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model/SGraFormer.py
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## Our model was revised from https://github.com/zczcwh/PoseFormer/blob/main/common/model_poseformer.py
import torch
import torch.nn as nn
from functools import partial
from einops import rearrange
from timm.models.layers import DropPath
from common.opt import opts
from model.Spatial_encoder import First_view_Spatial_features, Spatial_features
from model.Temporal_encoder import Temporal__features
opt = opts().parse()
#######################################################################################################################
class sgraformer(nn.Module):
def __init__(self, num_frame=9, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, norm_layer=None):
""" ##########hybrid_backbone=None, representation_size=None,
Args:
num_frame (int, tuple): input frame number
num_joints (int, tuple): joints number
in_chans (int): number of input channels, 2D joints have 2 channels: (x,y)
embed_dim_ratio (int): embedding dimension ratio
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer
"""
super().__init__()
embed_dim = embed_dim_ratio * num_joints
out_dim = num_joints * 3 #### output dimension is num_joints * 3
##Spatial_features
self.SF1 = First_view_Spatial_features(num_frame, num_joints, in_chans, embed_dim_ratio, depth,
num_heads, mlp_ratio, qkv_bias, qk_scale,
drop_rate, attn_drop_rate, drop_path_rate, norm_layer)
self.SF2 = Spatial_features(num_frame, num_joints, in_chans, embed_dim_ratio, depth,
num_heads, mlp_ratio, qkv_bias, qk_scale,
drop_rate, attn_drop_rate, drop_path_rate, norm_layer)
self.SF3 = Spatial_features(num_frame, num_joints, in_chans, embed_dim_ratio, depth,
num_heads, mlp_ratio, qkv_bias, qk_scale,
drop_rate, attn_drop_rate, drop_path_rate, norm_layer)
self.SF4 = Spatial_features(num_frame, num_joints, in_chans, embed_dim_ratio, depth,
num_heads, mlp_ratio, qkv_bias, qk_scale,
drop_rate, attn_drop_rate, drop_path_rate, norm_layer)
## MVF
self.view_pos_embed = nn.Parameter(torch.zeros(1, 4, num_frame, embed_dim))
self.pos_drop = nn.Dropout(p=0.)
self.conv = nn.Sequential(
nn.BatchNorm2d(4, momentum=0.1),
nn.Conv2d(4, 1, kernel_size=opt.mvf_kernel, stride=1, padding=int(opt.mvf_kernel // 2), bias=False),
nn.ReLU(inplace=True),
)
self.conv_hop = nn.Sequential(
nn.BatchNorm2d(4, momentum=0.1),
nn.Conv2d(4, 1, kernel_size=opt.mvf_kernel, stride=1, padding=int(opt.mvf_kernel // 2), bias=False),
nn.ReLU(inplace=True),
)
self.conv_norm = nn.LayerNorm(embed_dim)
self.conv_hop_norm = nn.LayerNorm(embed_dim)
# Time Serial
self.TF = Temporal__features(num_frame, num_joints, in_chans, embed_dim_ratio, depth,
num_heads, mlp_ratio, qkv_bias, qk_scale,
drop_rate, attn_drop_rate, drop_path_rate, norm_layer)
self.head = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, out_dim),
)
self.hop_w0 = nn.Parameter(torch.ones(17, 17))
self.hop_w1 = nn.Parameter(torch.ones(17, 17))
self.hop_w2 = nn.Parameter(torch.ones(17, 17))
self.hop_w3 = nn.Parameter(torch.ones(17, 17))
self.hop_w4 = nn.Parameter(torch.ones(17, 17))
self.hop_global = nn.Parameter(torch.ones(17, 17))
self.linear_hop = nn.Linear(8, 2)
# self.max_pool = nn.MaxPool1d(2)
self.edge_embedding = nn.Linear(17*17*4, 17*17)
def forward(self, x, hops):
b, f, v, j, c = x.shape
edge_embedding = self.edge_embedding(hops[0].reshape(1, -1))
###############golbal feature#################
x_hop_global = x.unsqueeze(3).repeat(1, 1, 1, 17, 1, 1)
x_hop_global = x_hop_global - x_hop_global.permute(0, 1, 2, 4, 3, 5)
x_hop_global = torch.sum(x_hop_global ** 2, dim=-1)
hop_global = x_hop_global / torch.sum(x_hop_global, dim=-1).unsqueeze(-1)
hops = hops.unsqueeze(1).unsqueeze(2).repeat(1, f, v, 1, 1, 1)
hops1 = hop_global * hops[:, :, :, 0]
hops2 = hop_global * hops[:, :, :, 1]
hops3 = hop_global * hops[:, :, :, 2]
hops4 = hop_global * hops[:, :, :, 3]
# hops = torch.cat((hops1,hops2,hops3,hops4), dim=-1)
hops = torch.cat((hops1,hops2,hops3,hops4), dim=-1)
x1 = x[:, :, 0]
x2 = x[:, :, 1]
x3 = x[:, :, 2]
x4 = x[:, :, 3]
x1 = x1.permute(0, 3, 1, 2)
x2 = x2.permute(0, 3, 1, 2)
x3 = x3.permute(0, 3, 1, 2)
x4 = x4.permute(0, 3, 1, 2)
hop1 = hops[:, :, 0]
hop2 = hops[:, :, 1]
hop3 = hops[:, :, 2]
hop4 = hops[:, :, 3]
