add keypoint_detection (keras-team#1589)

examples/vision/ipynb/keypoint_detection.ipynb

@@ -8,9 +8,9 @@
    "source": [
     "# Keypoint Detection with Transfer Learning\n",
     "\n",
-    "**Author:** [Sayak Paul](https://twitter.com/RisingSayak)<br>\n",
+    "**Author:** [Sayak Paul](https://twitter.com/RisingSayak), converted to Keras 3 by [Muhammad Anas Raza](https://anasrz.com)<br>\n",
     "**Date created:** 2021/05/02<br>\n",
-    "**Last modified:** 2021/05/02<br>\n",
+    "**Last modified:** 2023/07/19<br>\n",
     "**Description:** Training a keypoint detector with data augmentation and transfer learning."
    ]
   },
@@ -119,9 +119,8 @@
    },
    "outputs": [],
    "source": [
-    "from tensorflow.keras import layers\n",
-    "from tensorflow import keras\n",
-    "import tensorflow as tf\n",
+    "from keras import layers\n",
+    "import keras\n",
     "\n",
     "from imgaug.augmentables.kps import KeypointsOnImage\n",
     "from imgaug.augmentables.kps import Keypoint\n",
@@ -277,6 +276,7 @@
     "colours = [\"#\" + colour for colour in colours]\n",
     "labels = keypoint_def[\"Name\"].values.tolist()\n",
     "\n",
+    "\n",
     "# Utility for reading an image and for getting its annotations.\n",
     "def get_dog(name):\n",
     "    data = json_dict[name]\n",
@@ -311,6 +311,7 @@
    },
    "outputs": [],
    "source": [
+    "\n",
     "# Parts of this code come from here:\n",
     "# https://github.com/benjiebob/StanfordExtra/blob/master/demo.ipynb\n",
     "def visualize_keypoints(images, keypoints):\n",
@@ -326,7 +327,12 @@
     "        if isinstance(current_keypoint, KeypointsOnImage):\n",
     "            for idx, kp in enumerate(current_keypoint.keypoints):\n",
     "                ax_all.scatter(\n",
-    "                    [kp.x], [kp.y], c=colours[idx], marker=\"x\", s=50, linewidths=5\n",
+    "                    [kp.x],\n",
+    "                    [kp.y],\n",
+    "                    c=colours[idx],\n",
+    "                    marker=\"x\",\n",
+    "                    s=50,\n",
+    "                    linewidths=5,\n",
     "                )\n",
     "        else:\n",
     "            current_keypoint = np.array(current_keypoint)\n",
@@ -391,8 +397,9 @@
    "outputs": [],
    "source": [
     "\n",
-    "class KeyPointsDataset(keras.utils.Sequence):\n",
-    "    def __init__(self, image_keys, aug, batch_size=BATCH_SIZE, train=True):\n",
+    "class KeyPointsDataset(keras.utils.PyDataset):\n",
+    "    def __init__(self, image_keys, aug, batch_size=BATCH_SIZE, train=True, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
     "        self.image_keys = image_keys\n",
     "        self.aug = aug\n",
     "        self.batch_size = batch_size\n",
@@ -536,8 +543,12 @@
    },
    "outputs": [],
    "source": [
-    "train_dataset = KeyPointsDataset(train_keys, train_aug)\n",
-    "validation_dataset = KeyPointsDataset(validation_keys, test_aug, train=False)\n",
+    "train_dataset = KeyPointsDataset(\n",
+    "    train_keys, train_aug, workers=2, use_multiprocessing=True\n",
+    ")\n",
+    "validation_dataset = KeyPointsDataset(\n",
+    "    validation_keys, test_aug, train=False, workers=2, use_multiprocessing=True\n",
+    ")\n",
     "\n",
     "print(f\"Total batches in training set: {len(train_dataset)}\")\n",
     "print(f\"Total batches in validation set: {len(validation_dataset)}\")\n",
@@ -578,7 +589,9 @@
     "def get_model():\n",
     "    # Load the pre-trained weights of MobileNetV2 and freeze the weights\n",
     "    backbone = keras.applications.MobileNetV2(\n",
-    "        weights=\"imagenet\", include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3)\n",
+    "        weights=\"imagenet\",\n",
+    "        include_top=False,\n",
+    "        input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
     "    )\n",
     "    backbone.trainable = False\n",
     "\n",

examples/vision/keypoint_detection.py

@@ -1,8 +1,8 @@
 """
 Title: Keypoint Detection with Transfer Learning
-Author: [Sayak Paul](https://twitter.com/RisingSayak)
+Author: [Sayak Paul](https://twitter.com/RisingSayak), converted to Keras 3 by [Muhammad Anas Raza](https://anasrz.com)
 Date created: 2021/05/02
-Last modified: 2021/05/02
+Last modified: 2023/07/19
 Description: Training a keypoint detector with data augmentation and transfer learning.
 Accelerator: GPU
 """
@@ -56,10 +56,8 @@
 """
 ## Imports
 """
-
-from tensorflow.keras import layers
-from tensorflow import keras
-import tensorflow as tf
+from keras import layers
+import keras
 
 from imgaug.augmentables.kps import KeypointsOnImage
 from imgaug.augmentables.kps import Keypoint
@@ -205,7 +203,12 @@ def visualize_keypoints(images, keypoints):
         if isinstance(current_keypoint, KeypointsOnImage):
             for idx, kp in enumerate(current_keypoint.keypoints):
                 ax_all.scatter(
-                    [kp.x], [kp.y], c=colours[idx], marker="x", s=50, linewidths=5
+                    [kp.x],
+                    [kp.y],
+                    c=colours[idx],
+                    marker="x",
+                    s=50,
+                    linewidths=5,
                 )
         else:
             current_keypoint = np.array(current_keypoint)
@@ -251,8 +254,9 @@ def visualize_keypoints(images, keypoints):
 """
 
 
-class KeyPointsDataset(keras.utils.Sequence):
-    def __init__(self, image_keys, aug, batch_size=BATCH_SIZE, train=True):
+class KeyPointsDataset(keras.utils.PyDataset):
+    def __init__(self, image_keys, aug, batch_size=BATCH_SIZE, train=True, **kwargs):
+        super().__init__(**kwargs)
         self.image_keys = image_keys
         self.aug = aug
         self.batch_size = batch_size
@@ -349,8 +353,12 @@ def __data_generation(self, image_keys_temp):
 ## Data generator investigation
 """
 
-train_dataset = KeyPointsDataset(train_keys, train_aug)
-validation_dataset = KeyPointsDataset(validation_keys, test_aug, train=False)
+train_dataset = KeyPointsDataset(
+    train_keys, train_aug, workers=2, use_multiprocessing=True
+)
+validation_dataset = KeyPointsDataset(
+    validation_keys, test_aug, train=False, workers=2, use_multiprocessing=True
+)
 
 print(f"Total batches in training set: {len(train_dataset)}")
 print(f"Total batches in validation set: {len(validation_dataset)}")
@@ -377,7 +385,9 @@ def __data_generation(self, image_keys_temp):
 def get_model():
     # Load the pre-trained weights of MobileNetV2 and freeze the weights
     backbone = keras.applications.MobileNetV2(
-        weights="imagenet", include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3)
+        weights="imagenet",
+        include_top=False,
+        input_shape=(IMG_SIZE, IMG_SIZE, 3),
     )
     backbone.trainable = False