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from torch import nn, rand
from torch.autograd import Variable
class CNN(nn.Sequential):
def __init__(
self,
filters: list[int],
kernels: list[int],
max_sequence_length: int,
in_channels: int,
out_channels: int
):
super().__init__()
num_layer = len(filters)
channels = [in_channels] + filters
self.conv = nn.ModuleList([nn.Conv1d(in_channels=channels[i],
out_channels=channels[i+1],
kernel_size=kernels[i])
for i in range(num_layer)])
n_size = self._get_conv_output((in_channels, max_sequence_length))
self.fc1 = nn.Linear(n_size, out_channels)
def _forward_features(self, x):
for layer in self.conv:
x = nn.functional.relu(layer(x))
x = nn.functional.adaptive_max_pool1d(x, output_size=1)
return x
def _get_conv_output(self, shape):
bs = 1
input_feat = Variable(rand(bs, *shape))
output_feat = self._forward_features(input_feat)
n_size = output_feat.data.view(bs, -1).size(1)
return n_size
def forward(self, v):
v = self._forward_features(v.float())
v = v.view(v.size(0), -1)
v = self.fc1(v)
return v
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