| """ ONNX-runtime validation script |
| |
| This script was created to verify accuracy and performance of exported ONNX |
| models running with the onnxruntime. It utilizes the PyTorch dataloader/processing |
| pipeline for a fair comparison against the originals. |
| |
| Copyright 2020 Ross Wightman |
| """ |
| import argparse |
| import numpy as np |
| import onnxruntime |
| from data import create_loader, resolve_data_config, Dataset |
| from utils import AverageMeter |
| import time |
|
|
| parser = argparse.ArgumentParser(description='Caffe2 ImageNet Validation') |
| parser.add_argument('data', metavar='DIR', |
| help='path to dataset') |
| parser.add_argument('--onnx-input', default='', type=str, metavar='PATH', |
| help='path to onnx model/weights file') |
| parser.add_argument('--onnx-output-opt', default='', type=str, metavar='PATH', |
| help='path to output optimized onnx graph') |
| parser.add_argument('--profile', action='store_true', default=False, |
| help='Enable profiler output.') |
| parser.add_argument('-j', '--workers', default=2, type=int, metavar='N', |
| help='number of data loading workers (default: 2)') |
| parser.add_argument('-b', '--batch-size', default=256, type=int, |
| metavar='N', help='mini-batch size (default: 256)') |
| parser.add_argument('--img-size', default=None, type=int, |
| metavar='N', help='Input image dimension, uses model default if empty') |
| parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', |
| help='Override mean pixel value of dataset') |
| parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', |
| help='Override std deviation of of dataset') |
| parser.add_argument('--crop-pct', type=float, default=None, metavar='PCT', |
| help='Override default crop pct of 0.875') |
| parser.add_argument('--interpolation', default='', type=str, metavar='NAME', |
| help='Image resize interpolation type (overrides model)') |
| parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true', |
| help='use tensorflow mnasnet preporcessing') |
| parser.add_argument('--print-freq', '-p', default=10, type=int, |
| metavar='N', help='print frequency (default: 10)') |
|
|
|
|
| def main(): |
| args = parser.parse_args() |
| args.gpu_id = 0 |
|
|
| |
| sess_options = onnxruntime.SessionOptions() |
| sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL |
| if args.profile: |
| sess_options.enable_profiling = True |
| if args.onnx_output_opt: |
| sess_options.optimized_model_filepath = args.onnx_output_opt |
|
|
| session = onnxruntime.InferenceSession(args.onnx_input, sess_options) |
|
|
| data_config = resolve_data_config(None, args) |
| loader = create_loader( |
| Dataset(args.data, load_bytes=args.tf_preprocessing), |
| input_size=data_config['input_size'], |
| batch_size=args.batch_size, |
| use_prefetcher=False, |
| interpolation=data_config['interpolation'], |
| mean=data_config['mean'], |
| std=data_config['std'], |
| num_workers=args.workers, |
| crop_pct=data_config['crop_pct'], |
| tensorflow_preprocessing=args.tf_preprocessing) |
|
|
| input_name = session.get_inputs()[0].name |
|
|
| batch_time = AverageMeter() |
| top1 = AverageMeter() |
| top5 = AverageMeter() |
| end = time.time() |
| for i, (input, target) in enumerate(loader): |
| |
| output = session.run([], {input_name: input.data.numpy()}) |
| output = output[0] |
|
|
| |
| prec1, prec5 = accuracy_np(output, target.numpy()) |
| top1.update(prec1.item(), input.size(0)) |
| top5.update(prec5.item(), input.size(0)) |
|
|
| |
| batch_time.update(time.time() - end) |
| end = time.time() |
|
|
| if i % args.print_freq == 0: |
| print('Test: [{0}/{1}]\t' |
| 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s, {ms_avg:.3f} ms/sample) \t' |
| 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' |
| 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( |
| i, len(loader), batch_time=batch_time, rate_avg=input.size(0) / batch_time.avg, |
| ms_avg=100 * batch_time.avg / input.size(0), top1=top1, top5=top5)) |
|
|
| print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format( |
| top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg)) |
|
|
|
|
| def accuracy_np(output, target): |
| max_indices = np.argsort(output, axis=1)[:, ::-1] |
| top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean() |
| top1 = 100 * np.equal(max_indices[:, 0], target).mean() |
| return top1, top5 |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
