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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
def __init__(self, drop_prob=None, scale_by_keep=True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
with torch.no_grad():
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
tensor.uniform_(2 * l - 1, 2 * u - 1)
tensor.erfinv_()
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
def conv_branch_init(conv, branches):
weight = conv.weight
n = weight.size(0)
k1 = weight.size(1)
k2 = weight.size(2)
nn.init.normal_(weight, 0, math.sqrt(2. / (n * k1 * k2 * branches)))
if conv.bias is not None:
nn.init.constant_(conv.bias, 0)
def conv_init(conv):
if conv.weight is not None:
nn.init.kaiming_normal_(conv.weight, mode='fan_out')
if conv.bias is not None:
nn.init.constant_(conv.bias, 0)
def bn_init(bn, scale):
nn.init.constant_(bn.weight, scale)
nn.init.constant_(bn.bias, 0)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
if hasattr(m, 'weight'):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if hasattr(m, 'bias') and m.bias is not None and isinstance(m.bias, torch.Tensor):
nn.init.constant_(m.bias, 0)
elif classname.find('BatchNorm') != -1:
if hasattr(m, 'weight') and m.weight is not None:
m.weight.data.normal_(1.0, 0.02)
if hasattr(m, 'bias') and m.bias is not None:
m.bias.data.fill_(0)
class TemporalConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1):
super(TemporalConv, self).__init__()
pad = (kernel_size + (kernel_size-1) * (dilation-1) - 1) // 2
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=(kernel_size, 1),
padding=(pad, 0),
stride=(stride, 1),
dilation=(dilation, 1),
)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class MultiScale_TemporalConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
dilations=[1,2,3,4],
residual=False,
residual_kernel_size=1):
super().__init__()
assert out_channels % (len(dilations) + 2) == 0, '# out channels should be multiples of # branches'
self.num_branches = len(dilations) + 2
branch_channels = out_channels // self.num_branches
if type(kernel_size) == list:
assert len(kernel_size) == len(dilations)
else:
kernel_size = [kernel_size]*len(dilations)
self.branches = nn.ModuleList([
nn.Sequential(
nn.Conv2d(
in_channels,
branch_channels,
kernel_size=1,
padding=0
),
nn.BatchNorm2d(branch_channels),
nn.ReLU(inplace=True),
TemporalConv(
branch_channels,
branch_channels,
kernel_size=ks,
stride=stride,
dilation=dilation
),
)
for ks, dilation in zip(kernel_size, dilations)
])
self.branches.append(nn.Sequential(
nn.Conv2d(in_channels, branch_channels, kernel_size=1, padding=0),
nn.BatchNorm2d(branch_channels),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(3,1), stride=(stride,1), padding=(1,0)),
nn.BatchNorm2d(branch_channels)
))
self.branches.append(nn.Sequential(
nn.Conv2d(in_channels, branch_channels, kernel_size=1, padding=0, stride=(stride,1)),
nn.BatchNorm2d(branch_channels)
))
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels) and (stride == 1):
self.residual = lambda x: x
else:
self.residual = TemporalConv(in_channels, out_channels, kernel_size=residual_kernel_size, stride=stride)
self.apply(weights_init)
def forward(self, x):
res = self.residual(x)
branch_outs = []
for tempconv in self.branches:
out = tempconv(x)
branch_outs.append(out)
out = torch.cat(branch_outs, dim=1)
out += res
return out
class unit_tcn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=5, stride=1):
super(unit_tcn, self).__init__()
pad = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=(kernel_size, 1), padding=(pad, 0),
stride=(stride, 1), groups=1)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
conv_init(self.conv)
bn_init(self.bn, 1)
