{
"cells": [
{
"cell_type": "code",
"metadata": {},
"source": [
"# The MIT License (MIT)",
"#",
"# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.",
"#",
"# Permission is hereby granted, free of charge, to any person obtaining a copy",
"# of this software and associated documentation files (the 'Software'), to deal",
"# in the Software without restriction, including without limitation the rights",
"# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell",
"# copies of the Software, and to permit persons to whom the Software is",
"# furnished to do so, subject to the following conditions:",
"#",
"# The above copyright notice and this permission notice shall be included in",
"# all copies or substantial portions of the Software.",
"#",
"# THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE",
"# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,",
"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN",
"# THE SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NFNet Inference with AMD MIGraphX\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalizer-Free ResNet is a new residual convolutional network providing new state-of-the-art Top-1 accuracy of 86.5% at ImageNet dataset. The most important feature of the model is removing batch normalization. Instead of batch normalization, it uses adaptive gradient clipping to provide same regularization effect of BatchNorm.
Details of this network: https://arxiv.org/abs/2102.06171\n",
"\n",
"In this notebook, we are showing:
\n",
"- How to optimize NFNet ONNX model with AMD MIGraphX.\n",
"- How to run inference on AMD GPU with the optimized ONNX model.\n",
"\n",
"The NFNet utilized in this example is the smallest NFNet version, F0: 71.5M parameters (83.6% top-1 accuracy on ImageNet)\n",
"\n",
"Please make sure MIGraphX Python API is installed following the instructions at Github page."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!apt-get update\n",
"!apt-get install ffmpeg libsm6 libxext6 -y "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install --upgrade pip\n",
"!pip3 install -r requirements_nfnet.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import cv2\n",
"import json\n",
"from PIL import Image\n",
"import time\n",
"from os import path "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing AMD MIGraphX Python Module"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import migraphx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create NFNet ONNX file\n",
"Following repository provides functionality to create NFNet ONNX file from PyTorch model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!wget -nc https://www.dropbox.com/s/u4ga8zyxtppfzxc/dm_nfnet_f0.onnx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load ImageNet labels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('../python_resnet50/imagenet_simple_labels.json') as json_data:\n",
" labels = json.load(json_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## Load ONNX model using MIGraphX"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = migraphx.parse_onnx(\"dm_nfnet_f0.onnx\")\n",
"model.compile(migraphx.get_target(\"gpu\"))\n",
"\n",
"print(model.get_parameter_names())\n",
"print(model.get_parameter_shapes())\n",
"print(model.get_output_shapes())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Functions for image processing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def make_nxn(image, n):\n",
" height, width = image.shape[:2] \n",
" if height > width:\n",
" dif = height - width\n",
" bar = dif // 2 \n",
" square = image[(bar + (dif % 2)):(height - bar),:]\n",
" return cv2.resize(square, (n, n))\n",
" elif width > height:\n",
" dif = width - height\n",
" bar = dif // 2\n",
" square = image[:,(bar + (dif % 2)):(width - bar)]\n",
" return cv2.resize(square, (n, n))\n",
" else:\n",
" return cv2.resize(image, (n, n))\n",
" \n",
"def preprocess(img_data):\n",
" mean_vec = np.array([0.485, 0.456, 0.406])\n",
" stddev_vec = np.array([0.229, 0.224, 0.225])\n",
" norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
" for i in range(img_data.shape[0]): \n",
" norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
" return norm_img_data\n",
"\n",
"def input_process(frame, dim):\n",
" # Crop and resize original image\n",
" cropped = make_nxn(frame, dim)\n",
" # Convert from HWC to CHW\n",
" chw = cropped.transpose(2,0,1)\n",
" # Apply normalization\n",
" pp = preprocess(chw)\n",
" # Add singleton dimension (CHW to NCHW)\n",
" data = np.expand_dims(pp.astype('float32'),0)\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download example image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetch example image: traffic light\n",
"!wget -nc http://farm5.static.flickr.com/4072/4462811418_8bc2bd42ca_z_d.jpg -O traffic_light.jpg\n",
"# Read the image\n",
"im = cv2.imread('traffic_light.jpg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Process the read image to conform input requirements\n",
"data_input = input_process(im, 192)\n",
"\n",
"# Run the model\n",
"start = time.time()\n",
"results = model.run({'inputs':data_input}) # Your first inference would take longer than the following ones.\n",
"print(f\"Time inference took: {1000*(time.time() - start):.2f}ms\")\n",
"# Extract the index of the top prediction\n",
"res_npa = np.array(results[0])\n",
"print(f\"\\nResult: {labels[np.argmax(res_npa)]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Run the model again, first one would take long\n",
"start = time.time()\n",
"results = model.run({'inputs':data_input}) # Your first inference would take longer than the following ones.\n",
"print(f\"Time inference took: {1000*(time.time() - start):.2f}ms\")\n",
"# Extract the index of the top prediction\n",
"res_npa = np.array(results[0])\n",
"print(f\"\\nResult: {labels[np.argmax(res_npa)]}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}