replace global batch_size with self.batch_size (keras-team#304)

* Update oxford_pets_image_segmentation.py

Change batch_size to self.batch size; only works as written because there's a globally defined batch_size variable

* Update oxford_pets_image_segmentation.ipynb

* Update oxford_pets_image_segmentation.md

examples/vision/ipynb/oxford_pets_image_segmentation.ipynb

@@ -152,11 +152,11 @@
     "        i = idx * self.batch_size\n",
     "        batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]\n",
     "        batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]\n",
-    "        x = np.zeros((batch_size,) + self.img_size + (3,), dtype=\"float32\")\n",
+    "        x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype=\"float32\")\n",
     "        for j, path in enumerate(batch_input_img_paths):\n",
     "            img = load_img(path, target_size=self.img_size)\n",
     "            x[j] = img\n",
-    "        y = np.zeros((batch_size,) + self.img_size + (1,), dtype=\"uint8\")\n",
+    "        y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype=\"uint8\")\n",
     "        for j, path in enumerate(batch_target_img_paths):\n",
     "            img = load_img(path, target_size=self.img_size, color_mode=\"grayscale\")\n",
     "            y[j] = np.expand_dims(img, 2)\n",
@@ -390,4 +390,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/vision/md/oxford_pets_image_segmentation.md

@@ -142,11 +142,11 @@ class OxfordPets(keras.utils.Sequence):
         i = idx * self.batch_size
         batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
         batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
-        x = np.zeros((batch_size,) + self.img_size + (3,), dtype="float32")
+        x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype="float32")
         for j, path in enumerate(batch_input_img_paths):
             img = load_img(path, target_size=self.img_size)
             x[j] = img
-        y = np.zeros((batch_size,) + self.img_size + (1,), dtype="uint8")
+        y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype="uint8")
         for j, path in enumerate(batch_target_img_paths):
             img = load_img(path, target_size=self.img_size, color_mode="grayscale")
             y[j] = np.expand_dims(img, 2)

examples/vision/oxford_pets_image_segmentation.py

@@ -90,11 +90,11 @@ def __getitem__(self, idx):
         i = idx * self.batch_size
         batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
         batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
-        x = np.zeros((batch_size,) + self.img_size + (3,), dtype="float32")
+        x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype="float32")
         for j, path in enumerate(batch_input_img_paths):
             img = load_img(path, target_size=self.img_size)
             x[j] = img
-        y = np.zeros((batch_size,) + self.img_size + (1,), dtype="uint8")
+        y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype="uint8")
         for j, path in enumerate(batch_target_img_paths):
             img = load_img(path, target_size=self.img_size, color_mode="grayscale")
             y[j] = np.expand_dims(img, 2)