Added Conv2DTranspose and Conv3DTranspose layers (8b2312b8) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+159 −0

Original line number	Diff line number	Diff line
		@@ -1519,6 +1519,165 @@ class Conv3D(Layer):
		return out_tensor


		class Conv2DTranspose(Layer):
		"""A transposed 2D convolution on the input.

		This layer is typically used for upsampling in a deconvolutional network. It
		expects its input to be a four dimensional tensor of shape (batch size, height, width, # channels).
		If there is only one channel, the fourth dimension may optionally be omitted.
		"""

		def __init__(self,
		num_outputs,
		kernel_size=5,
		stride=1,
		padding='SAME',
		activation_fn=tf.nn.relu,
		normalizer_fn=None,
		scope_name=None,
		**kwargs):
		"""Create a Conv2DTranspose layer.

		Parameters
		----------
		num_outputs: int
		the number of outputs produced by the convolutional kernel
		kernel_size: int or tuple
		the width of the convolutional kernel. This can be either a two element tuple, giving
		the kernel size along each dimension, or an integer to use the same size along both
		dimensions.
		stride: int or tuple
		the stride between applications of the convolutional kernel. This can be either a two
		element tuple, giving the stride along each dimension, or an integer to use the same
		stride along both dimensions.
		padding: str
		the padding method to use, either 'SAME' or 'VALID'
		activation_fn: object
		the Tensorflow activation function to apply to the output
		normalizer_fn: object
		the Tensorflow normalizer function to apply to the output
		"""
		self.num_outputs = num_outputs
		self.kernel_size = kernel_size
		self.stride = stride
		self.padding = padding
		self.activation_fn = activation_fn
		self.normalizer_fn = normalizer_fn
		super(Conv2DTranspose, self).__init__(**kwargs)
		if scope_name is None:
		scope_name = self.name
		self.scope_name = scope_name
		try:
		parent_shape = self.in_layers[0].shape
		strides = stride
		if isinstance(stride, int):
		strides = (stride, stride)
		self._shape = (parent_shape[0], parent_shape[1] * strides[0],
		parent_shape[2] * strides[1], num_outputs)
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		parent_tensor = inputs[0]
		if len(parent_tensor.get_shape()) == 3:
		parent_tensor = tf.expand_dims(parent_tensor, 3)
		out_tensor = tf.contrib.layers.conv2d_transpose(
		parent_tensor,
		num_outputs=self.num_outputs,
		kernel_size=self.kernel_size,
		stride=self.stride,
		padding=self.padding,
		activation_fn=self.activation_fn,
		normalizer_fn=self.normalizer_fn,
		scope=self.scope_name)
		out_tensor = out_tensor
		if set_tensors:
		self._record_variable_scope(self.scope_name)
		self.out_tensor = out_tensor
		return out_tensor


		class Conv3DTranspose(Layer):
		"""A transposed 3D convolution on the input.

		This layer is typically used for upsampling in a deconvolutional network. It
		expects its input to be a five dimensional tensor of shape (batch size, height, width, depth, # channels).
		If there is only one channel, the fifth dimension may optionally be omitted.
		"""

		def __init__(self,
		num_outputs,
		kernel_size=5,
		stride=1,
		padding='SAME',
		activation_fn=tf.nn.relu,
		normalizer_fn=None,
		scope_name=None,
		**kwargs):
		"""Create a Conv3DTranspose layer.

