Merge pull request #67 from GabrielBG0/restructuring

Moving models to specific folders by use

minerva/models/nets/__init__.py

@@ -1,8 +1,8 @@
 from .base import SimpleSupervisedModel
-from .deeplabv3 import DeepLabV3
-from .setr import SETR_PUP
-from .unet import UNet
-from .wisenet import WiseNet
+from .image.deeplabv3 import DeepLabV3
+from .image.setr import SETR_PUP
+from .image.unet import UNet
+from .image.wisenet import WiseNet
 
 __all__ = [
     "SimpleSupervisedModel",

minerva/models/nets/deeplabv3.py → minerva/models/nets/image/deeplabv3.py

@@ -1,6 +1,6 @@
 from typing import Dict, Optional, Sequence
 
-from torch import Tensor, load, nn, optim
+from torch import Tensor, nn, optim
 from torchmetrics import Metric
 from torchvision.models.resnet import resnet50
 from torchvision.models.segmentation.deeplabv3 import ASPP

minerva/models/nets/setr.py → minerva/models/nets/image/setr.py

@@ -4,10 +4,10 @@
 import lightning as L
 import torch
 from torch import nn
-from torchmetrics import JaccardIndex, Metric
+from torchmetrics import Metric
 
-from minerva.models.nets.vit import _VisionTransformerBackbone
-from minerva.utils.upsample import Upsample, resize
+from minerva.models.nets.image.vit import _VisionTransformerBackbone
+from minerva.utils.upsample import Upsample
 
 
 class _SETRUPHead(nn.Module):

minerva/models/nets/unet.py → minerva/models/nets/image/unet.py

@@ -1,14 +1,10 @@
 """ Full assembly of the parts to form the complete network """
 
-import time
-from typing import Dict, Optional
+from typing import Optional
 
-import lightning as L
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.optim as optim
-from torch.optim.lr_scheduler import CyclicLR, StepLR
 
 from minerva.models.nets.base import SimpleSupervisedModel
 
@@ -227,5 +223,5 @@ def __init__(
             loss_fn=loss_fn or torch.nn.MSELoss(),
             learning_rate=learning_rate,
             flatten=False,
-            **kwargs
+            **kwargs,
         )

minerva/models/nets/imu_transformer.py → minerva/models/nets/time_series/imu_transformer.py

@@ -1,6 +1,5 @@
 from typing import Tuple
 
-import lightning as L
 import torch
 from torch import nn
 from torch.nn import TransformerEncoder, TransformerEncoderLayer

minerva/models/nets/inception_time.py → minerva/models/nets/time_series/inception_time.py

@@ -1,11 +1,6 @@
-import time
 from typing import Tuple
 
-import lightning as L
-import numpy as np
 import torch
-from torch import nn
-from torch.nn import TransformerEncoder, TransformerEncoderLayer
 from torchmetrics import Accuracy
 
 from minerva.models.nets.base import SimpleSupervisedModel

minerva/models/nets/resnet.py → minerva/models/nets/time_series/resnet.py

@@ -1,12 +1,6 @@
-import time
-from functools import partial
-from typing import Literal, Tuple
+from typing import Tuple
 
-import lightning as L
-import numpy as np
 import torch
-from torch import nn
-from torch.nn import TransformerEncoder, TransformerEncoderLayer
 from torchmetrics import Accuracy
 
 from minerva.models.nets.base import SimpleSupervisedModel

tests/models/nets/test_deeplabv3.py → tests/models/nets/image/test_deeplabv3.py

@@ -1,6 +1,6 @@
 import torch
 
-from minerva.models.nets.deeplabv3 import DeepLabV3, DeepLabV3Backbone
+from minerva.models.nets.image.deeplabv3 import DeepLabV3, DeepLabV3Backbone
 
 
 def test_deeplabv3_model():

tests/models/nets/test_sfm.py → tests/models/nets/image/test_sfm.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from minerva.models.nets.vit import (
+from minerva.models.nets.image.vit import (
     mae_vit_base_patch16,
     mae_vit_base_patch16D4d256,
     mae_vit_huge_patch14,

tests/models/nets/test_cnn_ha_etal.py → tests/models/nets/time_series/test_cnn_ha_etal.py

@@ -1,6 +1,6 @@
 import torch
 
-from minerva.models.nets.cnns import CNN_HaEtAl_1D, CNN_HaEtAl_2D
+from minerva.models.nets.time_series.cnns import CNN_HaEtAl_1D, CNN_HaEtAl_2D
 
 
 def test_cnn_ha_etal_1d_forward():

tests/models/nets/test_cnn_pf.py → tests/models/nets/time_series/test_cnn_pf.py

@@ -1,6 +1,6 @@
 import torch
 
-from minerva.models.nets.cnns import CNN_PF_2D, CNN_PFF_2D
+from minerva.models.nets.time_series.cnns import CNN_PF_2D, CNN_PFF_2D
 
 
 def test_cnn_pf_forward():

tests/models/nets/test_imu_transformer.py → tests/models/nets/time_series/test_imu_transformer.py

@@ -1,5 +1,10 @@
 import torch
-from minerva.models.nets.imu_transformer import IMUTransformerEncoder, IMUCNN
+
+from minerva.models.nets.time_series.imu_transformer import (
+    IMUCNN,
+    IMUTransformerEncoder,
+)
+
 
 def test_imu_transformer_forward():
     input_shape = (6, 60)
@@ -18,4 +23,4 @@ def test_imu_cnn_forward():
 
     x = torch.rand(1, *input_shape)
     y = model(x)
-    assert y is not None
+    assert y is not None

tests/models/nets/test_inception_time.py → tests/models/nets/time_series/test_inception_time.py

@@ -1,5 +1,7 @@
 import torch
-from minerva.models.nets.inception_time import InceptionTime
+
+from minerva.models.nets.time_series.inception_time import InceptionTime
+
 
 def test_inception_time_forward():
     input_shape = (6, 60)

tests/models/nets/test_resnet_1d.py → tests/models/nets/time_series/test_resnet_1d.py

@@ -1,6 +1,10 @@
 import torch
 
-from minerva.models.nets.resnet import ResNet1D_8, ResNetSE1D_5, ResNetSE1D_8
+from minerva.models.nets.time_series.resnet import (
+    ResNet1D_8,
+    ResNetSE1D_5,
+    ResNetSE1D_8,
+)
 
 
 def test_resnet_1d_8_forward():