From a1c1627f7cf4d928aed55aaa847109c8e680ca5d Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Tue, 14 May 2024 17:06:17 +0200
Subject: [PATCH 01/20] train with not rotated image and reconstruction loss as
 minimum over rotated images with reconstructed image of not rotated image

---
 ...otational_variational_autoencoder_power.py | 25 ++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/models/rotational_variational_autoencoder_power.py b/models/rotational_variational_autoencoder_power.py
index e4866a3..87df7b7 100644
--- a/models/rotational_variational_autoencoder_power.py
+++ b/models/rotational_variational_autoencoder_power.py
@@ -4,6 +4,7 @@
 import torch.linalg
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.transforms.v2.functional as functional
 from power_spherical import HypersphericalUniform, PowerSpherical
 from torch.optim import Adam
 
@@ -88,10 +89,28 @@ def forward(self, x):
         return (z_location, z_scale), (q_z, p_z), z, recon
 
     def training_step(self, batch, batch_idx):
-        best_scaled_image, _, _, _ = self.find_best_rotation(batch)
-        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(best_scaled_image)
 
-        loss_recon = self.reconstruction_loss(best_scaled_image, recon)
+        with torch.no_grad():
+            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
+            scaled = functional.resize(
+                crop, [self.input_size, self.input_size], antialias=True
+            )
+
+        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(scaled)
+        loss_recon = self.reconstruction_loss(scaled, recon)
+
+        for i in range(1, self.rotations):
+            with torch.no_grad():
+                rotate = functional.rotate(
+                    batch, 360.0 / self.rotations * i, expand=False
+                )
+                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
+                scaled = functional.resize(
+                    crop, [self.input_size, self.input_size], antialias=True
+                )
+
+            loss_recon = torch.min(loss_recon, self.reconstruction_loss(scaled, recon))
+
         loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
         loss = (loss_recon + loss_KL).mean()
         loss_recon = loss_recon.mean()

From c7a15c0c32c079c9f41b102a803e7a0e6c2b991e Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Fri, 17 May 2024 11:54:03 +0200
Subject: [PATCH 02/20] improve cnn encoder/decoder architecture: - remove one
 layers - double channels - smaller learning rate - upsampling in decoder

---
 models/__init__.py                |  4 ++++
 models/convolutional_decoder_2.py | 39 ++++++++++++++++++++++++++++++
 models/convolutional_encoder_2.py | 40 +++++++++++++++++++++++++++++++
 3 files changed, 83 insertions(+)
 create mode 100644 models/convolutional_decoder_2.py
 create mode 100644 models/convolutional_encoder_2.py

diff --git a/models/__init__.py b/models/__init__.py
index 9fa70b5..1d91252 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -13,9 +13,11 @@
 """
 
 from .convolutional_decoder import ConvolutionalDecoder
+from .convolutional_decoder_2 import ConvolutionalDecoder2
 from .convolutional_decoder_224 import ConvolutionalDecoder224
 from .convolutional_decoder_256 import ConvolutionalDecoder256
 from .convolutional_encoder import ConvolutionalEncoder
+from .convolutional_encoder_2 import ConvolutionalEncoder2
 from .rotational_autoencoder import RotationalAutoencoder
 from .rotational_variational_autoencoder import RotationalVariationalAutoencoder
 from .rotational_variational_autoencoder_power import (
@@ -24,9 +26,11 @@
 
 __all__ = [
     "ConvolutionalDecoder",
+    "ConvolutionalDecoder2",
     "ConvolutionalDecoder224",
     "ConvolutionalDecoder256",
     "ConvolutionalEncoder",
+    "ConvolutionalEncoder2",
     "RotationalAutoencoder",
     "RotationalVariationalAutoencoder",
     "RotationalVariationalAutoencoderPower",
diff --git a/models/convolutional_decoder_2.py b/models/convolutional_decoder_2.py
new file mode 100644
index 0000000..bd6313e
--- /dev/null
+++ b/models/convolutional_decoder_2.py
@@ -0,0 +1,39 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ConvolutionalDecoder2(nn.Module):
+    def __init__(self, h_dim: int = 256):
+        super().__init__()
+
+        self.fc = nn.Linear(h_dim, 256 * 8 * 8)
+        self.deconv0 = nn.ConvTranspose2d(
+            in_channels=256, out_channels=128, kernel_size=(3, 3), stride=1, padding=1
+        )
+        self.upsample0 = nn.Upsample(scale_factor=2)  # 16x16
+        self.deconv1 = nn.ConvTranspose2d(
+            in_channels=128, out_channels=64, kernel_size=(3, 3), stride=1, padding=1
+        )
+        self.upsample1 = nn.Upsample(scale_factor=2)  # 32x32
+        self.deconv2 = nn.ConvTranspose2d(
+            in_channels=64, out_channels=32, kernel_size=(3, 3), stride=1, padding=1
+        )
+        self.upsample2 = nn.Upsample(scale_factor=2)  # 64x64
+        self.deconv3 = nn.ConvTranspose2d(
+            in_channels=32, out_channels=3, kernel_size=(3, 3), stride=1, padding=1
+        )
+        self.upsample3 = nn.Upsample(scale_factor=2)  # 128x128
+
+    def forward(self, x: torch.tensor) -> torch.tensor:
+        x = F.relu(self.fc(x))
+        x = x.view(-1, 256, 8, 8)
+        x = F.relu(self.deconv0(x))
+        x = self.upsample0(x)
+        x = F.relu(self.deconv1(x))
+        x = self.upsample1(x)
+        x = F.relu(self.deconv2(x))
+        x = self.upsample2(x)
+        x = self.deconv3(x)
+        x = self.upsample3(x)
+        return x
diff --git a/models/convolutional_encoder_2.py b/models/convolutional_encoder_2.py
new file mode 100644
index 0000000..ccf9419
--- /dev/null
+++ b/models/convolutional_encoder_2.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ConvolutionalEncoder2(nn.Module):
+    def __init__(self, h_dim: int = 256):
+        super().__init__()
+
+        self.conv0 = nn.Conv2d(
+            in_channels=3, out_channels=32, kernel_size=(3, 3), stride=1, padding=1
+        )  # 128x128
+        self.pool0 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 64x64
+        self.conv1 = nn.Conv2d(
+            in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=1
+        )  # 64x64
+        self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 32x32
+        self.conv2 = nn.Conv2d(
+            in_channels=64, out_channels=128, kernel_size=(3, 3), stride=1, padding=1
+        )  # 32x32
+        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 16x16
+        self.conv3 = nn.Conv2d(
+            in_channels=128, out_channels=256, kernel_size=(3, 3), stride=1, padding=1
+        )  # 16x16
+        self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 8x8
+
+        self.fc1 = nn.Linear(256 * 8 * 8, h_dim)
+
+    def forward(self, x: torch.tensor) -> torch.tensor:
+        x = F.relu(self.conv0(x))
+        x = self.pool0(x)
+        x = F.relu(self.conv1(x))
+        x = self.pool1(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool2(x)
+        x = F.relu(self.conv3(x))
+        x = self.pool3(x)
+        x = torch.flatten(x, start_dim=1)
+        x = F.relu(self.fc1(x))
+        return x

From 5348385f67e87ac751511e20b7a0a2917bd16540 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 23 May 2024 10:12:57 +0200
Subject: [PATCH 03/20] add ImageDataset

---
 data/image_dataset.py | 70 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 data/image_dataset.py

diff --git a/data/image_dataset.py b/data/image_dataset.py
new file mode 100644
index 0000000..14af027
--- /dev/null
+++ b/data/image_dataset.py
@@ -0,0 +1,70 @@
+""" Create dataset with all image files in a directory.
+"""
+
+import os
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+def get_all_filenames(data_directory: str, extensions: list[str]):
+    result = []
+    for dirpath, dirnames, filenames in os.walk(data_directory):
+        for filename in filenames:
+            if Path(filename).suffix in extensions:
+                result.extend(os.path.join(dirpath, filename))
+        for dirname in dirnames:
+            result.extend(get_all_filenames(dirname, extensions))
+    return result
+
+
+class ImageDataset(Dataset):
+    """Create dataset with all image files in a directory."""
+
+    def __init__(
+        self,
+        data_directory: str,
+        extensions: list[str] = ["jpg"],
+        transform=None,
+    ):
+        """Initializes the data set.
+
+        Args:
+            data_directory (str): The data directory.
+            transform (torchvision.transforms, optional): A single or a set of
+                transformations to modify the images. Defaults to None.
+        """
+
+        self.transform = transform
+
+        filenames = get_all_filenames(data_directory, extensions)
+
+        for file in sorted(filenames):
+            images = np.load(os.path.join(data_directory, file)).astype(np.float32)
+            self.images = np.append(self.images, images, axis=0)
+
+    def __len__(self) -> int:
+        """Return the number of items in the dataset.
+
+        Returns:
+            int: Number of items in dataset.
+        """
+        return len(self.images)
+
+    def __getitem__(self, index: int) -> torch.Tensor:
+        """Retrieves the item/items with the given indices from the dataset.
+
+        Args:
+            index: The index of the item to retrieve.
+
+        Returns:
+            data: Data of the item/items with the given indices.
+
+        """
+        data = self.images[index]
+        data = torch.Tensor(data)
+        if self.transform:
+            data = self.transform(data)
+        return data

From 8e73df84fa4743e12f6c7e082f005215f85d44e5 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 23 May 2024 19:18:57 +0200
Subject: [PATCH 04/20] Add ImagesDataset and ImagesDataModule

---
 src/spherinator/data/__init__.py              |   4 +
 src/spherinator/data/images_data_module.py    |  85 ++++++++++++++++++
 .../{image_dataset.py => images_dataset.py}   |  13 +--
 tests/data/images/Abra/1.jpg                  | Bin 0 -> 9749 bytes
 tests/data/images/Zubat/1.jpg                 | Bin 0 -> 10144 bytes
 tests/test_images_data_module.py              |  30 +++++++
 tests/test_images_dataset.py                  |  29 ++++++
 7 files changed, 156 insertions(+), 5 deletions(-)
 create mode 100644 src/spherinator/data/images_data_module.py
 rename src/spherinator/data/{image_dataset.py => images_dataset.py} (79%)
 create mode 100755 tests/data/images/Abra/1.jpg
 create mode 100755 tests/data/images/Zubat/1.jpg
 create mode 100644 tests/test_images_data_module.py
 create mode 100644 tests/test_images_dataset.py

diff --git a/src/spherinator/data/__init__.py b/src/spherinator/data/__init__.py
index 751ade7..159e7b0 100644
--- a/src/spherinator/data/__init__.py
+++ b/src/spherinator/data/__init__.py
@@ -7,6 +7,8 @@
 from .illustris_sdss_data_module import IllustrisSdssDataModule
 from .illustris_sdss_dataset import IllustrisSdssDataset
 from .illustris_sdss_dataset_with_metadata import IllustrisSdssDatasetWithMetadata
+from .images_data_module import ImagesDataModule
+from .images_dataset import ImagesDataset
 from .shapes_data_module import ShapesDataModule
 from .shapes_dataset import ShapesDataset
 from .spherinator_data_module import SpherinatorDataModule
@@ -18,6 +20,8 @@
     "IllustrisSdssDataModule",
     "IllustrisSdssDataset",
     "IllustrisSdssDatasetWithMetadata",
+    "ImagesDataModule",
+    "ImagesDataset",
     "ShapesDataModule",
     "ShapesDataset",
     "SpherinatorDataModule",
diff --git a/src/spherinator/data/images_data_module.py b/src/spherinator/data/images_data_module.py
new file mode 100644
index 0000000..ded16f2
--- /dev/null
+++ b/src/spherinator/data/images_data_module.py
@@ -0,0 +1,85 @@
+import torch
+import torchvision.transforms.v2 as transforms
+from lightning.pytorch import LightningDataModule
+from torch.utils.data import DataLoader
+
+from spherinator.data.images_dataset import ImagesDataset
+
+
+class ImagesDataModule(LightningDataModule):
+    """Defines access to the ImagesDataset."""
+
+    def __init__(
+        self,
+        data_directory: str,
+        extensions: list[str] = ["jpg"],
+        shuffle: bool = True,
+        image_size: int = 64,
+        batch_size: int = 32,
+        num_workers: int = 1,
+    ):
+        """Initializes the data loader
+
+        Args:
+            data_directory (str): The data directory
+            shuffle (bool, optional): Wether or not to shuffle whe reading. Defaults to True.
+            image_size (int, optional): The size of the images. Defaults to 64.
+            batch_size (int, optional): The batch size for training. Defaults to 32.
+            num_workers (int, optional): How many worker to use for loading. Defaults to 1.
+            download (bool, optional): Wether or not to download the data. Defaults to False.
+        """
+        super().__init__()
+
+        self.data_directory = data_directory
+        self.extensions = extensions
+        self.shuffle = shuffle
+        self.image_size = image_size
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+
+        self.data_train = None
+        self.dataloader_train = None
+
+        self.transform_train = transforms.Compose(
+            [
+                transforms.Resize((self.image_size, self.image_size), antialias=True),
+                transforms.Lambda(  # Normalize
+                    lambda x: (x - torch.min(x)) / (torch.max(x) - torch.min(x))
+                ),
+            ]
+        )
+        self.transform_processing = self.transform_train
+        self.transform_images = self.transform_train
+        self.transform_thumbnail_images = transforms.Compose(
+            [
+                self.transform_train,
+                transforms.Resize((100, 100), antialias=True),
+            ]
+        )
+
+    def setup(self, stage: str):
+        """Sets up the data set and data loaders.
+
+        Args:
+            stage (str): Defines for which stage the data is needed.
+                         For the moment just fitting is supported.
+        """
+
+        if stage == "fit" and self.data_train is None:
+            self.data_train = ImagesDataset(
+                data_directory=self.data_directory,
+                extensions=self.extensions,
+                transform=self.transform_train,
+            )
+            self.dataloader_train = DataLoader(
+                self.data_train,
+                batch_size=self.batch_size,
+                shuffle=self.shuffle,
+                num_workers=self.num_workers,
+            )
+        else:
+            raise ValueError(f"Stage {stage} not supported.")
+
+    def train_dataloader(self):
+        """Gets the data loader for training."""
+        return self.dataloader_train
diff --git a/src/spherinator/data/image_dataset.py b/src/spherinator/data/images_dataset.py
similarity index 79%
rename from src/spherinator/data/image_dataset.py
rename to src/spherinator/data/images_dataset.py
index 14af027..5fd7160 100644
--- a/src/spherinator/data/image_dataset.py
+++ b/src/spherinator/data/images_dataset.py
@@ -6,6 +6,7 @@
 
 import numpy as np
 import torch
+from PIL import Image
 from torch.utils.data import Dataset
 
