def identity_block(input_tensor, kernel_size, filters, stage, block,model_name='resnet50_v20',dilate=(1,1)):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
conv_name_base = model_name+'_stage' + str(stage)
bn_name_base = conv_name_base
x = Norm(input_tensor,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+0))
x = Activation(x,name=bn_name_base + '_activation'+str(block+0),act_type='relu')
x = Convolution(x,kernel=(1,1),num_filter=filters1,no_bias=True,name=conv_name_base + '_conv'+str(block+1))
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+1))
x = Activation(x,name=bn_name_base + '_activation'+str(block+1),act_type='relu')
x = Convolution(x,kernel=(3,3),pad=dilate,no_bias=True,num_filter=filters2,name=conv_name_base + '_conv'+str(block+2),dilate=dilate)
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+2))
x = Activation(x,name=bn_name_base + '_activation'+str(block+2),act_type='relu')
x = Convolution(x,kernel=(1,1),num_filter=filters3,no_bias=True,name=conv_name_base + '_conv'+str(block+3))
x = x + input_tensor
return x
def residual_unit(data, num_filter, stride, dilate, dim_match, name):
s = stride
d = dilate
bn1 = BN(data=data,
fix_gamma=fix_gamma,
use_global_stats=use_global_stats,
eps=eps,
momentum=bn_mom,
name=name + '_bn1')
act1 = Relu(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = Conv(data=act1,
num_filter=int(num_filter * 0.25),
kernel=(1, 1),
no_bias=True,
name=name + '_conv1')
bn2 = BN(data=conv1,
fix_gamma=fix_gamma,
use_global_stats=use_global_stats,
eps=eps,
momentum=bn_mom,
name=name + '_bn2')
act2 = Relu(data=bn2, act_type='relu', name=name + '_relu2')
conv2 = Conv(data=act2,
num_filter=int(num_filter * 0.25),
kernel=(3, 3),
pad=(d, d),
stride=(s, s),
dilate=(d, d),
no_bias=True,
name=name + '_conv2')
bn3 = BN(data=conv2,
fix_gamma=fix_gamma,
use_global_stats=use_global_stats,
eps=eps,
momentum=bn_mom,
name=name + '_bn3')
act3 = Relu(data=bn3, act_type='relu', name=name + '_relu3')
conv3 = Conv(data=act3,
num_filter=num_filter,
kernel=(1, 1),
no_bias=True,
name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = Conv(data=act1,
num_filter=num_filter,
kernel=(1, 1),
stride=(s, s),
no_bias=True,
name=name + '_sc')
shortcut._set_attr(mirror_stage='True')
return conv3 + shortcut
def atrous_spatial_pyramid_pooling(feat, rate, aspp_with_separable_conv, oc_context=False):
conv_1x1 = Conv(feat, num_filter=256, kernel=(1, 1), name="aspp_1x1")
conv_1x1 = BN(conv_1x1, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_1x1_bn", eps=eps, **args)
conv_1x1 = Relu(conv_1x1, act_type='relu', name='aspp_1x1_relu')
if aspp_with_separable_conv:
conv_3x3_d6 = Sepconv(data=feat, in_channel=2048, num_filter=256, stride=1,
dilate=6 * rate, name="aspp_3x3_d6")
conv_3x3_d6 = BN(conv_3x3_d6, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d6_bn", eps=eps, **args)
conv_3x3_d6 = Relu(conv_3x3_d6, act_type='relu', name='aspp_3x3_d6_relu')
conv_3x3_d12 = Sepconv(data=feat, in_channel=2048, num_filter=256, stride=1,
dilate=12 * rate, name="aspp_3x3_d12")
conv_3x3_d12 = BN(conv_3x3_d12, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d12_bn", eps=eps, **args)
conv_3x3_d12 = Relu(conv_3x3_d12, act_type='relu', name='aspp_3x3_d12_relu')
conv_3x3_d18 = Sepconv(data=feat, in_channel=2048, num_filter=256, stride=1,
dilate=18 * rate, name="aspp_3x3_d18")
conv_3x3_d18 = BN(conv_3x3_d18, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d18_bn", eps=eps, **args)
conv_3x3_d18 = Relu(conv_3x3_d18, act_type='relu', name='aspp_3x3_d18_relu')
else:
conv_3x3_d6 = Conv(feat, num_filter=256, kernel=(3, 3), dilate=(6 * rate, 6 * rate),
pad=(6 * rate, 6 * rate), name="aspp_3x3_d6")
conv_3x3_d6 = BN(conv_3x3_d6, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d6_bn", eps=eps)
