[GitHub] ceiba-w commented on a change in pull request #6832: Add ConvRNN Cell, ConvLSTM Cell

[GitBox <gi...@apache.org>]

ceiba-w commented on a change in pull request #6832: Add ConvRNN Cell, ConvLSTM Cell
URL: https://github.com/apache/incubator-mxnet/pull/6832#discussion_r157361173
 
 

 ##########
 File path: python/mxnet/rnn/rnn_cell.py
 ##########
 @@ -1068,3 +1069,348 @@ def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=N
 
         states = [l_states, r_states]
         return outputs, states
+
+
+class BaseConvRNNCell(BaseRNNCell):
+    """Abstract base class for 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
+        Kernel of Convolution operator in state-to-state transitions.
+    h2h_dilate : tuple of int
+        Dilation of Convolution operator in state-to-state transitions.
+    i2h_kernel : tuple of int
+        Kernel of Convolution operator in input-to-state transitions.
+    i2h_stride : tuple of int
+        Stride of Convolution operator in input-to-state transitions.
+    i2h_pad : tuple of int
+        Pad of Convolution operator in input-to-state transitions.
+    i2h_dilate : tuple of int
+        Dilation of Convolution operator in input-to-state transitions.
+    activation : str or Symbol,
+        Type of activation function.
+    prefix : str, default ''
+        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, h2h_dilate,
+                 i2h_kernel, i2h_stride,
+                 i2h_pad, i2h_dilate,
+                 activation,
+                 prefix='', params=None, conv_layout='NCHW'):
+        super(BaseConvRNNCell, self).__init__(prefix=prefix, params=params)
+        # Convolution setting
+        self._h2h_kernel = h2h_kernel
+        assert (self._h2h_kernel[0] % 2 == 1) and (self._h2h_kernel[1] % 2 == 1), \
+            "Only support odd number, get h2h_kernel= %s" % str(h2h_kernel)
+        self._h2h_pad = (h2h_dilate[0] * (h2h_kernel[0] - 1) // 2,
+                         h2h_dilate[1] * (h2h_kernel[1] - 1) // 2)
+        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
+
+        # 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}]
+
+    def __call__(self, inputs, states):
+        raise NotImplementedError("BaseConvRNNCell is abstract class for convolutional RNN")
+
+class ConvRNNCell(BaseConvRNNCell):
+    """Convolutional RNN cells
 
 Review comment:
   Not only the ConvLSTMCell but  also LSTMCell has removed the contribution of C_{t-1} in the input gate, forget gate and output gate. 
   
   But I add the Hadamard product about C_{t-1} for personal use.

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