def dcn_resnet_unit(input, name, filter, stride, dilate, proj, norm, **kwargs):
conv1 = conv(input, name=name + "_conv1", filter=filter // 4)
bn1 = norm(conv1, name=name + "_bn1")
relu1 = relu(bn1, name=name + "_relu1")
# conv2 filter router
conv2_offset = conv(relu1, name=name + "_conv2_offset", filter=72, kernel=3, stride=stride, dilate=dilate)
conv2 = mx.sym.contrib.DeformableConvolution(relu1, conv2_offset, kernel=(3, 3),
stride=(stride, stride), dilate=(dilate, dilate), pad=(1, 1), num_filter=filter // 4,
num_deformable_group=4, no_bias=True, name=name + "_conv2")
bn2 = norm(conv2, name=name + "_bn2")
relu2 = relu(bn2, name=name + "_relu2")
conv3 = conv(relu2, name=name + "_conv3", filter=filter)
bn3 = norm(conv3, name=name + "_bn3")
if proj:
shortcut = conv(input, name=name + "_sc", filter=filter, stride=stride)
shortcut = norm(shortcut, name=name + "_sc_bn")
else:
shortcut = input
eltwise = add(bn3, shortcut, name=name + "_plus")
return relu(eltwise, name=name + "_relu")
def se_v2_resnet_v1b_unit(input, name, filter, stride, dilate, proj, norm,
**kwargs):
"""
diff with v1: move the SE module to 3x3 conv
"""
conv1 = conv(input, name=name + "_conv1", filter=filter // 4)
bn1 = norm(conv1, name=name + "_bn1")
relu1 = relu(bn1, name=name + "_relu1")
conv2 = conv(relu1,
name=name + "_conv2",
stride=stride,
filter=filter // 4,
kernel=3)
bn2 = norm(conv2, name=name + "_bn2")
relu2 = relu(bn2, name=name + "_relu2")
relu2 = se(relu2,
prefix=name + "_se2",
f_down=filter // 16,
f_up=filter // 4)
conv3 = conv(relu2, name=name + "_conv3", filter=filter)
bn3 = norm(conv3, name=name + "_bn3")
if proj:
shortcut = conv(input, name=name + "_sc", filter=filter, stride=stride)
shortcut = norm(shortcut, name=name + "_sc_bn")
else:
shortcut = input
eltwise = add(bn3, shortcut, name=name + "_plus")
return relu(eltwise, name=name + "_relu")
def trident_resnet_v1b_unit(input, name, id, filter, stride, dilate, proj, **kwargs):
"""
Compared with v1, v1b moves stride=2 to the 3x3 conv instead of the 1x1 conv and use std in pre-processing
This is also known as the facebook re-implementation of ResNet(a.k.a. the torch ResNet)
"""
p = kwargs["params"]
share_bn = p.branch_bn_shared
share_conv = p.branch_conv_shared
norm = p.normalizer
######################### prepare names #########################
if id is not None:
conv_postfix = ("_shared%s" if share_conv else "_branch%s") % id
bn_postfix = ("_shared%s" if share_bn else "_branch%s") % id
other_postfix = "_branch%s" % id
else:
conv_postfix = ""
bn_postfix = ""
other_postfix = ""
######################### prepare parameters #########################
conv_params = lambda x: dict(
weight=X.shared_var(name + "_%s_weight" % x) if share_conv else None,
name=name + "_%s" % x + conv_postfix
)
def bn_params(x):
ret = dict(
gamma=X.shared_var(name + "_%s_gamma" % x) if share_bn else None,
beta=X.shared_var(name + "_%s_beta" % x) if share_bn else None,
moving_mean=X.shared_var(name + "_%s_moving_mean" % x) if share_bn else None,
moving_var=X.shared_var(name + "_%s_moving_var" % x) if share_bn else None,
name=name + "_%s" % x + bn_postfix
)
if norm.__name__ == "gn":
del ret["moving_mean"], ret["moving_var"]
return ret
######################### construct graph #########################
conv1 = conv(input, filter=filter // 4, **conv_params("conv1"))
bn1 = norm(conv1, **bn_params("bn1"))
relu1 = relu(bn1, name=name + other_postfix)
conv2 = conv(relu1, filter=filter // 4, kernel=3, stride=stride, dilate=dilate, **conv_params("conv2"))
bn2 = norm(conv2, **bn_params("bn2"))
relu2 = relu(bn2, name=name + other_postfix)
conv3 = conv(relu2, filter=filter, **conv_params("conv3"))
bn3 = norm(conv3, **bn_params("bn3"))
if proj:
shortcut = conv(input, filter=filter, stride=stride, **conv_params("sc"))
shortcut = norm(shortcut, **bn_params("sc_bn"))
else:
shortcut = input
eltwise = add(bn3, shortcut, name=name + "_plus" + other_postfix)
return relu(eltwise, name=name + "_relu" + other_postfix)
def trident_resnet_v1_unit(input, name, id, filter, stride, dilate, proj, **kwargs):
p = kwargs["params"]
