Merge pull request #1023 from lilleswing/toint32 (c1d38548) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+40 −9

Original line number	Diff line number	Diff line
		@@ -644,6 +644,38 @@ class Reshape(Layer):
		return out_tensor


		class Cast(Layer):
		"""
		Wrapper around tf.cast. Changes the dtype of a single layer
		"""

		def __init__(self, in_layers=None, dtype=None, **kwargs):
		"""
		Parameters
		----------
		dtype: tf.DType
		the dtype to cast the in_layer to
		e.x. tf.int32
		"""
		if dtype is None:
		raise ValueError("Must cast to a dtype")
		self.dtype = dtype
		super(Cast, self).__init__(in_layers, **kwargs)
		try:
		parent_shape = self.in_layers[0].shape
		self._shape = parent_shape
		except:
		pass

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		inputs = self._get_input_tensors(in_layers)
		parent_tensor = inputs[0]
		out_tensor = tf.cast(parent_tensor, self.dtype)
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor


		class Squeeze(Layer):

		def __init__(self, in_layers=None, squeeze_dims=None, **kwargs):
		@@ -655,7 +687,8 @@ class Squeeze(Layer):
		self._shape = [i for i in parent_shape if i != 1]
		else:
		self._shape = [
		parent_shape[i] for i in range(len(parent_shape))
		parent_shape[i]
		for i in range(len(parent_shape))
		if i not in squeeze_dims
		]
		except:
		@@ -2817,15 +2850,13 @@ class VinaFreeEnergy(Layer):

		def hydrophobic(self, d):
		"""Computes Autodock Vina's hydrophobic interaction term."""
		out_tensor = tf.where(d < 0.5,
		tf.ones_like(d),
		out_tensor = tf.where(d < 0.5, tf.ones_like(d),
		tf.where(d < 1.5, 1.5 - d, tf.zeros_like(d)))
		return out_tensor

		def hydrogen_bond(self, d):
		"""Computes Autodock Vina's hydrogen bond interaction term."""
		out_tensor = tf.where(d < -0.7,
		tf.ones_like(d),
		out_tensor = tf.where(d < -0.7, tf.ones_like(d),
		tf.where(d < 0, (1.0 / 0.7) * (0 - d),
		tf.zeros_like(d)))
		return out_tensor

deepchem/models/tensorgraph/tests/test_layers.py

+12 −3

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, MaxPool2D, MaxPool3D, GraphCNN, GraphEmbedPoolLayer
		from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, GraphCNN, GraphEmbedPoolLayer, Cast
		from deepchem.models.tensorgraph.layers import AlphaShareLayer
		from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding
		from deepchem.models.tensorgraph.layers import BatchNorm
		@@ -276,8 +276,9 @@ class TestLayers(test_util.TensorFlowTestCase):
		value2 = np.random.uniform(size=(2, 3)).astype(np.float32)
		value3 = np.random.uniform(size=(2, 3)).astype(np.float32)
		with self.test_session() as sess:
		out_tensor = Add(weights=[1, 2, 1])(
		tf.constant(value1), tf.constant(value2), tf.constant(value3))
		out_tensor = Add(weights=[1, 2, 1])(tf.constant(value1),
		tf.constant(value2),
		tf.constant(value3))
		assert np.array_equal(value1 + 2 * value2 + value3, out_tensor.eval())

		def test_multiply(self):
		@@ -709,6 +710,14 @@ class TestLayers(test_util.TensorFlowTestCase):
		loss = np.mean(diff**2)
		assert (loss - result) / loss < 1e-6

		def test_cast(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		value1 = np.random.uniform(size=(2, 1)).astype(np.float32)
		with self.test_session() as sess:
		out_tensor = Cast(dtype=tf.int32)(tf.constant(value1))
		result = out_tensor.eval()
		assert result.dtype == np.int32

		def test_squeeze_inputs(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		value1 = np.random.uniform(size=(2, 1)).astype(np.float32)

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+11 −1

Original line number	Diff line number	Diff line
		@@ -10,7 +10,7 @@ from deepchem.models.tensorgraph.layers import Feature, Conv1D, Dense, Flatten,
		SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool2D, ReduceSum, GraphConv, GraphPool, \
		GraphGather, BatchNorm, WeightedError, \
		Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN
		LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast
		from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense


		@@ -64,6 +64,16 @@ def test_Squeeze_pickle():
		tg.save()


		def test_Cast_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 1))
		layer = Cast(in_layers=feature, dtype=tf.int32)
		tg.add_output(layer)
		tg.set_loss(layer)
		tg.build()
		tg.save()


		def test_Transpose_pickle():
		tg = TensorGraph()
		feature = Feature(shape=(tg.batch_size, 1))

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