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.
127 lines
3.8 KiB
127 lines
3.8 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 logging
|
|
import yaml
|
|
from fire import Fire
|
|
from addict import Dict
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from timm.models import create_model, apply_test_time_pool
|
|
from timm.data import Dataset, create_loader, resolve_data_config
|
|
from timm.utils import AverageMeter, setup_default_logging
|
|
|
|
torch.backends.cudnn.benchmark = True
|
|
_logger = logging.getLogger('inference')
|
|
|
|
|
|
def _update_config(config, params):
|
|
for k, v in params.items():
|
|
*path, key = k.split(".")
|
|
config.update({k: v})
|
|
print(f"Overwriting {k} = {v} (was {config.get(key)})")
|
|
return config
|
|
|
|
|
|
def _fit(config_path, **kwargs):
|
|
with open(config_path) as stream:
|
|
base_config = yaml.safe_load(stream)
|
|
|
|
if "config" in kwargs.keys():
|
|
cfg_path = kwargs["config"]
|
|
with open(cfg_path) as cfg:
|
|
cfg_yaml = yaml.load(cfg, Loader=yaml.FullLoader)
|
|
|
|
merged_cfg = _update_config(base_config, cfg_yaml)
|
|
else:
|
|
merged_cfg = base_config
|
|
|
|
update_cfg = _update_config(merged_cfg, kwargs)
|
|
return update_cfg
|
|
|
|
|
|
def _parse_args(config_path):
|
|
args = Dict(Fire(_fit(config_path)))
|
|
|
|
# Cache the args as a text string to save them in the output dir later
|
|
args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
|
|
return args, args_text
|
|
|
|
|
|
def main():
|
|
setup_default_logging()
|
|
args, args_text = _parse_args('configs/inference.yaml')
|
|
# 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(
|
|
Dataset(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()
|