add conv1d, conv1dcell, conv1d_transpose in to customized layers
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chenfeiyu
parent
fd9e198ab6
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907f777ab8
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@ -0,0 +1,157 @@
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from paddle import fluid
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import paddle.fluid.layers as F
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import paddle.fluid.dygraph as dg
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class Conv1D(dg.Conv2D):
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"""A standard Conv1D layer that use (B, C, T) data layout. It inherit Conv2D and
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use (B, C, 1, T) data layout to compute 1D convolution. Nothing more.
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NOTE: we inherit Conv2D instead of encapsulate a Conv2D layer to make it a simple
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layer, instead of a complex one. So we can easily apply weight norm to it.
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"""
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=None,
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param_attr=None,
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bias_attr=None,
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use_cudnn=True,
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act=None,
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dtype='float32'):
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super(Conv1D, self).__init__(num_channels,
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num_filters, (1, filter_size),
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stride=(1, stride),
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padding=(0, padding),
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dilation=(1, dilation),
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groups=groups,
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param_attr=param_attr,
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bias_attr=bias_attr,
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use_cudnn=use_cudnn,
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act=act,
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dtype=dtype)
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def forward(self, x):
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x = F.unsqueeze(x, [2])
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x = super(Conv1D, self).forward(x) # maybe risky here
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x = F.squeeze(x, [2])
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return x
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class Conv1DTranspose(dg.Conv2DTranspose):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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padding=0,
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stride=1,
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dilation=1,
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groups=None,
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param_attr=None,
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bias_attr=None,
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use_cudnn=True,
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act=None,
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dtype='float32'):
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super(Conv1DTranspose, self).__init__(num_channels,
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num_filters, (1, filter_size),
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output_size=None,
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padding=(0, padding),
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stride=(1, stride),
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dilation=(1, dilation),
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groups=groups,
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param_attr=param_attr,
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bias_attr=bias_attr,
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use_cudnn=use_cudnn,
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act=act,
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dtype=dtype)
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def forward(self, x):
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x = F.unsqueeze(x, [2])
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x = super(Conv1DTranspose, self).forward(x) # maybe risky here
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x = F.squeeze(x, [2])
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return x
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class Conv1DCell(Conv1D):
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"""A causal convolve-1d cell. It uses causal padding, padding(receptive_field -1, 0).
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But Conv2D in dygraph does not support asymmetric padding yet, we just pad
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(receptive_field -1, receptive_field -1) and drop last receptive_field -1 steps in
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the output.
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It is a cell that it acts like an RNN cell. It does not support stride > 1, and it
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ensures 1-to-1 mapping from input time steps to output timesteps.
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"""
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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dilation=1,
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causal=False,
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groups=None,
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param_attr=None,
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bias_attr=None,
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use_cudnn=True,
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act=None,
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dtype='float32'):
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receptive_field = 1 + dilation * (filter_size - 1)
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padding = receptive_field - 1 if causal else receptive_field // 2
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self._receptive_field = receptive_field
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self.causal = causal
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super(Conv1DCell, self).__init__(num_channels,
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num_filters,
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filter_size,
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stride=1,
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padding=padding,
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dilation=dilation,
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groups=groups,
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param_attr=param_attr,
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bias_attr=bias_attr,
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use_cudnn=use_cudnn,
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act=act,
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dtype=dtype)
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def forward(self, x):
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# it ensures that ouput time steps == input time steps
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time_steps = x.shape[-1]
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x = super(Conv1DCell, self).forward(x)
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if x.shape[-1] != time_steps:
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x = x[:, :, :time_steps]
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return x
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@property
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def receptive_field(self):
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return self._receptive_field
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def start_sequence(self):
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if not self.causal:
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raise ValueError(
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"Only causal conv1d shell should use start sequence")
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if self.receptive_field == 1:
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raise ValueError(
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"Convolution block with receptive field = 1 does not need"
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" to be implemented as a Conv1DCell. Conv1D suffices")
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self._buffer = None
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self._reshaped_weight = F.reshape(self.weight, (self._num_filters, -1))
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def add_input(self, x_t):
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batch_size, c_in, _ = x_t.shape
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if self._buffer is None:
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self._buffer = F.zeros((batch_size, c_in, self.receptive_field),
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dtype=x_t.dtype)
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self._buffer = F.concat([self._buffer[:, :, 1:], x_t], -1)
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if self._dilation[1] > 1:
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input = F.strided_slice(self._buffer,
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axes=[2],
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starts=[0],
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ends=[self.receptive_field],
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strides=[self._dilation[1]])
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else:
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input = self._buffer
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input = F.reshape(input, (batch_size, -1))
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y_t = F.matmul(input, self._reshaped_weight, transpose_y=True)
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y_t = y_t + self.bias
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y_t = F.unsqueeze(y_t, [-1])
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return y_t
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