def test_lambda():
net1 = mx.gluon.nn.HybridSequential()
net1.add(nn.Activation('tanh'), nn.LeakyReLU(0.1))
net2 = mx.gluon.nn.HybridSequential()
op3 = lambda F, x, *args: F.LeakyReLU(x, *args, slope=0.1)
net2.add(nn.HybridLambda('tanh'), nn.HybridLambda(op3))
op4 = lambda x: mx.nd.LeakyReLU(x, slope=0.1)
net3 = mx.gluon.nn.Sequential()
net3.add(nn.Lambda('tanh'), nn.Lambda(op4))
input_data = mx.nd.random.uniform(shape=(2, 3, 5, 7))
out1, out2, out3 = net1(input_data), net2(input_data), net3(input_data)
assert_almost_equal(out1.asnumpy(), out2.asnumpy())
assert_almost_equal(out1.asnumpy(), out3.asnumpy())
def __init__(self, act_func, **kwargs):
super(Activation, self).__init__(**kwargs)
if act_func == "relu":
self.act = nn.Activation('relu')
elif act_func == "relu6":
self.act = ReLU6()
elif act_func == "hard_sigmoid":
self.act = HardSigmoid()
elif act_func == "swish":
self.act = nn.Swish()
elif act_func == "hard_swish":
self.act = HardSwish()
elif act_func == "leaky":
self.act = nn.LeakyReLU(alpha=0.375)
else:
raise NotImplementedError
def tiny_darknet(output_size=125, token_op=False):
net = _nn.HybridSequential()
for idx, f in enumerate([16, 32, 64, 128, 256, 512, 1024, 1024], 1):
# Using NHWC would eliminate transposes before and after the base
# model. However, this is currently not supported by Conv2D in mxnet,
# so we have to use NCHW for now.
layout = 'NCHW'
c_axis = 3 if layout == 'NHWC' else 1
net.add(_nn.Conv2D(channels=f,
kernel_size=(3, 3),
use_bias=False,
layout=layout,
prefix='conv%d_' % (idx - 1),
padding=(1, 1)))
net.add(_nn.BatchNorm(axis=c_axis,
prefix='batchnorm%d_' % (idx - 1),
momentum=0.9,
epsilon=1e-5))
net.add(_nn.LeakyReLU(0.1, prefix='leakyrelu%d_' % (idx - 1)))
if idx < 6:
strides = (2, 2)
net.add(_nn.MaxPool2D(pool_size=(2, 2),
strides=strides,
layout=layout,
prefix='pool%d_' % (idx - 1),
))
elif idx == 6:
strides = (1, 1)
net.add(_nn.MaxPool2D(pool_size=(2, 2),
strides=strides,
layout=layout,
padding=(1, 1),
ceil_mode=False,
prefix='pool%d_' % (idx - 1),
))
net.add(_SpecialCrop(prefix='specialcrop%d' % (idx - 1)))
if output_size is not None:
net.add(_nn.Conv2D(channels=output_size,
kernel_size=(1, 1),
prefix='conv8_',
layout=layout))
return net
def __init__(self, dimension, num_channels, num_layers):
super(CnnEmbedding, self).__init__()
with self.name_scope():
self.convolutions = nn.Sequential()
for _ in range(num_layers - 1):
self.convolutions.add(
nn.Conv2D(channels=num_channels,
kernel_size=3,
padding=1,
weight_initializer=mx.init.Xavier()))
self.convolutions.add(nn.LeakyReLU(0.2))
