def __init__(self, indepth, branch=1, active='relu', nclass=1000, method='split'):
super(SummaryBlock, self).__init__()
assert len(indepth) == branch, '各分类分支的通道数必须全部给定，so， len of <indepth> == branch.'
assert method in self.METHOD, 'Unknown <method> %s for SummaryBlock.' % method
self.indepth = indepth
self.branch = branch
self.active = active
self.nclass = nclass
self.method = method
self.active_fc = True
if method == 'split':
for i in range(branch):
fc_layer = nn.Sequential(
nn.BatchNorm2d(indepth[i]),
Activate(active),
AdaAvgPool(),
ViewLayer(),
nn.Linear(indepth[i], nclass))
setattr(self, 'classifier%s' % (i + 1), fc_layer)
elif method == 'merge':
for i in range(branch):
view_layer = nn.Sequential(
nn.BatchNorm2d(indepth[i]),
Activate(active),
AdaAvgPool(),
ViewLayer())
setattr(self, 'pool_view%s' % (i + 1), view_layer)
self.classifier = nn.Linear(sum(indepth), nclass)
else:
raise NotImplementedError
def __init__(self,
indepth,
branch,
active='relu',
pool='avg',
nclass=1000,
method='split'):
super(RockSummary, self).__init__()
assert len(
indepth) == branch, '各输出分支的通道数必须全部给定，len of <indepth> == branch.'
assert method in self.METHOD, 'Unknown <methond> %s.' % method
self.indepth = indepth
self.branch = branch
self.active_fc = True
self.nclass = nclass
self.method = method
if method == 'split':
for b in range(1, branch + 1):
layer = nn.Sequential(nn.BatchNorm2d(indepth[b - 1]),
Activate(active), AdaAvgPool(),
ViewLayer(),
nn.Linear(indepth[b - 1], nclass))
setattr(self, 'classifier%s' % b, layer)
elif method == 'merge':
for b in range(1, branch + 1):
layer = nn.Sequential(nn.BatchNorm2d(indepth[b - 1]),
Activate(active), AdaAvgPool(),
ViewLayer())
setattr(self, 'pool_view%s' % b, layer)
self.classifier = nn.Linear(sum(indepth), nclass)
else:
raise NotImplementedError
def __init__(self, indepth, growth, active='relu', first=False, after=True, down=False,
trans='A', reduce=0.5, convkp='T', inmode='nearest', classify=0, nclass=1000,
last_branch=1, last_down=False, last_expand=0):
super(SingleCouple, self).__init__()
assert last_branch <= 2, '<last_branch> of SingleCouple should be <= 2...'
self.indepth = indepth
self.growth = growth
self.active = getattr(nn.functional, active)
self.first = first
self.after = after
self.down = down
self.trans = trans
self.reduce = reduce
self.trans_func = self._trans[trans]
self.classify = classify
self.nclass = nclass
self.last_branch = last_branch
self.last_down = last_down
self.last_expand = last_expand
self.inmode = inmode
self.convkp = convkp
kp = {'T': (3, 1), 'O': (1, 0)}[convkp]
first_outdepth = indepth + growth if self.first else growth
self.bn1 = nn.BatchNorm2d(indepth)
self.conv1 = nn.Conv2d(indepth, first_outdepth, 3, stride=2, padding=1, bias=False, dilation=1)
self.bn2 = nn.BatchNorm2d(indepth + growth)
self.conv2 = nn.Conv2d(indepth + growth, growth, kp[0], stride=1, padding=kp[1], bias=False, dilation=1)
if self.classify > 0:
self.classifier = nn.Sequential(
nn.BatchNorm2d(indepth + growth),
Activate(active),
AdaAvgPool(),
ViewLayer(dim=-1),
nn.Linear(indepth + growth, nclass)
)
if self.after:
if self.down:
outdepth = int(math.floor((indepth + growth) * reduce))
self.down_res2 = self.trans_func(indepth + growth, outdepth)
self.down_res1 = self.trans_func(indepth + growth, outdepth)
else:
if self.last_down:
outdepth = indepth + growth + last_expand
if self.last_branch >= 1:
self.down_last2 = self.trans_func(indepth + growth, outdepth)
if self.last_branch >= 2:
self.down_last1 = self.trans_func(indepth + growth, outdepth)
else:
if self.classify > 0 and self.last_branch == 2:
# 此时，最后一个Couple的中间层被当做branch输出而对接在Summary上.
# 因此，删除此Couple自带的Classifier,以免与Summary中的Classifier重复.
delattr(self, 'classifier')
self.classify = 0
print('Note: 1 xfc will be deleted because of duplicate with the last-fc!')