 
@@ -13,14 +14,14 @@ def get_all_filenames(data_directory: str, extensions: list[str]):
     result = []
     for dirpath, dirnames, filenames in os.walk(data_directory):
         for filename in filenames:
-            if Path(filename).suffix in extensions:
-                result.extend(os.path.join(dirpath, filename))
+            if Path(filename).suffix[1:] in extensions:
+                result.append(os.path.join(dirpath, filename))
         for dirname in dirnames:
             result.extend(get_all_filenames(dirname, extensions))
     return result
 
 
-class ImageDataset(Dataset):
+class ImagesDataset(Dataset):
     """Create dataset with all image files in a directory."""
 
     def __init__(
@@ -41,9 +42,11 @@ def __init__(
 
         filenames = get_all_filenames(data_directory, extensions)
 
+        self.images = np.empty((0, 3, 224, 224), np.float32)
         for file in sorted(filenames):
-            images = np.load(os.path.join(data_directory, file)).astype(np.float32)
-            self.images = np.append(self.images, images, axis=0)
+            # Swap axis 0 and 2 to bring the color channel to the front
+            image = np.asarray(Image.open(file)).swapaxes(0, 2)
+            self.images = np.append(self.images, [image], axis=0)
 
     def __len__(self) -> int:
         """Return the number of items in the dataset.
diff --git a/tests/data/images/Abra/1.jpg b/tests/data/images/Abra/1.jpg
new file mode 100755
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dHiB>E3*`Jh+22fPMFDg<|83X*llO(1`yV+%oF4!H

literal 0
HcmV?d00001

diff --git a/tests/test_images_data_module.py b/tests/test_images_data_module.py
new file mode 100644
index 0000000..3207613
--- /dev/null
+++ b/tests/test_images_data_module.py
@@ -0,0 +1,30 @@
+import numpy as np
+import torch
+
+from spherinator.data import ImagesDataModule
+
+
+def test_fit():
+    data = ImagesDataModule(
+        "tests/data/images",
+        image_size=224,
+        num_workers=1,
+        batch_size=2,
+    )
+    data.setup("fit")
+
+    assert len(data.data_train) == 2
+
+    dataloader = data.train_dataloader()
+
+    assert dataloader.batch_size == 2
+    assert len(dataloader) == 1
+    assert dataloader.num_workers == 1
+
+    batch = next(iter(dataloader))
+
+    assert batch.shape == (2, 3, 224, 224)
+    assert batch.dtype == torch.float32
+
+    assert np.isclose(batch.min(), 0.0)
+    assert np.isclose(batch.max(), 1.0)
diff --git a/tests/test_images_dataset.py b/tests/test_images_dataset.py
new file mode 100644
index 0000000..052bc08
--- /dev/null
+++ b/tests/test_images_dataset.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from spherinator.data import ImagesDataset
+
+
+def test_suffix():
+    filename = "test.jpg"
+    extensions = ["jpg", "png"]
+    assert Path(filename).suffix[1:] == "jpg"
+    assert Path(filename).suffix[1:] in extensions
+
+
+def test_load_image():
+    image = Image.open("tests/data/images/Abra/1.jpg")
+    assert image.size == (224, 224)
+    assert image.mode == "RGB"
+    assert image.format == "JPEG"
+    array = np.asarray(image)
+    assert array.shape == (224, 224, 3)
+
+
+def test_dataset():
+    dataset = ImagesDataset("tests/data/images")
+    assert len(dataset) == 2
+    data = dataset[0]
+    assert data.shape == (3, 224, 224)

From 430c4803d9057da9a73cdcca8306e9b2ffcda1ba Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 23 May 2024 20:18:54 +0200
Subject: [PATCH 05/20] replace PIL by skimage.io for better performance

---
 src/spherinator/data/images_dataset.py | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/src/spherinator/data/images_dataset.py b/src/spherinator/data/images_dataset.py
index 5fd7160..d14778d 100644
--- a/src/spherinator/data/images_dataset.py
+++ b/src/spherinator/data/images_dataset.py
@@ -5,8 +5,8 @@
 from pathlib import Path
 
 import numpy as np
+import skimage.io as io
 import torch
-from PIL import Image
 from torch.utils.data import Dataset
 
 
@@ -39,14 +39,7 @@ def __init__(
         """
 
         self.transform = transform
-
-        filenames = get_all_filenames(data_directory, extensions)
-
-        self.images = np.empty((0, 3, 224, 224), np.float32)
-        for file in sorted(filenames):
-            # Swap axis 0 and 2 to bring the color channel to the front
-            image = np.asarray(Image.open(file)).swapaxes(0, 2)
-            self.images = np.append(self.images, [image], axis=0)
+        self.filenames = sorted(get_all_filenames(data_directory, extensions))
 
     def __len__(self) -> int:
         """Return the number of items in the dataset.
@@ -54,7 +47,7 @@ def __len__(self) -> int:
         Returns:
             int: Number of items in dataset.
         """
-        return len(self.images)
+        return len(self.filenames)
 
     def __getitem__(self, index: int) -> torch.Tensor:
         """Retrieves the item/items with the given indices from the dataset.
@@ -66,7 +59,9 @@ def __getitem__(self, index: int) -> torch.Tensor:
             data: Data of the item/items with the given indices.
 
         """
-        data = self.images[index]
+        # Swap axis 0 and 2 to bring the color channel to the front
+        data = io.imread(self.filenames[index])
+        data = data.swapaxes(0, 2)
         data = torch.Tensor(data)
         if self.transform:
             data = self.transform(data)

From cc27bf7b69ea9b5f8679c6bba3ed0d0b465bd7f8 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Fri, 24 May 2024 18:57:28 +0200
Subject: [PATCH 06/20] update plain autoencoder

---
 .../models/rotational_autoencoder.py          | 174 +++++-------------
 tests/test_rotational_autoencoder.py          |  13 +-
 ...otational_variational_autoencoder_power.py |   3 +-
 3 files changed, 57 insertions(+), 133 deletions(-)

diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index bb98def..81b0266 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -4,137 +4,75 @@
 import torch.linalg
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision.transforms.functional as functional
+import torchvision.transforms.v2.functional as functional
 from torch.optim import Adam
 
+from .convolutional_decoder import ConvolutionalDecoder
+from .convolutional_encoder import ConvolutionalEncoder
 from .spherinator_module import SpherinatorModule
 
 
 class RotationalAutoencoder(SpherinatorModule):
-    def __init__(self, image_size: int = 363, rotations: int = 36, bottleneck: int = 3):
-        """
-        RotationalAutoencoder initializer
-
-        :param image_size: size of the input images
-        :param rotations: number of rotations
-        :param beta: factor for beta-VAE
+    def __init__(
+        self,
+        encoder: nn.Module = ConvolutionalEncoder(),
+        decoder: nn.Module = ConvolutionalDecoder(),
+        z_dim: int = 2,
+        image_size: int = 91,
+        input_size: int = 128,
+        rotations: int = 36,
+    ):
+        """Initializer
+
+        Args:
+            z_dim (int, optional): dimension of the latent representation. Defaults to 2.
+            image_size (int, optional): size of the input images. Defaults to 91.
+            rotations (int, optional): number of rotations. Defaults to 36.
         """
         super().__init__()
-        self.save_hyperparameters()
+        self.save_hyperparameters(ignore=["encoder", "decoder"])
 
+        self.encoder = encoder
+        self.decoder = decoder
+        self.z_dim = z_dim
         self.image_size = image_size
+        self.input_size = input_size
         self.rotations = rotations
-        self.bottleneck = bottleneck
 
         self.crop_size = int(self.image_size * math.sqrt(2) / 2)
-        self.input_size = 128
-
-        self.example_input_array = torch.randn(
-            1, bottleneck, self.input_size, self.input_size
-        )
+        self.total_input_size = self.input_size * self.input_size * 3
 
-        self.conv0 = nn.Conv2d(
-            in_channels=3, out_channels=16, kernel_size=(3, 3), stride=1, padding=1
-        )  # 128x128
-        self.pool0 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 64x64
-        self.conv1 = nn.Conv2d(
-            in_channels=16, out_channels=32, kernel_size=(3, 3), stride=1, padding=1
-        )  # 64x64
-        self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 32x32
-        self.conv2 = nn.Conv2d(
-            in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=1
-        )  # 32x32
-        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 16x16
-        self.conv3 = nn.Conv2d(
-            in_channels=64, out_channels=128, kernel_size=(3, 3), stride=1, padding=1
-        )  # 16x16
-        self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 8x8
-        self.conv4 = nn.Conv2d(
-            in_channels=128, out_channels=256, kernel_size=(3, 3), stride=1, padding=1
-        )  # 8x8
-        self.pool4 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 4x4
-
-        self.fc1 = nn.Linear(256 * 4 * 4, 256)
-        self.fc2 = nn.Linear(256, self.bottleneck)
-        self.fc3 = nn.Linear(self.bottleneck, 256)
-        self.fc4 = nn.Linear(256, 256 * 4 * 4)
-
-        self.deconv1 = nn.ConvTranspose2d(
-            in_channels=256, out_channels=128, kernel_size=(4, 4), stride=2, padding=1
-        )  # 8x8
-        self.deconv2 = nn.ConvTranspose2d(
-            in_channels=128, out_channels=128, kernel_size=(4, 4), stride=2, padding=1
-        )  # 16x16
-        self.deconv3 = nn.ConvTranspose2d(
-            in_channels=128, out_channels=64, kernel_size=(4, 4), stride=2, padding=1
-        )  # 32x32
-        self.deconv4 = nn.ConvTranspose2d(
-            in_channels=64, out_channels=32, kernel_size=(4, 4), stride=2, padding=1
-        )  # 64x64
-        self.deconv5 = nn.ConvTranspose2d(
-            in_channels=32, out_channels=16, kernel_size=(3, 3), stride=2, padding=1
-        )  # 127x127
-        self.deconv6 = nn.ConvTranspose2d(
-            in_channels=16, out_channels=3, kernel_size=(2, 2), stride=1, padding=0
-        )  # 128x128
+        self.example_input_array = torch.randn(1, 3, self.input_size, self.input_size)
 
     def get_input_size(self):
         return self.input_size
 
     def encode(self, x):
-        x = F.relu(self.conv0(x))
-        x = self.pool0(x)
-        x = F.relu(self.conv1(x))
-        x = self.pool1(x)
-        x = F.relu(self.conv2(x))
-        x = self.pool2(x)
-        x = F.relu(self.conv3(x))
-        x = self.pool3(x)
-        x = F.relu(self.conv4(x))
-        x = self.pool4(x)
-        x = x.view(-1, 256 * 4 * 4)
-        x = F.tanh(self.fc1(x))
-        x = self.fc2(x)
-        return x
+        z = self.encoder(x)
+        return z
 
-    def scale_to_unity(self, x):
-        length = torch.linalg.vector_norm(x, dim=1) + 1.0e-20
-        return (x.T / length).T
-
-    def decode(self, x):
-        x = F.tanh(self.fc3(x))
-        x = F.tanh(self.fc4(x))
-        x = x.view(-1, 256, 4, 4)
-        x = F.relu(self.deconv1(x))
-        x = F.relu(self.deconv2(x))
-        x = F.relu(self.deconv3(x))
-        x = F.relu(self.deconv4(x))
-        x = F.relu(self.deconv5(x))
-        x = self.deconv6(x)
+    def decode(self, z):
+        x = self.decoder(z)
         return x
 
     def forward(self, x):
-        coordinates = self.encode(x)
-        reconstruction = self.decode(self.scale_to_unity(coordinates))
-        return reconstruction, coordinates
-
-    def spherical_loss(self, images, reconstructions, coordinates):
-        coord_regularization = torch.square(
-            1 - torch.sum(torch.square(coordinates), dim=1)
-        )
-        reconstruction_loss = self.reconstruction_loss(images, reconstructions)
-        loss = reconstruction_loss + 1e-4 * coord_regularization
-        return loss
+        z = self.encode(x)
+        recon = self.decode(z)
+        return recon
 
     def training_step(self, batch, batch_idx):
-        best_recon = torch.ones(batch.shape[0], device=batch.device) * 1e10
-        best_scaled = torch.zeros(
-            (batch.shape[0], batch.shape[1], self.input_size, self.input_size),
-            device=batch.device,
-        )
 
         with torch.no_grad():
-            for i in range(self.rotations):
+            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
+            scaled = functional.resize(
+                crop, [self.input_size, self.input_size], antialias=True
+            )
+
+        recon = self.forward(scaled)
+        loss = self.reconstruction_loss(scaled, recon)
+
+        for i in range(1, self.rotations):
+            with torch.no_grad():
                 rotate = functional.rotate(
                     batch, 360.0 / self.rotations * i, expand=False
                 )
@@ -143,17 +81,11 @@ def training_step(self, batch, batch_idx):
                     crop, [self.input_size, self.input_size], antialias=True
                 )
 