# No ReLu for last convolution to allow for negative features.
self.convolutions.add(
nn.Conv2D(channels=dimension,
kernel_size=3,
padding=1,
weight_initializer=mx.init.Xavier()))
def __init__(self,
in_feats,
out_feats,
num_heads,
feat_drop=0.,
attn_drop=0.,
negative_slope=0.2,
residual=False,
activation=None):
super(GATConv, self).__init__()
self._num_heads = num_heads
self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
self._in_feats = in_feats
self._out_feats = out_feats
with self.name_scope():
if isinstance(in_feats, tuple):
self.fc_src = nn.Dense(out_feats * num_heads, use_bias=False,
weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
in_units=self._in_src_feats)
self.fc_dst = nn.Dense(out_feats * num_heads, use_bias=False,
weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
in_units=self._in_dst_feats)
else:
self.fc = nn.Dense(out_feats * num_heads, use_bias=False,
weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)),
in_units=in_feats)
self.attn_l = self.params.get('attn_l',
shape=(1, num_heads, out_feats),
init=mx.init.Xavier(magnitude=math.sqrt(2.0)))
self.attn_r = self.params.get('attn_r',
shape=(1, num_heads, out_feats),
init=mx.init.Xavier(magnitude=math.sqrt(2.0)))
self.feat_drop = nn.Dropout(feat_drop)
self.attn_drop = nn.Dropout(attn_drop)
self.leaky_relu = nn.LeakyReLU(negative_slope)
if residual:
if in_feats != out_feats:
self.res_fc = nn.Dense(out_feats * num_heads, use_bias=False,
weight_initializer=mx.init.Xavier(
magnitude=math.sqrt(2.0)),
in_units=in_feats)
else:
self.res_fc = Identity()
else:
self.res_fc = None
self.activation = activation
def __init__(self, alg, use_bias, **kwargs):
super(ConvBNSumAct, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(channels=64,
kernel_size=(3, 3),
strides=1,
use_bias=use_bias)
self.conv1 = nn.Conv2D(channels=64, kernel_size=(3, 3), strides=1)
self.conv1.share_parameters(self.conv0.collect_params())
self.bn = nn.BatchNorm()
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
else:
self.act = nn.Activation(activation=alg)
def __init__(self, image_size=784, h_dim=400, z_dim=20):
super(VAE, self).__init__()
with self.name_scope():
encoder = nn.HybridSequential()
encoder.add(
nn.Dense(h_dim),
nn.LeakyReLU(0.2),
nn.Dense(z_dim * 2) # 2 for mean and variance.
)
decoder = nn.HybridSequential()
decoder.add(
nn.Dense(h_dim, activation='relu'),
nn.Dense(image_size, activation='sigmoid')
)
self.encoder = encoder
self.decoder = decoder
def _encoder_module(in_channesl, out_channels, norm_layer=nn.BatchNorm, norm_kwargs=None):
"""
:param in_channesl:
:param out_channels:
:param act_fn:
:param norm_layer: None
:param norm_kwargs:
:return:
"""
module = nn.HybridSequential()
module.add(nn.LeakyReLU(0.2))
module.add(nn.Conv2D(channels=out_channels, kernel_size=4, strides=2, padding=1, in_channels=in_channesl))
if norm_layer is not None:
norm_kwargs = {} if norm_kwargs is None else {**norm_kwargs}
norm_kwargs['in_channels'] = out_channels
module.add(nn.BatchNorm(in_channels=out_channels))
return module
def __init__(self, slope=0.2, nfactor=1.0, **kwargs):
super(D_Net, self).__init__(**kwargs)
with self.name_scope():
self.d_net = nn.HybridSequential()
self.d_net.add(
nn.Conv2D(channels=intround(64 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 256->128
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(128 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 128->64
