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pytorch-image-models/inference.py

116 lines
4.1 KiB

"""Sample PyTorch Inference script
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import torch
from models import create_model
from data import Dataset, create_loader, get_model_meanstd
from utils import AverageMeter
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=224, type=int,
metavar='N', help='Input image dimension')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print 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='test_time_pool', action='store_false',
help='use pre-trained model')
def main():
args = parser.parse_args()
# create model
num_classes = 1000
model = create_model(
args.model,
num_classes=num_classes,
pretrained=args.pretrained,
test_time_pool=args.test_time_pool)
# resume from a checkpoint
if args.checkpoint and os.path.isfile(args.checkpoint):
print("=> loading checkpoint '{}'".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
model.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint)
print("=> loaded checkpoint '{}'".format(args.checkpoint))
elif not args.pretrained:
print("=> no checkpoint found at '{}'".format(args.checkpoint))
exit(1)
if args.num_gpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
else:
model = model.cuda()
data_mean, data_std = get_model_meanstd(args.model)
loader = create_loader(
Dataset(args.data),
img_size=args.img_size,
batch_size=args.batch_size,
use_prefetcher=True,
mean=data_mean,
std=data_std,
num_workers=args.workers)
model.eval()
batch_time = AverageMeter()
end = time.time()
top5_ids = []
with torch.no_grad():
for batch_idx, (input, _) in enumerate(loader):
input = input.cuda()
labels = model(input)
top5 = labels.topk(5)[1]
top5_ids.append(top5.cpu().numpy())
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % args.print_freq == 0:
print('Predict: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
batch_idx, len(loader), batch_time=batch_time))
top5_ids = np.concatenate(top5_ids, axis=0).squeeze()
with open(os.path.join(args.output_dir, './top5_ids.csv'), 'w') as out_file:
filenames = loader.dataset.filenames()
for filename, label in zip(filenames, top5_ids):
filename = os.path.basename(filename)
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()