Loading deepchem/models/tensorgraph/layers.py +41 −0 Original line number Diff line number Diff line Loading @@ -2511,6 +2511,47 @@ class MaxPool3D(Layer): return out_tensor class AvgPool2D(Layer): def __init__(self, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME", **kwargs): """Create a AvgPool2D layer. Parameters ---------- ksize: list size of the window for each dimension of the input tensor. Must have length of 4 and ksize[0] = ksize[3] = 1. strides: list stride of the sliding window for each dimension of input. Must have length of 4 and strides[0] = strides[3] = 1. padding: str the padding method to use, either 'SAME' or 'VALID' """ self.ksize = ksize self.strides = strides self.padding = padding super(AvgPool2D, self).__init__(**kwargs) try: parent_shape = self.in_layers[0].shape self._shape = tuple( None if p is None else p // s for p, s in zip(parent_shape, strides)) except: pass def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) in_tensor = inputs[0] out_tensor = tf.nn.avg_pool( in_tensor, ksize=self.ksize, strides=self.strides, padding=self.padding) if set_tensors: self.out_tensor = out_tensor return out_tensor class InputFifoQueue(Layer): """ This Queue Is used to allow asynchronous batching of inputs Loading deepchem/models/tensorgraph/tests/test_layers.py +15 −1 Original line number Diff line number Diff line Loading @@ -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, Cast from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, AvgPool2D, GraphCNN, GraphEmbedPoolLayer, Cast from deepchem.models.tensorgraph.layers import AlphaShareLayer from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding from deepchem.models.tensorgraph.layers import BatchNorm Loading Loading @@ -538,6 +538,20 @@ class TestLayers(test_util.TensorFlowTestCase): out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, 1, 1, 1, in_channels) def test_avgpool2D(self): """Test that AvgPool2D can be invoked.""" length = 4 width = 4 in_channels = 5 batch_size = 10 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 = AvgPool2D()(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, 2, 2, in_channels) def test_input_fifo_queue(self): """Test InputFifoQueue can be invoked.""" batch_size = 10 Loading Loading
deepchem/models/tensorgraph/layers.py +41 −0 Original line number Diff line number Diff line Loading @@ -2511,6 +2511,47 @@ class MaxPool3D(Layer): return out_tensor class AvgPool2D(Layer): def __init__(self, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME", **kwargs): """Create a AvgPool2D layer. Parameters ---------- ksize: list size of the window for each dimension of the input tensor. Must have length of 4 and ksize[0] = ksize[3] = 1. strides: list stride of the sliding window for each dimension of input. Must have length of 4 and strides[0] = strides[3] = 1. padding: str the padding method to use, either 'SAME' or 'VALID' """ self.ksize = ksize self.strides = strides self.padding = padding super(AvgPool2D, self).__init__(**kwargs) try: parent_shape = self.in_layers[0].shape self._shape = tuple( None if p is None else p // s for p, s in zip(parent_shape, strides)) except: pass def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) in_tensor = inputs[0] out_tensor = tf.nn.avg_pool( in_tensor, ksize=self.ksize, strides=self.strides, padding=self.padding) if set_tensors: self.out_tensor = out_tensor return out_tensor class InputFifoQueue(Layer): """ This Queue Is used to allow asynchronous batching of inputs Loading
deepchem/models/tensorgraph/tests/test_layers.py +15 −1 Original line number Diff line number Diff line Loading @@ -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, Cast from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, AvgPool2D, GraphCNN, GraphEmbedPoolLayer, Cast from deepchem.models.tensorgraph.layers import AlphaShareLayer from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding from deepchem.models.tensorgraph.layers import BatchNorm Loading Loading @@ -538,6 +538,20 @@ class TestLayers(test_util.TensorFlowTestCase): out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, 1, 1, 1, in_channels) def test_avgpool2D(self): """Test that AvgPool2D can be invoked.""" length = 4 width = 4 in_channels = 5 batch_size = 10 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 = AvgPool2D()(in_tensor) sess.run(tf.global_variables_initializer()) out_tensor = out_tensor.eval() assert out_tensor.shape == (batch_size, 2, 2, in_channels) def test_input_fifo_queue(self): """Test InputFifoQueue can be invoked.""" batch_size = 10 Loading