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(256 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 64->32
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 32->16
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 16->8
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 8->4
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=2,
kernel_size=(4, 4),
strides=(1, 1),
padding=(0, 0),
use_bias=True), # 4->1
nn.Flatten())
return
def __init__(self,
channels,
odd_pointwise,
avg_pool_size,
cls_activ,
alpha=0.1,
bn_use_global_stats=False,
in_channels=3,
in_size=(224, 224),
classes=1000,
**kwargs):
super(DarkNet, self).__init__(**kwargs)
self.in_size = in_size
self.classes = classes
with self.name_scope():
self.features = nn.HybridSequential(prefix="")
for i, channels_per_stage in enumerate(channels):
stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
with stage.name_scope():
for j, out_channels in enumerate(channels_per_stage):
stage.add(dark_convYxY(
in_channels=in_channels,
out_channels=out_channels,
bn_use_global_stats=bn_use_global_stats,
alpha=alpha,
pointwise=(len(channels_per_stage) > 1) and not(((j + 1) % 2 == 1) ^ odd_pointwise)))
in_channels = out_channels
if i != len(channels) - 1:
stage.add(nn.MaxPool2D(
pool_size=2,
strides=2))
self.features.add(stage)
self.output = nn.HybridSequential(prefix="")
self.output.add(nn.Conv2D(
channels=classes,
kernel_size=1,
in_channels=in_channels))
if cls_activ:
self.output.add(nn.LeakyReLU(alpha=alpha))
self.output.add(nn.AvgPool2D(
pool_size=avg_pool_size,
strides=1))
self.output.add(nn.Flatten())
def get_D_net(slope=0.2, nfactor=1.0, **kwargs):
d_net = nn.HybridSequential(**kwargs)
with d_net.name_scope():
d_net.add(
nn.Conv2D(channels=intround(64 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 256->128
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(128 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 128->64
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(256 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 64->32
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 32->16
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 16->8
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(512 * nfactor),
kernel_size=(4, 4),
strides=(2, 2),
padding=(1, 1),
use_bias=True), # 8->4
nn.BatchNorm(),
nn.LeakyReLU(alpha=slope),
nn.Conv2D(channels=intround(2),
kernel_size=(4, 4),
strides=(1, 1),
padding=(0, 0),
use_bias=True), # 4->1
)
return d_net
def ConvBNBlock(channels,
kernel_size,
strides,
pad,
use_bias=False,
leaky=True):
blk = nn.HybridSequential()
blk.add(
nn.Conv2D(int(channels),
kernel_size=kernel_size,
strides=strides,
padding=pad,
use_bias=use_bias))
if not use_bias:
blk.add(nn.BatchNorm(in_channels=int(channels)))
if leaky:
blk.add(nn.LeakyReLU(0.1))
return blk
def _conv2d(channel, kernel, padding, stride, num_sync_bn_devices=-1):
"""A common conv-bn-leakyrelu cell"""
cell = nn.HybridSequential(prefix='')
cell.add(
nn.Conv2D(channel,
kernel_size=kernel,
strides=stride,
padding=padding,
use_bias=False))
if num_sync_bn_devices < 1:
cell.add(nn.BatchNorm(epsilon=1e-5, momentum=0.9))
else:
cell.add(
gluon.contrib.nn.SyncBatchNorm(epsilon=1e-5,
momentum=0.9,
num_devices=num_sync_bn_devices))
cell.add(nn.LeakyReLU(0.1))
return cell
def _conv21d(out_channels,
kernel,
padding,
strides,
norm_layer=BatchNorm,
norm_kwargs=None):
"""R(2+1)D from 'A Closer Look at Spatiotemporal Convolutions for Action Recognition'"""
cell = nn.HybridSequential(prefix='R(2+1)D')
