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128 lines
5.2 KiB
128 lines
5.2 KiB
#!/usr/bin/env python3
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"""PyTorch Inference Script
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An example inference script that outputs top-k class ids for images in a folder into a csv.
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Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
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"""
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import os
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import time
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import argparse
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import logging
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import numpy as np
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import torch
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from timm.models import create_model, apply_test_time_pool
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from timm.data import ImageDataset, create_loader, resolve_data_config
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from timm.utils import AverageMeter, setup_default_logging
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torch.backends.cudnn.benchmark = True
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_logger = logging.getLogger('inference')
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
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parser.add_argument('data', metavar='DIR',
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help='path to dataset')
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parser.add_argument('--output_dir', metavar='DIR', default='./',
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help='path to output files')
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parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
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help='model architecture (default: dpn92)')
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parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
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help='number of data loading workers (default: 2)')
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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metavar='N', help='mini-batch size (default: 256)')
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parser.add_argument('--img-size', default=None, type=int,
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metavar='N', help='Input image dimension')
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parser.add_argument('--input-size', default=None, nargs=3, type=int,
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metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('--num-classes', type=int, default=1000,
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help='Number classes in dataset')
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parser.add_argument('--log-freq', default=10, type=int,
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metavar='N', help='batch logging frequency (default: 10)')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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help='path to latest checkpoint (default: none)')
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parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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help='use pre-trained model')
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
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help='disable test time pool')
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parser.add_argument('--topk', default=5, type=int,
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metavar='N', help='Top-k to output to CSV')
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def main():
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setup_default_logging()
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args = parser.parse_args()
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# might as well try to do something useful...
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args.pretrained = args.pretrained or not args.checkpoint
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# create model
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model = create_model(
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args.model,
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num_classes=args.num_classes,
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in_chans=3,
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pretrained=args.pretrained,
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checkpoint_path=args.checkpoint)
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_logger.info('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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config = resolve_data_config(vars(args), model=model)
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model, test_time_pool = (model, False) if args.no_test_pool else apply_test_time_pool(model, config)
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if args.num_gpu > 1:
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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else:
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model = model.cuda()
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loader = create_loader(
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ImageDataset(args.data),
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input_size=config['input_size'],
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batch_size=args.batch_size,
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use_prefetcher=True,
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interpolation=config['interpolation'],
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mean=config['mean'],
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std=config['std'],
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num_workers=args.workers,
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crop_pct=1.0 if test_time_pool else config['crop_pct'])
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model.eval()
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k = min(args.topk, args.num_classes)
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batch_time = AverageMeter()
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end = time.time()
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topk_ids = []
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with torch.no_grad():
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for batch_idx, (input, _) in enumerate(loader):
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input = input.cuda()
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labels = model(input)
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topk = labels.topk(k)[1]
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topk_ids.append(topk.cpu().numpy())
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if batch_idx % args.log_freq == 0:
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_logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
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batch_idx, len(loader), batch_time=batch_time))
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topk_ids = np.concatenate(topk_ids, axis=0).squeeze()
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with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
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filenames = loader.dataset.filenames(basename=True)
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for filename, label in zip(filenames, topk_ids):
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out_file.write('{0},{1},{2},{3},{4},{5}\n'.format(
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filename, label[0], label[1], label[2], label[3], label[4]))
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if __name__ == '__main__':
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main()
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