init

datasets/oxford_flowers.py

@@ -0,0 +1,67 @@
+import os
+import random
+from scipy.io import loadmat
+from collections import defaultdict
+
+from .oxford_pets import OxfordPets
+from .utils import Datum, DatasetBase, read_json
+
+
+template = ['a photo of a {}, a type of flower.']
+
+
+class OxfordFlowers(DatasetBase):
+
+    dataset_dir = 'oxford_flowers'
+
+    def __init__(self, root, num_shots):
+        self.dataset_dir = os.path.join(root, self.dataset_dir)
+        self.image_dir = os.path.join(self.dataset_dir, 'jpg')
+        self.label_file = os.path.join(self.dataset_dir, 'imagelabels.mat')
+        self.lab2cname_file = os.path.join(self.dataset_dir, 'cat_to_name.json')
+        self.split_path = os.path.join(self.dataset_dir, 'split_zhou_OxfordFlowers.json')
+
+        self.template = template
+
+        train, val, test = OxfordPets.read_split(self.split_path, self.image_dir)
+        train = self.generate_fewshot_dataset(train, num_shots=num_shots)
+        
+        super().__init__(train_x=train, val=val, test=test)
+    
+    def read_data(self):
+        tracker = defaultdict(list)
+        label_file = loadmat(self.label_file)['labels'][0]
+        for i, label in enumerate(label_file):
+            imname = f'image_{str(i + 1).zfill(5)}.jpg'
+            impath = os.path.join(self.image_dir, imname)
+            label = int(label)
+            tracker[label].append(impath)
+        
+        print('Splitting data into 50% train, 20% val, and 30% test')
+
+        def _collate(ims, y, c):
+            items = []
+            for im in ims:
+                item = Datum(
+                    impath=im,
+                    label=y-1, # convert to 0-based label
+                    classname=c
+                )
+                items.append(item)
+            return items
+
+        lab2cname = read_json(self.lab2cname_file)
+        train, val, test = [], [], []
+        for label, impaths in tracker.items():
+            random.shuffle(impaths)
+            n_total = len(impaths)
+            n_train = round(n_total * 0.5)
+            n_val = round(n_total * 0.2)
+            n_test = n_total - n_train - n_val
+            assert n_train > 0 and n_val > 0 and n_test > 0
+            cname = lab2cname[str(label)]
+            train.extend(_collate(impaths[:n_train], label, cname))
+            val.extend(_collate(impaths[n_train:n_train+n_val], label, cname))
+            test.extend(_collate(impaths[n_train+n_val:], label, cname))
+        
+        return train, val, test