def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(SEInitBlock, self).__init__(**kwargs)
mid_channels = out_channels // 2
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=mid_channels,
strides=2,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv2")
self.conv3 = conv3x3_block(in_channels=mid_channels,
out_channels=out_channels,
data_format=data_format,
name="conv3")
self.pool = MaxPool2d(pool_size=3,
strides=2,
padding=1,
data_format=data_format,
name="pool")
def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(LwopRefinementBlock, self).__init__(**kwargs)
self.pre_conv = conv1x1_block(in_channels=in_channels,
out_channels=out_channels,
use_bias=True,
use_bn=False,
data_format=data_format,
name="pre_conv")
self.body = SimpleSequential(name="body")
self.body.add(
conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
use_bias=True,
data_format=data_format,
name="block1"))
self.body.add(
conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
padding=2,
dilation=2,
use_bias=True,
data_format=data_format,
name="block2"))
def __init__(self,
out_channels,
use_bn,
activation,
data_format="channels_last",
**kwargs):
super(IbpPreBlock, self).__init__(**kwargs)
self.conv1 = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
use_bias=(not use_bn),
use_bn=use_bn,
activation=activation,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
use_bias=(not use_bn),
use_bn=use_bn,
activation=activation,
data_format=data_format,
name="conv2")
self.se = SEBlock(channels=out_channels,
use_conv=False,
mid_activation=activation,
data_format=data_format,
name="se")
def __init__(self,
in_channels,
out_channels,
mid_channels,
strides=1,
use_bias=True,
use_bn=True,
data_format="channels_last",
**kwargs):
super(InceptionDouble3x3Branch, self).__init__(**kwargs)
self.conv1 = conv1x1_block(in_channels=in_channels,
out_channels=mid_channels,
use_bias=use_bias,
use_bn=use_bn,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=out_channels,
use_bias=use_bias,
use_bn=use_bn,
data_format=data_format,
name="conv2")
self.conv3 = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
strides=strides,
use_bias=use_bias,
use_bn=use_bn,
data_format=data_format,
name="conv3")
def __init__(self,
in_channels,
out_channels,
mid_channels,
num_subblocks,
data_format="channels_last",
**kwargs):
super(HRInitBlock, self).__init__(**kwargs)
self.conv1 = conv3x3_block(
in_channels=in_channels,
out_channels=mid_channels,
strides=2,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(
in_channels=mid_channels,
out_channels=mid_channels,
strides=2,
data_format=data_format,
name="conv2")
in_channels = mid_channels
self.subblocks = SimpleSequential(name="subblocks")
for i in range(num_subblocks):
self.subblocks.add(ResUnit(
in_channels=in_channels,
out_channels=out_channels,
strides=1,
bottleneck=True,
data_format=data_format,
name="block{}".format(i + 1)))
in_channels = out_channels
def __init__(self,
in_channels,
out_channels,
pose_att=True,
data_format="channels_last",
**kwargs):
super(DANetHeadBranch, self).__init__(**kwargs)
mid_channels = in_channels // 4
dropout_rate = 0.1
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv1")
if pose_att:
self.att = PosAttBlock(mid_channels,
data_format=data_format,
name="att")
else:
self.att = ChaAttBlock(data_format=data_format, name="att")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv2")
self.conv3 = conv1x1(in_channels=mid_channels,
out_channels=out_channels,
use_bias=True,
data_format=data_format,
name="conv3")
self.dropout = nn.Dropout(rate=dropout_rate, name="dropout")
def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(VoVInitBlock, self).__init__(**kwargs)
mid_channels = out_channels // 2
self.conv1 = conv3x3_block(
in_channels=in_channels,
out_channels=mid_channels,
strides=2,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(
in_channels=mid_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv2")
self.conv3 = conv3x3_block(
in_channels=mid_channels,
out_channels=out_channels,
strides=2,
data_format=data_format,
name="conv3")
def __init__(self, in_channels, out_channels, stride):
super(ResBlock, self).__init__()
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
stride=stride)
self.conv2 = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
activation=None)
