Add Tests (85515af4) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+14 −4

Original line number	Diff line number	Diff line
		@@ -3133,7 +3133,6 @@ class GraphCNNPoolLayer(Layer):

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		"""
		TODO(LESWING) self.num_vertices = 1
		Parameters
		----------
		in_layers
		@@ -3167,7 +3166,10 @@ class GraphCNNPoolLayer(Layer):
		A.get_shape()[2].value, self.num_vertices))
		# We do not need the mask because every graph has self.num_vertices vertices now
		# result = make_bn(result, True, mask=None, name="%s_bn" % self.name)
		if set_tensors:
		self.out_tensor = result
		self.out_tensors = [result, result_A, factors]
		return result

		def embedding_factors(self, V, no_filters, name="default"):
		no_features = V.get_shape()[-1].value
		@@ -3192,17 +3194,26 @@ class GraphCNNPoolLayer(Layer):
		prob = tf.div(exp, tf.reduce_sum(exp, axis=axis, keep_dims=True))
		return prob

		def none_tensors(self):
		out_tensors, out_tensor = self.out_tensors, self.out_tensor
		self.out_tensors = None
		self.out_tensor = None
		return out_tensors, out_tensor

		def set_tensors(self, tensor):
		self.out_tensors, self.out_tensor = tensor


		def GraphCNNPool(num_vertices, **kwargs):
		gcnnpool_layer = GraphCNNPoolLayer(num_vertices, **kwargs)
		return [PassThroughLayer(x, in_layers=gcnnpool_layer) for x in range(3)]


		class GraphCNNLayer(Layer):
		class GraphCNN(Layer):

		def __init__(self, num_filters, **kwargs):
		self.num_filters = num_filters
		super(GraphCNNLayer, self).__init__(**kwargs)
		super(GraphCNN, self).__init__(**kwargs)

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		@@ -3210,7 +3221,6 @@ class GraphCNNLayer(Layer):
		V, A, mask = inputs
		else:
		V, A = inputs
		mask = None
		no_A = A.get_shape()[2].value
		no_features = V.get_shape()[2].value
		W = tf.get_variable(

deepchem/models/tensorgraph/models/graph_models.py

+4 −6

Original line number	Diff line number	Diff line
		@@ -12,7 +12,7 @@ from deepchem.models.tensorgraph.graph_layers import WeaveLayer, WeaveGather, \
		DAGGather, DTNNExtract, MessagePassing, SetGather
		from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, \
		SoftMaxCrossEntropy, GraphConv, BatchNorm, \
		GraphPool, GraphGather, WeightedError, Dropout, BatchNormalization, Stack, Layer, Flatten, GraphCNNLayer, GraphCNNPool
		GraphPool, GraphGather, WeightedError, Dropout, BatchNormalization, Stack, Layer, Flatten, GraphCNN, GraphCNNPool
		from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature
		from deepchem.models.tensorgraph.tensor_graph import TensorGraph
		from deepchem.trans import undo_transforms
		@@ -517,20 +517,18 @@ class PetroskiSuchTensorGraph(TensorGraph):
		self.mask = Feature(shape=(None, self.max_atoms, 1))

		gcnn1 = BatchNorm(
		GraphCNNLayer(
		GraphCNN(
		num_filters=64,
		in_layers=[self.vertex_features, self.adj_matrix, self.mask]))
		gcnn1 = Dropout(self.dropout, in_layers=gcnn1)
		gcnn2 = BatchNorm(
		GraphCNNLayer(
		num_filters=64, in_layers=[gcnn1, self.adj_matrix, self.mask]))
		GraphCNN(num_filters=64, in_layers=[gcnn1, self.adj_matrix, self.mask]))
		gcnn2 = Dropout(self.dropout, in_layers=gcnn2)
		gc_pool, adj_matrix, factors = GraphCNNPool(
		num_vertices=32, in_layers=[gcnn2, self.adj_matrix, self.mask])
		gc_pool = BatchNorm(gc_pool)
		gc_pool = Dropout(self.dropout, in_layers=gc_pool)
		gcnn3 = BatchNorm(
		GraphCNNLayer(num_filters=32, in_layers=[gc_pool, adj_matrix]))
		gcnn3 = BatchNorm(GraphCNN(num_filters=32, in_layers=[gc_pool, adj_matrix]))
		gcnn3 = Dropout(self.dropout, in_layers=gcnn3)
		gc_pool2, adj_matrix2, factors = GraphCNNPool(
		num_vertices=8, in_layers=[gcnn3, adj_matrix])

deepchem/models/tensorgraph/tests/test_layers.py

+23 −1

Original line number	Diff line number	Diff line
		@@ -5,7 +5,7 @@ from tensorflow.python.framework import test_util

		from deepchem.feat.graph_features import ConvMolFeaturizer
		from deepchem.feat.mol_graphs import ConvMol
		from deepchem.models.tensorgraph.layers import Add, Conv3D, MaxPool2D, MaxPool3D
		from deepchem.models.tensorgraph.layers import Add, Conv3D, MaxPool2D, MaxPool3D, GraphCNN, GraphCNNPoolLayer
		from deepchem.models.tensorgraph.layers import AlphaShareLayer
		from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding
		from deepchem.models.tensorgraph.layers import BatchNorm
		@@ -723,3 +723,25 @@ class TestLayers(test_util.TensorFlowTestCase):
		output_tensor = SluiceLoss()(input1, input2)
		sess.run(tf.global_variables_initializer())
		assert output_tensor.eval() == 40.0

		def test_graphcnn(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		V = np.random.uniform(size=(10, 100, 50)).astype(np.float32)
		adjs = np.random.uniform(size=(10, 100, 5, 100)).astype(np.float32)
		with self.test_session() as sess:
		out_tensor = GraphCNN(num_filters=6)(V, adjs)
		sess.run(tf.global_variables_initializer())
		result = out_tensor.eval()
		print(result.shape)
		assert result.shape == (10, 100, 6)

		def test_graphcnnpool(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		V = np.random.uniform(size=(10, 100, 50)).astype(np.float32)
		adjs = np.random.uniform(size=(10, 100, 5, 100)).astype(np.float32)
		with self.test_session() as sess:
		out_tensor = GraphCNNPoolLayer(num_vertices=6)(V, adjs)
		sess.run(tf.global_variables_initializer())
		result = out_tensor.eval()
		print(result.shape)
		assert result.shape == (10, 6, 50)

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+23 −1

Original line number	Diff line number	Diff line
		@@ -11,7 +11,7 @@ from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten,
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool2D, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, \
		Conv3D, MaxPool3D, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphCNNPoolLayer, GraphCNN
		from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense


		@@ -568,3 +568,25 @@ def test_AtomicDifferentialDense_pickle():
		tg.set_loss(atomic_differential_dense)
		tg.build()
		tg.save()


		def testGraphCNN_pickle():
		V = Feature(shape=(None, 200, 50))
		A = Feature(shape=(None, 200, 1, 200))
		gcnn = GraphCNN(32, in_layers=[V, A])
		tg = TensorGraph()
		tg.add_output(gcnn)
		tg.set_loss(gcnn)
		tg.build()
		tg.save()


		def testGraphCNNPoolLayer_pickle():
		V = Feature(shape=(None, 200, 50))
		A = Feature(shape=(None, 200, 1, 200))
		gcnnpool = GraphCNNPoolLayer(32, in_layers=[V, A])
		tg = TensorGraph()
		tg.add_output(gcnnpool)
		tg.set_loss(gcnnpool)
		tg.build()
		tg.save()

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