refactor: centralize transform constants in model.py

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>

model.py

@@ -1,6 +1,13 @@
 import torch.nn as nn
 from PIL import Image, ImageDraw
 
+# --- Model and Transform Constants ---
+# For the custom crop transform. User can adjust these.
+TRIANGLE_CROP_WIDTH = 556
+TRIANGLE_CROP_HEIGHT = 1184
+RESIZE_DIM = 64 # Resize cropped image to this dimension (square)
+
+
 # Custom transform to crop a triangle from the lower right corner
 class CropLowerRightTriangle(object):
     """

sort.py

@@ -5,7 +5,8 @@ from PIL import Image
 import os
 import shutil
 
-from model import CropLowerRightTriangle, GarageDoorCNN
+from model import (CropLowerRightTriangle, GarageDoorCNN, TRIANGLE_CROP_WIDTH,
+                   TRIANGLE_CROP_HEIGHT, RESIZE_DIM)
 
 def sort_images():
     # --- Configuration ---
@@ -13,10 +14,6 @@ def sort_images():
     SOURCE_DIR = 'data/hourly_photos/'
     DEST_DIR = 'data/sorted/open/'
     
-    # These must match the parameters used during training
-    TRIANGLE_CROP_WIDTH = 556
-    TRIANGLE_CROP_HEIGHT = 1184
-    RESIZE_DIM = 64
 
     # The classes are sorted alphabetically by ImageFolder: ['closed', 'open']
     CLASS_NAMES = ['closed', 'open']

train.py

@@ -6,7 +6,8 @@ from torchvision import datasets, transforms
 from PIL import Image, ImageDraw
 import os
 
-from model import CropLowerRightTriangle, GarageDoorCNN
+from model import (CropLowerRightTriangle, GarageDoorCNN, TRIANGLE_CROP_WIDTH,
+                   TRIANGLE_CROP_HEIGHT, RESIZE_DIM)
 
 
 def train_model():
@@ -16,10 +17,6 @@ def train_model():
     NUM_EPOCHS = 10
     BATCH_SIZE = 32
     LEARNING_RATE = 0.001
-    # For the custom crop transform. User can adjust these.
-    TRIANGLE_CROP_WIDTH = 556
-    TRIANGLE_CROP_HEIGHT = 1184
-    RESIZE_DIM = 64 # Resize cropped image to this dimension (square)
 
     # --- Data Preparation ---
     # Define transforms