63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
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# --------------------------------------------------------
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# mcan-vqa (Deep Modular Co-Attention Networks)
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Yuhao Cui https://github.com/cuiyuhao1996
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# --------------------------------------------------------
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import torch.nn as nn
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import os
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import torch
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class FC(nn.Module):
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def __init__(self, in_size, out_size, dropout_r=0., use_relu=True):
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super(FC, self).__init__()
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self.dropout_r = dropout_r
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self.use_relu = use_relu
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self.linear = nn.Linear(in_size, out_size)
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if use_relu:
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self.relu = nn.ReLU(inplace=True)
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if dropout_r > 0:
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self.dropout = nn.Dropout(dropout_r)
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def forward(self, x):
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x = self.linear(x)
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if self.use_relu:
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x = self.relu(x)
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if self.dropout_r > 0:
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x = self.dropout(x)
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return x
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class MLP(nn.Module):
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def __init__(self, in_size, mid_size, out_size, dropout_r=0., use_relu=True):
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super(MLP, self).__init__()
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self.fc = FC(in_size, mid_size, dropout_r=dropout_r, use_relu=use_relu)
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self.linear = nn.Linear(mid_size, out_size)
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def forward(self, x):
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return self.linear(self.fc(x))
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class LayerNorm(nn.Module):
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def __init__(self, size, eps=1e-6):
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super(LayerNorm, self).__init__()
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self.eps = eps
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self.a_2 = nn.Parameter(torch.ones(size))
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self.b_2 = nn.Parameter(torch.zeros(size))
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def forward(self, x):
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mean = x.mean(-1, keepdim=True)
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std = x.std(-1, keepdim=True)
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return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
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