Add NNCF quantization for CLIP notebook (#1173)

* Add NNCF quantization for CLIP notebook * Refactoring * Remove image from notebook * Add reduceSum to ignored scope * Move visualize_result to visualize.py * Fix comments * Fix message * Replace dataset * Add custom nncf version * Renamed notebooks * Fix names * Update notebooks/228-clip-zero-shot-image-classification/README.md Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com> * Update notebooks/228-clip-zero-shot-image-classification/README.md Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com> * make spell checker happy * Apply suggestions from code review --------- Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>
openvinotoolkit · Jul 14, 2023 · e68d657 · e68d657
1 parent d0ec891
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diff --git a/...clip-zero-shot-image-classification.ipynb → ...fication/228-clip-zero-shot-convert.ipynb b/...clip-zero-shot-image-classification.ipynb → ...fication/228-clip-zero-shot-convert.ipynb
@@ -58,46 +58,6 @@
     "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from PIL import Image\n",
-    "\n",
-    "\n",
-    "def visualize_result(image:Image, labels:List[str], probs:np.ndarray, top:int = 5):\n",
-    "    \"\"\" \n",
-    "    Utility function for visualization classification results\n",
-    "    params:\n",
-    "      image: input image\n",
-    "      labels: list of classification labels\n",
-    "      probs: model predicted softmaxed probabilities for each label\n",
-    "      top: number of the highest probability results for visualization\n",
-    "    returns:\n",
-    "      None\n",
-    "    \"\"\"\n",
-    "    plt.figure(figsize=(64, 64))\n",
-    "    top_labels = np.argsort(-probs)[:min(top, probs.shape[0])]\n",
-    "    top_probs = probs[top_labels]\n",
-    "    plt.subplot(8, 8, 1)\n",
-    "    plt.imshow(image)\n",
-    "    plt.axis(\"off\")\n",
-    "\n",
-    "    plt.subplot(8, 8, 2)\n",
-    "    y = np.arange(top_probs.shape[-1])\n",
-    "    plt.grid()\n",
-    "    plt.barh(y, top_probs)\n",
-    "    plt.gca().invert_yaxis()\n",
-    "    plt.gca().set_axisbelow(True)\n",
-    "    plt.yticks(y, [labels[index] for index in top_labels])\n",
-    "    plt.xlabel(\"probability\")   "
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -115,6 +75,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from PIL import Image\n",
+    "from visualize import visualize_result\n",
+    "\n",
     "image = Image.open('../data/image/coco.jpg')\n",
     "input_labels = ['cat', 'dog', 'wolf', 'tiger', 'man', 'horse', 'frog', 'tree', 'house', 'computer']\n",
     "text_descriptions = [f\"This is a photo of a {label}\" for label in input_labels]\n",
@@ -197,7 +160,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from scipy.special import softmax\n",
     "from openvino.runtime import Core\n",
     "\n",
@@ -290,6 +252,16 @@
     "# visualize prediction\n",
     "visualize_result(image, labels, probs[0])"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Open the [228-clip-zero-shot-quantize](228-clip-zero-shot-quantize.ipynb) notebook to quantize the IR model with the Post-training Quantization API of NNCF and compare `FP16` and `INT8` models."
+   ]
   }
  ],
  "metadata": {