Merge branch 'bert_to_merge' of https://github.com/alat-rights/deepchem into bert_to_merge (abd0b4e8) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_layers.py

+87 −2

Original line number	Diff line number	Diff line
		@@ -636,10 +636,95 @@ def test_multi_headed_mat_attention():
		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)
		result = layer(input_tensor, input_tensor, input_tensor, mask, adj_matrix,
		distance_matrix, 0.0)
		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)


		@pytest.mark.torch
		def test_position_wise_feed_forward():
		"""Test invoking PositionwiseFeedForward."""
		torch.manual_seed(0)
		input_ar = torch.tensor([[1., 2.], [5., 6.]])
		layer = torch_layers.PositionwiseFeedForward(
		d_input=2,
		d_hidden=2,
		d_output=2,
		activation='relu',
		n_layers=1,
		dropout_p=0.0)
		result = layer(input_ar)
		output_ar = torch.tensor([[0.4810, 0.0000], [1.9771, 0.0000]])
		assert torch.allclose(result, output_ar, rtol=1e-4)


		@pytest.mark.torch
		def test_sub_layer_connection():
		"""Test invoking SublayerConnection."""
		torch.manual_seed(0)
		input_ar = torch.tensor([[1., 2.], [5., 6.]])
		layer = torch_layers.SublayerConnection(2, 0.0)
		result = layer(input_ar, input_ar)
		output_ar = torch.tensor([[2.0027e-05, 3.0000e+00], [4.0000e+00, 7.0000e+00]])
		assert torch.allclose(result, output_ar)


		@pytest.mark.torch
		def test_mat_encoder_layer():
		"""Test invoking MATEncoderLayer."""
		torch.manual_seed(0)
		from rdkit import Chem
		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)
		layer = torch_layers.MATEncoderLayer(
		dist_kernel='softmax',
		lambda_attention=0.33,
		lambda_distance=0.33,
		h=2,
		sa_hsize=2,
		sa_dropout_p=0.0,
		output_bias=True,
		d_input=2,
		d_hidden=2,
		d_output=2,
		activation='relu',
		n_layers=2,
		ff_dropout_p=0.0,
		encoder_hsize=2,
		encoder_dropout_p=0.0)
		result = layer(input_ar, mask, adj_matrix, distance_matrix, 0.0)
		output_ar = torch.tensor([[[0.9988, 2.0012], [-0.9999, 3.9999],
		[0.9988, 2.0012], [-0.9999, 3.9999]],
		[[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)


		@pytest.mark.torch
		def test_mat_embedding():
		"""Test invoking MATEmbedding."""
		torch.manual_seed(0)
		input_ar = torch.tensor([1., 2., 3.])
		layer = torch_layers.MATEmbedding(3, 1, 0.0)
		result = layer(input_ar).detach()
		output_ar = torch.tensor([-1.2353])
		assert torch.allclose(result, output_ar, rtol=1e-4)


		@pytest.mark.torch
		def test_mat_generator():
		"""Test invoking MATGenerator."""
		torch.manual_seed(0)
		input_ar = torch.tensor([1., 2., 3.])
		layer = torch_layers.MATGenerator(3, 'mean', 1, 1, 0.0)
		mask = torch.tensor([1., 1., 1.])
		result = layer(input_ar, mask)
		output_ar = torch.tensor([-1.4436])
		assert torch.allclose(result, output_ar, rtol=1e-4)

deepchem/models/tests/test_weave_models.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -140,7 +140,7 @@ def test_weave_model():
		batch_size=batch_size,
		mode='classification',
		dropouts=0,
		learning_rate=0.002)
		learning_rate=0.0001)
		model.fit(dataset, nb_epoch=250)
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean-roc_auc_score'] >= 0.9

deepchem/models/torch_models/layers.py

+466 −22

File changed.

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deepchem/trans/transformers.py

+7 −12

Original line number	Diff line number	Diff line
		@@ -2488,12 +2488,7 @@ class RxnSplitTransformer(Transformer):
		>>> # When mixed training is toggled.
		>>> import numpy as np
		>>> from deepchem.trans.transformers import RxnSplitTransformer
		>>> reactions = np.array(
		[
		"CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1",
		"CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21"
		],
		dtype=object)
		>>> reactions = np.array(["CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1>C1CCOC1.[Cl-]>CC(C)CC(=O)c1ccc(O)nc1","CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO>>CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(NC=O)cc3)cc21"], dtype=object)
		>>> trans = RxnSplitTransformer(sep_reagent=True)
		>>> split_reactions = trans.transform_array(X=reactions, y=np.array([]), w=np.array([]), ids=np.array([]))
		>>> split_reactions

docs/source/api_reference/layers.rst

+15 −1

Original line number	Diff line number	Diff line
		@@ -125,9 +125,24 @@ Torch Layers
		.. autoclass:: deepchem.models.torch_models.layers.ScaleNorm
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.MATEncoderLayer
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.MultiHeadedMATAttention
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.SublayerConnection
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.PositionwiseFeedForward
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.MATEmbedding
		:members:

		.. autoclass:: deepchem.models.torch_models.layers.MATGenerator
		:members:

		.. autofunction:: deepchem.models.layers.cosine_dist

		Jax Layers
		@@ -135,4 +150,3 @@ Jax Layers

		.. autoclass:: deepchem.models.jax_models.layers.Linear
		:members:

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