1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071 |
- import paddlers
- from rs_models.test_model import TestModel
- __all__ = []
- class TestClasModel(TestModel):
- DEFAULT_HW = (224, 224)
- def check_output(self, output, target):
-
- self.check_output_equal(output.numpy().shape, target)
- 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):
- self.targets = [[self.DEFAULT_BATCH_SIZE, spec.get('num_classes', 2)]
- for spec in self.specs]
- class TestCondenseNetV2AModel(TestClasModel):
- MODEL_CLASS = paddlers.rs_models.clas.CondenseNetV2_A
- def set_specs(self):
- self.specs = [
- dict(in_channels=3, num_classes=2),
- dict(in_channels=10, num_classes=2),
- dict(in_channels=3, num_classes=100)
- ]
- class TestCondenseNetV2BModel(TestClasModel):
- MODEL_CLASS = paddlers.rs_models.clas.CondenseNetV2_B
- def set_specs(self):
- self.specs = [
- dict(in_channels=3, num_classes=2),
- dict(in_channels=10, num_classes=2),
- dict(in_channels=3, num_classes=100)
- ]
- class TestCondenseNetV2CModel(TestClasModel):
- MODEL_CLASS = paddlers.rs_models.clas.CondenseNetV2_C
- def set_specs(self):
- self.specs = [
- dict(in_channels=3, num_classes=2),
- dict(in_channels=10, num_classes=2),
- dict(in_channels=3, num_classes=100)
- ]
|