rename models -> arch

nndet/arch/blocks/__init__.py

+from nndet.arch.blocks.basic import AbstractBlock, StackedConvBlock, \
+    StackedResidualBlock, StackedConvBlock2
+from nndet.arch.blocks.res import ResBasic, ResBottleneck

nndet/models/blocks/basic.py → nndet/arch/blocks/basic.py

@@ -20,8 +20,8 @@ import torch.nn as nn
 from abc import abstractmethod
 from typing import Sequence, Callable, Union, Tuple
 
-from nndet.models.conv import NdParam
-from nndet.models.blocks.res import ResBasic
+from nndet.arch.conv import NdParam
+from nndet.arch.blocks.res import ResBasic
 
 
 class AbstractBlock(nn.Module):

nndet/models/blocks/res.py → nndet/arch/blocks/res.py

@@ -21,8 +21,8 @@ from typing import Sequence, Callable, Optional
 from functools import reduce 
 from loguru import logger
 
-from nndet.models.conv import nd_pool
-from nndet.models.conv import NdParam
+from nndet.arch.conv import nd_pool
+from nndet.arch.conv import NdParam
 
 
 class ResBasic(nn.Module):

nndet/models/blocks/se.py → nndet/arch/blocks/se.py

@@ -18,7 +18,7 @@ import torch
 import torch.nn as nn
 
 
-from nndet.models.conv import nd_pool, nd_conv
+from nndet.arch.conv import nd_pool, nd_conv
 
 
 class SELayer(nn.Module):

nndet/models/conv.py → nndet/arch/conv.py

@@ -18,8 +18,8 @@ import torch
 import torch.nn as nn
 from typing import Union, Callable, Any, Optional, Tuple, Sequence, Type
 
-from nndet.models.initializer import InitWeights_He
-from nndet.models.layers.norm import GroupNorm
+from nndet.arch.initializer import InitWeights_He
+from nndet.arch.layers.norm import GroupNorm
 
 
 NdParam = Union[int, Tuple[int, int], Tuple[int, int, int]]
@@ -415,7 +415,7 @@ def nd_norm(norm_type: str, dim: int, *args, **kwargs) -> torch.nn.Module:
                 * :class:`torch.nn.InstanceNorm2d`
                 * :class:`torch.nn.InstanceNorm3d`
                 * :class:`torch.nn.LocalResponseNorm`
-                * :class:`nndet.models.layers.norm.GroupNorm`
+                * :class:`nndet.arch.layers.norm.GroupNorm`
     """
     if dim is None:
         dim_str = ""

nndet/arch/decoder/__init__.py

+from nndet.arch.decoder.fpn import FPN, UFPN, FPN2

nndet/models/decoder/base.py → nndet/arch/decoder/base.py

@@ -20,7 +20,7 @@ from typing import Sequence, List, Tuple, Union, Callable, Optional, TypeVar
 
 from loguru import logger
 
-from nndet.models.conv import conv_kwargs_helper
+from nndet.arch.conv import conv_kwargs_helper
 from nndet.utils import to_dtype
 
 

nndet/arch/encoder/__init__.py

+from nndet.arch.encoder.abstract import AbstractEncoder
+from nndet.arch.encoder.modular import Encoder

nndet/models/encoder/modular.py → nndet/arch/encoder/modular.py

@@ -18,8 +18,8 @@ import torch
 import torch.nn as nn
 from typing import Callable, Tuple, Sequence, Union, List, Dict, Optional
 
-from nndet.models.encoder.abstract import AbstractEncoder
-from nndet.models.blocks.basic import AbstractBlock
+from nndet.arch.encoder.abstract import AbstractEncoder
+from nndet.arch.blocks.basic import AbstractBlock
 
 
 __all__ = ["Encoder"]

nndet/arch/heads/__init__.py

+from nndet.arch.heads.classifier import ClassifierType, Classifier
+from nndet.arch.heads.comb import HeadType, AbstractHead
+from nndet.arch.heads.regressor import RegressorType, Regressor
+from nndet.arch.heads.segmenter import SegmenterType, Segmenter

nndet/models/heads/comb.py → nndet/arch/heads/comb.py

@@ -23,8 +23,8 @@ from abc import abstractmethod
 
 from nndet.detection.boxes import BoxCoderND
 from nndet.detection.boxes.sampler import AbstractSampler
-from nndet.models.heads.classifier import Classifier
-from nndet.models.heads.regressor import Regressor
+from nndet.arch.heads.classifier import Classifier
+from nndet.arch.heads.regressor import Regressor
 
 
 class AbstractHead(nn.Module):

nndet/models/heads/regressor.py → nndet/arch/heads/regressor.py

@@ -23,7 +23,7 @@ from abc import abstractmethod
 from loguru import logger
 
 from nndet.detection.boxes import box_iou
-from nndet.models.layers.scale import Scale
+from nndet.arch.layers.scale import Scale
 from torch import Tensor
 
 from nndet.losses import SmoothL1Loss, GIoULoss

nndet/models/heads/segmenter.py → nndet/arch/heads/segmenter.py

@@ -20,10 +20,10 @@ import torch.nn as nn
 from torch import Tensor
 from typing import Dict, List, Union, Sequence, Optional, Tuple, TypeVar
 
-from nndet.models.conv import compute_padding_for_kernel, conv_kwargs_helper
-from nndet.models.heads.comb import AbstractHead
+from nndet.arch.conv import compute_padding_for_kernel, conv_kwargs_helper
+from nndet.arch.heads.comb import AbstractHead
+from nndet.arch.layers.interpolation import InterpolateToShapes
 from nndet.losses.segmentation import SoftDiceLoss, TopKLoss
-from nndet.models.layers.interpolation import InterpolateToShapes
 
 
 class Segmenter(AbstractHead):

nndet/models/layers/__init__.py → nndet/arch/layers/__init__.py

-from nndet.models.layers.interpolation import (
+from nndet.arch.layers.interpolation import (
     Interpolate,
     InterpolateToShapes,
     InterpolateToShape,
     MaxPoolToShapes,
 )
-from nndet.models.layers.norm import GroupNorm
+from nndet.arch.layers.norm import GroupNorm