-                recon, _ = self.forward(scaled)
-                loss_recon = self.reconstruction_loss(scaled, recon)
-                best_recon_idx = torch.where(loss_recon < best_recon)
-                best_recon[best_recon_idx] = loss_recon[best_recon_idx]
-                best_scaled[best_recon_idx] = scaled[best_recon_idx]
-
-        recon, coord = self.forward(best_scaled)
+            loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
 
-        loss = self.spherical_loss(best_scaled, recon, coord).mean()
+        loss = loss.mean()
 
-        self.log("train_loss", loss)
+        self.log("train_loss", loss, prog_bar=True)
         self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
         return loss
 
@@ -162,20 +94,16 @@ def configure_optimizers(self):
         return Adam(self.parameters(), lr=1e-3)
 
     def project(self, images):
-        return self.scale_to_unity(self.encode(images))
+        z = self.encode(images)
+        return z
 
     def reconstruct(self, coordinates):
         return self.decode(coordinates)
 
     def reconstruction_loss(self, images, reconstructions):
         return torch.sqrt(
-            torch.mean(
-                torch.square(
-                    images.reshape(-1, 3 * images.shape[-2] * images.shape[-1])
-                    - reconstructions.reshape(
-                        -1, 3 * images.shape[-2] * images.shape[-1]
-                    )
-                ),
-                dim=-1,
-            )
+            nn.MSELoss(reduction="none")(
+                reconstructions.reshape(-1, self.total_input_size),
+                images.reshape(-1, self.total_input_size),
+            ).mean(dim=1)
         )
diff --git a/tests/test_rotational_autoencoder.py b/tests/test_rotational_autoencoder.py
index f226e65..22c4832 100644
--- a/tests/test_rotational_autoencoder.py
+++ b/tests/test_rotational_autoencoder.py
@@ -6,18 +6,15 @@
 def test_forward():
     model = RotationalAutoencoder()
     input = model.example_input_array
-
-    recon, coord = model(input)
-
-    assert coord.shape == (1, 3)
+    recon = model(input)
     assert recon.shape == input.shape
 
 
 def test_reconstruction_loss():
     model = RotationalAutoencoder()
-    image1 = torch.zeros((2, 3, 64, 64))
-    image2 = torch.ones((2, 3, 64, 64))
-    image3 = torch.zeros((2, 3, 64, 64))
+    image1 = torch.zeros((2, 3, 128, 128))
+    image2 = torch.ones((2, 3, 128, 128))
+    image3 = torch.zeros((2, 3, 128, 128))
     image3[0, 0, 0, 0] = 1.0
 
     assert torch.isclose(
@@ -27,5 +24,5 @@ def test_reconstruction_loss():
         model.reconstruction_loss(image1, image2), torch.Tensor([1.0, 1.0]), atol=1e-3
     ).all()
     assert torch.isclose(
-        model.reconstruction_loss(image1, image3), torch.Tensor([0.009, 0.0]), atol=1e-3
+        model.reconstruction_loss(image1, image3), torch.Tensor([0.009, 0.0]), atol=1e-2
     ).all()
diff --git a/tests/test_rotational_variational_autoencoder_power.py b/tests/test_rotational_variational_autoencoder_power.py
index 272c49e..493700e 100644
--- a/tests/test_rotational_variational_autoencoder_power.py
+++ b/tests/test_rotational_variational_autoencoder_power.py
@@ -17,8 +17,7 @@ def test_forward():
 
 
 def test_reconstruction_loss():
-    z_dim = 2
-    model = RotationalVariationalAutoencoderPower(z_dim=z_dim)
+    model = RotationalVariationalAutoencoderPower()
     image1 = torch.zeros((2, 3, 128, 128))
     image2 = torch.ones((2, 3, 128, 128))
     image3 = torch.zeros((2, 3, 128, 128))

From 7c2fb24969cab601c9fd48f193bb4e8ce367e65e Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Fri, 24 May 2024 18:58:15 +0200
Subject: [PATCH 07/20] add pokemon config

---
 experiments/pokemon.yaml | 66 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 experiments/pokemon.yaml

diff --git a/experiments/pokemon.yaml b/experiments/pokemon.yaml
new file mode 100644
index 0000000..32631bb
--- /dev/null
+++ b/experiments/pokemon.yaml
@@ -0,0 +1,66 @@
+seed_everything: 42
+
+model:
+  class_path: spherinator.models.RotationalVariationalAutoencoderPower
+  init_args:
+    encoder: 
+      class_path: spherinator.models.ConvolutionalEncoder
+    decoder: 
+      class_path: spherinator.models.ConvolutionalDecoder
+    h_dim: 256
+    z_dim: 3
+    image_size: 224
+    rotations: 1
+    beta: 1.0e-3
+
+data:
+  class_path: spherinator.data.ImagesDataModule
+  init_args:
+    data_directory: /local_data/doserbd/data/pokemon
+    extensions: ['jpg']
+    image_size: 224
+    batch_size: 32
+    shuffle: True
+    num_workers: 16
+
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 1.e-3
+
+lr_scheduler:
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
+  init_args:
+    mode: min
+    factor: 0.1
+    patience: 5
+    cooldown: 5
+    min_lr: 1.e-6
+    monitor: train_loss
+    verbose: True
+
+trainer:
+  max_epochs: -1
+  accelerator: gpu
+  devices: [3]
+  precision: 32
+  callbacks:
+    - class_path: spherinator.callbacks.LogReconstructionCallback
+      init_args:
+        num_samples: 6
+#   - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+#     init_args:
+#       monitor: train_loss
+#       filename: "{epoch}-{train_loss:.2f}"
+#       save_top_k: 3
+#       mode: min
+#       every_n_epochs: 1
+  logger:
+    class_path: lightning.pytorch.loggers.WandbLogger
+    init_args:
+      project: spherinator
+      log_model: True
+      entity: ain-space
+      tags:
+        - rot-loss
+        - pokemon

From 741d758d93f9dce94b18f165215c0e97f33da107 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Fri, 24 May 2024 18:59:24 +0200
Subject: [PATCH 08/20] Improved CNN architecture

Source: https://www.biorxiv.org/content/10.1101/214247v2
---
 .../models/convolutional_decoder_2.py         | 66 +++++++++++--------
 .../models/convolutional_encoder_2.py         | 65 ++++++++++--------
 2 files changed, 74 insertions(+), 57 deletions(-)

diff --git a/src/spherinator/models/convolutional_decoder_2.py b/src/spherinator/models/convolutional_decoder_2.py
index bd6313e..441bbf0 100644
--- a/src/spherinator/models/convolutional_decoder_2.py
+++ b/src/spherinator/models/convolutional_decoder_2.py
@@ -4,36 +4,46 @@
 
 
 class ConvolutionalDecoder2(nn.Module):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, z_dim: int = 2):
         super().__init__()
 
-        self.fc = nn.Linear(h_dim, 256 * 8 * 8)
-        self.deconv0 = nn.ConvTranspose2d(
-            in_channels=256, out_channels=128, kernel_size=(3, 3), stride=1, padding=1
-        )
-        self.upsample0 = nn.Upsample(scale_factor=2)  # 16x16
-        self.deconv1 = nn.ConvTranspose2d(
-            in_channels=128, out_channels=64, kernel_size=(3, 3), stride=1, padding=1
-        )
-        self.upsample1 = nn.Upsample(scale_factor=2)  # 32x32
-        self.deconv2 = nn.ConvTranspose2d(
-            in_channels=64, out_channels=32, kernel_size=(3, 3), stride=1, padding=1
-        )
-        self.upsample2 = nn.Upsample(scale_factor=2)  # 64x64
-        self.deconv3 = nn.ConvTranspose2d(
-            in_channels=32, out_channels=3, kernel_size=(3, 3), stride=1, padding=1
-        )
-        self.upsample3 = nn.Upsample(scale_factor=2)  # 128x128
+        self.dec1 = nn.Sequential(
+            nn.Linear(z_dim, 1024 * 4 * 4),
+            nn.Unflatten(1, (1024, 4, 4)),
+            nn.BatchNorm2d(1024),
+            nn.ReLU(),
+        )  # 512 x 8 x 8
+        self.dec2 = nn.Sequential(
+            nn.ConvTranspose2d(1024, 512, 4, stride=2, padding=1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+        )  # 512 x 8 x 8
+        self.dec3 = nn.Sequential(
+            nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+        )  # 512 x 16 x 16
+        self.dec4 = nn.Sequential(
+            nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+        )  # 256 x 32 x 32
+        self.dec5 = nn.Sequential(
+            nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+        )  # 128 x 64 x 64
+        self.dec6 = nn.Sequential(
+            nn.ConvTranspose2d(128, 3, 4, stride=2, padding=1),
+            nn.BatchNorm2d(3),
+            nn.Sigmoid(),
+        )  # 3 x 128 x 128
 
     def forward(self, x: torch.tensor) -> torch.tensor:
-        x = F.relu(self.fc(x))
-        x = x.view(-1, 256, 8, 8)
-        x = F.relu(self.deconv0(x))
-        x = self.upsample0(x)
-        x = F.relu(self.deconv1(x))
-        x = self.upsample1(x)
-        x = F.relu(self.deconv2(x))
-        x = self.upsample2(x)
-        x = self.deconv3(x)
-        x = self.upsample3(x)
+        x = self.dec1(x)
+        x = self.dec2(x)
+        x = self.dec3(x)
+        x = self.dec4(x)
+        x = self.dec5(x)
+        x = self.dec6(x)
         return x
diff --git a/src/spherinator/models/convolutional_encoder_2.py b/src/spherinator/models/convolutional_encoder_2.py
index ccf9419..93b06b3 100644
--- a/src/spherinator/models/convolutional_encoder_2.py
+++ b/src/spherinator/models/convolutional_encoder_2.py
@@ -4,37 +4,44 @@
 
 
 class ConvolutionalEncoder2(nn.Module):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, z_dim: int = 2):
         super().__init__()
 
-        self.conv0 = nn.Conv2d(
-            in_channels=3, out_channels=32, kernel_size=(3, 3), stride=1, padding=1
-        )  # 128x128
-        self.pool0 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 64x64
-        self.conv1 = nn.Conv2d(
-            in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=1
-        )  # 64x64
-        self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 32x32
-        self.conv2 = nn.Conv2d(
-            in_channels=64, out_channels=128, kernel_size=(3, 3), stride=1, padding=1
-        )  # 32x32
-        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 16x16
-        self.conv3 = nn.Conv2d(
-            in_channels=128, out_channels=256, kernel_size=(3, 3), stride=1, padding=1
-        )  # 16x16
-        self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 8x8
-
-        self.fc1 = nn.Linear(256 * 8 * 8, h_dim)
+        self.enc1 = nn.Sequential(
+            nn.Conv2d(3, 128, 4, stride=2, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+        )  # 128 x 64 x 64
+        self.enc2 = nn.Sequential(
+            nn.Conv2d(128, 256, 4, stride=2, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+        )  # 256 x 32 x 32
+        self.enc3 = nn.Sequential(
+            nn.Conv2d(256, 512, 4, stride=2, padding=1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+        )  # 512 x 16 x 16
+        self.enc4 = nn.Sequential(
+            nn.Conv2d(512, 512, 4, stride=2, padding=1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+        )  # 512 x 8 x 8
+        self.enc5 = nn.Sequential(
+            nn.Conv2d(512, 1024, 4, stride=2, padding=1),
+            nn.BatchNorm2d(1024),
+            nn.ReLU(),
+        )  # 1024 x 4 x 4
+        self.enc6 = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(1024 * 4 * 4, z_dim),
+        )
 
     def forward(self, x: torch.tensor) -> torch.tensor:
-        x = F.relu(self.conv0(x))
-        x = self.pool0(x)
-        x = F.relu(self.conv1(x))
-        x = self.pool1(x)
-        x = F.relu(self.conv2(x))
-        x = self.pool2(x)
-        x = F.relu(self.conv3(x))
-        x = self.pool3(x)
-        x = torch.flatten(x, start_dim=1)
-        x = F.relu(self.fc1(x))
+        x = self.enc1(x)
+        x = self.enc2(x)
+        x = self.enc3(x)
+        x = self.enc4(x)
+        x = self.enc5(x)
+        x = self.enc6(x)
         return x

From 693a3002855cdef3f2ed96829cf5cd2f3c0bfd95 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 29 May 2024 14:35:17 +0200
Subject: [PATCH 09/20] rename h_dim and z_dim into latent_dim ... and use no
 default value