if isinstance(strides, int):
strides = (strides, strides, strides)
cell.add(
nn.Conv3D(out_channels,
kernel_size=(1, kernel, kernel),
strides=(1, strides[1], strides[2]),
padding=(0, padding, padding),
use_bias=False,
groups=1))
cell.add(
norm_layer(epsilon=1e-5,
momentum=0.9,
**({} if norm_kwargs is None else norm_kwargs)))
cell.add(nn.LeakyReLU(0.1))
if kernel == 3: # we need to do a special repeat pad as the zeros effect the middle correct 2d pathway
cell.add(
Conv3DRepPad(out_channels,
kernel_size=(kernel, 1, 1),
strides=(strides[0], 1, 1),
padding=(1, 0, 0),
use_bias=False,
groups=out_channels))
else:
cell.add(
nn.Conv3D(out_channels,
kernel_size=(kernel, 1, 1),
strides=(strides[0], 1, 1),
padding=(padding, 0, 0),
use_bias=False,
groups=out_channels))
# cell.add(nn.LeakyReLU(0.1)) # this breaks the imgnet pretrain flow
return cell
def __init__(self,
kernel,
stride,
dilation,
layers,
feature,
channel,
padding,
swap_in=True,
swap_out=True,
arch='',
auto=False,
norm=False,
device=None,
last=True,
flatten=False,
reconstruct=False):
super(Wave2DT, self).__init__()
self.arch = arch
self.swap_out = swap_out
self.swap_in = swap_in
self.reconstruct = reconstruct
self.layers = layers
self.kernel = kernel
self.stride = stride
self.dilation = dilation
self.sequence = []
self.norm = norm
self.channel = channel
with self.name_scope():
self.activation = nn.Activation('relu')
self.tanh = nn.Activation('tanh')
self.sigmoid = nn.Activation('sigmoid')
self.relu = nn.Activation('relu')
self.lrelu = nn.LeakyReLU(0.1)
if swap_out:
self.fc = nn.Dense(1, 'sigmoid', flatten=False)
self.dropout = nn.Dropout(0.5)
if self.norm:
self.norms = []
self.add(layers, channel, kernel, stride, padding, dilation)
if self.norm:
assert (len(self.norms) == len(self.sequence))
def discriminator_block(in_channels,
out_channels,
kernel_size,
stride,
padding=0,
last_layer=False):
net = nn.Sequential()
net.add(
nn.Conv2D(in_channels=in_channels,
channels=out_channels,
kernel_size=kernel_size,
strides=stride,
padding=padding))
if last_layer:
net.add(nn.Activation(activation='sigmoid'))
else:
net.add(nn.BatchNorm())
net.add(nn.LeakyReLU(alpha=0.2))
return net
def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
# Set the default Inputs.
# Default data is (32, 3, 256, 256) to mimic an input of batch_size=128 and a sample image of size 3*256*256.
default_parameters = {"data": (32, 3, 256, 256),
"data_initializer": nd.normal,
"alpha": 0.01,
"run_backward": True,
"dtype": "float32"}
super().__init__(ctx=ctx, warmup=warmup, runs=runs, default_parameters=default_parameters,
custom_parameters=inputs)
self.data = get_mx_ndarray(ctx=self.ctx, in_tensor=self.inputs["data"],
dtype=self.inputs["dtype"],
initializer=self.inputs["data_initializer"],
attach_grad=self.inputs["run_backward"])
self.block = nn.LeakyReLU(alpha=self.inputs["alpha"])
self.block.initialize(ctx=self.ctx)
def __init__(self,
in_channels,
out_channels,
kernel_size,
padding,
bn_use_global_stats,
**kwargs):
super(DarkConv, self).__init__(**kwargs)
with self.name_scope():
self.conv = nn.Conv2D(
channels=out_channels,
kernel_size=kernel_size,
padding=padding,
use_bias=False,
in_channels=in_channels)
self.bn = nn.BatchNorm(
in_channels=out_channels,
use_global_stats=bn_use_global_stats)
# self.bn = nn.BatchNorm(in_channels=out_channels, momentum=0.01)
self.activ = nn.LeakyReLU(alpha=0.1)
def __init__(self, num_classes=None):
super(ResNetBuilder, self).__init__()