def __init__(self,
channels,
init_block_channels,
alpha=0.1,
in_channels=3,
in_size=(224, 224),
classes=1000,
data_format="channels_last",
**kwargs):
super(DarkNet53, self).__init__(**kwargs)
self.in_size = in_size
self.classes = classes
self.data_format = data_format
self.features = tf.keras.Sequential(name="features")
self.features.add(
conv3x3_block(in_channels=in_channels,
out_channels=init_block_channels,
activation=nn.LeakyReLU(alpha=alpha),
data_format=data_format,
name="init_block"))
in_channels = init_block_channels
for i, channels_per_stage in enumerate(channels):
stage = tf.keras.Sequential(name="stage{}".format(i + 1))
for j, out_channels in enumerate(channels_per_stage):
if j == 0:
stage.add(
conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=2,
activation=nn.LeakyReLU(alpha=alpha),
data_format=data_format,
name="unit{}".format(j + 1)))
else:
stage.add(
DarkUnit(in_channels=in_channels,
out_channels=out_channels,
alpha=alpha,
data_format=data_format,
name="unit{}".format(j + 1)))
in_channels = out_channels
self.features.add(stage)
self.features.add(
nn.AveragePooling2D(pool_size=7,
strides=1,
data_format=data_format,
name="final_pool"))
self.output1 = nn.Dense(units=classes,
input_dim=in_channels,
name="output1")
def __init__(self, in_channels, data_format="channels_last", **kwargs):
super(XceptionInitBlock, self).__init__(**kwargs)
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=32,
strides=2,
padding=0,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=32,
out_channels=64,
strides=1,
padding=0,
data_format=data_format,
name="conv2")
def __init__(self,
in_channels_list,
out_channels_list,
num_modules,
num_branches,
num_subblocks,
data_format="channels_last",
**kwargs):
super(HRStage, self).__init__(**kwargs)
self.branches = num_branches
self.in_channels_list = out_channels_list
in_branches = len(in_channels_list)
out_branches = len(out_channels_list)
self.transition = SimpleSequential(name="transition")
for i in range(out_branches):
if i < in_branches:
if out_channels_list[i] != in_channels_list[i]:
self.transition.add(conv3x3_block(
in_channels=in_channels_list[i],
out_channels=out_channels_list[i],
strides=1,
data_format=data_format,
name="transition/block{}".format(i + 1)))
else:
self.transition.add(Identity(name="transition/block{}".format(i + 1)))
else:
conv3x3_seq = SimpleSequential(name="transition/conv3x3_seq{}".format(i + 1))
for j in range(i + 1 - in_branches):
in_channels_i = in_channels_list[-1]
out_channels_i = out_channels_list[i] if j == i - in_branches else in_channels_i
conv3x3_seq.add(conv3x3_block(
in_channels=in_channels_i,
out_channels=out_channels_i,
strides=2,
data_format=data_format,
name="subblock{}".format(j + 1)))
self.transition.add(conv3x3_seq)
self.layers = SimpleSequential(name="layers")
for i in range(num_modules):
self.layers.add(HRBlock(
in_channels_list=self.in_channels_list,
out_channels_list=out_channels_list,
num_branches=num_branches,
num_subblocks=num_subblocks,
data_format=data_format,
name="block{}".format(i + 1)))
self.in_channels_list = list(self.layers[-1].in_channels_list)
def __init__(self,
in_channels,
out_channels,
skip_channels,
mid_channels,
strides,
data_format="channels_last",
**kwargs):
super(SelecSLSUnit, self).__init__(**kwargs)
self.data_format = data_format
self.resize = (strides == 2)
mid2_channels = mid_channels // 2
last_channels = 2 * mid_channels + (skip_channels if strides == 1 else 0)
self.branch1 = conv3x3_block(
in_channels=in_channels,
out_channels=mid_channels,
strides=strides,
data_format=data_format,
name="branch1")
self.branch2 = SelecSLSBlock(
in_channels=mid_channels,
out_channels=mid2_channels,
data_format=data_format,
name="branch2")
self.branch3 = SelecSLSBlock(
in_channels=mid2_channels,
out_channels=mid2_channels,
data_format=data_format,
name="branch3")
self.last_conv = conv1x1_block(
in_channels=last_channels,
out_channels=out_channels,
data_format=data_format,
name="last_conv")
def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(StemBlock, self).__init__(**kwargs)
mid1_channels = out_channels // 2
mid2_channels = out_channels * 2
self.first_conv = conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=2,
data_format=data_format,
name="first_conv")
