Loading deepchem/models/tensorgraph/layers.py +127 −0 Original line number Diff line number Diff line Loading @@ -1222,6 +1222,86 @@ class Conv2D(Layer): return out_tensor class Conv3D(Layer): """A 3D convolution on the input. This layer 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 Conv3D 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(Conv3D, 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( 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 MaxPool(Layer): def __init__(self, Loading Loading @@ -1250,6 +1330,53 @@ class MaxPool(Layer): return out_tensor class MaxPool3D(Layer): """A 3D max pooling on the input. This layer expects its input to be a five dimensional tensor of shape (batch size, height, width, depth, # channels). """ def __init__(self, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding='SAME', **kwargs): """Create a MaxPool3D layer. Parameters ---------- ksize: list size of the window for each dimension of the input tensor. Must have length of 5 and ksize[0] = ksize[4] = 1. strides: list stride of the sliding window for each dimension of input. Must have length of 5 and strides[0] = strides[4] = 1. padding: str the padding method to use, either 'SAME' or 'VALID' """ self.ksize = ksize self.strides = strides self.padding = padding super(MaxPool3D, 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.max_pool3d( 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 Loading
deepchem/models/tensorgraph/layers.py +127 −0 Original line number Diff line number Diff line Loading @@ -1222,6 +1222,86 @@ class Conv2D(Layer): return out_tensor class Conv3D(Layer): """A 3D convolution on the input. This layer 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 Conv3D 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(Conv3D, 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( 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 MaxPool(Layer): def __init__(self, Loading Loading @@ -1250,6 +1330,53 @@ class MaxPool(Layer): return out_tensor class MaxPool3D(Layer): """A 3D max pooling on the input. This layer expects its input to be a five dimensional tensor of shape (batch size, height, width, depth, # channels). """ def __init__(self, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding='SAME', **kwargs): """Create a MaxPool3D layer. Parameters ---------- ksize: list size of the window for each dimension of the input tensor. Must have length of 5 and ksize[0] = ksize[4] = 1. strides: list stride of the sliding window for each dimension of input. Must have length of 5 and strides[0] = strides[4] = 1. padding: str the padding method to use, either 'SAME' or 'VALID' """ self.ksize = ksize self.strides = strides self.padding = padding super(MaxPool3D, 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.max_pool3d( 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
