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- """ 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 timm.data import create_loader, resolve_data_config, create_dataset
- from timm.utils import AverageMeter
- import time
-
- parser = argparse.ArgumentParser(description='ONNX 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('--print-freq', '-p', default=10, type=int,
- metavar='N', help='print frequency (default: 10)')
-
-
- def main():
- args = parser.parse_args()
- args.gpu_id = 0
-
- # Set graph optimization level
- 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(vars(args))
- loader = create_loader(
- create_dataset('', args.data),
- 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']
- )
-
- input_name = session.get_inputs()[0].name
-
- batch_time = AverageMeter()
- top1 = AverageMeter()
- top5 = AverageMeter()
- end = time.time()
- for i, (input, target) in enumerate(loader):
- # run the net and return prediction
- output = session.run([], {input_name: input.data.numpy()})
- output = output[0]
-
- # measure accuracy and record loss
- prec1, prec5 = accuracy_np(output, target.numpy())
- top1.update(prec1.item(), input.size(0))
- top5.update(prec5.item(), input.size(0))
-
- # measure elapsed time
- batch_time.update(time.time() - end)
- end = time.time()
-
- if i % args.print_freq == 0:
- print(
- f'Test: [{i}/{len(loader)}]\t'
- f'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {input.size(0) / batch_time.avg:.3f}/s, '
- f'{100 * batch_time.avg / input.size(0):.3f} ms/sample) \t'
- f'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
- f'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'
- )
-
- print(f' * Prec@1 {top1.avg:.3f} ({100-top1.avg:.3f}) Prec@5 {top5.avg:.3f} ({100.-top5.avg:.3f})')
-
-
- 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()
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