add evaluation transform method

src/transforms.py

@@ -23,19 +23,16 @@ def make_transforms(
     horizontal_flip=False,
     color_distortion=False,
     gaussian_blur=False,
-    normalization=((0.485, 0.456, 0.406),
-                   (0.229, 0.224, 0.225))
+    normalization=((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
 ):
-    logger.info('making imagenet data transforms')
+    logger.info("making imagenet data transforms")
 
     def get_color_distortion(s=1.0):
         # s is the strength of color distortion.
         color_jitter = transforms.ColorJitter(0.8 * s, 0.8 * s, 0.8 * s, 0.2 * s)
         rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)
         rnd_gray = transforms.RandomGrayscale(p=0.2)
-        color_distort = transforms.Compose([
-            rnd_color_jitter,
-            rnd_gray])
+        color_distort = transforms.Compose([rnd_color_jitter, rnd_gray])
         return color_distort
 
     transform_list = []
@@ -53,8 +50,25 @@ def make_transforms(
     return transform
 
 
+def make_transform_eval(
+    crop_size=224, normalization=((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+):
+    logger.info("making imagenet evaluation transforms")
+
+    resize_size = int(crop_size * 256 / 224)
+    transform = transforms.Compose(
+        [
+            transforms.Resize(resize_size),
+            transforms.CenterCrop(crop_size),
+            transforms.ToTensor(),
+            transforms.Normalize(normalization[0], normalization[1]),
+        ]
+    )
+    return transform
+
+
 class GaussianBlur(object):
-    def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
+    def __init__(self, p=0.5, radius_min=0.1, radius_max=2.0):
         self.prob = p
         self.radius_min = radius_min
         self.radius_max = radius_max