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- import paddlers
- from rs_models.test_model import TestModel
- __all__ = ['TestFarSegModel']
- class TestSegModel(TestModel):
- DEFAULT_HW = (512, 512)
- def check_output(self, output, target):
- self.assertIsInstance(output, list)
- self.check_output_equal(len(output), len(target))
- for o, t in zip(output, target):
- o = o.numpy()
- self.check_output_equal(o.shape[0], t.shape[0])
- self.check_output_equal(len(o.shape), 4)
- self.check_output_equal(o.shape[2:], t.shape[2:])
- def set_inputs(self):
- def _gen_data(specs):
- for spec in specs:
- c = spec.get('in_channels', 3)
- yield self.get_randn_tensor(c)
- self.inputs = _gen_data(self.specs)
- def set_targets(self):
- def _gen_data(specs):
- for spec in specs:
- c = spec.get('num_classes', 2)
- yield [self.get_zeros_array(c)]
- self.targets = _gen_data(self.specs)
- class TestFarSegModel(TestSegModel):
- MODEL_CLASS = paddlers.rs_models.seg.FarSeg
- def set_specs(self):
- self.specs = [
- dict(), dict(num_classes=20), dict(encoder_pretrained=False)
- ]
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