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Complete Super Linear

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This commit is contained in:
D-X-Y
2021-03-19 15:17:49 +08:00
parent 9c5ae93494
commit 51c626c96d
8 changed files with 161 additions and 31 deletions
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24
tests/test_super_model.py
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@@ -25,6 +25,26 @@ class TestSuperLinear(unittest.TestCase):
out_features = spaces.Categorical(12, 24, 36)
bias = spaces.Categorical(True, False)
model = super_core.SuperLinear(10, out_features, bias=bias)
print(model)
print("The simple super linear module is:\n{:}".format(model))
print(model.super_run_type)
print(model.abstract_search_space())
self.assertTrue(model.bias)
inputs = torch.rand(32, 10)
print("Input shape: {:}".format(inputs.shape))
print("Weight shape: {:}".format(model._super_weight.shape))
print("Bias shape: {:}".format(model._super_bias.shape))
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), (32, 36))
abstract_space = model.abstract_search_space
abstract_child = abstract_space.random()
print("The abstract searc space:\n{:}".format(abstract_space))
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.apply_candiate(abstract_child)
output_shape = (32, abstract_child["_out_features"].value)
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), output_shape)