
+        reduction: Mean
+  train_name: Curvas
+  manual_seed: 32
+  gpu_checkpoints: None
+  images_log: None
+  combine: Mean
+  autocast: false
+  data_log: None

Prediction_MR.yml ADDED Viewed

	@@ -0,0 +1,92 @@

+Predictor:
+  Model:
+    classpath: Model:Unet_TS
+    Unet_TS:
+      outputs_criterions: None
+      channels:
+      - 1
+      - 32
+      - 64
+      - 128
+      - 256
+      - 320
+      - 320
+      nb_class: 30
+  Dataset:
+    groups_src:
+      Volume:
+        groups_dest:
+          Volume:
+            transforms:
+              TensorCast:
+                dtype: float32
+                inverse: false
+              Canonical:
+                inverse: true
+              Standardize:
+                lazy: false
+                mean: None
+                std: None
+                mask: None
+                inverse: false
+              ResampleToResolution:
+                spacing:
+                - 1.5
+                - 1.5
+                - 1.5
+                inverse: true
+              Padding:
+                padding:
+                - 32
+                - 32
+                - 32
+                - 32
+                - 32
+                - 32
+                mode: constant
+                inverse: true
+            patch_transforms: None
+            is_input: true
+    augmentations: None
+    Patch:
+      patch_size:
+      - 96
+      - 128
+      - 160
+      overlap: 32
+      mask: None
+      pad_value: 0
+      extend_slice: 0
+    subset: None
+    filter: None
+    dataset_filenames:
+    - ./Dataset/:nii.gz
+    use_cache: false
+    batch_size: 1
+  outputs_dataset:
+    Head:Conv:
+      OutputDataset:
+        name_class: OutSameAsGroupDataset
+        before_reduction_transforms: None
+        after_reduction_transforms: None
+        final_transforms:
+          Softmax:
+            dim: 0
+          Argmax:
+            dim: 0
+          TensorCast:
+            dtype: uint8
+            inverse: true
+        dataset_filename: Dataset:mha
+        group: MASK
+        same_as_group: Volume:Volume
+        patch_combine: Cosinus
+        inverse_transform: true
+        reduction: Mean
+  train_name: Curvas
+  manual_seed: 32
+  gpu_checkpoints: None
+  images_log: None
+  combine: Mean
+  autocast: false
+  data_log: None