def forward(self, x):
x = self.bn(self.conv(x))
return x
class MHSA(nn.Module):
def __init__(self, dim_in, dim, A, num_heads=6, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., insert_cls_layer=0, pe=False, num_point=25,
outer=True, layer=0,
**kwargs):
super().__init__()
self.num_heads = num_heads
self.dim = dim
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.num_point = num_point
self.layer = layer
h1 = A.sum(0)
h1[h1 != 0] = 1
h = [None for _ in range(num_point)]
h[0] = np.eye(num_point)
h[1] = h1
self.hops = 0*h[0]
for i in range(2, num_point):
h[i] = h[i-1] @ h1.transpose(0, 1)
h[i][h[i] != 0] = 1
for i in range(num_point-1, 0, -1):
if np.any(h[i]-h[i-1]):
h[i] = h[i] - h[i - 1]
self.hops += i*h[i]
else:
continue
self.hops = torch.tensor(self.hops).long()
self.rpe = nn.Parameter(torch.zeros((self.hops.max()+1, dim)))
self.w1 = nn.Parameter(torch.zeros(num_heads, head_dim))
A = A.sum(0)
A[:, :] = 0
self.outer = nn.Parameter(torch.stack([torch.eye(A.shape[-1]) for _ in range(num_heads)], dim=0), requires_grad=True)
self.alpha = nn.Parameter(torch.zeros(1), requires_grad=True)
self.kv = nn.Conv2d(dim_in, dim * 2, 1, bias=qkv_bias)
self.q = nn.Conv2d(dim_in, dim, 1, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Conv2d(dim, dim, 1, groups=6)
self.proj_drop = nn.Dropout(proj_drop)
self.apply(self._init_weights)
self.insert_cls_layer = insert_cls_layer
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, e):
N, C, T, V = x.shape
kv = self.kv(x).reshape(N, 2, self.num_heads, self.dim // self.num_heads, T, V).permute(1, 0, 4, 2, 5, 3)
k, v = kv[0], kv[1]
q = self.q(x).reshape(N, self.num_heads, self.dim // self.num_heads, T, V).permute(0, 3, 1, 4, 2)
e_k = e.reshape(N, self.num_heads, self.dim // self.num_heads, T, V).permute(0, 3, 1, 4, 2)
pos_emb = self.rpe[self.hops]
pos_emb = pos_emb.to(torch.float16)
k_r = pos_emb.view(V, V, self.num_heads, self.dim // self.num_heads)
q = q.to(torch.float16)
e_k = e_k.to(torch.float16)
b = torch.einsum("bthnc, nmhc->bthnm", q, k_r)
c = torch.einsum("bthnc, bthmc->bthnm", q, e_k)
w1 = self.w1.to(torch.float16)
d = torch.einsum("hc, bthmc->bthm", w1, e_k).unsqueeze(-2)
a = q @ k.transpose(-2, -1)
attn = a + b + c + d
attn = attn * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (self.alpha * attn + self.outer) @ v
x = x.transpose(3, 4).reshape(N, T, -1, V).transpose(1, 2)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.,
num_heads=None):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class unit_vit(nn.Module):
def __init__(self, dim_in, dim, A, num_of_heads, add_skip_connection=True, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer=0,
insert_cls_layer=0, pe=False, num_point=25, **kwargs):
super().__init__()
self.norm1 = norm_layer(dim_in)
self.dim_in = dim_in
self.dim = dim
self.add_skip_connection = add_skip_connection
self.num_point = num_point
self.attn = MHSA(dim_in, dim, A, num_heads=num_of_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, insert_cls_layer=insert_cls_layer, pe=pe, num_point=num_point, layer=layer, **kwargs)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
if self.dim_in != self.dim:
self.skip_proj = nn.Conv2d(dim_in, dim, (1, 1), padding=(0, 0), bias=False)
self.pe_proj = nn.Conv2d(dim_in, dim, 1, bias=False)
self.pe = pe
def forward(self, x, joint_label, groups):
label = F.one_hot(torch.tensor(joint_label)).float().to(x.device)
z = x @ (label / label.sum(dim=0, keepdim=True))
z = self.pe_proj(z).permute(3, 0, 1, 2)
e = z[joint_label].permute(1, 2, 3, 0)
if self.add_skip_connection:
if self.dim_in != self.dim:
x = self.skip_proj(x) + self.drop_path(self.attn(self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2), e))