self.branches = Concurrent(data_format=data_format, name="branches")
self.branches.add(
PeleeBranch1(in_channels=out_channels,
out_channels=out_channels,
mid_channels=mid1_channels,
strides=2,
data_format=data_format,
name="branch1"))
self.branches.add(
MaxPool2d(pool_size=2,
strides=2,
padding=0,
data_format=data_format,
name="branch2"))
self.last_conv = conv1x1_block(in_channels=mid2_channels,
out_channels=out_channels,
data_format=data_format,
name="last_conv")
def __init__(self,
in_channels_list,
out_channels_list,
data_format="channels_last",
**kwargs):
super(HRFinalBlock, self).__init__(**kwargs)
self.inc_blocks = SimpleSequential(name="inc_blocks")
for i, in_channels_i in enumerate(in_channels_list):
self.inc_blocks.add(ResUnit(
in_channels=in_channels_i,
out_channels=out_channels_list[i],
strides=1,
bottleneck=True,
data_format=data_format,
name="inc_blocks/block{}".format(i + 1)))
self.down_blocks = SimpleSequential(name="down_blocks")
for i in range(len(in_channels_list) - 1):
self.down_blocks.add(conv3x3_block(
in_channels=out_channels_list[i],
out_channels=out_channels_list[i + 1],
strides=2,
use_bias=True,
data_format=data_format,
name="down_blocks/block{}".format(i + 1)))
self.final_layer = conv1x1_block(
in_channels=1024,
out_channels=2048,
strides=1,
use_bias=True,
data_format=data_format,
name="final_layer")
def __init__(self,
in_channels,
mid_channels,
out_channels,
bn_eps,
data_format="channels_last",
**kwargs):
super(SBEncoderInitBlock, self).__init__(**kwargs)
self.conv1 = conv3x3_block(
in_channels=in_channels,
out_channels=mid_channels,
strides=2,
bn_eps=bn_eps,
activation=(lambda: PReLU2(
mid_channels, data_format=data_format, name="activ")),
data_format=data_format,
name="conv1")
self.conv2 = dwsconv3x3_block(
in_channels=mid_channels,
out_channels=out_channels,
strides=2,
dw_use_bn=False,
bn_eps=bn_eps,
dw_activation=None,
pw_activation=(lambda: PReLU2(
out_channels, data_format=data_format, name="activ")),
se_reduction=1,
data_format=data_format,
name="conv2")
def __init__(self,
in_channels,
out_channels,
strides,
conv1_ibn,
data_format="channels_last",
**kwargs):
super(IBNResBottleneck, self).__init__(**kwargs)
mid_channels = out_channels // 4
self.conv1 = ibn_conv1x1_block(in_channels=in_channels,
out_channels=mid_channels,
use_ibn=conv1_ibn,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=mid_channels,
strides=strides,
data_format=data_format,
name="conv2")
self.conv3 = conv1x1_block(in_channels=mid_channels,
out_channels=out_channels,
activation=None,
data_format=data_format,
name="conv3")
def __init__(self,
in_channels,
out_channels,
strides,
cardinality,
bottleneck_width,
data_format="channels_last",
**kwargs):
super(SENetBottleneck, self).__init__(**kwargs)
mid_channels = out_channels // 4
D = int(math.floor(mid_channels * (bottleneck_width / 64.0)))
group_width = cardinality * D
group_width2 = group_width // 2
self.conv1 = conv1x1_block(in_channels=in_channels,
out_channels=group_width2,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=group_width2,
out_channels=group_width,
strides=strides,
groups=cardinality,
data_format=data_format,
name="conv2")
self.conv3 = conv1x1_block(in_channels=group_width,
out_channels=out_channels,
activation=None,
data_format=data_format,
name="conv3")
def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(LwopEncoderFinalBlock, self).__init__(**kwargs)
self.pre_conv = conv1x1_block(in_channels=in_channels,
out_channels=out_channels,
use_bias=True,
use_bn=False,
data_format=data_format,
name="pre_conv")
self.body = SimpleSequential(name="body")
for i in range(3):
self.body.add(
dwsconv3x3_block(in_channels=out_channels,
out_channels=out_channels,
dw_use_bn=False,
pw_use_bn=False,
dw_activation=(lambda: nn.ELU()),
pw_activation=(lambda: nn.ELU()),
data_format=data_format,
name="block{}".format(i + 1)))
self.post_conv = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
use_bias=True,
use_bn=False,
data_format=data_format,
name="post_conv")
def __init__(self,
channels,
reduction_ratio=16,
num_dil_convs=2,
dilation=4,
data_format="channels_last",
**kwargs):
super(SpatialGate, self).__init__(**kwargs)
mid_channels = channels // reduction_ratio
self.init_conv = conv1x1_block(in_channels=channels,
out_channels=mid_channels,
strides=1,
use_bias=True,