def __init__(self,
indepth,
branch=1,
active='relu',
pool='avg',
nclass=1000):
super(RockSummary, self).__init__()
self.indepth = indepth
self.branch = branch
self.active_fc = True
self.pool = pool
for b in range(1, branch + 1):
layer = nn.Sequential(nn.BatchNorm2d(indepth), Activate(active),
AdaAvgPool(), ViewLayer(),
nn.Linear(indepth, nclass))
setattr(self, 'classifier%s' % b, layer)
def __init__(self, indepth, growth, bottle=4, active='relu', first=False, after=True, down=False, trans='A',
reduce=0.5, classify=0, nclass=1000, last_branch=1, last_down=False, last_expand=0):
super(DoubleCouple, self).__init__()
assert last_branch <= 4, '<last_branch> of DoubleCouple should be <= 4...'
self.indepth = indepth
self.growth = growth
self.bottle = bottle
self.classify = classify
self.nclass = nclass
self.active = getattr(nn.functional, active)
self.first = first
self.after = after
self.down = down
self.trans = trans
self.reduce = reduce
self.trans_func = self._trans[trans]
self.last_branch = last_branch
self.last_down = last_down
self.last_expand = last_expand
if bottle > 0:
interdepth = int(bottle * growth)
assert interdepth == math.ceil(bottle * growth), \
'<bottle> * <growth> cannot be a fraction number (带小数位), but %s !\n' % bottle * growth
first_outdepth = indepth + growth if self.first else growth
self.bn1_b = nn.BatchNorm2d(indepth)
self.conv1_b = nn.Conv2d(indepth, interdepth, 1, stride=1, padding=0, dilation=1, bias=False)
self.bn1 = nn.BatchNorm2d(interdepth)
self.conv1 = nn.Conv2d(interdepth, first_outdepth, 3, stride=2, padding=1, bias=False, dilation=1)
self.bn2_b = nn.BatchNorm2d(indepth + growth)
self.conv2_b = nn.Conv2d(indepth + growth, interdepth, 1, stride=1, padding=0, dilation=1, bias=False)
self.bn2 = nn.BatchNorm2d(interdepth)
self.conv2 = nn.Conv2d(interdepth, growth, 3, stride=2, padding=1, bias=False, dilation=1)
self.bn3_b = nn.BatchNorm2d(indepth + growth)
self.conv3_b = nn.Conv2d(indepth + growth, interdepth, 1, stride=1, padding=0, dilation=1, bias=False)
self.bn3 = nn.BatchNorm2d(interdepth)
self.deconv3 = nn.ConvTranspose2d(interdepth, growth, 3, stride=2, padding=1,
output_padding=1, bias=False, dilation=1)
self.bn4_b = nn.BatchNorm2d(indepth + growth)
self.conv4_b = nn.Conv2d(indepth + growth, interdepth, 1, stride=1, padding=0, dilation=1, bias=False)
self.bn4 = nn.BatchNorm2d(interdepth)
self.deconv4 = nn.ConvTranspose2d(interdepth, growth, 3, stride=2, padding=1,
output_padding=1, bias=False, dilation=1)
elif bottle == 0:
first_outdepth = indepth + growth if self.first else growth
self.bn1 = nn.BatchNorm2d(indepth)
self.conv1 = nn.Conv2d(indepth, first_outdepth, 3, stride=2, padding=1, bias=False, dilation=1)
self.bn2 = nn.BatchNorm2d(indepth + growth)
self.conv2 = nn.Conv2d(indepth + growth, growth, 3, stride=2, padding=1, bias=False, dilation=1)
self.bn3 = nn.BatchNorm2d(indepth + growth)
self.deconv3 = nn.ConvTranspose2d(indepth + growth, growth, 3, stride=2, padding=1,
output_padding=1, bias=False, dilation=1)
self.bn4 = nn.BatchNorm2d(indepth + growth)
self.deconv4 = nn.ConvTranspose2d(indepth + growth, growth, 3, stride=2, padding=1,
output_padding=1, bias=False, dilation=1)
else:
