import of the example from torchcvnn

mnist_conv/README.md

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+# MNIST classification in the spectral domain
+
+This simple example demonstrates how to code and run a complex valued neural network for classification.
+
+The task does not necessarily make sense but provides complex valued inputs : we classifiy the MNIST digits from their spectral representation.
+
+```bash
+python -m pip install -r requirements.txt
+python mnist.py
+```
+
+An expected output is :
+
+```bash
+Logging to ./logs/CMNIST_0
+>> Training
+100%|██████| 844/844 [00:17<00:00, 48.61it/s]
+>> Testing
+[Step 0] Train : CE  0.20 Acc  0.94 | Valid : CE  0.08 Acc  0.97 | Test : CE 0.06 Acc  0.98[>> BETTER <<]
+
+>> Training
+100%|██████| 844/844 [00:16<00:00, 51.69it/s]
+>> Testing
+[Step 1] Train : CE  0.06 Acc  0.98 | Valid : CE  0.06 Acc  0.98 | Test : CE 0.05 Acc  0.98[>> BETTER <<]
+
+>> Training
+100%|██████| 844/844 [00:15<00:00, 53.47it/s]
+>> Testing
+[Step 2] Train : CE  0.04 Acc  0.99 | Valid : CE  0.04 Acc  0.99 | Test : CE 0.04 Acc  0.99[>> BETTER <<]
+```

mnist_conv/mnist.py

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+# MIT License
+
+# Copyright (c) 2023 Jérémy Fix
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+# Example using complex valued neural networks to classify MNIST from the Fourier Transform of the digits.
+
+
+
+Requires dependencies :
+    python3 -m pip install torchvision tqdm
+"""
+
+# Standard imports
+import random
+import sys
+from typing import List
+
+# External imports
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms.v2 as v2_transforms
+
+import torchcvnn.nn as c_nn
+
+# Local imports
+import utils
+
+
+def conv_block(in_c: int, out_c: int, cdtype: torch.dtype) -> List[nn.Module]:
+    """
+    Builds a basic building block of
+    `Conv2d`-`Cardioid`-`Conv2d`-`Cardioid`-`AvgPool2d`
+
+    Arguments:
+        in_c : the number of input channels
+        out_c : the number of output channels
+        cdtype : the dtype of complex values (expected to be torch.complex64 or torch.complex32)
+    """
+    return [
+        nn.Conv2d(in_c, out_c, kernel_size=3, stride=1, padding=1, dtype=cdtype),
+        c_nn.BatchNorm2d(out_c),
+        c_nn.Cardioid(),
+        nn.Conv2d(out_c, out_c, kernel_size=3, stride=1, padding=1, dtype=cdtype),
+        c_nn.BatchNorm2d(out_c),
+        c_nn.Cardioid(),
+        c_nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+    ]
+
+
+def train():
+    """
+    Train function
+
+    Sample output :
+        ```.bash
+        (venv) me@host:~$ python mnist.py
+        Logging to ./logs/CMNIST_0
+        >> Training
+        100%|██████| 844/844 [00:17<00:00, 48.61it/s]
+        >> Testing
+        [Step 0] Train : CE  0.20 Acc  0.94 | Valid : CE  0.08 Acc  0.97 | Test : CE 0.06 Acc  0.98[>> BETTER <<]
+
+        >> Training
+        100%|██████| 844/844 [00:16<00:00, 51.69it/s]
+        >> Testing
+        [Step 1] Train : CE  0.06 Acc  0.98 | Valid : CE  0.06 Acc  0.98 | Test : CE 0.05 Acc  0.98[>> BETTER <<]
+
+        >> Training
+        100%|██████| 844/844 [00:15<00:00, 53.47it/s]
+        >> Testing
+        [Step 2] Train : CE  0.04 Acc  0.99 | Valid : CE  0.04 Acc  0.99 | Test : CE 0.04 Acc  0.99[>> BETTER <<]
+
+        [...]
+        ```
+
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    valid_ratio = 0.1
+    batch_size = 64
+    epochs = 10
+    cdtype = torch.complex64
+
+    # Dataloading
+    train_valid_dataset = torchvision.datasets.MNIST(
+        root="./data",
+        train=True,
+        download=True,
+        transform=v2_transforms.Compose([v2_transforms.PILToTensor(), torch.fft.fft]),
+    )
+    test_dataset = torchvision.datasets.MNIST(
+        root="./data",
+        train=False,
+        download=True,
+        transform=v2_transforms.Compose([v2_transforms.PILToTensor(), torch.fft.fft]),
+    )
+
+    all_indices = list(range(len(train_valid_dataset)))
+    random.shuffle(all_indices)
+    split_idx = int(valid_ratio * len(train_valid_dataset))
+    valid_indices, train_indices = all_indices[:split_idx], all_indices[split_idx:]
+
+    # Train dataloader
+    train_dataset = torch.utils.data.Subset(train_valid_dataset, train_indices)
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=batch_size, shuffle=True
+    )
+
+    # Valid dataloader
+    valid_dataset = torch.utils.data.Subset(train_valid_dataset, valid_indices)
+    valid_loader = torch.utils.data.DataLoader(
+        valid_dataset, batch_size=batch_size, shuffle=False
+    )
+
+    # Test dataloader
+    test_loader = torch.utils.data.DataLoader(
+        test_dataset, batch_size=batch_size, shuffle=False
+    )
+
+    # Model
+    conv_model = nn.Sequential(
+        *conv_block(1, 16, cdtype),
+        *conv_block(16, 16, cdtype),
+        *conv_block(16, 32, cdtype),
+        *conv_block(32, 32, cdtype),
+        nn.Flatten(),
+    )
+
+    with torch.no_grad():
+        conv_model.eval()
+        dummy_input = torch.zeros((64, 1, 28, 28), dtype=cdtype, requires_grad=False)
+        out_conv = conv_model(dummy_input).view(64, -1)
+    lin_model = nn.Sequential(
+        nn.Linear(out_conv.shape[-1], 124, dtype=cdtype),
+        c_nn.Cardioid(),
+        nn.Linear(124, 10, dtype=cdtype),
+        c_nn.Mod(),
+    )
+    model = nn.Sequential(conv_model, lin_model)
+    model.to(device)
+
+    # Loss, optimizer, callbacks
+    f_loss = nn.CrossEntropyLoss()
+    optim = torch.optim.Adam(model.parameters(), lr=3e-4)
+    logpath = utils.generate_unique_logpath("./logs", "CMNIST")
+    print(f"Logging to {logpath}")
+    checkpoint = utils.ModelCheckpoint(model, logpath, 4, min_is_best=True)
+
+    # Training loop
+    for e in range(epochs):
+        print(">> Training")
+        train_loss, train_acc = utils.train_epoch(
+            model, train_loader, f_loss, optim, device
+        )
+
+        print(">> Testing")
+        valid_loss, valid_acc = utils.test_epoch(model, valid_loader, f_loss, device)
+        test_loss, test_acc = utils.test_epoch(model, test_loader, f_loss, device)
+        updated = checkpoint.update(valid_loss)
+        better_str = "[>> BETTER <<]" if updated else ""
+
+        print(
+            f"[Step {e}] Train : CE {train_loss:5.2f} Acc {train_acc:5.2f} | Valid : CE {valid_loss:5.2f} Acc {valid_acc:5.2f} | Test : CE {test_loss:5.2f} Acc {test_acc:5.2f}"
+            + better_str
+        )
+
+
+if __name__ == "__main__":
+    train()

mnist_conv/requirements.txt

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+torchcvnn
+torchvision
+tqdm