data_format=data_format,
name="init_conv")
self.dil_convs = SimpleSequential(name="dil_convs")
for i in range(num_dil_convs):
self.dil_convs.children.append(
conv3x3_block(in_channels=mid_channels,
out_channels=mid_channels,
strides=1,
padding=dilation,
dilation=dilation,
use_bias=True,
data_format=data_format,
name="conv{}".format(i + 1)))
self.final_conv = conv1x1(in_channels=mid_channels,
out_channels=1,
strides=1,
use_bias=True,
data_format=data_format,
name="final_conv")
def __init__(self,
in_channels,
keypoints,
data_format="channels_last",
**kwargs):
super(LwopDecoderInitBlock, self).__init__(**kwargs)
self.data_format = data_format
num_heatmap = keypoints
num_paf = 2 * keypoints
bend_mid_channels = 512
self.body = SimpleSequential(name="body")
for i in range(3):
self.body.add(
conv3x3_block(in_channels=in_channels,
out_channels=in_channels,
use_bias=True,
use_bn=False,
data_format=data_format,
name="block{}".format(i + 1)))
self.heatmap_bend = LwopDecoderBend(in_channels=in_channels,
mid_channels=bend_mid_channels,
out_channels=num_heatmap,
data_format=data_format,
name="heatmap_bend")
self.paf_bend = LwopDecoderBend(in_channels=in_channels,
mid_channels=bend_mid_channels,
out_channels=num_paf,
data_format=data_format,
name="paf_bend")
def __init__(self,
in_channels,
out_channels,
strides,
use_bias=True,
bottleneck_factor=2,
squeeze_out=False,
data_format="channels_last",
**kwargs):
super(LwopResBottleneck, self).__init__(**kwargs)
mid_channels = out_channels // bottleneck_factor if squeeze_out else in_channels // bottleneck_factor
self.conv1 = conv1x1_block(in_channels=in_channels,
out_channels=mid_channels,
use_bias=use_bias,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=mid_channels,
strides=strides,
use_bias=use_bias,
data_format=data_format,
name="conv2")
self.conv3 = conv1x1_block(in_channels=mid_channels,
out_channels=out_channels,
use_bias=use_bias,
activation=None,
data_format=data_format,
name="conv3")
def __init__(self,
in_channels,
out_channels,
use_deptwise,
activation,
data_format="channels_last",
**kwargs):
super(HarDInitBlock, self).__init__(**kwargs)
mid_channels = out_channels // 2
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=mid_channels,
strides=2,
activation=activation,
data_format=data_format,
name="conv1")
conv2_block_class = conv1x1_block if use_deptwise else conv3x3_block
self.conv2 = conv2_block_class(in_channels=mid_channels,
out_channels=out_channels,
activation=activation,
data_format=data_format,
name="conv2")
if use_deptwise:
self.downsample = dwconv3x3_block(in_channels=out_channels,
out_channels=out_channels,
strides=2,
activation=None,
data_format=data_format,
name="downsample")
else:
self.downsample = MaxPool2d(pool_size=3,
strides=2,
padding=1,
data_format=data_format,
name="downsample")
def __init__(self,
in_channels_low,
in_channels_high,
out_channels,
classes,
data_format="channels_last",
**kwargs):
super(CFFBlock, self).__init__(**kwargs)
self.up = InterpolationBlock(scale_factor=2,
data_format=data_format,
name="up")
self.conv_low = conv3x3_block(in_channels=in_channels_low,
out_channels=out_channels,
padding=2,
dilation=2,
activation=None,
data_format=data_format,
name="conv_low")
self.conv_hign = conv1x1_block(in_channels=in_channels_high,
out_channels=out_channels,
activation=None,
data_format=data_format,
name="conv_hign")
self.activ = nn.ReLU()
self.conv_cls = conv1x1(in_channels=out_channels,
out_channels=classes,
data_format=data_format,
name="conv_cls")
def __init__(self,
in_channels,
out_channels,
bottleneck_factor=4,
data_format="channels_last",
**kwargs):
super(FCNFinalBlock, self).__init__(**kwargs)
assert (in_channels % bottleneck_factor == 0)
self.data_format = data_format
mid_channels = in_channels // bottleneck_factor
self.conv1 = conv3x3_block(
in_channels=in_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv1")
self.dropout = nn.Dropout(
rate=0.1,
name="dropout")
self.conv2 = conv1x1(
in_channels=mid_channels,
out_channels=out_channels,
use_bias=True,
data_format=data_format,
name="conv2")
def dark_convYxY(in_channels,
out_channels,
alpha,
pointwise,
data_format="channels_last",
**kwargs):
"""
DarkNet unit.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
alpha : float
Slope coefficient for Leaky ReLU activation.
