def test_from_to_instances(self):
orig = Instances((30, 30))
orig.proposal_boxes = Boxes(torch.rand(3, 4))
fields = {"proposal_boxes": Boxes, "a": Tensor}
with patch_instances(fields) as NewInstances:
# convert to NewInstances and back
new1 = NewInstances.from_instances(orig)
new2 = convert_scripted_instances(new1)
self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new1.proposal_boxes.tensor))
self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new2.proposal_boxes.tensor))
def _test_retinanet_model(self, config_path):
model = model_zoo.get(config_path, trained=True)
model.eval()
fields = {
"pred_boxes": Boxes,
"scores": Tensor,
"pred_classes": Tensor,
}
script_model = export_torchscript_with_instances(model, fields)
img = get_sample_coco_image()
inputs = [{"image": img}]
with torch.no_grad():
instance = model(inputs)[0]["instances"]
scripted_instance = convert_scripted_instances(script_model(inputs)[0])
scripted_instance = detector_postprocess(scripted_instance, img.shape[1], img.shape[2])
assert_instances_allclose(instance, scripted_instance)