		Parameters
		----------
		num_outputs: int
		the number of outputs produced by the convolutional kernel
		kernel_size: int or tuple
		the width of the convolutional kernel. This can be either a three element tuple, giving
		the kernel size along each dimension, or an integer to use the same size along both
		dimensions.
		stride: int or tuple
		the stride between applications of the convolutional kernel. This can be either a three
		element tuple, giving the stride along each dimension, or an integer to use the same
		stride along both dimensions.
		padding: str
		the padding method to use, either 'SAME' or 'VALID'
		activation_fn: object
		the Tensorflow activation function to apply to the output
		normalizer_fn: object
		the Tensorflow normalizer function to apply to the output
		"""
		self.num_outputs = num_outputs
		self.kernel_size = kernel_size
		self.stride = stride
		self.padding = padding
		self.activation_fn = activation_fn
		self.normalizer_fn = normalizer_fn
		super(Conv3DTranspose, self).__init__(**kwargs)
		if scope_name is None:
		scope_name = self.name
		self.scope_name = scope_name
		try:
		parent_shape = self.in_layers[0].shape
		strides = stride
		if isinstance(stride, int):
		strides = (stride, stride, stride)
		self._shape = (parent_shape[0], parent_shape[1] * strides[0],
		parent_shape[2] * strides[1], parent_shape[3] * strides[2],
		num_outputs)
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		parent_tensor = inputs[0]
		if len(parent_tensor.get_shape()) == 4:
		parent_tensor = tf.expand_dims(parent_tensor, 4)
		out_tensor = tf.layers.conv3d_transpose(
		parent_tensor,
		filters=self.num_outputs,
		kernel_size=self.kernel_size,
		strides=self.stride,
		padding=self.padding,
		activation=self.activation_fn,
		activity_regularizer=self.normalizer_fn,
		name=self.scope_name)
		out_tensor = out_tensor
		if set_tensors:
		self._record_variable_scope(self.scope_name)
		self.out_tensor = out_tensor
		return out_tensor


		class MaxPool2D(Layer):

		def __init__(self,

deepchem/models/tensorgraph/tests/test_layers.py

+39 −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, GraphCNN, GraphEmbedPoolLayer
		from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, GraphCNN, GraphEmbedPoolLayer
		from deepchem.models.tensorgraph.layers import AlphaShareLayer
		from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding
		from deepchem.models.tensorgraph.layers import BatchNorm
		@@ -15,6 +15,9 @@ from deepchem.models.tensorgraph.layers import Concat
		from deepchem.models.tensorgraph.layers import Constant
		from deepchem.models.tensorgraph.layers import Conv1D, Squeeze
		from deepchem.models.tensorgraph.layers import Conv2D
		from deepchem.models.tensorgraph.layers import Conv2DTranspose
		from deepchem.models.tensorgraph.layers import Conv3D
		from deepchem.models.tensorgraph.layers import Conv3DTranspose
		from deepchem.models.tensorgraph.layers import Dense
		from deepchem.models.tensorgraph.layers import Exp
		from deepchem.models.tensorgraph.layers import Flatten
		@@ -395,6 +398,41 @@ class TestLayers(test_util.TensorFlowTestCase):
		assert out_tensor.shape == (batch_size, length, width, depth,
		out_channels)

		def test_conv_2D_transpose(self):
		"""Test that Conv2DTranspose can be invoked."""
		length = 4
		width = 5
		in_channels = 2
		out_channels = 3
		batch_size = 20
		in_tensor = np.random.rand(batch_size, length, width, in_channels)
		with self.test_session() as sess:
		in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
		out_tensor = Conv2DTranspose(
		out_channels, kernel_size=1, stride=2)(in_tensor)
		sess.run(tf.global_variables_initializer())
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, 2 * length, 2 * width,
		out_channels)

		def test_conv_3D_transpose(self):
		"""Test that Conv3DTranspose 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 = Conv3DTranspose(
		out_channels, kernel_size=1, stride=(2, 3, 1))(in_tensor)
		sess.run(tf.global_variables_initializer())
		out_tensor = out_tensor.eval()
		assert out_tensor.shape == (batch_size, 2 * length, 3 * width, depth,
		out_channels)

		def test_maxpool2D(self):
		"""Test that MaxPool2D can be invoked."""
		length = 2

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+21 −1

Original line number	Diff line number	Diff line
		@@ -9,7 +9,7 @@ from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten,
		Constant, Variable, StopGradient, Add, Multiply, Log, Exp, InteratomicL2Distances, \
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool2D, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, \
		Conv3D, MaxPool3D, \
		Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN
		from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense

		@@ -288,6 +288,26 @@ def test_Conv3D_pickle():
		tg.save()


		def test_Conv2DTranspose_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 1))
		layer = Conv2DTranspose(num_outputs=3, in_layers=feature)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_Conv3DTranspose_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 10, 1))
		layer = Conv3DTranspose(num_outputs=3, in_layers=feature)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_MaxPool2D_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 10, 10, 10))

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