Initial commit

1 files changed, 75 insertions, 0 deletions

diff --git a/code/sunlab/common/data/image_dataset.py b/code/sunlab/common/data/image_dataset.py
new file mode 100644
index 0000000..46e77b6
--- /dev/null
+++ b/code/sunlab/common/data/image_dataset.py
@@ -0,0 +1,75 @@
+class ImageDataset:
+    def __init__(
+        self,
+        base_directory,
+        ext="png",
+        channels=[0],
+        batch_size=None,
+        shuffle=False,
+        rotate=False,
+        rotate_p=1.,
+    ):
+        """# Image Dataset
+
+        Load a directory of images"""
+        from glob import glob
+        from matplotlib.pyplot import imread
+        from numpy import newaxis, vstack
+        from numpy.random import permutation, rand
+
+        self.base_directory = base_directory
+        files = glob(self.base_directory + "*." + ext)
+        self.dataset = []
+        for file in files:
+            im = imread(file)[newaxis, :, :, channels].transpose(0, 3, 1, 2)
+            self.dataset.append(im)
+            # Also add rotations of the image to the dataset
+            if rotate:
+                if rand() < rotate_p:
+                    self.dataset.append(im[:, :, ::-1, :])
+                if rand() < rotate_p:
+                    self.dataset.append(im[:, :, :, ::-1])
+                if rand() < rotate_p:
+                    self.dataset.append(im[:, :, ::-1, ::-1])
+                if rand() < rotate_p:
+                    self.dataset.append(im.transpose(0, 1, 3, 2))
+                if rand() < rotate_p:
+                    self.dataset.append(im.transpose(0, 1, 3, 2)[:, :, ::-1, :])
+                if rand() < rotate_p:
+                    self.dataset.append(im.transpose(0, 1, 3, 2)[:, :, :, ::-1])
+                if rand() < rotate_p:
+                    self.dataset.append(im.transpose(0, 1, 3, 2)[:, :, ::-1, ::-1])
+        self.dataset = vstack(self.dataset)
+        if shuffle:
+            self.dataset = self.dataset[permutation(self.dataset.shape[0]), ...]
+        self.batch_size = (
+            batch_size if batch_size is not None else self.dataset.shape[0]
+        )
+
+    def torch(self, device=None):
+        """# Cast to Torch Tensor"""
+        import torch
+
+        if device is None:
+            device = torch.device("cpu")
+        return torch.tensor(self.dataset).to(device)
+
+    def numpy(self):
+        """# Cast to Numpy Array"""
+        return self.dataset
+
+    def __len__(self):
+        """# Return Number of Cases
+
+        (or Number in each Batch)"""
+        return self.dataset.shape[0] // self.batch_size
+
+    def __getitem__(self, index):
+        """# Slice By Batch"""
+        if type(index) == tuple:
+            return self.dataset[index]
+        elif type(index) == int:
+            return self.dataset[
+                index * self.batch_size : (index + 1) * self.batch_size, ...
+            ]
+        return