add eval_detection_multi

examples/detection/eval_detection_multi.py

@@ -0,0 +1,50 @@
+import argparse
+
+import chainer
+from chainer import iterators
+
+import chainermn
+
+from chainercv.utils import apply_to_iterator
+from chainercv.utils import ProgressHook
+
+from eval_detection import models
+from eval_detection import setup
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset', ('voc', 'coco'))
+    parser.add_argument('--model', sorted(models.keys()))
+    parser.add_argument('--pretrained-model')
+    parser.add_argument('--batchsize', type=int)
+    args = parser.parse_args()
+
+    comm = chainermn.create_communicator()
+    device = comm.intra_rank
+
+    dataset, label_names, eval_, model, batchsize = setup(
+        args.dataset, args.model, args.pretrained_model, args.batchsize)
+
+    chainer.cuda.get_device_from_id(device).use()
+    model.to_gpu()
+
+    model.use_preset('evaluate')
+
+    if not comm.rank == 0:
+        apply_to_iterator(model.predict, None, comm=comm)
+        return
+
+    iterator = iterators.MultithreadIterator(
+        dataset, batchsize, repeat=False, shuffle=False)
+
+    in_values, out_values, rest_values = apply_to_iterator(
+        model.predict, iterator, hook=ProgressHook(len(dataset)))
+    # delete unused iterators explicitly
+    del in_values
+
+    eval_(out_values, rest_values)
+
+
+if __name__ == '__main__':
+    main()