conv_3x3_d6 = Relu(conv_3x3_d6, act_type='relu', name='aspp_3x3_d6_relu')
conv_3x3_d12 = Conv(feat, num_filter=256, kernel=(3, 3), dilate=(12 * rate, 12 * rate),
pad=(12 * rate, 12 * rate), name="aspp_3x3_d12")
conv_3x3_d12 = BN(conv_3x3_d12, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d12_bn", eps=eps)
conv_3x3_d12 = Relu(conv_3x3_d12, act_type='relu', name='aspp_3x3_d12_relu')
conv_3x3_d18 = Conv(feat, num_filter=256, kernel=(3, 3), dilate=(18 * rate, 18 * rate),
pad=(18 * rate, 18 * rate), name="aspp_3x3_d18")
conv_3x3_d18 = BN(conv_3x3_d18, use_global_stats=use_global_stats, fix_gamma=fix_gamma,
momentum=bn_mom, name="aspp_3x3_d18_bn", eps=eps)
conv_3x3_d18 = Relu(conv_3x3_d18, act_type='relu', name='aspp_3x3_d18_relu')
if oc_context:
gap = oc_context_block(feat, 128, 256, 256, resample_rate=2)
else:
gap = Pool(feat, kernel=(1, 1), global_pool=True, pool_type="avg", name="aspp_gap")
gap = Conv(gap, num_filter=256, kernel=(1, 1), name="aspp_gap_1x1")
gap = BN(gap, use_global_stats=use_global_stats, fix_gamma=fix_gamma, momentum=bn_mom,
name="aspp_gap_1x1_bn", eps=eps, **args)
if not oc_context:
gap = Relu(gap, act_type='relu', name='aspp_gap_1x1_relu')
gap = broadcast_like(gap, conv_1x1, name="aspp_gap_broadcast")
aspp = concat(conv_1x1, conv_3x3_d6, conv_3x3_d12, conv_3x3_d18, gap, dim=1, name="aspp_concat")
aspp_1x1 = Conv(aspp, num_filter=256, kernel=(1, 1), name="aspp_concat_1x1")
aspp_1x1 = BN(aspp_1x1, use_global_stats=use_global_stats, fix_gamma=fix_gamma, momentum=bn_mom,
name="aspp_concat_1x1_bn", eps=eps, **args)
aspp_1x1._set_attr(mirror_stage='True')
aspp_1x1 = Relu(aspp_1x1, act_type='relu', name='aspp_concat_1x1_relu')
return aspp_1x1
def detai_net(inputs,classes=1000,use_att=False,model_name='resnetv20'):
"""Instantiates the ResNet50 architecture.
"""
if use_att:
x = inputs[0]
else:
x = inputs
x = Norm(x,fix_gamma=True,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm0')
x = Convolution(x,
num_filter=64, kernel=(7, 7), stride=(2, 2), pad=(3,3), no_bias=True, name=model_name+'_conv0')
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm1')
x = Activation(x,name=model_name+'_relu0',act_type='relu')
x = Pooling(x,kernel=(3,3),stride=(2,2),pad=(1,1),pool_type='max',name=model_name+'_pool0')
x = conv_block(x, 3, [64, 64, 256], stage=1, block=0, strides=(1, 1),model_name=model_name)
x = identity_block(x, 3, [64, 64, 256], stage=1, block=3,model_name=model_name)
x = identity_block(x, 3, [64, 64, 256], stage=1, block=6,model_name=model_name)
x = conv_block(x, 3, [128, 128, 512], stage=2, block=0,model_name=model_name)
x = identity_block(x, 3, [128, 128, 512], stage=2, block=3,model_name=model_name)
x = identity_block(x, 3, [128, 128, 512], stage=2, block=6,model_name=model_name)
x = identity_block(x, 3, [128, 128, 512], stage=2, block=9,model_name=model_name)
x = conv_block(x, 3, [256, 256, 1024], stage=3, block=0,model_name=model_name)
x = identity_block(x, 3, [256, 256, 1024], stage=3, block=3,model_name=model_name)
x = identity_block(x, 3, [256, 256, 1024], stage=3, block=6,model_name=model_name)
x = identity_block(x, 3, [256, 256, 1024], stage=3, block=9,model_name=model_name)
x = identity_block(x, 3, [256, 256, 1024], stage=3, block=12,model_name=model_name)
x = identity_block(x, 3, [256, 256, 1024], stage=3, block=15,model_name=model_name)
x = conv_block(x, 3, [512, 512, 2048], stage=4, block=0,model_name=model_name)
x = identity_block(x, 3, [512, 512, 2048], stage=4, block=3,model_name=model_name)
x = identity_block(x, 3, [512, 512, 2048], stage=4, block=6,model_name=model_name)
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm2')
x = Activation(x,name=model_name+'_relu1',act_type='relu')
x = Pooling(x,kernel=(3,3),stride=(2, 2),pool_type='avg',global_pool=True,name=model_name+'_pool1')
