mirror of
https://github.com/babysor/MockingBird.git
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80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.autograd import Function
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def tile(x, count, dim=0):
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"""
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Tiles x on dimension dim count times.
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"""
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perm = list(range(len(x.size())))
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if dim != 0:
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perm[0], perm[dim] = perm[dim], perm[0]
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x = x.permute(perm).contiguous()
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out_size = list(x.size())
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out_size[0] *= count
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batch = x.size(0)
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x = x.view(batch, -1) \
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.transpose(0, 1) \
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.repeat(count, 1) \
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.transpose(0, 1) \
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.contiguous() \
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.view(*out_size)
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if dim != 0:
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x = x.permute(perm).contiguous()
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return x
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class Linear(torch.nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
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super(Linear, self).__init__()
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self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
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torch.nn.init.xavier_uniform_(
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self.linear_layer.weight,
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gain=torch.nn.init.calculate_gain(w_init_gain))
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def forward(self, x):
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return self.linear_layer(x)
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class Conv1d(torch.nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
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padding=None, dilation=1, bias=True, w_init_gain='linear', param=None):
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super(Conv1d, self).__init__()
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if padding is None:
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assert(kernel_size % 2 == 1)
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padding = int(dilation * (kernel_size - 1)/2)
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self.conv = torch.nn.Conv1d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation,
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bias=bias)
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torch.nn.init.xavier_uniform_(
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self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain, param=param))
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def forward(self, x):
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# x: BxDxT
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return self.conv(x)
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def tile(x, count, dim=0):
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"""
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Tiles x on dimension dim count times.
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"""
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perm = list(range(len(x.size())))
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if dim != 0:
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perm[0], perm[dim] = perm[dim], perm[0]
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x = x.permute(perm).contiguous()
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out_size = list(x.size())
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out_size[0] *= count
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batch = x.size(0)
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x = x.view(batch, -1) \
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.transpose(0, 1) \
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.repeat(count, 1) \
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.transpose(0, 1) \
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.contiguous() \
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.view(*out_size)
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if dim != 0:
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x = x.permute(perm).contiguous()
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return x
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