unet_inference.ipynb

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			"#  The MIT License (MIT)\n",
			"#\n",
			"#  Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.\n",
			"#\n",
			"#  Permission is hereby granted, free of charge, to any person obtaining a copy\n",
			"#  of this software and associated documentation files (the 'Software'), to deal\n",
			"#  in the Software without restriction, including without limitation the rights\n",
			"#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
			"#  copies of the Software, and to permit persons to whom the Software is\n",
			"#  furnished to do so, subject to the following conditions:\n",
			"#\n",
			"#  The above copyright notice and this permission notice shall be included in\n",
			"#  all copies or substantial portions of the Software.\n",
			"#\n",
			"#  THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
			"#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
			"#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE\n",
			"#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
			"#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
			"#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n",
			"#  THE SOFTWARE.\n"
		]
	},
  {
   "cell_type": "markdown",
   "id": "cd7a3990",
   "metadata": {},
   "source": [
    "## Import MIGraphX Python Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3930d7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import migraphx\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b350c333",
   "metadata": {},
   "source": [
    "## Fetch U-NET ONNX Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02a7b7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -nc https://www.dropbox.com/s/3ntkhyk30x05uuv/unet_13_256.onnx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cfe6e9",
   "metadata": {},
   "source": [
    "## Load ONNX Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e05a13dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = migraphx.parse_onnx(\"unet_13_256.onnx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c67023",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(migraphx.get_target(\"gpu\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80edb6f1",
   "metadata": {},
   "source": [
    "## Print model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd5c3269",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.get_parameter_names())\n",
    "print(model.get_parameter_shapes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47f956c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(pil_img, newW, newH):\n",
    "    w, h = pil_img.size\n",
    "    assert newW > 0 and newH > 0, 'Scale is too small'\n",
    "    pil_img = pil_img.resize((newW, newH))\n",
    "\n",
    "    img_nd = np.array(pil_img)\n",
    "\n",
    "    if len(img_nd.shape) == 2:\n",
    "        img_nd = np.expand_dims(img_nd, axis=2)\n",
    "\n",
    "    # HWC to CHW\n",
    "    img_print = pil_img\n",
    "    img_trans = img_nd.transpose((2, 0, 1))\n",
    "    if img_trans.max() > 1:\n",
    "        img_trans = img_trans / 255\n",
    "        \n",
    "    img_trans = np.expand_dims(img_trans, 0)\n",
    "\n",
    "    return img_trans, img_print\n",
    "\n",
    "def plot_img_and_mask(img, mask):\n",
    "    classes = mask.shape[0] if len(mask.shape) > 3 else 1\n",
    "    print(classes)\n",
    "    fig, ax = plt.subplots(1, classes + 1)\n",
    "    ax[0].set_title('Input image')\n",
    "    ax[0].imshow(img)\n",
    "    if classes > 1:\n",
    "        for i in range(classes):\n",
    "            ax[i+1].set_title(f'Output mask (class {i+1})')\n",
    "            ax[i+1].imshow(mask[:, :, i])\n",
    "    else:\n",
    "        ax[1].set_title(f'Output mask')\n",
    "        ax[1].imshow(mask[0,0])\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "389ddc4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open(\"./car1.jpeg\")\n",
    "img, imPrint = preprocess(img, 256, 256)\n",
    "input_im = np.zeros((1,3,256,256),dtype='float32') \n",
    "np.lib.stride_tricks.as_strided(input_im, shape=img.shape, strides=input_im.strides)[:] = img #getting correct stride\n",
    "print(input_im.strides)\n",
    "print(input_im.shape)\n",
    "imPrint.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9de6f2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = model.run({'inputs':input_im}) # Your first inference would take longer than the following ones.\n",
    "output_mask = np.array(mask[0])\n",
    "print(output_mask.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acbd68e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "  return 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58e3062c",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = sigmoid(output_mask)\n",
    "full_mask = probs > 0.996\n",
    "plot_img_and_mask(imPrint, full_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6126df0b",
   "metadata": {},
   "source": [
    "<b>NOTE:</b> The model weights utilized here are trained by using car images with plain backgrounds. The imperfect result on a \"real-world\" image as shown above is expected. To get a better result fine-tuning the model on a dataset of real-world examples is recommended. "
   ]
  }
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