---
 src/spherinator/models/convolutional_decoder.py       |  4 ++--
 src/spherinator/models/convolutional_decoder_2.py     |  5 ++---
 src/spherinator/models/convolutional_decoder_224.py   |  4 ++--
 src/spherinator/models/convolutional_decoder_256.py   |  4 ++--
 src/spherinator/models/convolutional_encoder.py       |  4 ++--
 src/spherinator/models/convolutional_encoder_2.py     |  5 ++---
 src/spherinator/models/rotational_autoencoder.py      | 11 ++++++++---
 .../rotational_variational_autoencoder_power.py       | 11 ++++++++---
 tests/test_cnn.py                                     | 10 ++++++----
 tests/test_hipster.py                                 |  4 ++--
 tests/test_resnet.py                                  |  4 ++--
 11 files changed, 38 insertions(+), 28 deletions(-)

diff --git a/src/spherinator/models/convolutional_decoder.py b/src/spherinator/models/convolutional_decoder.py
index c81f6ab..f6ecaed 100644
--- a/src/spherinator/models/convolutional_decoder.py
+++ b/src/spherinator/models/convolutional_decoder.py
@@ -5,10 +5,10 @@
 
 
 class ConvolutionalDecoder(pl.LightningModule):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, latent_dim: int):
         super().__init__()
 
-        self.fc = nn.Linear(h_dim, 256 * 4 * 4)
+        self.fc = nn.Linear(latent_dim, 256 * 4 * 4)
         self.deconv1 = nn.ConvTranspose2d(
             in_channels=256, out_channels=128, kernel_size=(4, 4), stride=2, padding=1
         )  # 8x8
diff --git a/src/spherinator/models/convolutional_decoder_2.py b/src/spherinator/models/convolutional_decoder_2.py
index 441bbf0..96e08d3 100644
--- a/src/spherinator/models/convolutional_decoder_2.py
+++ b/src/spherinator/models/convolutional_decoder_2.py
@@ -1,14 +1,13 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class ConvolutionalDecoder2(nn.Module):
-    def __init__(self, z_dim: int = 2):
+    def __init__(self, latent_dim: int):
         super().__init__()
 
         self.dec1 = nn.Sequential(
-            nn.Linear(z_dim, 1024 * 4 * 4),
+            nn.Linear(latent_dim, 1024 * 4 * 4),
             nn.Unflatten(1, (1024, 4, 4)),
             nn.BatchNorm2d(1024),
             nn.ReLU(),
diff --git a/src/spherinator/models/convolutional_decoder_224.py b/src/spherinator/models/convolutional_decoder_224.py
index 426f759..be74981 100644
--- a/src/spherinator/models/convolutional_decoder_224.py
+++ b/src/spherinator/models/convolutional_decoder_224.py
@@ -5,7 +5,7 @@
 
 
 class ConvolutionalDecoder224(pl.LightningModule):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, latent_dim: int):
         """Convolutional decoder for 224x224 images.
         Input: h_dim
         Output: 3x224x224
@@ -13,7 +13,7 @@ def __init__(self, h_dim: int = 256):
         """
         super().__init__()
 
-        self.fc = nn.Linear(h_dim, 256 * 4 * 4)
+        self.fc = nn.Linear(latent_dim, 256 * 4 * 4)
         self.deconv1 = nn.ConvTranspose2d(
             in_channels=256, out_channels=128, kernel_size=(3, 3), stride=2, padding=1
         )  # 7 = 6 - 2 + 2 + 1
diff --git a/src/spherinator/models/convolutional_decoder_256.py b/src/spherinator/models/convolutional_decoder_256.py
index 93b08c3..5ad328d 100644
--- a/src/spherinator/models/convolutional_decoder_256.py
+++ b/src/spherinator/models/convolutional_decoder_256.py
@@ -5,7 +5,7 @@
 
 
 class ConvolutionalDecoder256(pl.LightningModule):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, latent_dim: int):
         """Convolutional decoder for 256x256 images.
         Input: h_dim
         Output: 3x256x256
@@ -13,7 +13,7 @@ def __init__(self, h_dim: int = 256):
         """
         super().__init__()
 
-        self.fc = nn.Linear(h_dim, 256 * 4 * 4)
+        self.fc = nn.Linear(latent_dim, 256 * 4 * 4)
         self.deconv1 = nn.ConvTranspose2d(
             in_channels=256, out_channels=128, kernel_size=(4, 4), stride=2, padding=1
         )  # 8x8 = 6 - 2 + 3 + 1
diff --git a/src/spherinator/models/convolutional_encoder.py b/src/spherinator/models/convolutional_encoder.py
index 6035a67..31af737 100644
--- a/src/spherinator/models/convolutional_encoder.py
+++ b/src/spherinator/models/convolutional_encoder.py
@@ -5,7 +5,7 @@
 
 
 class ConvolutionalEncoder(pl.LightningModule):
-    def __init__(self, h_dim: int = 256):
+    def __init__(self, latent_dim: int):
         super().__init__()
 
         self.conv0 = nn.Conv2d(
@@ -29,7 +29,7 @@ def __init__(self, h_dim: int = 256):
         )  # 8x8
         self.pool4 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)  # 4x4
 
-        self.fc1 = nn.Linear(256 * 4 * 4, h_dim)
+        self.fc1 = nn.Linear(256 * 4 * 4, latent_dim)
 
     def forward(self, x: torch.tensor) -> torch.tensor:
         x = F.relu(self.conv0(x))
diff --git a/src/spherinator/models/convolutional_encoder_2.py b/src/spherinator/models/convolutional_encoder_2.py
index 93b06b3..76b5d2f 100644
--- a/src/spherinator/models/convolutional_encoder_2.py
+++ b/src/spherinator/models/convolutional_encoder_2.py
@@ -1,10 +1,9 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class ConvolutionalEncoder2(nn.Module):
-    def __init__(self, z_dim: int = 2):
+    def __init__(self, latent_dim: int):
         super().__init__()
 
         self.enc1 = nn.Sequential(
@@ -34,7 +33,7 @@ def __init__(self, z_dim: int = 2):
         )  # 1024 x 4 x 4
         self.enc6 = nn.Sequential(
             nn.Flatten(),
-            nn.Linear(1024 * 4 * 4, z_dim),
+            nn.Linear(1024 * 4 * 4, latent_dim),
         )
 
     def forward(self, x: torch.tensor) -> torch.tensor:
diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index 81b0266..db22ce0 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -15,9 +15,9 @@
 class RotationalAutoencoder(SpherinatorModule):
     def __init__(
         self,
-        encoder: nn.Module = ConvolutionalEncoder(),
-        decoder: nn.Module = ConvolutionalDecoder(),
-        z_dim: int = 2,
+        encoder: nn.Module | None = None,
+        decoder: nn.Module | None = None,
+        z_dim: int = 3,
         image_size: int = 91,
         input_size: int = 128,
         rotations: int = 36,
@@ -32,6 +32,11 @@ def __init__(
         super().__init__()
         self.save_hyperparameters(ignore=["encoder", "decoder"])
 
+        if encoder is None:
+            encoder = ConvolutionalEncoder(latent_dim=z_dim)
+        if decoder is None:
+            decoder = ConvolutionalDecoder(latent_dim=z_dim)
+
         self.encoder = encoder
         self.decoder = decoder
         self.z_dim = z_dim
diff --git a/src/spherinator/models/rotational_variational_autoencoder_power.py b/src/spherinator/models/rotational_variational_autoencoder_power.py
index 87df7b7..154cf25 100644
--- a/src/spherinator/models/rotational_variational_autoencoder_power.py
+++ b/src/spherinator/models/rotational_variational_autoencoder_power.py
@@ -16,10 +16,10 @@
 class RotationalVariationalAutoencoderPower(SpherinatorModule):
     def __init__(
         self,
-        encoder: nn.Module = ConvolutionalEncoder(),
-        decoder: nn.Module = ConvolutionalDecoder(),
+        encoder: nn.Module | None = None,
+        decoder: nn.Module | None = None,
         h_dim: int = 256,
-        z_dim: int = 2,
+        z_dim: int = 3,
         image_size: int = 91,
         input_size: int = 128,
         rotations: int = 36,
@@ -37,6 +37,11 @@ def __init__(
         super().__init__()
         self.save_hyperparameters(ignore=["encoder", "decoder"])
 
+        if encoder is None:
+            encoder = ConvolutionalEncoder(latent_dim=h_dim)
+        if decoder is None:
+            decoder = ConvolutionalDecoder(latent_dim=h_dim)
+
         self.encoder = encoder
         self.decoder = decoder
         self.h_dim = h_dim
diff --git a/tests/test_cnn.py b/tests/test_cnn.py
index f5d3697..957d284 100644
--- a/tests/test_cnn.py
+++ b/tests/test_cnn.py
@@ -9,7 +9,7 @@
 
 def test_convolutional_encoder():
     """Check if weights are reproducible"""
-    model = ConvolutionalEncoder()
+    model = ConvolutionalEncoder(latent_dim=3)
 
     assert model.conv0.weight.shape == torch.Size([16, 3, 3, 3])
     assert torch.isclose(
@@ -19,7 +19,9 @@ def test_convolutional_encoder():
 
 def test_model():
     """Check if weights are reproducible"""
-    model = RotationalVariationalAutoencoderPower(encoder=ConvolutionalEncoder())
+    model = RotationalVariationalAutoencoderPower(
+        encoder=ConvolutionalEncoder(latent_dim=3)
+    )
 
     assert model.encoder.conv0.weight.shape == torch.Size([16, 3, 3, 3])
     assert torch.isclose(
@@ -28,8 +30,8 @@ def test_model():
 
 
 def test_convolutional_decoder():
-    model = ConvolutionalDecoder256()
-    data = torch.randn([2, 256])
+    model = ConvolutionalDecoder256(latent_dim=3)
+    data = torch.randn([2, 3])
 
     output = model(data)
 
diff --git a/tests/test_hipster.py b/tests/test_hipster.py
index 0c7b3ed..c30f89e 100644
--- a/tests/test_hipster.py
+++ b/tests/test_hipster.py
@@ -16,8 +16,8 @@
 @pytest.fixture
 def model():
     model = RotationalVariationalAutoencoderPower(
-        encoder=ConvolutionalEncoder(),
-        decoder=ConvolutionalDecoder(),
+        encoder=ConvolutionalEncoder(latent_dim=256),
+        decoder=ConvolutionalDecoder(latent_dim=256),
         z_dim=3,
         rotations=4,
     )
diff --git a/tests/test_resnet.py b/tests/test_resnet.py
index 0de8ab1..adb54d2 100644
--- a/tests/test_resnet.py
+++ b/tests/test_resnet.py
@@ -9,12 +9,12 @@
     [
         (
             torchvision.models.resnet18(num_classes=256),
-            spherinator.models.ConvolutionalDecoder(),
+            spherinator.models.ConvolutionalDecoder(latent_dim=256),
             128,
         ),
         (
             torchvision.models.vit_b_16(num_classes=256),
-            spherinator.models.ConvolutionalDecoder224(),
+            spherinator.models.ConvolutionalDecoder224(latent_dim=256),
             224,
         ),
     ],

From 601eaf7fa4f6915324a1591820d052fdc2878786 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 29 May 2024 18:58:10 +0200
Subject: [PATCH 10/20] normalize loss by brightness

---
 .../models/rotational_autoencoder.py          |  6 ++++++
 tests/test_rotational_autoencoder.py          | 21 +++++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index db22ce0..5ed8e7a 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -21,6 +21,7 @@ def __init__(
         image_size: int = 91,
         input_size: int = 128,
         rotations: int = 36,
+        norm_brightness: bool = False,
     ):
         """Initializer
 
@@ -43,6 +44,7 @@ def __init__(
         self.image_size = image_size
         self.input_size = input_size
         self.rotations = rotations
+        self.norm_brightness = norm_brightness
 
         self.crop_size = int(self.image_size * math.sqrt(2) / 2)
         self.total_input_size = self.input_size * self.input_size * 3
@@ -88,6 +90,10 @@ def training_step(self, batch, batch_idx):
 
             loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
 
+            # divide by the brightness of the image
+            if self.norm_brightness:
+                loss = loss / torch.sum(scaled, (1, 2, 3)) * self.total_input_size
+
         loss = loss.mean()
 
         self.log("train_loss", loss, prog_bar=True)
diff --git a/tests/test_rotational_autoencoder.py b/tests/test_rotational_autoencoder.py
index 22c4832..f9fbca9 100644
--- a/tests/test_rotational_autoencoder.py
+++ b/tests/test_rotational_autoencoder.py
@@ -1,5 +1,7 @@
 import torch
+from lightning.pytorch.trainer import Trainer
 
+from spherinator.data import ImagesDataModule
 from spherinator.models import RotationalAutoencoder
 
 
@@ -10,6 +12,25 @@ def test_forward():
     assert recon.shape == input.shape
 
 
+def test_training(shape_path):
+    model = RotationalAutoencoder(norm_brightness=True)
+
+    datamodule = ImagesDataModule(
+        "tests/data/images",
+        image_size=224,
+        num_workers=1,
+        batch_size=2,
+    )
+
+    trainer = Trainer(
+        max_epochs=1,
+        overfit_batches=2,
+        enable_checkpointing=False,
+        accelerator="cpu",
+    )
+    trainer.fit(model, datamodule=datamodule)
+
+
 def test_reconstruction_loss():
     model = RotationalAutoencoder()
     image1 = torch.zeros((2, 3, 128, 128))