raise NotImplementedError('<bottle> should be >= 0, but %s' % bottle)
if self.classify > 0:
self.classifier = nn.Sequential(
nn.BatchNorm2d(indepth + growth),
Activate(active),
AdaAvgPool(),
ViewLayer(dim=-1),
nn.Linear(indepth + growth, nclass))
if self.after:
if self.down and self.reduce != 1:
outdepth = int(math.floor((indepth + growth) * reduce))
self.down_res4 = self.trans_func(indepth + growth, outdepth)
self.down_res3 = self.trans_func(indepth + growth, outdepth)
self.down_res2 = self.trans_func(indepth + growth, outdepth)
self.down_res1 = self.trans_func(indepth + growth, outdepth)
else:
if self.last_down:
outdepth = indepth + growth + last_expand
if self.last_branch >= 1:
self.down_last4 = self.trans_func(indepth + growth, outdepth)
if self.last_branch >= 2:
self.down_last3 = self.trans_func(indepth + growth, outdepth)
if self.last_branch >= 3:
self.down_last2 = self.trans_func(indepth + growth, outdepth)
if self.last_branch >= 4:
self.down_last1 = self.trans_func(indepth + growth, outdepth)
else:
if self.classify > 0 and self.last_branch == 4:
# 最后一个Couple的中间层被当做branch输出而对接在Summary上.
# 因此，删除此Couple自带的Classifier,以免与Summary中的Classifier重复.
delattr(self, 'classifier')
self.classify = 0
print('\nNote: 1 xfc will be deleted because of duplicated with the lfc!\n')
def __init__(self,
indepth,
insize,
branch,
active='relu',
pool='avg',
nclass=1000,
method='split'):
super(RockSummary, self).__init__()
assert len(
indepth) == branch, '各输出分支的通道数必须全部给定，len of <indepth> == branch.'
assert method in self.METHOD, 'Unknown <methond> %s.' % method
if method in ['convf', 'convs', 'convt']:
assert insize >= 1, '进行卷积分类，输入特征图尺寸<insize> >= 1.'
if method == 'convf':
assert branch >= 1, '对last-1进行卷积分类，输出分支<last-branch>>=1'
elif method == 'convs':
assert branch >= 2, '对last-2进行卷积分类，输出分支<last-branch>必须>=2'
elif method == 'convt':
assert branch >= 3, '对last-3进行卷积分类，输出分支<last-branch>必须>=3'
self.indepth = indepth
self.branch = branch
self.active_fc = True
self.nclass = nclass
insize = int(insize)
self.insize = insize
self.method = method
if method == 'split':
for b in range(1, branch + 1):
layer = nn.Sequential(nn.BatchNorm2d(indepth[b - 1]),
Activate(active), AdaAvgPool(),
ViewLayer(),
nn.Linear(indepth[b - 1], nclass))
setattr(self, 'classifier%s' % b, layer)
elif method == 'merge':
for b in range(1, branch + 1):
layer = nn.Sequential(nn.BatchNorm2d(indepth[b - 1]),
Activate(active), AdaAvgPool(),
ViewLayer())
setattr(self, 'pool_view%s' % b, layer)
self.classifier = nn.Linear(sum(indepth), nclass)
elif method == 'convx':
for b in range(1, branch + 1):
ksize = int(insize * ((1 / 2)**(b - 1)))
layer = ConvClassifier(indepth[b - 1],
nclass,
ksize=ksize,
stride=1,
padding=0)
setattr(self, 'classifier%s' % b, layer)
elif method == 'convf':
self.classifier = ConvClassifier(indepth[0],
nclass,
ksize=insize // 1,
stride=1,
padding=0)
elif method == 'convs':
self.classifier = ConvClassifier(indepth[1],
nclass,
ksize=insize // 2,
stride=1,
padding=0)
elif method == 'convt':
self.classifier = ConvClassifier(indepth[2],
nclass,
ksize=insize // 4,
stride=1,
padding=0)
else:
raise NotImplementedError('Unknow <method> for SummaryBlock, %s' %
method)