share_bn = p.branch_bn_shared
share_conv = p.branch_conv_shared
norm = p.normalizer
######################### prepare names #########################
if id is not None:
conv_postfix = ("_shared%s" if share_conv else "_branch%s") % id
bn_postfix = ("_shared%s" if share_bn else "_branch%s") % id
other_postfix = "_branch%s" % id
else:
conv_postfix = ""
bn_postfix = ""
other_postfix = ""
######################### prepare parameters #########################
conv_params = lambda x: dict(
weight=X.shared_var(name + "_%s_weight" % x) if share_conv else None,
name=name + "_%s" % x + conv_postfix
)
bn_params = lambda x: dict(
gamma=X.shared_var(name + "_%s_gamma" % x) if share_bn else None,
beta=X.shared_var(name + "_%s_beta" % x) if share_bn else None,
moving_mean=X.shared_var(name + "_%s_moving_mean" % x) if share_bn else None,
moving_var=X.shared_var(name + "_%s_moving_var" % x) if share_bn else None,
name=name + "_%s" % x + bn_postfix
)
######################### construct graph #########################
conv1 = conv(input, filter=filter // 4, stride=stride, **conv_params("conv1"))
bn1 = norm(conv1, **bn_params("bn1"))
relu1 = relu(bn1, name=name + other_postfix)
conv2 = conv(relu1, filter=filter // 4, kernel=3, dilate=dilate, **conv_params("conv2"))
bn2 = norm(conv2, **bn_params("bn2"))
relu2 = relu(bn2, name=name + other_postfix)
conv3 = conv(relu2, filter=filter, **conv_params("conv3"))
bn3 = norm(conv3, **bn_params("bn3"))
if proj:
shortcut = conv(input, filter=filter, stride=stride, **conv_params("sc"))
shortcut = norm(shortcut, **bn_params("sc_bn"))
else:
shortcut = input
eltwise = add(bn3, shortcut, name=name + "_plus" + other_postfix)
return relu(eltwise, name=name + "_relu" + other_postfix)
def resnet_trident_unit(cls,
data,
name,
filter,
stride,
dilate,
proj,
norm_type,
norm_mom,
ndev,
branch_ids,
branch_bn_shared,
branch_conv_shared,
branch_deform=False):
"""
One resnet unit is comprised of 2 or 3 convolutions and a shortcut.
:param data:
:param name:
:param filter:
:param stride:
:param dilate:
:param proj:
:param norm_type:
:param norm_mom:
:param ndev:
:param branch_ids:
:param branch_bn_shared:
:param branch_conv_shared:
:param branch_deform:
:return:
"""
if branch_ids is None:
branch_ids = range(len(data))
norm = X.normalizer_factory(type=norm_type, ndev=ndev, mom=norm_mom)
bn1 = cls.bn_shared(data,
name=name + "_bn1",
normalizer=norm,
branch_ids=branch_ids,
share_weight=branch_bn_shared)
relu1 = [X.relu(bn) for bn in bn1]
conv1 = cls.conv_shared(relu1,
name=name + "_conv1",
num_filter=filter // 4,
kernel=(1, 1),
branch_ids=branch_ids,
share_weight=branch_conv_shared)
bn2 = cls.bn_shared(conv1,
name=name + "_bn2",
normalizer=norm,
branch_ids=branch_ids,
share_weight=branch_bn_shared)
relu2 = [X.relu(bn) for bn in bn2]
if not branch_deform:
conv2 = cls.conv_shared(relu2,
name=name + "_conv2",
num_filter=filter // 4,
kernel=(3, 3),
pad=dilate,
stride=stride,
dilate=dilate,
branch_ids=branch_ids,
share_weight=branch_conv_shared)
else:
conv2_offset = cls.conv_shared(relu2,
name=name + "_conv2_offset",
num_filter=72,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
dilate=(1, 1),
no_bias=False,
branch_ids=branch_ids,
share_weight=branch_conv_shared)
conv2 = cls.deform_conv_shared(relu2,
name=name + "_conv2",
conv_offset=conv2_offset,
num_filter=filter // 4,
kernel=(3, 3),
pad=dilate,
stride=stride,
dilate=dilate,
num_deformable_group=4,
branch_ids=branch_ids,
share_weight=branch_conv_shared)
bn3 = cls.bn_shared(conv2,
name=name + "_bn3",
normalizer=norm,
branch_ids=branch_ids,
share_weight=branch_bn_shared)
relu3 = [X.relu(bn) for bn in bn3]
conv3 = cls.conv_shared(relu3,
name=name + "_conv3",
num_filter=filter,
kernel=(1, 1),
branch_ids=branch_ids,
share_weight=branch_conv_shared)
if proj:
shortcut = cls.conv_shared(relu1,
name=name + "_sc",
num_filter=filter,
kernel=(1, 1),
branch_ids=branch_ids,
share_weight=branch_conv_shared)
else:
shortcut = data
return [X.add(conv3_i, shortcut_i, name=name + "_plus_branch{}".format(i)) \
for i, conv3_i, shortcut_i in zip(branch_ids, conv3, shortcut)]