pointwise : bool
Whether use 1x1 (pointwise) convolution or 3x3 convolution.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
if pointwise:
return conv1x1_block(in_channels=in_channels,
out_channels=out_channels,
activation=nn.LeakyReLU(alpha=alpha),
data_format=data_format,
**kwargs)
else:
return conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
activation=nn.LeakyReLU(alpha=alpha),
data_format=data_format,
**kwargs)
def __init__(self,
channels,
init_block_channels,
final_block_channels,
classifier_mid_channels,
kernels3,
exp_factors,
use_se,
first_stride,
bn_use_global_stats=False,
in_channels=3,
in_size=(224, 224),
classes=1000,
dropout=0.2,
**kwargs):
super(GhostNet, self).__init__(**kwargs)
self.in_size = in_size
self.classes = classes
with self.name_scope():
self.features = nn.HybridSequential(prefix="")
self.features.add(
conv3x3_block(in_channels=in_channels,
out_channels=init_block_channels,
strides=2,
bn_use_global_stats=bn_use_global_stats))
in_channels = init_block_channels
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):
strides = 2 if (j == 0) and (
(i != 0) or first_stride) else 1
use_kernel3 = kernels3[i][j] == 1
exp_factor = exp_factors[i][j]
use_se_flag = use_se[i][j] == 1
stage.add(
GhostUnit(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
use_kernel3=use_kernel3,
exp_factor=exp_factor,
use_se=use_se_flag,
bn_use_global_stats=bn_use_global_stats))
in_channels = out_channels
self.features.add(stage)
self.features.add(
conv1x1_block(in_channels=in_channels,
out_channels=final_block_channels,
bn_use_global_stats=bn_use_global_stats))
in_channels = final_block_channels
self.features.add(nn.AvgPool2D(pool_size=7, strides=1))
self.output = nn.HybridSequential(prefix="")
self.output.add(
GhostClassifier(in_channels=in_channels,
out_channels=classes,
mid_channels=classifier_mid_channels,
dropout=dropout))
self.output.add(nn.Flatten())
def __init__(self,
in_channels_list,
out_channels_list,
links_list,
use_deptwise,
use_dropout,
downsampling,
activation,
data_format="channels_last",
**kwargs):
super(HarDUnit, self).__init__(**kwargs)
self.data_format = data_format
self.links_list = links_list
self.use_dropout = use_dropout
self.downsampling = downsampling
self.blocks = SimpleSequential(name="blocks")
for i in range(len(links_list)):
in_channels = in_channels_list[i]
out_channels = out_channels_list[i]
if use_deptwise:
unit = invdwsconv3x3_block(in_channels=in_channels,
out_channels=out_channels,
pw_activation=activation,
dw_activation=None,
data_format=data_format,
name="block{}".format(i + 1))
else:
unit = conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
data_format=data_format,
name="block{}".format(i + 1))
self.blocks.add(unit)
if self.use_dropout:
self.dropout = nn.Dropout(rate=0.1, name="dropout")
self.conv = conv1x1_block(in_channels=in_channels_list[-1],
out_channels=out_channels_list[-1],
activation=activation,
data_format=data_format,
name="conv")
if self.downsampling:
if use_deptwise:
self.downsample = dwconv3x3_block(
in_channels=out_channels_list[-1],
out_channels=out_channels_list[-1],
strides=2,
activation=None,
data_format=data_format,
name="downsample")
else:
self.downsample = MaxPool2d(pool_size=2,
strides=2,
data_format=data_format,
name="downsample")
def __init__(self,
in_channels,
out_channels,
mid_channels,
data_format="channels_last",
**kwargs):
super(PeleeBranch2, self).__init__(**kwargs)
self.conv1 = conv1x1_block(in_channels=in_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=mid_channels,
out_channels=out_channels,
data_format=data_format,
name="conv2")
self.conv3 = conv3x3_block(in_channels=out_channels,
out_channels=out_channels,
data_format=data_format,
name="conv3")
def __init__(self, in_channels, data_format="channels_last", **kwargs):
super(PolyInitBlock, self).__init__(**kwargs)
self.conv1 = conv3x3_block(in_channels=in_channels,
out_channels=32,
strides=2,
padding=0,
data_format=data_format,
name="conv1")
self.conv2 = conv3x3_block(in_channels=32,
out_channels=32,
padding=0,
data_format=data_format,
name="conv2")
self.conv3 = conv3x3_block(in_channels=32,
out_channels=64,
data_format=data_format,
name="conv3")
self.block1 = PolyBlock3a(data_format=data_format, name="block1")
self.block2 = PolyBlock4a(data_format=data_format, name="block2")
self.block3 = PolyBlock5a(data_format=data_format, name="block3")
def __init__(self,
in_channels,
out_channels,
data_format="channels_last",
**kwargs):
super(Conv3x3Branch, self).__init__(**kwargs)
self.conv = conv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=2,
padding=0,
data_format=data_format,
name="conv")