From 715ed7addb8ffc4fdbe8fa1ef3c06809910f1345 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Tue, 4 Jun 2024 17:39:07 +0200
Subject: [PATCH 11/20] remove sigmoid from last layer of decoder

---
 src/spherinator/models/convolutional_decoder_2.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/spherinator/models/convolutional_decoder_2.py b/src/spherinator/models/convolutional_decoder_2.py
index 96e08d3..3b82083 100644
--- a/src/spherinator/models/convolutional_decoder_2.py
+++ b/src/spherinator/models/convolutional_decoder_2.py
@@ -35,7 +35,6 @@ def __init__(self, latent_dim: int):
         self.dec6 = nn.Sequential(
             nn.ConvTranspose2d(128, 3, 4, stride=2, padding=1),
             nn.BatchNorm2d(3),
-            nn.Sigmoid(),
         )  # 3 x 128 x 128
 
     def forward(self, x: torch.tensor) -> torch.tensor:

From beee3eacc46aa22f744f423864d1074edfe029f1 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 26 Jun 2024 18:48:42 +0200
Subject: [PATCH 12/20] use Optional to support python 3.9

---
 src/spherinator/models/rotational_autoencoder.py             | 5 +++--
 .../models/rotational_variational_autoencoder_power.py       | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index 5ed8e7a..a29802b 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -1,4 +1,5 @@
 import math
+from typing import Optional
 
 import torch
 import torch.linalg
@@ -15,8 +16,8 @@
 class RotationalAutoencoder(SpherinatorModule):
     def __init__(
         self,
-        encoder: nn.Module | None = None,
-        decoder: nn.Module | None = None,
+        encoder: Optional[nn.Module] = None,
+        decoder: Optional[nn.Module] = None,
         z_dim: int = 3,
         image_size: int = 91,
         input_size: int = 128,
diff --git a/src/spherinator/models/rotational_variational_autoencoder_power.py b/src/spherinator/models/rotational_variational_autoencoder_power.py
index 154cf25..6f7b33f 100644
--- a/src/spherinator/models/rotational_variational_autoencoder_power.py
+++ b/src/spherinator/models/rotational_variational_autoencoder_power.py
@@ -1,4 +1,5 @@
 import math
+from typing import Optional
 
 import torch
 import torch.linalg
@@ -16,8 +17,8 @@
 class RotationalVariationalAutoencoderPower(SpherinatorModule):
     def __init__(
         self,
-        encoder: nn.Module | None = None,
-        decoder: nn.Module | None = None,
+        encoder: Optional[nn.Module] = None,
+        decoder: Optional[nn.Module] = None,
         h_dim: int = 256,
         z_dim: int = 3,
         image_size: int = 91,

From bfb5f602c7961d3729c33198344647705ef49e01 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 27 Jun 2024 10:15:56 +0200
Subject: [PATCH 13/20] Revert "train with not rotated image and reconstruction
 loss as minimum over rotated images with reconstructed image of not rotated
 image"

This reverts commit a1c1627f7cf4d928aed55aaa847109c8e680ca5d.
---
 ...otational_variational_autoencoder_power.py | 25 +++----------------
 1 file changed, 3 insertions(+), 22 deletions(-)

diff --git a/src/spherinator/models/rotational_variational_autoencoder_power.py b/src/spherinator/models/rotational_variational_autoencoder_power.py
index 6f7b33f..faccc31 100644
--- a/src/spherinator/models/rotational_variational_autoencoder_power.py
+++ b/src/spherinator/models/rotational_variational_autoencoder_power.py
@@ -5,7 +5,6 @@
 import torch.linalg
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision.transforms.v2.functional as functional
 from power_spherical import HypersphericalUniform, PowerSpherical
 from torch.optim import Adam
 
@@ -95,28 +94,10 @@ def forward(self, x):
         return (z_location, z_scale), (q_z, p_z), z, recon
 
     def training_step(self, batch, batch_idx):
+        best_scaled_image, _, _, _ = self.find_best_rotation(batch)
+        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(best_scaled_image)
 
-        with torch.no_grad():
-            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
-            scaled = functional.resize(
-                crop, [self.input_size, self.input_size], antialias=True
-            )
-
-        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(scaled)
-        loss_recon = self.reconstruction_loss(scaled, recon)
-
-        for i in range(1, self.rotations):
-            with torch.no_grad():
-                rotate = functional.rotate(
-                    batch, 360.0 / self.rotations * i, expand=False
-                )
-                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
-                scaled = functional.resize(
-                    crop, [self.input_size, self.input_size], antialias=True
-                )
-
-            loss_recon = torch.min(loss_recon, self.reconstruction_loss(scaled, recon))
-
+        loss_recon = self.reconstruction_loss(best_scaled_image, recon)
         loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
         loss = (loss_recon + loss_KL).mean()
         loss_recon = loss_recon.mean()

From a16f96717d06d762afea27187101c9414da8d062 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 27 Jun 2024 10:23:40 +0200
Subject: [PATCH 14/20] git ignore sarif files

---
 .gitignore | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 8b5ded0..38d6f57 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,10 +2,11 @@ __pycache__
 .venv*/
 *.ckpt
 *.ncu-rep
+*.sarif
 config.yaml
 dist/
+docs/_build/
+docs/html/
 HiPSter/
 lightning_logs/
 wandb/
-docs/_build/
-docs/html/

From ea158f8cec4c8cf92287c1bb065ba7e1bf6bfda2 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Thu, 27 Jun 2024 10:31:06 +0200
Subject: [PATCH 15/20] Rotational2Autoencoder, which takes the minimal loss of
 all rotated input image and the reconstructed image using the not rotated
 image.

---
 src/spherinator/models/__init__.py            |   6 +
 .../models/rotational2_autoencoder.py         | 120 ++++++++++++++
 ...tational2_variational_autoencoder_power.py | 150 ++++++++++++++++++
 .../models/rotational_autoencoder.py          |  33 +---
 4 files changed, 284 insertions(+), 25 deletions(-)
 create mode 100644 src/spherinator/models/rotational2_autoencoder.py
 create mode 100644 src/spherinator/models/rotational2_variational_autoencoder_power.py

diff --git a/src/spherinator/models/__init__.py b/src/spherinator/models/__init__.py
index 1afb6e5..0e3d906 100644
--- a/src/spherinator/models/__init__.py
+++ b/src/spherinator/models/__init__.py
@@ -18,6 +18,10 @@
 from .convolutional_decoder_256 import ConvolutionalDecoder256
 from .convolutional_encoder import ConvolutionalEncoder
 from .convolutional_encoder_2 import ConvolutionalEncoder2
+from .rotational2_autoencoder import Rotational2Autoencoder
+from .rotational2_variational_autoencoder_power import (
+    Rotational2VariationalAutoencoderPower,
+)
 from .rotational_autoencoder import RotationalAutoencoder
 from .rotational_variational_autoencoder_power import (
     RotationalVariationalAutoencoderPower,
@@ -31,6 +35,8 @@
     "ConvolutionalDecoder256",
     "ConvolutionalEncoder",
     "ConvolutionalEncoder2",
+    "Rotational2Autoencoder",
+    "Rotational2VariationalAutoencoderPower",
     "RotationalAutoencoder",
     "RotationalVariationalAutoencoderPower",
     "SpherinatorModule",
diff --git a/src/spherinator/models/rotational2_autoencoder.py b/src/spherinator/models/rotational2_autoencoder.py
new file mode 100644
index 0000000..cfb2f82
--- /dev/null
+++ b/src/spherinator/models/rotational2_autoencoder.py
@@ -0,0 +1,120 @@
+import math
+from typing import Optional
+
+import torch
+import torch.linalg
+import torch.nn as nn
+import torchvision.transforms.v2.functional as functional
+from torch.optim import Adam
+
+from .convolutional_decoder import ConvolutionalDecoder
+from .convolutional_encoder import ConvolutionalEncoder
+from .spherinator_module import SpherinatorModule
+
+
+class Rotational2Autoencoder(SpherinatorModule):
+    def __init__(
+        self,
+        encoder: Optional[nn.Module] = None,
+        decoder: Optional[nn.Module] = None,
+        z_dim: int = 3,
+        image_size: int = 91,
+        input_size: int = 128,
+        rotations: int = 36,
+        norm_brightness: bool = False,
+    ):
+        """Initializer
+
+        Args:
+            z_dim (int, optional): dimension of the latent representation. Defaults to 2.
+            image_size (int, optional): size of the input images. Defaults to 91.
+            rotations (int, optional): number of rotations. Defaults to 36.
+        """
+        super().__init__()
+        self.save_hyperparameters(ignore=["encoder", "decoder"])
+
+        if encoder is None:
+            encoder = ConvolutionalEncoder(latent_dim=z_dim)
+        if decoder is None:
+            decoder = ConvolutionalDecoder(latent_dim=z_dim)
+
+        self.encoder = encoder
+        self.decoder = decoder
+        self.z_dim = z_dim
+        self.image_size = image_size
+        self.input_size = input_size
+        self.rotations = rotations
+        self.norm_brightness = norm_brightness
+
+        self.crop_size = int(self.image_size * math.sqrt(2) / 2)
+        self.total_input_size = self.input_size * self.input_size * 3
+
+        self.example_input_array = torch.randn(1, 3, self.input_size, self.input_size)
+
+    def get_input_size(self):
+        return self.input_size
+
+    def encode(self, x):
+        z = self.encoder(x)
+        return z
+
+    def decode(self, z):
+        x = self.decoder(z)
+        return x
+
+    def forward(self, x):
+        z = self.encode(x)
+        recon = self.decode(z)
+        return recon
+
+    def training_step(self, batch, batch_idx):
+
+        with torch.no_grad():
+            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
+            scaled = functional.resize(
+                crop, [self.input_size, self.input_size], antialias=True
+            )
+
+        recon = self.forward(scaled)
+        loss = self.reconstruction_loss(scaled, recon)
+
+        for i in range(1, self.rotations):
+            with torch.no_grad():
+                rotate = functional.rotate(
+                    batch, 360.0 / self.rotations * i, expand=False
+                )
+                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
+                scaled = functional.resize(
+                    crop, [self.input_size, self.input_size], antialias=True
+                )
+
+            loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
+
+            # divide by the brightness of the image
+            if self.norm_brightness:
+                loss = loss / torch.sum(scaled, (1, 2, 3)) * self.total_input_size
+
+        loss = loss.mean()
+
+        self.log("train_loss", loss, prog_bar=True)
+        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+        return loss
+
+    def configure_optimizers(self):
+        """Default Adam optimizer if missing from the configuration file."""
+        return Adam(self.parameters(), lr=1e-3)
+
+    def project(self, images):
+        z = self.encode(images)
+        return z
+
+    def reconstruct(self, coordinates):
+        return self.decode(coordinates)
+
+    def reconstruction_loss(self, images, reconstructions):
+        return torch.sqrt(
+            nn.MSELoss(reduction="none")(
+                reconstructions.reshape(-1, self.total_input_size),
+                images.reshape(-1, self.total_input_size),
+            ).mean(dim=1)
+        )
diff --git a/src/spherinator/models/rotational2_variational_autoencoder_power.py b/src/spherinator/models/rotational2_variational_autoencoder_power.py
new file mode 100644
index 0000000..b402ea6
--- /dev/null
+++ b/src/spherinator/models/rotational2_variational_autoencoder_power.py
@@ -0,0 +1,150 @@
+import math
+from typing import Optional
+
+import torch
+import torch.linalg
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms.v2.functional as functional
+from power_spherical import HypersphericalUniform, PowerSpherical
+from torch.optim import Adam
+
+from .convolutional_decoder import ConvolutionalDecoder
+from .convolutional_encoder import ConvolutionalEncoder
+from .spherinator_module import SpherinatorModule
+
+
+class Rotational2VariationalAutoencoderPower(SpherinatorModule):
+    def __init__(
+        self,
+        encoder: Optional[nn.Module] = None,
+        decoder: Optional[nn.Module] = None,
+        h_dim: int = 256,
+        z_dim: int = 3,
+        image_size: int = 91,
+        input_size: int = 128,
+        rotations: int = 36,
+        beta: float = 1.0,
+    ):
+        """RotationalVariationalAutoencoderPower initializer
+
+        Args:
+            h_dim (int, optional): dimension of the hidden layers. Defaults to 256.
+            z_dim (int, optional): dimension of the latent representation. Defaults to 2.
+            image_size (int, optional): size of the input images. Defaults to 91.
+            rotations (int, optional): number of rotations. Defaults to 36.
+            beta (float, optional): factor for beta-VAE. Defaults to 1.0.
+        """
+        super().__init__()
+        self.save_hyperparameters(ignore=["encoder", "decoder"])
+
+        if encoder is None:
+            encoder = ConvolutionalEncoder(latent_dim=h_dim)
+        if decoder is None:
+            decoder = ConvolutionalDecoder(latent_dim=h_dim)
+
+        self.encoder = encoder
+        self.decoder = decoder
+        self.h_dim = h_dim
+        self.z_dim = z_dim
+        self.image_size = image_size
+        self.input_size = input_size
+        self.rotations = rotations
+        self.beta = beta
+
+        self.crop_size = int(self.image_size * math.sqrt(2) / 2)
+        self.total_input_size = self.input_size * self.input_size * 3
+
+        self.example_input_array = torch.randn(1, 3, self.input_size, self.input_size)
+
+        self.fc_location = nn.Linear(h_dim, z_dim)
+        self.fc_scale = nn.Linear(h_dim, 1)
+        self.fc2 = nn.Linear(z_dim, h_dim)
+
+        with torch.no_grad():
+            self.fc_scale.bias.fill_(1.0e3)
+
+    def get_input_size(self):
+        return self.input_size
+
+    def encode(self, x):
+        x = self.encoder(x)
+
+        z_location = self.fc_location(x)
+        z_location = torch.nn.functional.normalize(z_location, p=2.0, dim=1)
+        # SVAE code: the `+ 1` prevent collapsing behaviors
+        z_scale = F.softplus(self.fc_scale(x)) + 1
+
+        return z_location, z_scale
+
+    def decode(self, z):
+        x = F.relu(self.fc2(z))
+        x = self.decoder(x)
+        return x
+
+    def reparameterize(self, z_location, z_scale):
+        q_z = PowerSpherical(z_location, z_scale)
+        p_z = HypersphericalUniform(self.z_dim, device=z_location.device)
+        return q_z, p_z
+
+    def forward(self, x):
+        z_location, z_scale = self.encode(x)
+        q_z, p_z = self.reparameterize(z_location, z_scale.squeeze())
+        z = q_z.rsample()
+        recon = self.decode(z)
+        return (z_location, z_scale), (q_z, p_z), z, recon
+
+    def training_step(self, batch, batch_idx):
+
+        with torch.no_grad():
+            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
+            scaled = functional.resize(
+                crop, [self.input_size, self.input_size], antialias=True
+            )
+
+        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(scaled)
+        loss_recon = self.reconstruction_loss(scaled, recon)
+
+        for i in range(1, self.rotations):
+            with torch.no_grad():
+                rotate = functional.rotate(
+                    batch, 360.0 / self.rotations * i, expand=False
+                )
+                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
+                scaled = functional.resize(
+                    crop, [self.input_size, self.input_size], antialias=True
+                )
+
+            loss_recon = torch.min(loss_recon, self.reconstruction_loss(scaled, recon))
+
+        loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
+        loss = (loss_recon + loss_KL).mean()
+        loss_recon = loss_recon.mean()
+        loss_KL = loss_KL.mean()
+
+        self.log("train_loss", loss, prog_bar=True)
+        self.log("loss_recon", loss_recon, prog_bar=True)
+        self.log("loss_KL", loss_KL)
+        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+        self.log("mean(z_location)", torch.mean(z_location))
+        self.log("mean(z_scale)", torch.mean(z_scale))
+        return loss
+
+    def configure_optimizers(self):
+        """Default Adam optimizer if missing from the configuration file."""
+        return Adam(self.parameters(), lr=1e-3)
+
+    def project(self, images):
+        z_location, _ = self.encode(images)
+        return z_location
+
+    def reconstruct(self, coordinates):
+        return self.decode(coordinates)
+
+    def reconstruction_loss(self, images, reconstructions):
+        return torch.sqrt(
+            nn.MSELoss(reduction="none")(
+                reconstructions.reshape(-1, self.total_input_size),
+                images.reshape(-1, self.total_input_size),
+            ).mean(dim=1)
+        )
diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index a29802b..c3e5c9e 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -4,8 +4,6 @@
 import torch
 import torch.linalg
 import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms.v2.functional as functional
 from torch.optim import Adam
 
