def get_symbol(num_classes=10, flag='training', add_stn=False, **kwargs):
data = sym.Variable('data')
if add_stn:
data = sym.SpatialTransformer(data=data,
loc=get_loc(data),
target_shape=(28, 28),
transform_type="affine",
sampler_type="bilinear")
# first conv
conv1 = sym.Convolution(data=data, kernel=(5, 5), num_filter=10)
relu1 = sym.Activation(data=conv1, act_type="relu")
pool1 = sym.Pooling(data=relu1,
pool_type="max",
kernel=(2, 2),
stride=(2, 2))
# second conv
conv2 = sym.Convolution(data=pool1, kernel=(5, 5), num_filter=20)
relu2 = sym.Activation(data=conv2, act_type="relu")
pool2 = sym.Pooling(data=relu2,
pool_type="max",
kernel=(2, 2),
stride=(2, 2))
drop1 = mx.sym.Dropout(data=pool2)
# first fullc
flatten = sym.Flatten(data=drop1)
fc1 = sym.FullyConnected(data=flatten, num_hidden=50)
relu3 = sym.Activation(data=fc1, act_type="relu")
# second fullc
drop2 = mx.sym.Dropout(data=relu3, mode=flag)
fc2 = sym.FullyConnected(data=drop2, num_hidden=num_classes)
# loss
lenet = sym.SoftmaxOutput(data=fc2, name='softmax')
return lenet
def get_loc(data, attr={'lr_mult': '0.01'}):
"""
the localisation network in lenet-stn, it will increase acc about more than 1%,
when num-epoch >=15
"""
loc = sym.Convolution(data=data,
num_filter=8,
kernel=(7, 7),
stride=(1, 1))
loc = sym.Activation(data=loc, act_type='relu')
loc = sym.Pooling(data=loc, kernel=(2, 2), stride=(2, 2), pool_type='max')
loc = sym.Convolution(data=loc,
num_filter=10,
kernel=(5, 5),
stride=(1, 1))
loc = sym.Activation(data=loc, act_type='relu')
loc = sym.Pooling(data=loc, kernel=(2, 2), stride=(2, 2), pool_type='max')
loc = sym.FullyConnected(data=loc,
num_hidden=32,
name="stn_loc_fc1",
attr=attr)
loc = sym.Activation(data=loc, act_type='relu')
# loc = sym.Flatten(data=loc)
loc = sym.FullyConnected(data=loc,
num_hidden=6,
name="stn_loc_fc2",
attr=attr)
return loc
def _conv(x,
name,
channels,
kernel=1,
stride=1,
pad=0,
dilate=1,
groups=1,
no_bias=False,
norm_layer=None,
norm_kwargs=None,
act_type=None):
if norm_kwargs is None:
norm_kwargs = {}
y = sym.Convolution(x,
num_filter=channels,
kernel=(kernel, kernel),
stride=(stride, stride),
pad=(pad, pad),
dilate=(dilate, dilate),
num_group=groups,
no_bias=no_bias,
name=f'{name}',
attr={
'__init__':
mx.init.Xavier(rnd_type='uniform',
factor_type='in',
magnitude=1.)
})
if norm_layer is not None:
y = norm_layer(y, name=f'{name}_bn', **norm_kwargs)
y = _activate(y, act_type, name)
return y
def getSSD():
"""
This is for generation of ssd network symbols
:return:
"""
net = sym.Variable('data')
net = sym.Convolution(data=net, num_filter=128)
pass
def _deformable_conv(x,
name,
channels,
kernel=1,
stride=1,
pad=0,
groups=1,
dilate=1,
no_bias=False,
num_deformable_group=1,
offset_use_bias=True,
norm_layer=None,
norm_kwargs=None,
act_type=None):
if norm_kwargs is None:
norm_kwargs = {}
offset_channels = 2 * (kernel**2) * num_deformable_group
offset = sym.Convolution(x,
num_filter=offset_channels,
kernel=(kernel, kernel),
stride=(stride, stride),
pad=(pad, pad),
num_group=groups,
dilate=(dilate, dilate),
no_bias=not offset_use_bias,
cudnn_off=True,
name=f'{name}_offset')
y = sym.contrib.DeformableConvolution(
x,
offset=offset,
num_filter=channels,
kernel=(kernel, kernel),
stride=(stride, stride),
pad=(pad, pad),
num_group=groups,
dilate=(dilate, dilate),
no_bias=no_bias,
num_deformable_group=num_deformable_group,
name=f'{name}',
attr={
'__init__':
mx.init.Xavier(rnd_type='uniform', factor_type='in', magnitude=1.)