self.base = ResNetV1()
self.avgpool = nn.GlobalAvgPool2D()
self.flatten = nn.Flatten()
self.num_classes = num_classes
self.bottleneck = nn.HybridSequential()
self.bottleneck.add(
nn.Dense(512,
in_units=2048,
weight_initializer=mx.initializer.MSRAPrelu('out', 0)))
self.bottleneck.add(nn.BatchNorm(in_channels=512))
self.bottleneck.add(nn.LeakyReLU(0.1))
self.bottleneck.add(nn.Dropout(0.5))
self.classifier = nn.Dense(
self.num_classes,
in_units=512,
weight_initializer=mx.initializer.Normal(0.001))
def _conv2d(channel,
kernel,
padding,
stride,
norm_layer=BatchNorm,
norm_kwargs=None):
"""A common conv-bn-leakyrelu cell"""
cell = nn.HybridSequential(prefix='')
cell.add(
nn.Conv2D(channel,
kernel_size=kernel,
strides=stride,
padding=padding,
use_bias=False))
cell.add(
norm_layer(epsilon=1e-5,
momentum=0.9,
**({} if norm_kwargs is None else norm_kwargs)))
cell.add(nn.LeakyReLU(0.1))
return cell
def __init__(self, use_bias, alg, **kwargs):
super(ConvActAdd, self).__init__(**kwargs)
self.conv0 = nn.Conv2D(
channels=64,
kernel_size=(3, 3),
strides=1,
use_bias=use_bias,
weight_initializer=mx.init.Xavier(magnitude=2.24))
if alg == "relu6":
self.act = RELU6()
elif alg == "leakyrelu":
self.act = nn.LeakyReLU(0.25)
elif alg == "gelu":
self.act = nn.GELU()
else:
self.act = nn.Activation(activation=alg)
self.conv1 = nn.Conv2D(channels=64,
kernel_size=(3, 3),
strides=1,
use_bias=use_bias)
self.conv1.share_parameters(self.conv0.collect_params())
def Conv2dBatchLeaky(in_channels,
out_channels,
kernel,
stride,
padding,
norm_layer=BatchNorm,
norm_kwargs=None):
"""A common conv-bn-leakyrelu cell"""
cell = nn.HybridSequential(prefix='')
cell.add(
nn.Conv2D(channels=out_channels,
kernel_size=kernel,
strides=stride,
padding=padding,
in_channels=in_channels,
use_bias=False))
cell.add(
norm_layer(axis=1, epsilon=1e-5, momentum=0.9, use_global_stats=False))
cell.add(nn.LeakyReLU(0.1))
return cell
def get_activation(act):
"""Get the activation based on the act string
Parameters
----------
act: str or HybridBlock
Returns
-------
ret: HybridBlock
"""
if isinstance(act, str):
if act == 'leaky':
return nn.LeakyReLU(0.1)
elif act == 'identity':
return IdentityActivation()
elif act == 'elu':
return ELU()
else:
return nn.Activation(act)
else:
return act
def get_activation(act):
"""Get the activation based on the act string
Parameters
----------
act: str or HybridBlock
Returns
-------
ret: HybridBlock
"""
if act is None:
return lambda x: x
if isinstance(act, str):
if act == "leaky":
return nn.LeakyReLU(0.1)
elif act in ["relu", "sigmoid", "tanh", "softrelu", "softsign"]:
return nn.Activation(act)
else:
raise NotImplementedError
else:
return act
def make_crit_block(self,
input_dim,
output_dim,
kernel_size=4,
strides=2,
final_layer=False):
layer = nn.HybridSequential()
if not final_layer:
layer.add(
nn.Conv2D(in_channels=input_dim,
channels=output_dim,
kernel_size=kernel_size,
strides=strides,
use_bias=False),
nn.BatchNorm(in_channels=output_dim), nn.LeakyReLU(alpha=0.2))
else:
layer.add(
nn.Conv2D(in_channels=input_dim,
channels=output_dim,
kernel_size=kernel_size,
strides=strides))
return layer
def convolution(num_filters,
filter_size,
norm_layer,
norm_kwargs,
stride=1,
pad=1,
use_mish=False):
out = nn.HybridSequential()
out.add(nn.Conv2D(num_filters, filter_size, padding=pad, strides=stride))
if norm_layer == nn.BatchNorm:
out.add(nn.BatchNorm(in_channels=num_filters))
elif norm_layer == contrib_nn.SyncBatchNorm:
out.add(
mx.gluon.contrib.nn.SyncBatchNorm(
