273 lines
10 KiB
Python
273 lines
10 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 Pool1D(dg.Layer):
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"""
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A Pool 1D block implemented with Pool2D.
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"""
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def __init__(self,
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pool_size=-1,
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pool_type='max',
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pool_stride=1,
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pool_padding=0,
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global_pooling=False,
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use_cudnn=True,
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ceil_mode=False,
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exclusive=True,
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data_format='NCT'):
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super(Pool1D, self).__init__()
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self.pool_size = pool_size
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self.pool_type = pool_type
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self.pool_stride = pool_stride
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self.pool_padding = pool_padding
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self.global_pooling = global_pooling
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self.use_cudnn = use_cudnn
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self.ceil_mode = ceil_mode
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self.exclusive = exclusive
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self.data_format = data_format
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self.pool2d = dg.Pool2D(
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[1, pool_size],
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pool_type=pool_type,
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pool_stride=[1, pool_stride],
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pool_padding=[0, pool_padding],
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global_pooling=global_pooling,
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use_cudnn=use_cudnn,
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ceil_mode=ceil_mode,
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exclusive=exclusive)
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def forward(self, x):
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"""
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Args:
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x (Variable): Shape(B, C_in, 1, T), the input, where C_in means
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input channels.
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Returns:
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x (Variable): Shape(B, C_out, 1, T), the outputs, where C_out means
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output channels (num_filters).
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"""
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if self.data_format == 'NTC':
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x = fluid.layers.transpose(x, [0, 2, 1])
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x = fluid.layers.unsqueeze(x, [2])
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x = self.pool2d(x)
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x = fluid.layers.squeeze(x, [2])
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if self.data_format == 'NTC':
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x = fluid.layers.transpose(x, [0, 2, 1])
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return x
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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=1,
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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__(
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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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"""Compute Conv1D by unsqueeze the input and squeeze the output.
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Args:
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x (Variable): shape(B, C_in, T_in), dtype float32, input of Conv1D.
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Returns:
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Variable: shape(B, C_out, T_out), dtype float32, output of Conv1D.
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"""
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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=1,
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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__(
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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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"""Compute Conv1DTranspose by unsqueeze the input and squeeze the output.
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Args:
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x (Variable): shape(B, C_in, T_in), dtype float32, input of Conv1DTranspose.
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Returns:
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Variable: shape(B, C_out, T_out), dtype float32, output of Conv1DTranspose.
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"""
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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=1,
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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__(
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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=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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"""Compute Conv1D by unsqueeze the input and squeeze the output.
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Args:
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x (Variable): shape(B, C_in, T), dtype float32, input of Conv1D.
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Returns:
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Variable: shape(B, C_out, T), dtype float32, output of Conv1D.
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"""
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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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"""Prepare the Conv1DCell to generate a new sequence, this method should be called before calling add_input multiple times.
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WARNING:
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This method accesses `self.weight` directly. If a `Conv1DCell` object is wrapped in a `WeightNormWrapper`, make sure this method is called only after the `WeightNormWrapper`'s hook is called.
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`WeightNormWrapper` removes the wrapped layer's `weight`, add has a `weight_v` and `weight_g` to re-compute the wrapped layer's weight as $weight = weight_g * weight_v / ||weight_v||$. (Recomputing the `weight` is a hook before calling the wrapped layer's `forward` method.)
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Whenever a `WeightNormWrapper`'s `forward` method is called, the wrapped layer's weight is updated. But when loading from a checkpoint, `weight_v` and `weight_g` are updated but the wrapped layer's weight is not, since it is no longer a `Parameter`. You should manually call `remove_weight_norm` or `hook` to re-compute the wrapped layer's weight before calling this method if you don't call `forward` first.
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So when loading a model which uses `Conv1DCell` objects wrapped in `WeightNormWrapper`s, remember to call `remove_weight_norm` for all `WeightNormWrapper`s before synthesizing. Also, removing weight norm speeds up computation.
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"""
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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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"""This method works similarily with forward but in a `step-in-step-out` fashion.
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Args:
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x (Variable): shape(B, C_in, T=1), dtype float32, input of Conv1D.
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Returns:
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Variable: shape(B, C_out, T=1), dtype float32, output of Conv1D.
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"""
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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(
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(batch_size, c_in, self.receptive_field), 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(
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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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