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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import torch
import torch.nn as nn
import pytorch_lightning as pl
class MLP(pl.LightningModule):
def __init__(self,
filter_channels,
name=None,
res_layers=[],
norm='group',
last_op=None):
super(MLP, self).__init__()
self.filters = nn.ModuleList()
self.norms = nn.ModuleList()
self.res_layers = res_layers
self.norm = norm
self.last_op = last_op
self.name = name
self.activate = nn.LeakyReLU(inplace=True)
for l in range(0, len(filter_channels) - 1):
if l in self.res_layers:
self.filters.append(
nn.Conv1d(filter_channels[l] + filter_channels[0],
filter_channels[l + 1], 1))
else:
self.filters.append(
nn.Conv1d(filter_channels[l], filter_channels[l + 1], 1))
if l != len(filter_channels) - 2:
if norm == 'group':
self.norms.append(nn.GroupNorm(32, filter_channels[l + 1]))
elif norm == 'batch':
self.norms.append(nn.BatchNorm1d(filter_channels[l + 1]))
elif norm == 'instance':
self.norms.append(nn.InstanceNorm1d(filter_channels[l +
1]))
elif norm == 'weight':
self.filters[l] = nn.utils.weight_norm(self.filters[l],
name='weight')
# print(self.filters[l].weight_g.size(),
# self.filters[l].weight_v.size())
def forward(self, feature):
'''
feature may include multiple view inputs
args:
feature: [B, C_in, N]
return:
[B, C_out, N] prediction
'''
y = feature
tmpy = feature
for i, f in enumerate(self.filters):
y = f(y if i not in self.res_layers else torch.cat([y, tmpy], 1))
if i != len(self.filters) - 1:
if self.norm not in ['batch', 'group', 'instance']:
y = self.activate(y)
else:
y = self.activate(self.norms[i](y))
if self.last_op is not None:
y = self.last_op(y)
return y
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