in_channels=num_filters,
num_devices=norm_kwargs['num_devices']))
else:
raise ValueError("Unknown norm layer type: {}".format(norm_layer))
if use_mish:
out.add(Mish())
else:
out.add(nn.LeakyReLU(alpha=0.25))
return out
def __init__(self,
block,
layers,
channels,
classes=400,
return_features=False,
**kwargs):
super(R21DV1, self).__init__(**kwargs)
assert len(layers) == len(channels) - 1
self.return_features = return_features
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
self.features.add(
_conv21d(channels[0], [3, 7, 7],
strides=[1, 2, 2],
padding=[1, 3, 3],
mid_channels=45,
prefix='init_'))
self.features.add(
nn.BatchNorm(epsilon=1e-3,
momentum=0.9,
use_global_stats=True,
prefix='init_'))
self.features.add(nn.LeakyReLU(0.0))
for i, num_layer in enumerate(layers):
stride = 1 if i == 0 else 2
self.features.add(
self._make_layer(block,
num_layer,
channels[i + 1],
stride,
i + 1,
in_channels=channels[i]))
self.avg = nn.GlobalAvgPool3D()
self.dense = nn.Dense(classes, in_units=channels[-1])
def _conv21d(out_channels,
kernel,
strides=(1, 1, 1),
padding=(0, 0, 0),
in_channels=0,
mid_channels=None,
norm_layer=nn.BatchNorm,
norm_kwargs=None,
prefix=''):
"""R(2+1)D from 'A Closer Look at Spatiotemporal Convolutions for Action Recognition'"""
cell = nn.HybridSequential(prefix='R(2+1)D')
if mid_channels is None:
mid_channels = int(
math.floor((kernel[0] * kernel[1] * kernel[2] * in_channels *
out_channels) / (kernel[1] * kernel[2] * in_channels +
kernel[0] * out_channels)))
cell.add(
_conv3d(mid_channels, (1, kernel[1], kernel[2]),
strides=(1, strides[1], strides[2]),
padding=(0, padding[1], padding[2]),
prefix=prefix + 'middle_'))
cell.add(
norm_layer(epsilon=1e-3,
momentum=0.9,
prefix=prefix + 'middle_',
**({} if norm_kwargs is None else norm_kwargs)))
cell.add(nn.LeakyReLU(0.0))
cell.add(
_conv3d(out_channels, (kernel[0], 1, 1),
strides=(strides[0], 1, 1),
padding=(padding[0], 0, 0),
prefix=prefix))
return cell
def get_activation(act: Optional[Union[str, HybridBlock]]) -> HybridBlock:
"""Get the activation based on the string
Parameters
----------
act
The activation
Returns
-------
ret
The activation layer
"""
if act is None:
return lambda x: x
if isinstance(act, str):
if act == 'leaky':
# TODO(sxjscience) Add regex matching here to parse `leaky(0.1)`
return nn.LeakyReLU(0.1)
elif act == 'identity':
return IdentityActivation()
elif act == 'elu':
return ELU()
elif act == 'gelu':
return GELU(mode='erf')
elif act == 'gelu(tanh)':
return GELU(mode='tanh')
elif act == 'gelu(sigmoid)':
return GELU(mode='sigmoid')
elif act in ['relu', 'sigmoid', 'tanh', 'softrelu', 'softsign']:
return nn.Activation(act)
else:
raise NotImplementedError('act={} is not supported'.format(act))
else:
return act
def __init__(self, features, config, **kwargs):
super().__init__(**kwargs)
self.feature_layers = [4, 5, 6, 7]
self.feature_dims = [64, 128, 256, 512]
self.features = features
config = Reader(config)
with self.name_scope():
# self.backbone = vision.resnet50_v1()
self.reduce_dim = nn.HybridSequential()
self.reduce_dim.add(nn.Conv2D(64, 1))
self.reduce_dim.add(nn.BatchNorm())
self.reduce_dim.add(nn.Activation('relu'))
channels = config.network.flow.channels.get([64, 32, 16, 2])
self.flow = nn.HybridSequential(prefix='flow')
for i, c in enumerate(channels):
if i != 0:
self.flow.add(nn.LeakyReLU(0.1))
self.flow.add(
nn.Conv2D(c,
7,
padding=3,
weight_initializer=Xavier(rnd_type='gaussian',
magnitude=2)))
