Detach numpy test scalenorm (2a289bd0) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_layers.py

+4 −37

Original line number	Diff line number	Diff line
		@@ -612,24 +612,6 @@ def test_scale_norm():
		"""Test invoking ScaleNorm."""
		input_ar = torch.tensor([[1., 99., 10000.], [0.003, 999.37, 23.]])
		layer = torch_layers.ScaleNorm(0.35)
		<<<<<<< HEAD
		result1 = layer(input_ar)
		output_ar = torch.tensor([[5.9157897e-05, 5.8566318e-03, 5.9157896e-01],
		[1.7754727e-06, 5.9145141e-01, 1.3611957e-02]])
		assert torch.allclose(result1, output_ar)


		@pytest.mark.torch
		def test_multi_headed_mat_attention():
		"""Test invoking MultiHeadedMATAttention."""
		from rdkit import Chem
		torch.manual_seed(0)
		input_smile = "CC"
		mol = Chem.MolFromSmiles(input_smile)
		adj_matrix = Chem.GetAdjacencyMatrix(mol)
		distance_matrix = Chem.GetDistanceMatrix(mol)
		layer = torch_layers.MultiHeadedMATAttention(
		=======
		result1 = layer.forward(input_ar)
		output_ar = np.array([[5.9157897e-05, 5.8566318e-03, 5.9157896e-01],
		[1.7754727e-06, 5.9145141e-01, 1.3611957e-02]])
		@@ -668,31 +650,17 @@ def test_sub_layer_connection():
		def test_mat_encoder_layer():
		"""Test invoking MATEncoderLayer."""
		torch.manual_seed(0)
		from rdkit import Chem
		import rdkit
		input_ar = torch.Tensor([[1., 2.], [5., 6.]])
		mask = torch.Tensor([[1., 1.], [1., 1.]])
		mol = Chem.MolFromSmiles("CC")
		adj_matrix = Chem.GetAdjacencyMatrix(mol)
		distance_matrix = Chem.GetDistanceMatrix(mol)
		mol = rdkit.Chem.rdmolfiles.MolFromSmiles("CC")
		adj_matrix = rdkit.Chem.rdmolops.GetAdjacencyMatrix(mol)
		distance_matrix = rdkit.Chem.rdmolops.GetDistanceMatrix(mol)
		layer = torch_layers.MATEncoderLayer(
		>>>>>>> Tests for encoder
		dist_kernel='softmax',
		lambda_attention=0.33,
		lambda_distance=0.33,
		h=2,
		<<<<<<< HEAD
		hsize=2,
		dropout_p=0.0)
		input_tensor = torch.tensor([[1., 2.], [5., 6.]])
		mask = torch.tensor([[1., 1.], [1., 1.]])
		result = layer(input_tensor, input_tensor, input_tensor, mask, 0.0,
		adj_matrix, distance_matrix)
		output_ar = torch.tensor([[[0.0492, -0.0792], [-0.9971, -0.3172],
		[0.0492, -0.0792], [-0.9971, -0.3172]],
		[[0.8671, 0.1069], [-3.4075, -0.8656],
		[0.8671, 0.1069], [-3.4075, -0.8656]]])
		assert torch.allclose(result, output_ar, rtol=1e-3)
		=======
		sa_hsize=2,
		sa_dropout_p=0.0,
		output_bias=True,
		@@ -710,4 +678,3 @@ def test_mat_encoder_layer():
		[[5.0000, 6.0000], [3.0000, 8.0000],
		[5.0000, 6.0000], [3.0000, 8.0000]]])
		assert torch.allclose(result, output_ar, rtol=1e-4)
		>>>>>>> Tests for encoder

deepchem/models/torch_models/layers.py

+198 −315

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