(self)
| 27 | self.model = get_model_zoo("COCO-Detection/retinanet_R_50_FPN_1x.yaml") |
| 28 | |
| 29 | def test_flop(self): |
| 30 | # RetinaNet supports flop-counting with random inputs |
| 31 | inputs = [{"image": torch.rand(3, 800, 800)}] |
| 32 | res = flop_count_operators(self.model, inputs) |
| 33 | self.assertTrue(int(res["conv"]), 146) # 146B flops |
| 34 | |
| 35 | def test_param_count(self): |
| 36 | res = parameter_count(self.model) |
nothing calls this directly
no test coverage detected