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Posted to commits@mxnet.apache.org by gi...@git.apache.org on 2017/08/04 02:43:04 UTC
[GitHub] szha commented on a change in pull request #6832: Add ConvRNN Cell, ConvLSTM Cell
szha commented on a change in pull request #6832: Add ConvRNN Cell, ConvLSTM Cell
URL: https://github.com/apache/incubator-mxnet/pull/6832#discussion_r131301824
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File path: python/mxnet/rnn/rnn_cell.py
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@@ -1068,3 +1068,346 @@ def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=N
states = [l_states, r_states]
return outputs, states
+
+class ConvRNNCell(BaseRNNCell):
+ """Convolutional RNN cells
+
+ Parameters
+ ----------
+ input_shape : tuple of int
+ Shape of input in single timestep.
+ num_hidden : int
+ Number of units in output symbol.
+ h2h_kernel : tuple of int, default (3, 3)
+ Kernel of Convolution operator in state-to-state transitions.
+ h2h_pad : tuple of int, default (1, 1)
+ Pad of Convolution operator in state-to-state transitions.
+ h2h_dilate : tuple of int, default (1, 1)
+ Dilation of Convolution operator in state-to-state transitions.
+ i2h_kernel : tuple of int, default (3, 3)
+ Kernel of Convolution operator in input-to-state transitions.
+ i2h_stride : tuple of int, default (1, 1)
+ Stride of Convolution operator in input-to-state transitions.
+ i2h_pad : tuple of int, default (1, 1)
+ Pad of Convolution operator in input-to-state transitions.
+ i2h_dilate : tuple of int, default (1, 1)
+ Dilation of Convolution operator in input-to-state transitions.
+ activation : str or Symbol, default 'tanh'
+ Type of activation function. Options are 'relu' and 'tanh'.
+ prefix : str, default 'ConvRNN_'
+ Prefix for name of layers (and name of weight if params is None).
+ params : RNNParams, default None
+ Container for weight sharing between cells. Created if None.
+ conv_layout : str, , default 'NCHW'
+ Layout of ConvolutionOp
+ """
+ def __init__(self, input_shape, num_hidden,
+ h2h_kernel=(3, 3), h2h_pad=(1, 1), h2h_dilate=(1, 1),
+ i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1), i2h_dilate=(1, 1),
+ activation='tanh', prefix='ConvRNN_', params=None, conv_layout='NCHW'):
+ super(ConvRNNCell, self).__init__(prefix=prefix, params=params)
+ # Convolution setting
+ self._h2h_kernel = h2h_kernel
+ self._h2h_pad = h2h_pad
+ self._h2h_dilate = h2h_dilate
+ self._i2h_kernel = i2h_kernel
+ self._i2h_stride = i2h_stride
+ self._i2h_pad = i2h_pad
+ self._i2h_dilate = i2h_dilate
+
+ self._num_hidden = num_hidden
+ self._input_shape = input_shape
+ self._conv_layout = conv_layout
+ self._activation = activation
+
+ # Get params
+ self._iW = self.params.get('i2h_weight')
+ self._hW = self.params.get('h2h_weight')
+ self._iB = self.params.get('i2h_bias')
+ self._hB = self.params.get('h2h_bias')
+
+ # Infer state shape
+ data = symbol.Variable('data')
+ self._state_shape = symbol.Convolution(data=data,
+ num_filter=self._num_hidden,
+ kernel=self._i2h_kernel,
+ stride=self._i2h_stride,
+ pad=self._i2h_pad,
+ dilate=self._i2h_dilate,
+ layout=conv_layout)
+ self._state_shape = self._state_shape.infer_shape(data=input_shape)[1][0]
+ self._state_shape = (0, ) + self._state_shape[1:]
+
+ @property
+ def state_info(self):
+ return [{'shape': self._state_shape, '__layout__': self._conv_layout},
+ {'shape': self._state_shape, '__layout__': self._conv_layout}]
+
+ @property
+ def _gate_names(self):
+ return ('',)
+
+ def __call__(self, inputs, states):
+ self._counter += 1
+ name = '%st%d_'%(self._prefix, self._counter)
+ i2h = symbol.Convolution(name='%si2h'%name,
+ data=inputs,
+ num_filter=self._num_hidden,
+ kernel=self._i2h_kernel,
+ stride=self._i2h_stride,
+ pad=self._i2h_pad,
+ dilate=self._i2h_dilate,
+ weight=self._iW,
+ bias=self._iB,)
+ h2h = symbol.Convolution(name='%sh2h'%name,
+ data=states[0],
+ num_filter=self._num_hidden,
+ kernel=self._h2h_kernel,
+ dilate=self._h2h_dilate,
+ pad=self._h2h_pad,
+ stride=(1, 1),
+ weight=self._hW,
+ bias=self._hB)
+ output = self._get_activation(i2h + h2h, self._activation,
+ name='%sout'%name)
+ return output, [output]
+
+class ConvLSTMCell(BaseRNNCell):
+ """Convolutional LSTM network cell.
+
+ Reference:
+ Xingjian et al. NIPS2015
+
+ Parameters
+ ----------
+ input_shape : tuple of int
+ Shape of input in single timestep.
+ num_hidden : int
+ Number of units in output symbol.
+ h2h_kernel : tuple of int, default (3, 3)
+ Kernel of Convolution operator in state-to-state transitions.
+ h2h_pad : tuple of int, default (1, 1)
+ Pad of Convolution operator in state-to-state transitions.
+ h2h_dilate : tuple of int, default (1, 1)
+ Dilation of Convolution operator in state-to-state transitions.
+ i2h_kernel : tuple of int, default (3, 3)
+ Kernel of Convolution operator in input-to-state transitions.
+ i2h_stride : tuple of int, default (1, 1)
+ Stride of Convolution operator in input-to-state transitions.
+ i2h_pad : tuple of int, default (1, 1)
+ Pad of Convolution operator in input-to-state transitions.
+ i2h_dilate : tuple of int, default (1, 1)
+ Dilation of Convolution operator in input-to-state transitions.
+ activation : str or Symbol, default 'tanh'
+ Type of activation function. Options are 'relu' and 'tanh'.
Review comment:
@sxjscience thanks. I will set the default to tanh for now in #7264
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