Update evaluation dataset to use all 8 orients

tests/model_tests/chestnut_dec_may/train_fixmatch.py

@@ -24,7 +24,7 @@
 from frdc.utils.training import predict, plot_confusion_matrix
 from model_tests.utils import (
     val_preprocess,
-    FRDCDatasetFlipped,
+    FRDCDatasetStaticEval,
     n_weak_strong_aug,
     get_y_encoder,
     get_x_scaler,
@@ -116,7 +116,7 @@ def main(
         )
 
     y_true, y_pred = predict(
-        ds=FRDCDatasetFlipped(
+        ds=FRDCDatasetStaticEval(
             "chestnut_nature_park",
             "20210510",
             "90deg43m85pct255deg",

tests/model_tests/chestnut_dec_may/train_mixmatch.py

@@ -23,7 +23,7 @@
 from frdc.utils.training import predict, plot_confusion_matrix
 from model_tests.utils import (
     val_preprocess,
-    FRDCDatasetFlipped,
+    FRDCDatasetStaticEval,
     n_strong_aug,
     strong_aug,
     get_y_encoder,
@@ -107,7 +107,7 @@ def main(
         )
 
     y_true, y_pred = predict(
-        ds=FRDCDatasetFlipped(
+        ds=FRDCDatasetStaticEval(
             "chestnut_nature_park",
             "20210510",
             "90deg43m85pct255deg",

tests/model_tests/utils.py

@@ -5,6 +5,7 @@
 import numpy as np
 import torch
 from sklearn.preprocessing import OrdinalEncoder, StandardScaler
+from torch import rot90
 from torchvision.transforms import RandomVerticalFlip
 from torchvision.transforms.v2 import (
     Compose,
@@ -18,6 +19,7 @@
     Resize,
 )
 from torchvision.transforms.v2 import RandomHorizontalFlip
+from torchvision.transforms.v2.functional import hflip
 
 from frdc.load.dataset import FRDCDataset
 
@@ -26,27 +28,41 @@
 BANDS = ["NB", "NG", "NR", "RE", "NIR"]
 
 
-class FRDCDatasetFlipped(FRDCDataset):
+class FRDCDatasetStaticEval(FRDCDataset):
     def __len__(self):
-        """Assume that the dataset is 4x larger than it actually is.
+        """Assume that the dataset is 8x larger than it actually is.
 
-        For example, for index 0, we return the original image. For index 1, we
-        return the horizontally flipped image and so on, until index 3.
-        Then, return the next image for index 4, and so on.
+        There are 8 possible orientations for each image.
+        1.       As-is
+        2, 3, 4. Rotated 90, 180, 270 degrees
+        5.       Horizontally flipped
+        6, 7, 8. Horizontally flipped and rotated 90, 180, 270 degrees
         """
-        return super().__len__() * 4
+        return super().__len__() * 8
 
     def __getitem__(self, idx):
         """Alter the getitem method to implement the logic above."""
-        x, y = super().__getitem__(int(idx // 4))
-        if idx % 4 == 0:
-            return x, y
-        elif idx % 4 == 1:
-            return RandomHorizontalFlip(p=1)(x), y
-        elif idx % 4 == 2:
-            return RandomVerticalFlip(p=1)(x), y
-        elif idx % 4 == 3:
-            return RandomHorizontalFlip(p=1)(RandomVerticalFlip(p=1)(x)), y
+        x, y = super().__getitem__(int(idx // 8))
+        assert x.ndim == 3, "x must be a 3D tensor"
+        x_ = None
+        if idx % 8 == 0:
+            x_ = x
+        elif idx % 8 == 1:
+            x_ = rot90(x, 1, (1, 2))
+        elif idx % 8 == 2:
+            x_ = rot90(x, 2, (1, 2))
+        elif idx % 8 == 3:
+            x_ = rot90(x, 3, (1, 2))
+        elif idx % 8 == 4:
+            x_ = hflip(x)
+        elif idx % 8 == 5:
+            x_ = hflip(rot90(x, 1, (1, 2)))
+        elif idx % 8 == 6:
+            x_ = hflip(rot90(x, 2, (1, 2)))
+        elif idx % 8 == 7:
+            x_ = hflip(rot90(x, 3, (1, 2)))
+
+        return x_, y
 
 
 def val_preprocess(size: int):