})
if norm_layer is not None:
y = norm_layer(y, name=f'{name}_bn', **norm_kwargs)
y = _activate(y, act_type, name)
return y
def getConvLayer(data,
name,
num_filter,
kernel_size,
pad=(0, 0),
stride=(1, 1),
activation='relu',
bn=True):
"""
return a conv layer with act, batchnorm, and pooling layers, if any
:return:
"""
convunit = sym.Convolution(data=data,
num_filter=num_filter,
pad=pad,
stride=stride,
kernel=kernel_size,
name=name + "conv")
if bn:
convunit = sym.BatchNorm(data=convunit, name=name + "bn")
convunit = sym.Activation(data=convunit,
act_type=activation,
name=name + "act")
return convunit
def _cbam(x, name, channels, reduction, act_type='relu', spatial_dilate=0):
"""
启用 CBAM
:param x: 输入
:param name: operator name
:param channels: 输出 channels
:param reduction: MLP reduction
:param act_type: MLP activation
:param spatial_dilate: spatial_dilate > 0，对于 spatial 新添加一个 dilate conv
:return:
"""
# =============================== channel
# Pooling [N, C, 1, 1]
max_pool = sym.Pooling(x,
pool_type='max',
global_pool=True,
name=f'{name}_max')
avg_pool = sym.Pooling(x,
pool_type='avg',
global_pool=True,
name=f'{name}_avg')
# MLP FC1 [N, C // reduction, 1, 1]
mlp_fc1_weight = sym.Variable(f'{name}_mlp_fc1_weight',
shape=(channels // reduction, 0, 1, 1))
mlp_fc1_bias = sym.Variable(f'{name}_mlp_fc1_bias',
shape=(channels // reduction, ),
init=mx.init.Constant(0.))
max_pool = sym.Convolution(max_pool,
num_filter=channels // reduction,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
weight=mlp_fc1_weight,
bias=mlp_fc1_bias,
name=f'{name}_max_fc1')
avg_pool = sym.Convolution(avg_pool,
num_filter=channels // reduction,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
weight=mlp_fc1_weight,
bias=mlp_fc1_bias,
name=f'{name}_avg_fc1')
max_pool = _activate(max_pool, act_type, name=f'{name}_max_fc1')
avg_pool = _activate(avg_pool, act_type, name=f'{name}_avg_fc1')
# MLP FC2 [N, C, 1, 1]
mlp_fc2_weight = sym.Variable(f'{name}_mlp_fc2_weight',
shape=(channels, 0, 1, 1))
mlp_fc2_bias = sym.Variable(f'{name}_mlp_fc2_bias',
shape=(channels, ),
init=mx.init.Constant(0.))
max_pool = sym.Convolution(max_pool,
num_filter=channels,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
weight=mlp_fc2_weight,
bias=mlp_fc2_bias,
name=f'{name}_max_fc2')
avg_pool = sym.Convolution(avg_pool,
num_filter=channels,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
weight=mlp_fc2_weight,
bias=mlp_fc2_bias,
name=f'{name}_avg_fc2')
channel_attention = _activate(max_pool + avg_pool,
'sigmoid',
name=f'{name}_channel')
y = sym.broadcast_mul(x, channel_attention, name=f'{name}_channel_out')
# =============================== spatial
max_spatial = sym.max(y, axis=1, keepdims=True, name=f'{name}_max_spatial')
avg_spatial = sym.mean(y,
axis=1,
keepdims=True,
name=f'{name}_avg_spatial')
spatial = sym.Concat(max_spatial,
avg_spatial,
dim=1,
name=f'{name}_spatial_concat')
if spatial_dilate > 0:
dilate_spatial = _conv(y,
f'{name}_spatial_dilate{spatial_dilate}',
1,
kernel=3,
stride=1,
pad=spatial_dilate,
dilate=spatial_dilate,
no_bias=False)
spatial = sym.Concat(spatial,
dilate_spatial,
dim=1,
name=f'{name}_spatial_concat_dilate')
spatial_attention = _conv(spatial,
f'{name}_spatial_conv',
1,
kernel=7,
stride=1,
pad=3,
groups=1,
act_type='sigmoid')
y = sym.broadcast_mul(y, spatial_attention, name=f'{name}_spatial_out')
return y
def getPredBranch():
net = sym.Convolution()
pass