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@@ -71,6 +71,42 @@ def test_input_layer_03(): |
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assert layer.node_reps[1].device == device
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def test_one_hot_input_layer_01():
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d = _some_data()
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layer = OneHotInputLayer(d)
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assert layer.output_dim == [1000, 100]
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assert len(layer.node_reps) == 2
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assert layer.node_reps[0].shape == (1000, 1000)
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assert layer.node_reps[1].shape == (100, 100)
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assert layer.data == d
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assert layer.is_sparse
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def test_one_hot_input_layer_02():
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d = _some_data()
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layer = OneHotInputLayer(d)
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res = layer()
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assert isinstance(res[0], torch.Tensor)
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assert isinstance(res[1], torch.Tensor)
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assert res[0].shape == (1000, 1000)
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assert res[1].shape == (100, 100)
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assert torch.all(res[0].to_dense() == layer.node_reps[0].to_dense())
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assert torch.all(res[1].to_dense() == layer.node_reps[1].to_dense())
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def test_one_hot_input_layer_03():
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if torch.cuda.device_count() == 0:
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pytest.skip('No CUDA devices on this host')
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d = _some_data()
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layer = OneHotInputLayer(d)
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device = torch.device('cuda:0')
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layer = layer.to(device)
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print(list(layer.parameters()))
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# assert layer.device.type == 'cuda:0'
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assert layer.node_reps[0].device == device
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assert layer.node_reps[1].device == device
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def test_decagon_layer_01():
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d = _some_data_with_interactions()
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in_layer = InputLayer(d)
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@@ -82,3 +118,7 @@ def test_decagon_layer_02(): |
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in_layer = OneHotInputLayer(d)
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d_layer = DecagonLayer(d, in_layer, output_dim=32)
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_ = d_layer() # dummy call
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def test_decagon_layer_03():
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pass
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