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import torch
import torch.nn as nn
from .skeleton_DME import SkeletonConv, SkeletonPool, SkeletonUnpool
def calc_node_depth(topology):
def dfs(node, topology):
if topology[node] < 0:
return 0
return 1 + dfs(topology[node], topology)
depth = []
for i in range(len(topology)):
depth.append(dfs(i, topology))
return depth
def residual_ratio(k):
return 1 / (k + 1)
class Affine(nn.Module):
def __init__(self, num_parameters, scale=True, bias=True, scale_init=1.0):
super(Affine, self).__init__()
if scale:
self.scale = nn.Parameter(torch.ones(num_parameters) * scale_init)
else:
self.register_parameter("scale", None)
if bias:
self.bias = nn.Parameter(torch.zeros(num_parameters))
else:
self.register_parameter("bias", None)
def forward(self, input):
output = input
if self.scale is not None:
scale = self.scale.unsqueeze(0)
while scale.dim() < input.dim():
scale = scale.unsqueeze(2)
output = output.mul(scale)
if self.bias is not None:
bias = self.bias.unsqueeze(0)
while bias.dim() < input.dim():
bias = bias.unsqueeze(2)
output += bias
return output
class BatchStatistics(nn.Module):
def __init__(self, affine=-1):
super(BatchStatistics, self).__init__()
self.affine = nn.Sequential() if affine == -1 else Affine(affine)
self.loss = 0
def clear_loss(self):
self.loss = 0
def compute_loss(self, input):
input_flat = input.view(input.size(1), input.numel() // input.size(1))
mu = input_flat.mean(1)
logvar = (input_flat.pow(2).mean(1) - mu.pow(2)).sqrt().log()
self.loss = mu.pow(2).mean() + logvar.pow(2).mean()
def forward(self, input):
self.compute_loss(input)
return self.affine(input)
class ResidualBlock(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, stride, padding, residual_ratio, activation, batch_statistics=False, last_layer=False
):
super(ResidualBlock, self).__init__()
self.residual_ratio = residual_ratio
self.shortcut_ratio = 1 - residual_ratio
residual = []
residual.append(nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding))
if batch_statistics:
residual.append(BatchStatistics(out_channels))
if not last_layer:
residual.append(nn.PReLU() if activation == "relu" else nn.Tanh())
self.residual = nn.Sequential(*residual)
self.shortcut = nn.Sequential(
nn.AvgPool1d(kernel_size=2) if stride == 2 else nn.Sequential(),
nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0),
BatchStatistics(out_channels) if (in_channels != out_channels and batch_statistics is True) else nn.Sequential(),
)
def forward(self, input):
return self.residual(input).mul(self.residual_ratio) + self.shortcut(input).mul(self.shortcut_ratio)
class ResidualBlockTranspose(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, residual_ratio, activation):
super(ResidualBlockTranspose, self).__init__()
self.residual_ratio = residual_ratio
self.shortcut_ratio = 1 - residual_ratio
self.residual = nn.Sequential(
nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding), nn.PReLU() if activation == "relu" else nn.Tanh()
)
self.shortcut = nn.Sequential(
nn.Upsample(scale_factor=2, mode="linear", align_corners=False) if stride == 2 else nn.Sequential(),
nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, padding=0),
)
def forward(self, input):
return self.residual(input).mul(self.residual_ratio) + self.shortcut(input).mul(self.shortcut_ratio)
class SkeletonResidual(nn.Module):
def __init__(
self,
topology,
neighbour_list,
joint_num,
in_channels,
out_channels,
kernel_size,
stride,
padding,
padding_mode,
bias,
extra_conv,
pooling_mode,
activation,
last_pool,
):
super(SkeletonResidual, self).__init__()
kernel_even = False if kernel_size % 2 else True
seq = []
for _ in range(extra_conv):
# (T, J, D) => (T, J, D)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=in_channels,
joint_num=joint_num,
kernel_size=kernel_size - 1 if kernel_even else kernel_size,
stride=1,
padding=padding,
padding_mode=padding_mode,
bias=bias,
)
)
seq.append(nn.PReLU() if activation == "relu" else nn.Tanh())
# (T, J, D) => (T/2, J, 2D)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=out_channels,
joint_num=joint_num,
kernel_size=kernel_size,
stride=stride,
padding=padding,
padding_mode=padding_mode,
bias=bias,
add_offset=False,
)
)
seq.append(nn.GroupNorm(10, out_channels)) # FIXME: REMEMBER TO CHANGE BACK !!!
self.residual = nn.Sequential(*seq)
# (T, J, D) => (T/2, J, 2D)
self.shortcut = SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=out_channels,
joint_num=joint_num,
kernel_size=1,
stride=stride,
padding=0,
bias=True,
add_offset=False,
)
seq = []
# (T/2, J, 2D) => (T/2, J', 2D)
pool = SkeletonPool(
edges=topology, pooling_mode=pooling_mode, channels_per_edge=out_channels // len(neighbour_list), last_pool=last_pool
)
if len(pool.pooling_list) != pool.edge_num:
seq.append(pool)
seq.append(nn.PReLU() if activation == "relu" else nn.Tanh())
self.common = nn.Sequential(*seq)
def forward(self, input):
output = self.residual(input) + self.shortcut(input)
return self.common(output)
class SkeletonResidualTranspose(nn.Module):
def __init__(
self,
neighbour_list,
joint_num,
in_channels,
out_channels,
kernel_size,
padding,
padding_mode,
bias,
extra_conv,
pooling_list,
upsampling,
activation,
last_layer,
):
super(SkeletonResidualTranspose, self).__init__()
kernel_even = False if kernel_size % 2 else True
seq = []
# (T, J, D) => (2T, J, D)
if upsampling is not None:
seq.append(nn.Upsample(scale_factor=2, mode=upsampling, align_corners=False))
# (2T, J, D) => (2T, J', D)
unpool = SkeletonUnpool(pooling_list, in_channels // len(neighbour_list))
if unpool.input_edge_num != unpool.output_edge_num:
seq.append(unpool)
self.common = nn.Sequential(*seq)
seq = []
for _ in range(extra_conv):
# (2T, J', D) => (2T, J', D)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=in_channels,
joint_num=joint_num,
kernel_size=kernel_size - 1 if kernel_even else kernel_size,
stride=1,
padding=padding,
padding_mode=padding_mode,
bias=bias,
)
)
seq.append(nn.PReLU() if activation == "relu" else nn.Tanh())
# (2T, J', D) => (2T, J', D/2)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=out_channels,
joint_num=joint_num,
kernel_size=kernel_size - 1 if kernel_even else kernel_size,
stride=1,
padding=padding,
padding_mode=padding_mode,
bias=bias,
add_offset=False,
)
)
self.residual = nn.Sequential(*seq)
# (2T, J', D) => (2T, J', D/2)
self.shortcut = SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=out_channels,
joint_num=joint_num,
kernel_size=1,
stride=1,
padding=0,
bias=True,
add_offset=False,
)
if activation == "relu":
self.activation = nn.PReLU() if not last_layer else None
else:
self.activation = nn.Tanh() if not last_layer else None
def forward(self, input):
output = self.common(input)
output = self.residual(output) + self.shortcut(output)
if self.activation is not None:
return self.activation(output)
else:
return output
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