Return adjs from Graphcnnpool (f87fa51a) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+3 −4

Original line number	Diff line number	Diff line
		@@ -3179,11 +3179,10 @@ class GraphCNNPoolLayer(Layer):
		result_A = tf.reshape(result_A, (tf.shape(A)[0], self.num_vertices,
		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
		self.out_tensors = [result, result_A]
		return result, result_A

		def embedding_factors(self, V, no_filters, name="default"):
		no_features = V.get_shape()[-1].value
		@@ -3220,7 +3219,7 @@ class GraphCNNPoolLayer(Layer):

		def GraphCNNPool(num_vertices, **kwargs):
		gcnnpool_layer = GraphCNNPoolLayer(num_vertices, **kwargs)
		return [PassThroughLayer(x, in_layers=gcnnpool_layer) for x in range(3)]
		return [PassThroughLayer(x, in_layers=gcnnpool_layer) for x in range(2)]


		class GraphCNN(Layer):

+3 −3

Original line number	Diff line number	Diff line
		@@ -495,7 +495,7 @@ class PetroskiSuchTensorGraph(TensorGraph):
		def __init__(self,
		n_tasks,
		max_atoms=200,
		dropout=0.2,
		dropout=0.0,
		mode="classification",
		**kwargs):
		"""
		@@ -528,13 +528,13 @@ class PetroskiSuchTensorGraph(TensorGraph):
		gcnn2 = BatchNorm(
		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(
		gc_pool, adj_matrix = 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(GraphCNN(num_filters=32, in_layers=[gc_pool, adj_matrix]))
		gcnn3 = Dropout(self.dropout, in_layers=gcnn3)
		gc_pool2, adj_matrix2, factors = GraphCNNPool(
		gc_pool2, adj_matrix2 = GraphCNNPool(
		num_vertices=8, in_layers=[gcnn3, adj_matrix])
		gc_pool2 = BatchNorm(gc_pool2)
		gc_pool2 = Dropout(self.dropout, in_layers=gc_pool2)

+4 −5

Original line number	Diff line number	Diff line
		@@ -732,7 +732,6 @@ class TestLayers(test_util.TensorFlowTestCase):
		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):
		@@ -740,8 +739,8 @@ class TestLayers(test_util.TensorFlowTestCase):
		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)
		vertex_props, adjs = 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)
		vertex_props, adjs = vertex_props.eval(), adjs.eval()
		assert vertex_props.shape == (10, 6, 50)
		assert adjs.shape == (10, 6, 5, 6)