def __init__(self,
in_channels,
out_channels,
strides,
use_kernel3,
exp_factor,
use_se,
bn_use_global_stats=False,
**kwargs):
super(GhostUnit, self).__init__(**kwargs)
self.resize_identity = (in_channels != out_channels) or (strides != 1)
with self.name_scope():
self.body = GhostExpBlock(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
use_kernel3=use_kernel3,
exp_factor=exp_factor,
use_se=use_se,
bn_use_global_stats=bn_use_global_stats)
if self.resize_identity:
self.identity_conv = dwsconv3x3_block(
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
bn_use_global_stats=bn_use_global_stats,
pw_activation=None)
def __init__(self,
in_channels,
out_channels,
strides,
use_kernel3,
exp_factor,
use_se,
data_format="channels_last",
**kwargs):
super(GhostUnit, self).__init__(**kwargs)
self.resize_identity = (in_channels != out_channels) or (strides != 1)
self.body = GhostExpBlock(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
use_kernel3=use_kernel3,
exp_factor=exp_factor,
use_se=use_se,
data_format=data_format,
name="body")
if self.resize_identity:
self.identity_conv = dwsconv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
pw_activation=None,
data_format=data_format,
name="identity_conv")
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,
first_stage_stride,
dw_use_bn=True,
dw_activation="relu",
in_channels=3,
in_size=(224, 224),
classes=1000,
data_format="channels_last",
**kwargs):
super(MobileNet, self).__init__(**kwargs)
self.in_size = in_size
self.classes = classes
self.data_format = data_format
self.features = SimpleSequential(name="features")
init_block_channels = channels[0][0]
self.features.add(
conv3x3_block(in_channels=in_channels,
out_channels=init_block_channels,
strides=2,
data_format=data_format,
name="init_block"))
in_channels = init_block_channels
for i, channels_per_stage in enumerate(channels[1:]):
stage = SimpleSequential(name="stage{}".format(i + 1))
for j, out_channels in enumerate(channels_per_stage):
strides = 2 if (j == 0) and (
(i != 0) or first_stage_stride) else 1
stage.add(
dwsconv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
dw_use_bn=dw_use_bn,
dw_activation=dw_activation,
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,
encoder_channels,
encoder_paddings,
encoder_init_block_channels,
encoder_final_block_channels,
refinement_units,
calc_3d_features,
return_heatmap=True,
in_channels=3,
in_size=(368, 368),
keypoints=19,
data_format="channels_last",
**kwargs):
super(LwOpenPose, self).__init__(**kwargs)
assert (in_channels == 3)
self.in_size = in_size
self.keypoints = keypoints
self.data_format = data_format
self.return_heatmap = return_heatmap
self.calc_3d_features = calc_3d_features
num_heatmap_paf = 3 * keypoints
self.encoder = tf.keras.Sequential(name="encoder")
backbone = SimpleSequential(name="backbone")
backbone.add(
conv3x3_block(in_channels=in_channels,
out_channels=encoder_init_block_channels,
strides=2,
data_format=data_format,
name="init_block"))
in_channels = encoder_init_block_channels
for i, channels_per_stage in enumerate(encoder_channels):
stage = SimpleSequential(name="stage{}".format(i + 1))
for j, out_channels in enumerate(channels_per_stage):
strides = 2 if (j == 0) and (i != 0) else 1
padding = encoder_paddings[i][j]
stage.add(
dwsconv3x3_block(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
padding=padding,
dilation=padding,
data_format=data_format,
name="unit{}".format(j + 1)))
in_channels = out_channels
backbone.add(stage)
self.encoder.add(backbone)
self.encoder.add(
LwopEncoderFinalBlock(in_channels=in_channels,
out_channels=encoder_final_block_channels,
data_format=data_format,
name="final_block"))
in_channels = encoder_final_block_channels
self.decoder = tf.keras.Sequential(name="decoder")
self.decoder.add(
LwopDecoderInitBlock(in_channels=in_channels,
keypoints=keypoints,
data_format=data_format,
name="init_block"))
in_channels = encoder_final_block_channels + num_heatmap_paf
for i in range(refinement_units):
self.decoder.add(
LwopDecoderUnit(in_channels=in_channels,
keypoints=keypoints,
data_format=data_format,
name="unit{}".format(i + 1)))
self.decoder.add(
LwopDecoderFinalBlock(in_channels=in_channels,
keypoints=keypoints,
bottleneck_factor=2,
calc_3d_features=calc_3d_features,
data_format=data_format,
name="final_block"))