x = Flatten(x,name=model_name+'_flatten0')
if use_att:
x = mx.sym.Concat(x,inputs[1],dim=-1)
weight = mx.sym.Variable(model_name+'_concat_dense_weight',shape=(classes,4096))
x = FullyConnected(x,num_hidden=classes,weight=weight,no_bias=True,name=model_name+'_dense1')
else:
x = FullyConnected(x,num_hidden=classes,name=model_name+'_dense1')
return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2),model_name='resnet50_v20',dilate=(1,1)):
"""A block that has a conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
And the shortcut should have strides=(2,2) as well
"""
filters1, filters2, filters3 = filters
conv_name_base = model_name+'_stage' + str(stage)
bn_name_base = conv_name_base
x = Norm(input_tensor,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+0))
x_sc = Activation(x,name=bn_name_base + '_activation'+str(block+0),act_type='relu')
x = Convolution(x_sc,kernel=(1,1),num_filter=filters1,no_bias=True,name=conv_name_base + '_conv'+str(block+0))
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+1))
x = Activation(x,name=bn_name_base + '_activation'+str(block+1),act_type='relu')
x = Convolution(x,kernel=(kernel_size,kernel_size),stride=strides,pad=dilate,num_filter=filters2,
no_bias=True, dilate=dilate,name=conv_name_base + '_conv'+str(block+1))
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=bn_name_base + '_batchnorm'+str(block+2))
x = Activation(x,name=bn_name_base + '_activation'+str(block+2),act_type='relu')
x = Convolution(x,kernel=(1,1),num_filter=filters3,no_bias=True,name=conv_name_base + '_conv'+str(block+2))
shortcut = Convolution(x_sc,kernel=(1,1),stride=strides,num_filter=filters3,
no_bias=True,name=conv_name_base + '_conv'+str(block+3))
x = x + shortcut
return x
def ResNet18_V2(inputs,classes=1000,model_name='resnetv20'):
"""Instantiates the ResNet18 architecture.
"""
x = inputs
x = Norm(x,fix_gamma=True,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm0')
x = Convolution(x,
num_filter=64, kernel=(7, 7), stride=(2, 2), pad=(3,3), no_bias=True, name=model_name+'_conv0')
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm1')
x = Activation(x,name=model_name+'_relu0',act_type='relu')
x = Pooling(x,kernel=(3,3),stride=(2,2),pad=(1,1),pool_type='max',name=model_name+'_pool0')
x = conv_block(x, 3, [64, 64], stage=1, block=0, strides=(1, 1),short_connect=False,model_name=model_name)
x = identity_block(x, 3, [64, 64], stage=1, block=2,model_name=model_name)
x = conv_block(x, 3, [128, 128], stage=2, block=0,model_name=model_name)
x = identity_block(x, 3, [128, 128], stage=2, block=3,model_name=model_name)
x = conv_block(x, 3, [256, 256], stage=3, block=0,model_name=model_name)
x = identity_block(x, 3, [256, 256], stage=3, block=3,model_name=model_name)
x = conv_block(x, 3, [512, 512], stage=4, block=0, model_name=model_name)
x = identity_block(x, 3, [512, 512], stage=4, block=3, model_name=model_name)
x = Norm(x,fix_gamma=False,use_global_stats=False,eps=1e-5,name=model_name+'_batchnorm2')
x = Activation(x,name=model_name+'_relu1',act_type='relu')
x = Pooling(x,kernel=(3,3),stride=(2, 2),pool_type='avg',global_pool=True,name=model_name+'_pool1')
x = Flatten(x,name=model_name+'_flatten0')
xl2 = mx.sym.norm(x,axis=1,keepdims=True)
xl2 = mx.sym.clip(xl2,0,8)
x1 = mx.sym.L2Normalization(x)
x1 = mx.sym.broadcast_mul(x1,xl2)
weight = mx.sym.Variable(model_name+'_dense1_weight')
bias = mx.sym.Variable(model_name+'_dense1_bias')
output = FullyConnected(x,num_hidden=classes,weight=weight,bias=bias,name=model_name+'_dense1')
# weight1 = mx.sym.L2Normalization(weight)
output1 = FullyConnected(x1,num_hidden=classes,weight=weight,bias=bias,name=model_name+'_dense2')
return output,output1
#
def atten_net(inputs, classes=200, model_name='resnet50_v10'):
"""Instantiates the ResNet50 architecture.