 from .convolutional_decoder import ConvolutionalDecoder
@@ -70,30 +68,15 @@ def forward(self, x):
 
     def training_step(self, batch, batch_idx):
 
-        with torch.no_grad():
-            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
-            scaled = functional.resize(
-                crop, [self.input_size, self.input_size], antialias=True
-            )
-
-        recon = self.forward(scaled)
-        loss = self.reconstruction_loss(scaled, recon)
-
-        for i in range(1, self.rotations):
-            with torch.no_grad():
-                rotate = functional.rotate(
-                    batch, 360.0 / self.rotations * i, expand=False
-                )
-                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
-                scaled = functional.resize(
-                    crop, [self.input_size, self.input_size], antialias=True
-                )
+        best_scaled_image, _, _, _ = self.find_best_rotation(batch)
+        recon = self.forward(best_scaled_image)
+        loss = self.reconstruction_loss(best_scaled_image, recon)
 
-            loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
-
-            # divide by the brightness of the image
-            if self.norm_brightness:
-                loss = loss / torch.sum(scaled, (1, 2, 3)) * self.total_input_size
+        # divide by the brightness of the image
+        if self.norm_brightness:
+            loss = (
+                loss / torch.sum(best_scaled_image, (1, 2, 3)) * self.total_input_size
+            )
 
         loss = loss.mean()
 

From 8a76c7c0fdbfc7fd4c02f78b16289c74ce65b184 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 10 Jul 2024 17:20:39 +0200
Subject: [PATCH 16/20] switch off autocast for torch.no_grad

---
 ...otational_variational_autoencoder_power.py | 37 +++++++++++--------
 1 file changed, 21 insertions(+), 16 deletions(-)

diff --git a/src/spherinator/models/rotational_variational_autoencoder_power.py b/src/spherinator/models/rotational_variational_autoencoder_power.py
index faccc31..78c2da0 100644
--- a/src/spherinator/models/rotational_variational_autoencoder_power.py
+++ b/src/spherinator/models/rotational_variational_autoencoder_power.py
@@ -94,22 +94,27 @@ def forward(self, x):
         return (z_location, z_scale), (q_z, p_z), z, recon
 
     def training_step(self, batch, batch_idx):
-        best_scaled_image, _, _, _ = self.find_best_rotation(batch)
-        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(best_scaled_image)
-
-        loss_recon = self.reconstruction_loss(best_scaled_image, recon)
-        loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
-        loss = (loss_recon + loss_KL).mean()
-        loss_recon = loss_recon.mean()
-        loss_KL = loss_KL.mean()
-
-        self.log("train_loss", loss, prog_bar=True)
-        self.log("loss_recon", loss_recon, prog_bar=True)
-        self.log("loss_KL", loss_KL)
-        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
-        self.log("mean(z_location)", torch.mean(z_location))
-        self.log("mean(z_scale)", torch.mean(z_scale))
-        return loss
+        with torch.autocast("cuda", enabled=False):
+            best_scaled_image, _, _, _ = self.find_best_rotation(batch)
+
+        with torch.autocast("cuda", enabled=True):
+            (z_location, z_scale), (q_z, p_z), _, recon = self.forward(
+                best_scaled_image
+            )
+
+            loss_recon = self.reconstruction_loss(best_scaled_image, recon)
+            loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
+            loss = (loss_recon + loss_KL).mean()
+            loss_recon = loss_recon.mean()
+            loss_KL = loss_KL.mean()
+
+            self.log("train_loss", loss, prog_bar=True)
+            self.log("loss_recon", loss_recon, prog_bar=True)
+            self.log("loss_KL", loss_KL)
+            self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+            self.log("mean(z_location)", torch.mean(z_location))
+            self.log("mean(z_scale)", torch.mean(z_scale))
+            return loss
 
     def configure_optimizers(self):
         """Default Adam optimizer if missing from the configuration file."""

From 9de13533c66d72d5e93fad9b47bc08f96629b4af Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 10 Jul 2024 17:48:37 +0200
Subject: [PATCH 17/20] torch dynamo export not supported for python 3.12, skip
 tests

---
 tests/test_dynamo_export.py | 6 ++++++
 tests/test_onnx_export.py   | 6 ++++++
 2 files changed, 12 insertions(+)

diff --git a/tests/test_dynamo_export.py b/tests/test_dynamo_export.py
index 5ff78b7..ed9b096 100644
--- a/tests/test_dynamo_export.py
+++ b/tests/test_dynamo_export.py
@@ -1,3 +1,5 @@
+import sys
+
 import pytest
 import torch
 import torch.nn as nn
@@ -13,6 +15,10 @@ def forward(self, x):
         return self.dist.rsample(x.shape)
 
 
+@pytest.mark.skipif(
+    sys.version_info > (3, 11),
+    reason="torch dynamo export not supported for python 3.12",
+)
 @pytest.mark.parametrize(
     ("module", "input"),
     [
diff --git a/tests/test_onnx_export.py b/tests/test_onnx_export.py
index 7dc7168..01e01da 100644
--- a/tests/test_onnx_export.py
+++ b/tests/test_onnx_export.py
@@ -1,3 +1,5 @@
+import sys
+
 import pytest
 import torch
 import torch.nn as nn
@@ -44,6 +46,10 @@ def forward(self, x):
         return self.dist.rsample(x.shape)
 
 
+@pytest.mark.skipif(
+    sys.version_info > (3, 11),
+    reason="torch dynamo export not supported for python 3.12",
+)
 @pytest.mark.parametrize(
     ("module", "input"),
     [

From 8578d9affbaa7054778f737b43419bca05bb3af7 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Mon, 22 Jul 2024 13:18:26 +0200
Subject: [PATCH 18/20] fix flake8 warnings

---
 src/spherinator/data/galaxy_zoo_data_module.py     | 2 +-
 src/spherinator/data/illustris_sdss_data_module.py | 2 +-
 src/spherinator/data/images_dataset.py             | 1 -
 src/spherinator/data/shapes_data_module.py         | 2 +-
 tests/test_illustris_sdss_data_module.py           | 2 +-
 tests/test_power_spherical.py                      | 2 +-
 6 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/spherinator/data/galaxy_zoo_data_module.py b/src/spherinator/data/galaxy_zoo_data_module.py
index 221eed3..9759d95 100644
--- a/src/spherinator/data/galaxy_zoo_data_module.py
+++ b/src/spherinator/data/galaxy_zoo_data_module.py
@@ -65,7 +65,7 @@ def setup(self, stage: str):
         Args:
             stage (str): Defines for which stage the data is needed.
         """
-        if not stage in ["fit", "processing", "images", "thumbnail_images"]:
+        if stage not in ["fit", "processing", "images", "thumbnail_images"]:
             raise ValueError(f"Stage {stage} not supported.")
 
         if stage == "fit" and self.data_train is None:
diff --git a/src/spherinator/data/illustris_sdss_data_module.py b/src/spherinator/data/illustris_sdss_data_module.py
index 318c63a..4c2af4d 100644
--- a/src/spherinator/data/illustris_sdss_data_module.py
+++ b/src/spherinator/data/illustris_sdss_data_module.py
@@ -93,7 +93,7 @@ def setup(self, stage: str):
         Args:
             stage (str): Defines for which stage the data is needed.
         """
-        if not stage in ["fit", "processing", "images", "thumbnail_images"]:
+        if stage not in ["fit", "processing", "images", "thumbnail_images"]:
             raise ValueError(f"Stage {stage} not supported.")
 
         if stage == "fit" and self.data_train is None:
diff --git a/src/spherinator/data/images_dataset.py b/src/spherinator/data/images_dataset.py
index d14778d..163ab90 100644
--- a/src/spherinator/data/images_dataset.py
+++ b/src/spherinator/data/images_dataset.py
@@ -4,7 +4,6 @@
 import os
 from pathlib import Path
 
-import numpy as np
 import skimage.io as io
 import torch
 from torch.utils.data import Dataset
diff --git a/src/spherinator/data/shapes_data_module.py b/src/spherinator/data/shapes_data_module.py
index 951eb0c..851f750 100644
--- a/src/spherinator/data/shapes_data_module.py
+++ b/src/spherinator/data/shapes_data_module.py
@@ -76,7 +76,7 @@ def setup(self, stage: str):
             stage (str): Defines for which stage the data is needed.
                          For the moment just fitting is supported.
         """
-        if not stage in ["fit", "processing", "images", "thumbnail_images"]:
+        if stage not in ["fit", "processing", "images", "thumbnail_images"]:
             raise ValueError(f"Stage {stage} not supported.")
 
         if stage == "fit" and self.data_train is None:
diff --git a/tests/test_illustris_sdss_data_module.py b/tests/test_illustris_sdss_data_module.py
index c0865f3..644bacb 100644
--- a/tests/test_illustris_sdss_data_module.py
+++ b/tests/test_illustris_sdss_data_module.py
@@ -14,7 +14,7 @@
 def test_empty():
     data = IllustrisSdssDataModule(["tests/data/"], num_workers=1)
 
-    assert data.train_dataloader() == None
+    assert data.train_dataloader() is None
 
     try:
         data.setup("fit")
diff --git a/tests/test_power_spherical.py b/tests/test_power_spherical.py
index 46a6a4e..f62a105 100644
--- a/tests/test_power_spherical.py
+++ b/tests/test_power_spherical.py
@@ -5,7 +5,7 @@
 def test_power_spherical_2d():
     dist = PowerSpherical(torch.Tensor([0.0, 0.0]), torch.Tensor([1.0]))
 
-    assert dist.has_rsample == True
+    assert dist.has_rsample is True
     assert torch.allclose(dist.rsample(), torch.Tensor([-0.9971, 0.0766]), rtol=1e-3)
     assert torch.allclose(dist.rsample(), torch.Tensor([-0.5954, 0.8034]), rtol=1e-3)
 

