You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
5.2 KiB
128 lines
5.2 KiB
#!/usr/bin/env python
|
|
"""PyTorch Inference Script
|
|
|
|
An example inference script that outputs top-k class ids for images in a folder into a csv.
|
|
|
|
Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
|
|
"""
|
|
import os
|
|
import time
|
|
import argparse
|
|
import logging
|
|
import numpy as np
|
|
import torch
|
|
|
|
from timm.models import create_model, apply_test_time_pool
|
|
from timm.data import ImageDataset, create_loader, resolve_data_config
|
|
from timm.utils import AverageMeter, setup_default_logging
|
|
|
|
torch.backends.cudnn.benchmark = True
|
|
_logger = logging.getLogger('inference')
|
|
|
|
|
|
parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
|
|
parser.add_argument('data', metavar='DIR',
|
|
help='path to dataset')
|
|
parser.add_argument('--output_dir', metavar='DIR', default='./',
|
|
help='path to output files')
|
|
parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
|
|
help='model architecture (default: dpn92)')
|
|
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')
|
|
parser.add_argument('--input-size', default=None, nargs=3, type=int,
|
|
metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), 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('--interpolation', default='', type=str, metavar='NAME',
|
|
help='Image resize interpolation type (overrides model)')
|
|
parser.add_argument('--num-classes', type=int, default=1000,
|
|
help='Number classes in dataset')
|
|
parser.add_argument('--log-freq', default=10, type=int,
|
|
metavar='N', help='batch logging frequency (default: 10)')
|
|
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
|
|
help='path to latest checkpoint (default: none)')
|
|
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
|
|
help='use pre-trained model')
|
|
parser.add_argument('--num-gpu', type=int, default=1,
|
|
help='Number of GPUS to use')
|
|
parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
|
|
help='disable test time pool')
|
|
parser.add_argument('--topk', default=5, type=int,
|
|
metavar='N', help='Top-k to output to CSV')
|
|
|
|
|
|
def main():
|
|
setup_default_logging()
|
|
args = parser.parse_args()
|
|
# might as well try to do something useful...
|
|
args.pretrained = args.pretrained or not args.checkpoint
|
|
|
|
# create model
|
|
model = create_model(
|
|
args.model,
|
|
num_classes=args.num_classes,
|
|
in_chans=3,
|
|
pretrained=args.pretrained,
|
|
checkpoint_path=args.checkpoint)
|
|
|
|
_logger.info('Model %s created, param count: %d' %
|
|
(args.model, sum([m.numel() for m in model.parameters()])))
|
|
|
|
config = resolve_data_config(vars(args), model=model)
|
|
model, test_time_pool = (model, False) if args.no_test_pool else apply_test_time_pool(model, config)
|
|
|
|
if args.num_gpu > 1:
|
|
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
|
|
else:
|
|
model = model.cuda()
|
|
|
|
loader = create_loader(
|
|
ImageDataset(args.data),
|
|
input_size=config['input_size'],
|
|
batch_size=args.batch_size,
|
|
use_prefetcher=True,
|
|
interpolation=config['interpolation'],
|
|
mean=config['mean'],
|
|
std=config['std'],
|
|
num_workers=args.workers,
|
|
crop_pct=1.0 if test_time_pool else config['crop_pct'])
|
|
|
|
model.eval()
|
|
|
|
k = min(args.topk, args.num_classes)
|
|
batch_time = AverageMeter()
|
|
end = time.time()
|
|
topk_ids = []
|
|
with torch.no_grad():
|
|
for batch_idx, (input, _) in enumerate(loader):
|
|
input = input.cuda()
|
|
labels = model(input)
|
|
topk = labels.topk(k)[1]
|
|
topk_ids.append(topk.cpu().numpy())
|
|
|
|
# measure elapsed time
|
|
batch_time.update(time.time() - end)
|
|
end = time.time()
|
|
|
|
if batch_idx % args.log_freq == 0:
|
|
_logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
|
|
batch_idx, len(loader), batch_time=batch_time))
|
|
|
|
topk_ids = np.concatenate(topk_ids, axis=0).squeeze()
|
|
|
|
with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
|
|
filenames = loader.dataset.filenames(basename=True)
|
|
for filename, label in zip(filenames, topk_ids):
|
|
out_file.write('{0},{1},{2},{3},{4},{5}\n'.format(
|
|
filename, label[0], label[1], label[2], label[3], label[4]))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|