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inference.py
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inference.py
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import argparse
import torch
from tqdm import tqdm
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
from config_parser import ConfigParser
def main(config):
logger = config.get_logger('test')
# 테스트를 위한 args 셋팅
data_loader = getattr(module_data, config['data_loader']['type'])(
config['data_loader']['args']['data_dir'],
batch_size=512,
shuffle=False,
validation_split=0.0,
training=False,
num_workers=2
)
model = config.init_obj('arch', module_arch)
logger.info(model)
criterion = getattr(module_loss, config["loss"])
metrics = [getattr(module_metric, metric) for metric in config["metrics"]]
logger.info('Loading checkpoint: {} ...'.format(config.resume))
checkpoint = torch.load(config.resume)
state_dict = checkpoint['state_dict']
model.load_state_dict(state_dict)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
total_loss = 0.0
total_metrics = torch.zeros(len(metrics))
with torch.no_grad():
for i, (data, target) in enumerate(tqdm(data_loader)):
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(output, target)
batch_size = data.shape[0]
total_loss += loss.item() * batch_size
for i, metric in enumerate(metrics):
total_metrics[i] += metric(output, target) * batch_size
if __name__ == '__main__':
args = argparse.ArgumentParser(description='PyTorch Template')
args.add_argument('-c', '--config', default=None, type=str,
help='config file path (default: None)')
args.add_argument('-r', '--resume', default=None, type=str,
help='path to latest checkpoint (default: None)')
args.add_argument('-d', '--device', default=None, type=str,
help='indices of GPUs to enable (default: all)')
config = ConfigParser.from_args(args)
main(config)