Merge pull request #152 from bojiang/torch

chore(torch): add pytorch yolov5 example with torchhub usage

pytorch_mnist/pytorch_mnist_demo.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# BentoML PyTorch MNIST Tutorial\n",
     "\n",
-    "Link to source code: https://github.com/bentoml/gallery/tree/main/pytorch_mnist_demo/\n",
+    "Link to source code: https://github.com/bentoml/gallery/tree/main/pytorch_mnist/\n",
     "\n",
     "Install required dependencies:"
    ]
@@ -494,9 +494,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "tf2-py3.7",
    "language": "python",
-   "name": "python3"
+   "name": "tf2-py3.7"
   },
   "language_info": {
    "codemirror_mode": {
@@ -508,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.13"
   },
   "name": "pytorch_mnist.ipynb"
  },

pytorch_yolov5_torchhub/.bentoignore

@@ -0,0 +1,4 @@
+__pycache__/
+*.py[cod]
+*$py.class
+.ipynb_checkpoints

pytorch_yolov5_torchhub/.gitignore

@@ -0,0 +1 @@
+MNIST/

pytorch_yolov5_torchhub/bentofile.yaml

@@ -0,0 +1,13 @@
+service: "service:svc"
+description: "file: ./README.md"
+labels:
+  owner: bentoml-team
+  stage: demo
+include:
+- "*.py"
+- "/home/agent/.cache/torch/hub/ultralytics_yolov5_master/models/common.py"
+exclude:
+- "tests/"
+- "locustfile.py"
+python:
+  requirements_txt: ./requirements.txt

pytorch_yolov5_torchhub/locustfile.py

@@ -0,0 +1,15 @@
+from locust import HttpUser, task, between
+
+with open("yolov5/data/images/bus.jpg", "rb") as f:
+    test_image_bytes = f.read()
+
+
+class PyTorchMNISTLoadTestUser(HttpUser):
+
+    wait_time = between(0.3, 1.7)
+
+    @task
+    def predict_image(self):
+        files = {"upload_files": ("bus.jpg", test_image_bytes, "image/png")}
+        self.client.post("/predict_image", files=files)
+