"""
x = inputs
x = Convolution(x,
num_filter=64,
kernel=(7, 7),
stride=(2, 2),
pad=(3, 3),
no_bias=True,
name=model_name + '_conv0')
x = Norm(x,
fix_gamma=False,
use_global_stats=False,
eps=1e-5,
name=model_name + '_batchnorm0')
x = Activation(x, name=model_name + '_relu0', act_type='relu')
x = Pooling(x,
kernel=(3, 3),
stride=(2, 2),
pad=(1, 1),
pool_type='max',
name=model_name + '_pool0')
x = conv_block(x,
3, [64, 64, 256],
stage=1,
block=0,
strides=(1, 1),
model_name=model_name)
x = identity_block(x,
3, [64, 64, 256],
stage=1,
block=4,
model_name=model_name)
x = identity_block(x,
3, [64, 64, 256],
stage=1,
block=7,
model_name=model_name)
x = conv_block(x,
3, [128, 128, 512],
stage=2,
block=0,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=4,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=7,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=10,
model_name=model_name)
x = conv_block(x,
3, [256, 256, 1024],
stage=3,
block=0,
strides=(1, 1),
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=4,
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=7,
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=10,
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=13,
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=16,
dilate=(2, 2),
model_name=model_name)
x = conv_block(x,
3, [512, 512, 2048],
stage=4,
block=0,
strides=(1, 1),
dilate=(2, 2),
model_name=model_name)
x = identity_block(x,
3, [512, 512, 2048],
stage=4,
block=4,
dilate=(1, 1),
model_name=model_name)
x = identity_block(x,
3, [512, 512, 2048],
stage=4,
block=7,
dilate=(1, 1),
model_name=model_name)
att_fea = x
x = Pooling(x,
kernel=(3, 3),
stride=(2, 2),
pool_type='avg',
global_pool=True,
name=model_name + '_pool1')
x = flatten(x)
weight = mx.sym.Variable(model_name + '_dense1_weight')
x = FullyConnected(x,
num_hidden=classes,
weight=weight,
name=model_name + '_dense1')
# x = SoftmaxOutput(x, name = 'softmax')
return x, att_fea, weight
def Conv_BN_ACT(data, num_filter, kernel, pad=(0, 0), stride=(1,1), name=None, no_bias=True, num_group=1, act_type='relu'):
conv = Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, num_group=num_group, no_bias=no_bias, name=('%s_conv' % name))
b_act = BN_ACT(data=conv, act_type=act_type, name=name)
return b_act
def Conv_BN(data, num_filter, kernel, pad=(0, 0), stride=(1,1), name=None, no_bias=True, num_group=1, zero_init_gamma=False):
conv = Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, num_group=num_group, no_bias=no_bias, name=('%s_conv' % name))
bn = BatchNorm(data=conv, zero_init_gamma=zero_init_gamma, name=('%s_bn' % name))
return bn
def ResNet50_V2(inputs, classes=1000, batch_size=144, model_name='resnetv20'):
"""Instantiates the ResNet50 architecture.
