(self)
| 22 | self.assertEqual(output[0].shape, torch.Size([batch_size, 2048, 7, 7])) |
| 23 | |
| 24 | def test_resnest_withfc(self): |
| 25 | batch_size = 2 |
| 26 | num_classes = 5 |
| 27 | images = torch.rand(batch_size, 3, 224, 224).to('cuda') |
| 28 | model = ResNeSt(101, num_classes=num_classes).to('cuda') |
| 29 | model.init_weights() |
| 30 | output = model(images) |
| 31 | self.assertEqual(output[0].shape, torch.Size([batch_size, |
| 32 | num_classes])) |
| 33 | |
| 34 | def test_resnest_jit(self): |
| 35 | with torch.no_grad(): |
nothing calls this directly
no test coverage detected