pytorch_yolov5_torchhub/pytorch_yolov5_torchhub_demo.ipynb

@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a682ea0b",
+   "metadata": {},
+   "source": [
+    "# BentoML PyTorch MNIST Tutorial\n",
+    "\n",
+    "Link to source code: https://github.com/bentoml/gallery/tree/main/pytorch_yolov5_torchhub/\n",
+    "\n",
+    "Install required dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ad00863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45393b74",
+   "metadata": {},
+   "source": [
+    "## Load the pre-trained model from Torch Hub\n",
+    "\n",
+    "take `ultralytics/yolov5` as the example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee440480-fec6-4da1-a38b-e70fd5dab70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone 'https://github.com/ultralytics/yolov5.git'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ae5cf59-8423-49d3-a2d4-d5837ce94c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# Model\n",
+    "original_model = torch.hub.load('./yolov5', 'yolov5s', pretrained=True, source=\"local\")\n",
+    "\n",
+    "\n",
+    "class WrapperModel(torch.nn.Module):\n",
+    "    def __init__(self, model):\n",
+    "        super().__init__()\n",
+    "        self.model = model\n",
+    "        \n",
+    "    def forward(self, imgs):\n",
+    "        outputs = self.model(imgs)\n",
+    "\n",
+    "        # convert outputs to a json serializable list\n",
+    "        results = []\n",
+    "        for det in outputs.pred:\n",
+    "            detections = []\n",
+    "            for i in det:\n",
+    "                d = {}\n",
+    "                d['obj'] = outputs.names[int(i[5])]\n",
+    "                d['position'] = i[:4].tolist()\n",
+    "                d['prob'] = i[4].tolist()\n",
+    "                detections.append(d)\n",
+    "            results.append(detections)\n",
+    "\n",
+    "        return results\n",
+    "\n",
+    "\n",
+    "model = WrapperModel(original_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0950faa2-256f-46ef-b7c1-62737a85ff32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import PIL.Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c821e28-1001-4aae-91b0-5684d458be61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Images\n",
+    "imgs = [\n",
+    "    np.array(PIL.Image.open('yolov5/data/images/bus.jpg')),\n",
+    "    np.array(PIL.Image.open('yolov5/data/images/zidane.jpg')),\n",
+    "]  # batch of images\n",
+    "\n",
+    "model(imgs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a8b2ec-875b-43d7-9d23-c9fa4c938a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38888f0a",
+   "metadata": {},
+   "source": [
+    "## Training and Saving the model\n",
+    "\n",
+    "Then we define a simple PyTorch network and some helper functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92d9b23c",
+   "metadata": {},
+   "source": [
+    "### saving the model with some metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "048518a2-9a87-4672-8807-2e180534bab7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bentoml\n",
+    "\n",
+    "bentoml.pytorch.save_model(\n",
+    "    \"pytorch_yolov5\",\n",
+    "    model,\n",
+    "    signatures={\"__call__\": {\"batchable\": True, \"batchdim\": 0}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf35e55",
+   "metadata": {},
+   "source": [
+    "## Create a BentoML Service for serving the model\n",
+    "\n",
+    "Note: using `%%writefile` here because `bentoml.Service` instance must be created in a separate `.py` file\n",
+    "\n",
+    "Even though we have only one model, we can create as many api endpoints as we want. Here we create two end points `predict_ndarray` and `predict_image`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3e2f590",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile service.py\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "import typing as t\n",
+    "\n",
+    "import numpy as np\n",
+    "import PIL.Image\n",
+    "\n",
+    "import bentoml\n",
+    "from bentoml.io import Image\n",
+    "from bentoml.io import JSON\n",
+    "\n",
+    "\n",
+    "yolo_runner = bentoml.pytorch.get(\"pytorch_yolov5\").to_runner()\n",
+    "\n",
+    "svc = bentoml.Service(\n",
+    "    name=\"pytorch_yolo_demo\",\n",
+    "    runners=[yolo_runner],\n",
+    ")\n",
+    "\n",
+    "\n",
+    "sys.path.append('yolov5')\n",
+    "\n",
+    "@svc.api(input=Image(), output=JSON())\n",
+    "async def predict_image(img: PIL.Image.Image) -> list:\n",
+    "    assert isinstance(img, PIL.Image.Image)\n",
+    "    return await yolo_runner.async_run([np.array(img)])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "590147aa",
+   "metadata": {},
+   "source": [
+    "Start a dev model server to test out the service defined above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29173871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!bentoml serve service.py:svc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "606c1b36",
+   "metadata": {},
+   "source": [
+    "Now you can use something like:\n",
+    "\n",
+    "`curl -H \"Content-Type: multipart/form-data\" -F'fileobj=@yolov5/data/images/bus.jpg;type=image/png' http://127.0.0.1:3000/predict_image`\n",
+    "    \n",
+    "to send an image to the digit recognition service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7f03564",
+   "metadata": {},
+   "source": [
+    "## Build a Bento for distribution and deployment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36306933",
+   "metadata": {},
+   "source": [
+    "Starting a dev server with the Bento build:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f01436c-a244-4504-bf05-1bbdef5d815a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!bentoml build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec4b9dff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!bentoml serve pytorch_yolo_demo:latest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f05fae93",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch-py3.7",
+   "language": "python",
+   "name": "torch-py3.7"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "name": "pytorch_mnist.ipynb"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pytorch_yolov5_torchhub/requirements.txt

@@ -0,0 +1,20 @@
+# Usage: pip install -r requirements.txt
+
+# Bento requirements
+pydantic
+
+
+
+# YOLOv5 requirements
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.64.0
+protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+seaborn