"""
x = inputs[0]
lam = inputs[1]
x = Norm(x,
fix_gamma=True,
use_global_stats=False,
eps=1e-5,
name=model_name + '_batchnorm0')
x = Convolution(x,
num_filter=64,
kernel=(7, 7),
stride=(2, 2),
pad=(3, 3),
no_bias=True,
name=model_name + '_conv0')
x = Norm(x,
fix_gamma=False,
use_global_stats=False,
eps=1e-5,
name=model_name + '_batchnorm1')
x = Activation(x, name=model_name + '_relu0', act_type='relu')
x = Pooling(x,
kernel=(3, 3),
stride=(2, 2),
pad=(1, 1),
pool_type='max',
name=model_name + '_pool0')
x = conv_block(x,
3, [64, 64, 256],
stage=1,
block=0,
strides=(1, 1),
model_name=model_name)
x = identity_block(x,
3, [64, 64, 256],
stage=1,
block=3,
model_name=model_name)
x = identity_block(x,
3, [64, 64, 256],
stage=1,
block=6,
model_name=model_name)
x_f = mx.sym.slice(x,
begin=(None, None, None, None),
end=(None, None, None, None),
step=(-1, 1, 1, 1))
lam1 = mx.sym.slice(lam, begin=(0), end=(1))
lam2 = mx.sym.slice(lam, begin=(1), end=(2))
x_mix = mx.sym.broadcast_mul(x, lam1) + broadcast_mul(x_f, lam2)
x = mx.sym.concat(x, x_mix, dim=0)
x = conv_block(x,
3, [128, 128, 512],
stage=2,
block=0,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=3,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=6,
model_name=model_name)
x = identity_block(x,
3, [128, 128, 512],
stage=2,
block=9,
model_name=model_name)
x = conv_block(x,
3, [256, 256, 1024],
stage=3,
block=0,
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=3,
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=6,
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=9,
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=12,
model_name=model_name)
x = identity_block(x,
3, [256, 256, 1024],
stage=3,
block=15,
model_name=model_name)
x = conv_block(x,
3, [512, 512, 2048],
stage=4,
block=0,
model_name=model_name)
x = identity_block(x,
3, [512, 512, 2048],
stage=4,
block=3,
model_name=model_name)
x = identity_block(x,
3, [512, 512, 2048],
stage=4,
block=6,
model_name=model_name)
x = Norm(x,
fix_gamma=False,
use_global_stats=False,
eps=1e-5,
name=model_name + '_batchnorm2')
x = Activation(x, name=model_name + '_relu1', act_type='relu')
x = Pooling(x,
kernel=(3, 3),
stride=(2, 2),
pool_type='avg',
global_pool=True,
name=model_name + '_pool1')
x = Flatten(x, name=model_name + '_flatten0')
# x = mx.sym.L2Normalization(x)
weight = mx.sym.Variable(model_name + '_dense_weight')
# weight = mx.sym.L2Normalization(weight)
output = FullyConnected(x,
num_hidden=classes,
weight=weight,
no_bias=True,
name=model_name + '_dense1')
#
output1 = mx.sym.slice_axis(output, axis=0, begin=0, end=batch_size)
mix_output = mx.sym.slice_axis(output,
axis=0,
begin=batch_size,
end=2 * batch_size)
output1_f = mx.sym.slice(output1,
begin=(None, None),
end=(None, None),
step=(-1, 1))
output_mix = mx.sym.broadcast_mul(output1, lam1) + broadcast_mul(
output1_f, lam2)
# x = mx.sym.L2Normalization(x)
# weight = mx.sym.Variable(model_name+'_dense1_weight',shape=(classes,2048))
# weight = mx.sym.L2Normalization(weight)
# alpha_w = mx.sym.Variable(model_name+'_alpha',shape=(1,1),init=mx.init.Constant(3))
# alpha_w = mx.sym.clip(alpha_w,2,6)
# weight = mx.sym.broadcast_mul(weight, alpha_w)
# x = mx.sym.broadcast_mul(x, alpha_w+1)
# weight = weight * alpha
# x = x * (alpha+2)
# weight = mx.sym.expand_dims(weight,0)
# x = mx.sym.expand_dims(x,1)
# x = mx.sym.tile(x,(1,classes,1))
# dis = mx.sym.broadcast_minus(x,weight)
# x = -mx.sym.sum(dis*dis,axis=2)
# x = x / alpha
return output1, output_mix, mix_output
#inputs = mx.sym.Variable('data')
#lam = mx.sym.Variable('lam')
#outputs = ResNet50_V2([inputs,lam],200,batch_size=144)
#net = mx.gluon.SymbolBlock(outputs,[inputs,lam])
##import gluoncv
##gluoncv.utils.viz.plot_network(net,shape=)
#
#a = mx.viz.plot_network(outputs[1], shape={'data':(144,3,224,224),'lam':(2,)},save_format='png')
#a.view('12')