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nets.py
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nets.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from functools import reduce
import torch.nn as nn
import torch.nn.functional as F
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class FlexibleAvgPool2d(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
return F.avg_pool2d(inputs, kernel_size=inputs.size(2))
class WeightPool(nn.Module):
def __init__(self, in_planes, kernel_size):
super(WeightPool, self).__init__()
self.conv = nn.Conv2d(in_planes, in_planes, kernel_size=kernel_size,
stride=kernel_size, groups=in_planes, bias=False)
self.conv.unit_gain = True
def forward(self, x):
return self.conv(x)
class WeightPoolOut(nn.Module):
def __init__(self, in_planes, plane_size, categories, unit_gain=False):
super(WeightPoolOut, self).__init__()
self.in_planes = in_planes
self.conv = nn.Conv2d(in_planes, in_planes, kernel_size=plane_size,
groups=in_planes, bias=False)
self.linear = nn.Linear(in_planes, categories)
self.linear.unit_gain = unit_gain
def forward(self, x):
out = self.conv(x)
out = out.view(-1, self.in_planes)
return self.linear(out)
class MaxPoolOut(nn.Module):
def __init__(self, in_planes, plane_size, categories, unit_gain=False):
super(MaxPoolOut, self).__init__()
self.in_planes = in_planes
self.maxpool = nn.MaxPool2d(kernel_size=plane_size)
self.linear = nn.Linear(in_planes, categories)
self.linear.unit_gain = unit_gain
def forward(self, x):
out = self.maxpool(x)
out = out.view(-1, self.in_planes)
return self.linear(out)
class AvgPoolOut(nn.Module):
def __init__(self, in_planes, plane_size, categories, unit_gain=False):
super(AvgPoolOut, self).__init__()
self.in_planes = in_planes
self.avgpool = nn.AvgPool2d(kernel_size=plane_size)
self.linear = nn.Linear(in_planes, categories)
self.linear.unit_gain = unit_gain
def forward(self, x):
out = self.avgpool(x)
out = out.view(-1, self.in_planes)
return self.linear(out)
class FCout(nn.Module):
def __init__(self, in_planes, plane_size, categories, unit_gain=False):
super(FCout, self).__init__()
if type(plane_size) == tuple and len(plane_size) == 2:
plane_size = reduce(lambda x, y: x * y, plane_size)
else:
plane_size = plane_size ** 2
print('Plane size = ', plane_size)
self.in_planes = in_planes
self.plane_size = plane_size
self.linear = nn.Linear(in_planes * plane_size, categories)
self.linear.unit_gain = unit_gain
def forward(self, x):
out = x.view(-1, self.in_planes * self.plane_size)
return self.linear(out)
class ConvLayer(nn.Module):
def __init__(self, in_planes, planes, pooltype=None, no_BN=False,
no_act=False, dilation=1):
super(ConvLayer, self).__init__()
self.pad = nn.ReflectionPad2d(dilation)
if pooltype is None: # Usual conv
self.conv = nn.Conv2d(in_planes, planes, 3, padding=0,
stride=1, dilation=dilation)
elif pooltype == 'avgpool': # Average Pool
self.conv = nn.Sequential(
nn.Conv2d(in_planes, planes, 3, dilation=dilation),
nn.AvgPool2d(2))
elif pooltype == 'subsamp': # Strided Conv
self.conv = nn.Conv2d(
in_planes, planes, 3, stride=2, dilation=dilation)
elif pooltype == 'maxpool': # Max Pool
self.conv = nn.Sequential(
nn.Conv2d(in_planes, planes, 3, dilation=dilation),
nn.MaxPool2d(2))
elif pooltype == 'weightpool':
self.conv = nn.Sequential(
nn.Conv2d(in_planes, planes, 3, dilation=dilation),
WeightPool(planes, 2))
else:
raise NotImplementedError
if no_act:
self.act = lambda x: x
else:
self.act = nn.ReLU()
if no_BN:
self.bn = lambda x: x # Identity()
else:
self.bn = nn.BatchNorm2d(planes)
def forward(self, x):
out = self.act(self.bn(self.conv(self.pad(x))))
return out
class ConvNet(nn.Module):
def __init__(
self, categories=10, n_layers=3, in_size=32, poolings=None,
pooltype='avgpool', no_BN=False, no_act=False, dilations=1,
normalize_inputs=False, last_layers='maxpool', in_planes=3):
# last_layers in {'maxpool', 'fc', 'weightpool'}
super(ConvNet, self).__init__()
poolings = [] if poolings is None else poolings
if type(dilations) != list:
dilations = [dilations] * n_layers
self.in_planes = in_planes
if normalize_inputs or no_BN:
self.bn = (lambda x: x)
else:
self.bn = nn.BatchNorm2d(self.in_planes)
self.layers = self._make_layers(
ConvLayer, 64, n_layers, poolings, pooltype,
no_BN, no_act, dilations)
# compute input-size to last layers from input-size of the net
# self.in_planes is changed by _make_layers to the nbr of out-planes
out_size = int(in_size / (2 ** (len(poolings))))
self.last_layers = self._make_last_layers(
out_size, categories, last_layers)
def _make_layers(self, block, planes, num_blocks, poolings,
pooltype, no_BN, no_act, dilations):
# pooltypes = [0] + [0] * (num_blocks - 1)
pooltypes = [None] * num_blocks
for pool in poolings:
pooltypes[pool] = pooltype
layers = []
for pool, dilation in zip(pooltypes, dilations):
layers.append(block(self.in_planes, planes, pool, no_BN, no_act,
dilation))
self.in_planes = planes
return nn.Sequential(*layers)
def _make_last_layers(self, in_size, categories, last_layers):
if last_layers == 'maxpool':
last_layers = MaxPoolOut(
self.in_planes, in_size, categories, unit_gain=True)
elif last_layers == 'avgpool':
last_layers = AvgPoolOut(
self.in_planes, in_size, categories, unit_gain=True)
elif last_layers == 'weightpool':
last_layers = WeightPoolOut(
self.in_planes, in_size, categories, unit_gain=True)
elif last_layers == 'fc':
last_layers = FCout(
self.in_planes, in_size, categories, unit_gain=True)
else:
raise NotImplementedError(
'Argument last_layers must be maxpool, fc, weightpool. '
'But got: %s' % last_layers)
return last_layers
def forward(self, x):
out = self.layers(self.bn(x))
out = self.last_layers(out)
return out