From a1c54da86ae6df0049b56f93d4ceafb02581c22d Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 24 Jul 2024 10:43:30 +0200
Subject: [PATCH 19/20] disable autocast for torch.no_grad for all autoencoders

---
 .../models/rotational2_autoencoder.py         | 55 +++++++++--------
 ...tational2_variational_autoencoder_power.py | 61 ++++++++++---------
 .../models/rotational_autoencoder.py          | 36 ++++++-----
 3 files changed, 82 insertions(+), 70 deletions(-)

diff --git a/src/spherinator/models/rotational2_autoencoder.py b/src/spherinator/models/rotational2_autoencoder.py
index cfb2f82..cb5e916 100644
--- a/src/spherinator/models/rotational2_autoencoder.py
+++ b/src/spherinator/models/rotational2_autoencoder.py
@@ -68,37 +68,40 @@ def forward(self, x):
         return recon
 
     def training_step(self, batch, batch_idx):
-
-        with torch.no_grad():
-            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
-            scaled = functional.resize(
-                crop, [self.input_size, self.input_size], antialias=True
-            )
-
-        recon = self.forward(scaled)
-        loss = self.reconstruction_loss(scaled, recon)
-
-        for i in range(1, self.rotations):
+        with torch.autocast("cuda", enabled=False):
             with torch.no_grad():
-                rotate = functional.rotate(
-                    batch, 360.0 / self.rotations * i, expand=False
-                )
-                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
+                crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
                 scaled = functional.resize(
                     crop, [self.input_size, self.input_size], antialias=True
                 )
 
-            loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
-
-            # divide by the brightness of the image
-            if self.norm_brightness:
-                loss = loss / torch.sum(scaled, (1, 2, 3)) * self.total_input_size
-
-        loss = loss.mean()
-
-        self.log("train_loss", loss, prog_bar=True)
-        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
-        return loss
+        with torch.autocast("cuda", enabled=True):
+            recon = self.forward(scaled)
+            loss = self.reconstruction_loss(scaled, recon)
+
+            for i in range(1, self.rotations):
+                with torch.no_grad():
+                    rotate = functional.rotate(
+                        batch, 360.0 / self.rotations * i, expand=False
+                    )
+                    crop = functional.center_crop(
+                        rotate, [self.crop_size, self.crop_size]
+                    )
+                    scaled = functional.resize(
+                        crop, [self.input_size, self.input_size], antialias=True
+                    )
+
+                loss = torch.min(loss, self.reconstruction_loss(scaled, recon))
+
+                # divide by the brightness of the image
+                if self.norm_brightness:
+                    loss = loss / torch.sum(scaled, (1, 2, 3)) * self.total_input_size
+
+            loss = loss.mean()
+
+            self.log("train_loss", loss, prog_bar=True)
+            self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+            return loss
 
     def configure_optimizers(self):
         """Default Adam optimizer if missing from the configuration file."""
diff --git a/src/spherinator/models/rotational2_variational_autoencoder_power.py b/src/spherinator/models/rotational2_variational_autoencoder_power.py
index b402ea6..f71e43d 100644
--- a/src/spherinator/models/rotational2_variational_autoencoder_power.py
+++ b/src/spherinator/models/rotational2_variational_autoencoder_power.py
@@ -95,40 +95,45 @@ def forward(self, x):
         return (z_location, z_scale), (q_z, p_z), z, recon
 
     def training_step(self, batch, batch_idx):
-
-        with torch.no_grad():
-            crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
-            scaled = functional.resize(
-                crop, [self.input_size, self.input_size], antialias=True
-            )
-
-        (z_location, z_scale), (q_z, p_z), _, recon = self.forward(scaled)
-        loss_recon = self.reconstruction_loss(scaled, recon)
-
-        for i in range(1, self.rotations):
+        with torch.autocast("cuda", enabled=False):
             with torch.no_grad():
-                rotate = functional.rotate(
-                    batch, 360.0 / self.rotations * i, expand=False
-                )
-                crop = functional.center_crop(rotate, [self.crop_size, self.crop_size])
+                crop = functional.center_crop(batch, [self.crop_size, self.crop_size])
                 scaled = functional.resize(
                     crop, [self.input_size, self.input_size], antialias=True
                 )
 
-            loss_recon = torch.min(loss_recon, self.reconstruction_loss(scaled, recon))
-
-        loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
-        loss = (loss_recon + loss_KL).mean()
-        loss_recon = loss_recon.mean()
-        loss_KL = loss_KL.mean()
+        with torch.autocast("cuda", enabled=True):
+            (z_location, z_scale), (q_z, p_z), _, recon = self.forward(scaled)
+            loss_recon = self.reconstruction_loss(scaled, recon)
+
+            for i in range(1, self.rotations):
+                with torch.no_grad():
+                    rotate = functional.rotate(
+                        batch, 360.0 / self.rotations * i, expand=False
+                    )
+                    crop = functional.center_crop(
+                        rotate, [self.crop_size, self.crop_size]
+                    )
+                    scaled = functional.resize(
+                        crop, [self.input_size, self.input_size], antialias=True
+                    )
+
+                loss_recon = torch.min(
+                    loss_recon, self.reconstruction_loss(scaled, recon)
+                )
 
-        self.log("train_loss", loss, prog_bar=True)
-        self.log("loss_recon", loss_recon, prog_bar=True)
-        self.log("loss_KL", loss_KL)
-        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
-        self.log("mean(z_location)", torch.mean(z_location))
-        self.log("mean(z_scale)", torch.mean(z_scale))
-        return loss
+            loss_KL = torch.distributions.kl.kl_divergence(q_z, p_z) * self.beta
+            loss = (loss_recon + loss_KL).mean()
+            loss_recon = loss_recon.mean()
+            loss_KL = loss_KL.mean()
+
+            self.log("train_loss", loss, prog_bar=True)
+            self.log("loss_recon", loss_recon, prog_bar=True)
+            self.log("loss_KL", loss_KL)
+            self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+            self.log("mean(z_location)", torch.mean(z_location))
+            self.log("mean(z_scale)", torch.mean(z_scale))
+            return loss
 
     def configure_optimizers(self):
         """Default Adam optimizer if missing from the configuration file."""
diff --git a/src/spherinator/models/rotational_autoencoder.py b/src/spherinator/models/rotational_autoencoder.py
index c3e5c9e..1879cd1 100644
--- a/src/spherinator/models/rotational_autoencoder.py
+++ b/src/spherinator/models/rotational_autoencoder.py
@@ -67,22 +67,26 @@ def forward(self, x):
         return recon
 
     def training_step(self, batch, batch_idx):
-
-        best_scaled_image, _, _, _ = self.find_best_rotation(batch)
-        recon = self.forward(best_scaled_image)
-        loss = self.reconstruction_loss(best_scaled_image, recon)
-
-        # divide by the brightness of the image
-        if self.norm_brightness:
-            loss = (
-                loss / torch.sum(best_scaled_image, (1, 2, 3)) * self.total_input_size
-            )
-
-        loss = loss.mean()
-
-        self.log("train_loss", loss, prog_bar=True)
-        self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
-        return loss
+        with torch.autocast("cuda", enabled=False):
+            best_scaled_image, _, _, _ = self.find_best_rotation(batch)
+
+        with torch.autocast("cuda", enabled=True):
+            recon = self.forward(best_scaled_image)
+            loss = self.reconstruction_loss(best_scaled_image, recon)
+
+            # divide by the brightness of the image
+            if self.norm_brightness:
+                loss = (
+                    loss
+                    / torch.sum(best_scaled_image, (1, 2, 3))
+                    * self.total_input_size
+                )
+
+            loss = loss.mean()
+
+            self.log("train_loss", loss, prog_bar=True)
+            self.log("learning_rate", self.optimizers().param_groups[0]["lr"])
+            return loss
 
     def configure_optimizers(self):
         """Default Adam optimizer if missing from the configuration file."""

From 574960f6c08fa0004cac66b26719a58be3fc5b29 Mon Sep 17 00:00:00 2001
From: Bernd Doser <bernd.doser@h-its.org>
Date: Wed, 24 Jul 2024 11:10:01 +0200
Subject: [PATCH 20/20] poetry update

---
 poetry.lock | 1049 ++++++++++++++++++++++++++-------------------------
 1 file changed, 531 insertions(+), 518 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 1209068..c75c4f9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -197,13 +197,13 @@ test-all = ["astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "s
 
 [[package]]
 name = "astropy-iers-data"
-version = "0.2024.6.24.0.31.11"
+version = "0.2024.7.22.0.34.13"
 description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "astropy_iers_data-0.2024.6.24.0.31.11-py3-none-any.whl", hash = "sha256:0b3799034b0b76af8f915ef822d38cc90e00e235db0cb688018e4f567a8babb9"},
-    {file = "astropy_iers_data-0.2024.6.24.0.31.11.tar.gz", hash = "sha256:ef0197b7b84dea248031e553687ea1dc58d7ac9473043693b2d33b46d81a9a12"},
+    {file = "astropy_iers_data-0.2024.7.22.0.34.13-py3-none-any.whl", hash = "sha256:567a6cb261dd62f60862ee8d38e70fb2c88dfad03e962bc8138397a22e33003d"},
+    {file = "astropy_iers_data-0.2024.7.22.0.34.13.tar.gz", hash = "sha256:9bbb4bfc28bc8e834a6b3946a312ce3490c285abeab8fd9b1e98b11fdee6f92c"},
 ]
 
 [package.extras]
@@ -274,13 +274,13 @@ dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -567,63 +567,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.5.4"
+version = "7.6.0"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "coverage-7.5.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6cfb5a4f556bb51aba274588200a46e4dd6b505fb1a5f8c5ae408222eb416f99"},
-    {file = "coverage-7.5.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2174e7c23e0a454ffe12267a10732c273243b4f2d50d07544a91198f05c48f47"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2214ee920787d85db1b6a0bd9da5f8503ccc8fcd5814d90796c2f2493a2f4d2e"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1137f46adb28e3813dec8c01fefadcb8c614f33576f672962e323b5128d9a68d"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b385d49609f8e9efc885790a5a0e89f2e3ae042cdf12958b6034cc442de428d3"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b4a474f799456e0eb46d78ab07303286a84a3140e9700b9e154cfebc8f527016"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:5cd64adedf3be66f8ccee418473c2916492d53cbafbfcff851cbec5a8454b136"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:e564c2cf45d2f44a9da56f4e3a26b2236504a496eb4cb0ca7221cd4cc7a9aca9"},
-    {file = "coverage-7.5.4-cp310-cp310-win32.whl", hash = "sha256:7076b4b3a5f6d2b5d7f1185fde25b1e54eb66e647a1dfef0e2c2bfaf9b4c88c8"},
-    {file = "coverage-7.5.4-cp310-cp310-win_amd64.whl", hash = "sha256:018a12985185038a5b2bcafab04ab833a9a0f2c59995b3cec07e10074c78635f"},
-    {file = "coverage-7.5.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:db14f552ac38f10758ad14dd7b983dbab424e731588d300c7db25b6f89e335b5"},
-    {file = "coverage-7.5.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3257fdd8e574805f27bb5342b77bc65578e98cbc004a92232106344053f319ba"},
-    {file = "coverage-7.5.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3a6612c99081d8d6134005b1354191e103ec9705d7ba2754e848211ac8cacc6b"},
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-    {file = "coverage-7.5.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed550e7442f278af76d9d65af48069f1fb84c9f745ae249c1a183c1e9d1b025c"},
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 ]
 
 [package.dependencies]
@@ -1172,13 +1172,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.0.0"
+version = "8.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-8.0.0-py3-none-any.whl", hash = "sha256:15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f"},
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 ]
 
 [package.dependencies]
@@ -1234,13 +1234,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.4"
+version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1351,13 +1351,13 @@ files = [
 
 [[package]]
 name = "jsonargparse"
-version = "4.30.0"
+version = "4.32.0"
 description = "Implement minimal boilerplate CLIs derived from type hints and parse from command line, config files and environment variables."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1370,7 +1370,7 @@ typing-extensions = {version = ">=3.10.0.0", optional = true, markers = "python_
 all = ["jsonargparse[fsspec]", "jsonargparse[jsonnet]", "jsonargparse[jsonschema]", "jsonargparse[omegaconf]", "jsonargparse[reconplogger]", "jsonargparse[ruyaml]", "jsonargparse[signatures]", "jsonargparse[typing-extensions]", "jsonargparse[urls]"]
 argcomplete = ["argcomplete (>=2.0.0)", "argcomplete (>=3.3.0)"]
 coverage = ["jsonargparse[test-no-urls]", "pytest-cov (>=4.0.0)"]
-dev = ["build (>=0.10.0)", "jsonargparse[coverage]", "jsonargparse[doc]", "jsonargparse[mypy]", "jsonargparse[test]", "pre-commit (>=2.19.0)", "tox (>=3.25.0)"]
+dev = ["build (>=0.10.0)", "jsonargparse[coverage]", "jsonargparse[doc]", "jsonargparse[test]", "pre-commit (>=2.19.0)", "tox (>=3.25.0)"]
 doc = ["Sphinx (>=1.7.9)", "autodocsumm (>=0.1.10)", "sphinx-autodoc-typehints (>=1.19.5)", "sphinx-rtd-theme (>=1.2.2)"]
 fsspec = ["fsspec (>=0.8.4)"]
 jsonnet = ["jsonnet (>=0.13.0)", "jsonnet-binary (>=0.17.0)"]
@@ -1388,13 +1388,13 @@ urls = ["requests (>=2.18.4)"]
 