else:
x = x + self.drop_path(self.attn(self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2), e))
else:
x = self.drop_path(self.attn(self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2), e))
return x
class TCN_ViT_unit(nn.Module):
def __init__(self, in_channels, out_channels, A, stride=1, num_of_heads=6, residual=True, kernel_size=5, dilations=[1,2], pe=False, num_point=25, layer=0):
super(TCN_ViT_unit, self).__init__()
self.vit1 = unit_vit(in_channels, out_channels, A, add_skip_connection=residual, num_of_heads=num_of_heads, pe=pe, num_point=num_point, layer=layer)
self.tcn1 = MultiScale_TemporalConv(out_channels, out_channels, kernel_size=kernel_size, stride=stride,
dilations=dilations,
residual=False)
self.act = nn.ReLU(inplace=True)
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels) and (stride == 1):
self.residual = lambda x: x
else:
self.residual = unit_tcn(in_channels, out_channels, kernel_size=1, stride=stride)
def forward(self, x, joint_label, groups):
y = self.act(self.tcn1(self.vit1(x, joint_label, groups)) + self.residual(x))
return y
class Hyperformer_Model(nn.Module):
def __init__(self, A, joint_label, num_point, in_channels=3, num_person=1,
drop_out=0, num_of_heads=9, out_dim=512):
super(Hyperformer_Model, self).__init__()
A = np.asarray(A, dtype=np.float32)
assert A.shape[-1] == num_point and len(joint_label) == num_point
self.num_of_heads = num_of_heads
self.num_point = num_point
self.num_person = num_person
self.feat_dim = 24 * num_of_heads
self.out_dim = out_dim
self.data_bn = nn.BatchNorm1d(num_person * in_channels * num_point)
self.joint_label = list(joint_label)
self.l1 = TCN_ViT_unit(in_channels, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=1)
self.l2 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=2)
self.l3 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=3)
self.l4 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=4)
self.l5 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, stride=2, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=5)
self.l6 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=6)
self.l7 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=7)
self.l8 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, stride=2, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=8)
self.l9 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=9)
self.l10 = TCN_ViT_unit(24*num_of_heads, 24*num_of_heads, A, residual=True, num_of_heads=num_of_heads, pe=True, num_point=num_point, layer=10)
self.fc2 = nn.Linear(24*num_of_heads, out_dim)
nn.init.normal_(self.fc2.weight, 0, math.sqrt(2. / out_dim))
bn_init(self.data_bn, 1)
if drop_out:
self.drop_out = nn.Dropout(drop_out)
else:
self.drop_out = lambda x: x
def forward(self, x):
groups = []
for num in range(max(self.joint_label)+1):
groups.append([ind for ind, element in enumerate(self.joint_label) if element==num])
N, C, T, V, M = x.size()
x = x.permute(0, 4, 3, 1, 2).contiguous().view(N, M * V * C, T)
x = self.data_bn(x)
x = x.view(N, M, V, C, T).contiguous().view(N * M, V, C, T).permute(0, 2, 3, 1)
x = self.l1(x, self.joint_label, groups)
x = self.l2(x, self.joint_label, groups)
x = self.l3(x, self.joint_label, groups)
x = self.l4(x, self.joint_label, groups)
x = self.l5(x, self.joint_label, groups)
x = self.l6(x, self.joint_label, groups)
x = self.l7(x, self.joint_label, groups)
x = self.l8(x, self.joint_label, groups)
x = self.l9(x, self.joint_label, groups)
x = self.l10(x, self.joint_label, groups)
_ , C, T, V = x.size()
z = x.view(N, M, C, T, V)
z1 = z.mean(4).mean(1)
z2 = z1.view(N,T,C)
x = x.view(N, M, C, -1)
x = x.mean(3).mean(1)
x2 = self.fc2(x)
return x, x2 |