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110 lines
3.8 KiB
110 lines
3.8 KiB
"""Sample PyTorch Inference script
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import argparse
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import numpy as np
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import torch
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from models import create_model, load_checkpoint, TestTimePoolHead
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from data import Dataset, create_loader, get_model_meanstd
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from utils import AverageMeter
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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=224, type=int,
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metavar='N', help='Input image dimension')
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parser.add_argument('--print-freq', '-p', default=10, type=int,
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metavar='N', help='print 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='test_time_pool', action='store_false',
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help='use pre-trained model')
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def main():
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args = parser.parse_args()
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# create model
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num_classes = 1000
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model = create_model(
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args.model,
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num_classes=num_classes,
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pretrained=args.pretrained)
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print('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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# load a checkpoint
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if not args.pretrained:
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if not load_checkpoint(model, args.checkpoint):
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exit(1)
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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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data_mean, data_std = get_model_meanstd(args.model)
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loader = create_loader(
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Dataset(args.data),
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img_size=args.img_size,
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batch_size=args.batch_size,
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use_prefetcher=True,
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mean=data_mean,
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std=data_std,
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num_workers=args.workers)
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model.eval()
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batch_time = AverageMeter()
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end = time.time()
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top5_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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top5 = labels.topk(5)[1]
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top5_ids.append(top5.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.print_freq == 0:
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print('Predict: [{0}/{1}]\t'
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'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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top5_ids = np.concatenate(top5_ids, axis=0).squeeze()
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with open(os.path.join(args.output_dir, './top5_ids.csv'), 'w') as out_file:
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filenames = loader.dataset.filenames()
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for filename, label in zip(filenames, top5_ids):
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filename = os.path.basename(filename)
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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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