 [[package]]
 name = "jsonschema"
-version = "4.22.0"
+version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
 ]
 
 [package.dependencies]
@@ -1405,7 +1405,7 @@ rpds-py = ">=0.7.1"
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
 
 [[package]]
 name = "jsonschema-specifications"
@@ -1466,13 +1466,13 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "jupytext"
-version = "1.16.2"
+version = "1.16.3"
 description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupytext-1.16.3-py3-none-any.whl", hash = "sha256:870e0d7a716dcb1303df6ad1cec65e3315a20daedd808a55cb3dae2d56e4ed20"},
+    {file = "jupytext-1.16.3.tar.gz", hash = "sha256:1ebac990461dd9f477ff7feec9e3003fa1acc89f3c16ba01b73f79fd76f01a98"},
 ]
 
 [package.dependencies]
@@ -1484,11 +1484,11 @@ pyyaml = "*"
 tomli = {version = "*", markers = "python_version < \"3.11\""}
 
 [package.extras]
-dev = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
+dev = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (>=1.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
 docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
 test = ["pytest", "pytest-randomly", "pytest-xdist"]
 test-cov = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-cov (>=2.6.1)", "pytest-randomly", "pytest-xdist"]
-test-external = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (<0.4.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
+test-external = ["autopep8", "black", "flake8", "gitpython", "ipykernel", "isort", "jupyter-fs (>=1.0)", "jupyter-server (!=2.11)", "nbconvert", "pre-commit", "pytest", "pytest-randomly", "pytest-xdist", "sphinx-gallery (<0.8)"]
 test-functional = ["pytest", "pytest-randomly", "pytest-xdist"]
 test-integration = ["ipykernel", "jupyter-server (!=2.11)", "nbconvert", "pytest", "pytest-randomly", "pytest-xdist"]
 test-ui = ["calysto-bash"]
@@ -1627,18 +1627,18 @@ test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 
 [[package]]
 name = "lightning"
-version = "2.3.0"
+version = "2.3.3"
 description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "lightning-2.3.0.tar.gz", hash = "sha256:4bb4d6e3650d2d5f544ad60853a22efc4e164aa71b9596d13f0454b29df05130"},
+    {file = "lightning-2.3.3-py3-none-any.whl", hash = "sha256:5b4950f20b5117f30aad7820709cfa56669567cf4ded93df502b3dee443a6a5d"},
+    {file = "lightning-2.3.3.tar.gz", hash = "sha256:7f454711895c1c6e455766f01fa39522e25e5ab54c15c5e5fbad342fa92bc93c"},
 ]
 
 [package.dependencies]
 fsspec = {version = ">=2022.5.0,<2026.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0,<2.0"
+lightning-utilities = ">=0.10.0,<2.0"
 numpy = ">=1.17.2,<3.0"
 packaging = ">=20.0,<25.0"
 pytorch-lightning = "*"
@@ -1649,45 +1649,36 @@ tqdm = ">=4.57.0,<6.0"
 typing-extensions = ">=4.4.0,<6.0"
 
 [package.extras]
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 [package.dependencies]
@@ -1813,40 +1804,40 @@ files = [
 
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 python-versions = ">=3.9"
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 ]
 
 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
@@ -2613,20 +2616,21 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "pip-licenses"
-version = "4.4.0"
+version = "4.5.1"
 description = "Dump the software license list of Python packages installed with pip."
 optional = false
 python-versions = "~=3.8"
 files = [
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 ]
 
 [package.dependencies]
 prettytable = ">=2.3.0"
+tomli = ">=2"
 
 [package.extras]
-test = ["docutils", "mypy", "pytest-cov", "pytest-pycodestyle", "pytest-runner"]
+test = ["docutils", "mypy", "pytest-cov", "pytest-pycodestyle", "pytest-runner", "tomli-w"]
 
 [[package]]
 name = "platformdirs"
@@ -2672,13 +2676,13 @@ files = [
 
 [[package]]
 name = "prettytable"
-version = "3.10.0"
+version = "3.10.2"
 description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2762,13 +2766,13 @@ files = [
 
 [[package]]
 name = "pure-eval"
-version = "0.2.2"
+version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -2864,13 +2868,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.2.2"
+version = "8.3.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2878,7 +2882,7 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.5,<2.0"
+pluggy = ">=1.5,<2"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
@@ -2918,18 +2922,18 @@ six = ">=1.5"
 
 [[package]]
 name = "pytorch-lightning"
-version = "2.3.0"
+version = "2.3.3"
 description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 fsspec = {version = ">=2022.5.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0"
+lightning-utilities = ">=0.10.0"
 numpy = ">=1.17.2"
 packaging = ">=20.0"
 PyYAML = ">=5.4"
@@ -2939,11 +2943,11 @@ tqdm = ">=4.57.0"
 typing-extensions = ">=4.4.0"
 
 [package.extras]
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@@ -3006,6 +3010,7 @@ files = [
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@@ -3194,110 +3199,110 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rpds-py"
-version = "0.18.1"
+version = "0.19.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -3386,8 +3391,8 @@ threadpoolctl = ">=3.1.0"
 
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@@ -3437,13 +3442,13 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
 
 [[package]]
 name = "sentry-sdk"
-version = "2.7.0"
+version = "2.11.0"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
@@ -3483,7 +3488,7 @@ sanic = ["sanic (>=0.8)"]
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 starlette = ["starlette (>=0.19.1)"]
 starlite = ["starlite (>=1.48)"]
-tornado = ["tornado (>=5)"]
+tornado = ["tornado (>=6)"]
 
 [[package]]
 name = "setproctitle"
@@ -3587,18 +3592,19 @@ test = ["pytest"]
 
 [[package]]
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-version = "70.1.1"
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 python-versions = ">=3.8"
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 [[package]]
 name = "six"
@@ -3635,27 +3641,27 @@ files = [
 
 [[package]]
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+babel = ">=2.13"
+colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\""}
+docutils = ">=0.20,<0.22"
 imagesize = ">=1.3"
-importlib-metadata = {version = ">=4.8", markers = "python_version < \"3.10\""}
-Jinja2 = ">=3.0"
-packaging = ">=21.0"
-Pygments = ">=2.14"
-requests = ">=2.25.0"
-snowballstemmer = ">=2.0"
+importlib-metadata = {version = ">=6.0", markers = "python_version < \"3.10\""}
+Jinja2 = ">=3.1"
+packaging = ">=23.0"
+Pygments = ">=2.17"
+requests = ">=2.30.0"
+snowballstemmer = ">=2.2"
 sphinxcontrib-applehelp = "*"
 sphinxcontrib-devhelp = "*"
 sphinxcontrib-htmlhelp = ">=2.0.0"
@@ -3666,18 +3672,18 @@ tomli = {version = ">=2", markers = "python_version < \"3.11\""}
 
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
-lint = ["flake8 (>=3.5.0)", "importlib_metadata", "mypy (==1.9.0)", "pytest (>=6.0)", "ruff (==0.3.7)", "sphinx-lint", "tomli", "types-docutils", "types-requests"]
-test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=6.0)", "setuptools (>=67.0)"]
+lint = ["flake8 (>=6.0)", "importlib-metadata (>=6.0)", "mypy (==1.10.1)", "pytest (>=6.0)", "ruff (==0.5.2)", "sphinx-lint (>=0.9)", "tomli (>=2)", "types-docutils (==0.21.0.20240711)", "types-requests (>=2.30.0)"]
+test = ["cython (>=3.0)", "defusedxml (>=0.7.1)", "pytest (>=8.0)", "setuptools (>=70.0)", "typing_extensions (>=4.9)"]
 
 [[package]]
 name = "sphinx-autodoc-typehints"
-version = "2.2.2"
+version = "2.2.3"
 description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinx_autodoc_typehints-2.2.2-py3-none-any.whl", hash = "sha256:b98337a8530c95b73ba0c65465847a8ab0a13403bdc81294d5ef396bbd1f783e"},
-    {file = "sphinx_autodoc_typehints-2.2.2.tar.gz", hash = "sha256:128e600eeef63b722f3d8dac6403594592c8cade3ba66fd11dcb997465ee259d"},
+    {file = "sphinx_autodoc_typehints-2.2.3-py3-none-any.whl", hash = "sha256:b7058e8c5831e5598afca1a78fda0695d3291388d954464a6e480c36198680c0"},
+    {file = "sphinx_autodoc_typehints-2.2.3.tar.gz", hash = "sha256:fde3d888949bd0a91207cf1e54afda58121dbb4bf1f183d0cc78a0826654c974"},
 ]
 
 [package.dependencies]
@@ -3741,13 +3747,13 @@ test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.5"
+version = "2.0.6"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_htmlhelp-2.0.5-py3-none-any.whl", hash = "sha256:393f04f112b4d2f53d93448d4bce35842f62b307ccdc549ec1585e950bc35e04"},
-    {file = "sphinxcontrib_htmlhelp-2.0.5.tar.gz", hash = "sha256:0dc87637d5de53dd5eec3a6a01753b1ccf99494bd756aafecd74b4fa9e729015"},
+    {file = "sphinxcontrib_htmlhelp-2.0.6-py3-none-any.whl", hash = "sha256:1b9af5a2671a61410a868fce050cab7ca393c218e6205cbc7f590136f207395c"},
+    {file = "sphinxcontrib_htmlhelp-2.0.6.tar.gz", hash = "sha256:c6597da06185f0e3b4dc952777a04200611ef563882e0c244d27a15ee22afa73"},
 ]
 
 [package.extras]
@@ -3785,19 +3791,19 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.7"
+version = "1.0.8"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_qthelp-1.0.7-py3-none-any.whl", hash = "sha256:e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182"},
-    {file = "sphinxcontrib_qthelp-1.0.7.tar.gz", hash = "sha256:053dedc38823a80a7209a80860b16b722e9e0209e32fea98c90e4e6624588ed6"},
+    {file = "sphinxcontrib_qthelp-1.0.8-py3-none-any.whl", hash = "sha256:323d6acc4189af76dfe94edd2a27d458902319b60fcca2aeef3b2180c106a75f"},
+    {file = "sphinxcontrib_qthelp-1.0.8.tar.gz", hash = "sha256:db3f8fa10789c7a8e76d173c23364bdf0ebcd9449969a9e6a3dd31b8b7469f03"},
 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
 standalone = ["Sphinx (>=5)"]
-test = ["pytest"]
+test = ["defusedxml (>=0.7.1)", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
@@ -3836,17 +3842,20 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "sympy"
-version = "1.12.1"
+version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
-    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
+    {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
+    {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
 ]
 
 [package.dependencies]
-mpmath = ">=1.1.0,<1.4.0"
+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
 [[package]]
 name = "tbb"
@@ -3907,13 +3916,13 @@ files = [
 
 [[package]]
 name = "tifffile"
-version = "2024.6.18"
+version = "2024.7.24"
 description = "Read and write TIFF files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "tifffile-2024.6.18-py3-none-any.whl", hash = "sha256:67299c0445fc47463bbc71f3cb4676da2ab0242b0c6c6542a0680801b4b97d8a"},
-    {file = "tifffile-2024.6.18.tar.gz", hash = "sha256:57e0d2a034bcb6287ea3155d8716508dfac86443a257f6502b57ee7f8a33b3b6"},
+    {file = "tifffile-2024.7.24-py3-none-any.whl", hash = "sha256:f5cce1a915c37bc44ae4a792e3b42c07a30a3fa88406f5c6060a3de076487ed1"},
+    {file = "tifffile-2024.7.24.tar.gz", hash = "sha256:723456ebf2b4918878ae05a7b50fa366ff3b3a686293317eb7a0f294c3eea050"},
 ]
 
 [package.dependencies]
@@ -3921,6 +3930,10 @@ numpy = "*"
 
 [package.extras]
 all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
+codecs = ["imagecodecs (>=2023.8.12)"]
+plot = ["matplotlib"]
+xml = ["defusedxml", "lxml"]
+zarr = ["fsspec", "zarr"]
 
 [[package]]
 name = "tomli"
@@ -4133,13 +4146,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "typeshed-client"
-version = "2.5.1"
+version = "2.7.0"
 description = "A library for accessing stubs in typeshed."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typeshed_client-2.5.1-py3-none-any.whl", hash = "sha256:8cc254766b38f949effd3c9d4e0934d706d787127d7aedcabc648e4f094f7292"},
-    {file = "typeshed_client-2.5.1.tar.gz", hash = "sha256:6f1127f4853d216d5bfbb77260bb25e760fe00fcc67204a891be6c23471cd221"},
+    {file = "typeshed_client-2.7.0-py3-none-any.whl", hash = "sha256:97084e5abc58a76ace2c4618ecaebd625f2d19bbd85aa1b3fb86216bf174bbea"},
+    {file = "typeshed_client-2.7.0.tar.gz", hash = "sha256:e63df1e738588ad39f1226de042f4407ab6a99c456f0837063afd83b1415447c"},
 ]
 
 [package.dependencies]
@@ -4187,18 +4200,18 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wandb"
-version = "0.17.3"
+version = "0.17.5"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "wandb-0.17.5-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10e4b954ce7ff8615ee64b2dd5a04e51e8c64568d47ec39f5995dbbc9df172db"},
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 ]
 
 [package.dependencies]