tests for Conv3D and MaxPool3D (b6ced203) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tests/test_layers.py

+34 −0

Original line number	Diff line number	Diff line
		@@ -34,7 +34,9 @@ from deepchem.models.tensorgraph.layers import ToFloat
		from deepchem.models.tensorgraph.layers import ReduceSum
		from deepchem.models.tensorgraph.layers import ReduceSquareDifference
		from deepchem.models.tensorgraph.layers import Conv2D
		from deepchem.models.tensorgraph.layers import Conv3D
		from deepchem.models.tensorgraph.layers import MaxPool
		from deepchem.models.tensorgraph.layers import MaxPool3D
		from deepchem.models.tensorgraph.layers import InputFifoQueue
		from deepchem.models.tensorgraph.layers import GraphConv
		from deepchem.models.tensorgraph.layers import GraphPool
		@@ -368,6 +370,23 @@ class TestLayers(test_util.TensorFlowTestCase):
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, length, width, out_channels)

		def test_conv_3D(self):
		"""Test that Conv3D can be invoked."""
		length = 4
		width = 5
		depth = 6
		in_channels = 2
		out_channels = 3
		batch_size = 20
		in_tensor = np.random.rand(batch_size, length, width, depth, in_channels)
		with self.test_session() as sess:
		in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
		out_tensor = Conv3D(out_channels, kernel_size=1)(in_tensor)
		sess.run(tf.global_variables_initializer())
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, length, width, depth,
		out_channels)

		def test_max_pool(self):
		"""Test that MaxPool can be invoked."""
		length = 2
		@@ -382,6 +401,21 @@ class TestLayers(test_util.TensorFlowTestCase):
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, 1, 1, in_channels)

		def test_max_pool_3D(self):
		"""Test that MaxPool3D can be invoked."""
		length = 2
		width = 2
		depth = 2
		in_channels = 2
		batch_size = 20
		in_tensor = np.random.rand(batch_size, length, width, depth, in_channels)
		with self.test_session() as sess:
		in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
		out_tensor = MaxPool3D()(in_tensor)
		sess.run(tf.global_variables_initializer())
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, 1, 1, 1, in_channels)

		def test_input_fifo_queue(self):
		"""Test InputFifoQueue can be invoked."""
		batch_size = 10

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+21 −0

Original line number	Diff line number	Diff line
		@@ -5,6 +5,7 @@ from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten,
		CombineMeanStd, Repeat, Gather, GRU, L2Loss, Concat, SoftMax, Constant, Variable, Add, Multiply, Log, InteratomicL2Distances, \
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, \
		Conv3D, MaxPool3D, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding
		from deepchem.models.tensorgraph.graph_layers import Combine_AP, Separate_AP, \
		WeaveLayer, WeaveGather, DTNNEmbedding, DTNNGather, DTNNStep, \
		@@ -255,6 +256,16 @@ def test_Conv2D_pickle():
		tg.save()


		def test_Conv3D_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 10, 1))
		layer = Conv3D(num_outputs=3, in_layers=feature)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_MaxPool_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 10))
		@@ -265,6 +276,16 @@ def test_MaxPool_pickle():
		tg.save()


		def test_MaxPool3D_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 10, 10))
		layer = MaxPool3D(in_layers=feature)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_GraphConv_pickle():
		tg = TensorGraph()
		atom_features = Feature(shape=(None, 75))

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