hop1 = hop1.permute(0, 3, 1, 2)
hop2 = hop2.permute(0, 3, 1, 2)
hop3 = hop3.permute(0, 3, 1, 2)
hop4 = hop4.permute(0, 3, 1, 2)
### Semantic graph transformer encoder
x1, hop1, MSA1, MSA2, MSA3, MSA4 = self.SF1(x1, hop1, edge_embedding)
x2, hop2, MSA1, MSA2, MSA3, MSA4 = self.SF2(x2, hop2, MSA1, MSA2, MSA3, MSA4, edge_embedding)
x3, hop3, MSA1, MSA2, MSA3, MSA4 = self.SF3(x3, hop3, MSA1, MSA2, MSA3, MSA4, edge_embedding)
x4, hop4, MSA1, MSA2, MSA3, MSA4 = self.SF4(x4, hop4, MSA1, MSA2, MSA3, MSA4, edge_embedding)
### Multi-view cross-channel fusion
x = torch.cat((x1.unsqueeze(1), x2.unsqueeze(1), x3.unsqueeze(1), x4.unsqueeze(1)), dim=1) + self.view_pos_embed
x = self.pos_drop(x)
x = self.conv(x).squeeze(1) + x1 + x2 + x3 + x4
x = self.conv_norm(x)
hop = torch.cat((hop1.unsqueeze(1), hop2.unsqueeze(1), hop3.unsqueeze(1), hop4.unsqueeze(1)), dim=1) + self.view_pos_embed
hop = self.pos_drop(hop)
# hop = self.conv_hop(hop).squeeze(1) + hop1 + hop2 + hop3 + hop4
# hop = self.conv_hop_norm(hop)
hop = self.conv(hop).squeeze(1) + hop1 + hop2 + hop3 + hop4
hop = self.conv_norm(hop)
x = x * hop
### Temporal transformer encoder
x = self.TF(x)
x = self.head(x)
x = x.view(b, opt.frames, j, -1)
print("=============> x.shape", x.shape)
return x
# x = torch.rand((8, 27, 4, 17 , 2))
# hops = torch.rand((8,4,17,17))
# mvft = hmvformer(num_frame=opt.frames, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
# num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None, drop_path_rate=0.1)
# print(mvft(x, hops).shape)
model/Spatial_encoder.py
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## Our model was revised from https://github.com/zczcwh/PoseFormer/blob/main/common/model_poseformer.py
import torch
import torch.nn as nn
from functools import partial
from einops import rearrange
from timm.models.layers import DropPath
#######################################################################################################################
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
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 Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.edge_embedding = nn.Linear(17*17, 17*17)
def forward(self, x, edge_embedding):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale
edge_embedding = self.edge_embedding(edge_embedding)
edge_embedding = edge_embedding.reshape(1, 17, 17).unsqueeze(0).repeat(B, self.num_heads, 1, 1)
# print(edge_embedding.shape)
attn = attn + edge_embedding
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#######################################################################################################################
class CVA_Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.Qnorm = nn.LayerNorm(dim)
self.Knorm = nn.LayerNorm(dim)
self.Vnorm = nn.LayerNorm(dim)
self.QLinear = nn.Linear(dim, dim)
self.KLinear = nn.Linear(dim, dim)
self.VLinear = nn.Linear(dim, dim)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.edge_embedding = nn.Linear(17*17, 17*17)
def forward(self, x, CVA_input, edge_embedding):
B, N, C = x.shape
# CVA_input = self.max_pool(CVA_input)
# print(CVA_input.shape)
q = self.QLinear(self.Qnorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = self.KLinear(self.Knorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
v = self.VLinear(self.Vnorm(x)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale
edge_embedding = self.edge_embedding(edge_embedding)
edge_embedding = edge_embedding.reshape(1, 17, 17).unsqueeze(0).repeat(B, self.num_heads, 1, 1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#######################################################################################################################
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x), edge_embedding))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
#######################################################################################################################
class Multi_Out_Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.norm_hop1 = norm_layer(dim)
self.norm_hop2 = norm_layer(dim)
self.mlp_hop = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, hops, edge_embedding):
MSA = self.drop_path(self.attn(self.norm1(x), edge_embedding))
MSA = self.norm_hop1(hops) * MSA
x = x + MSA
x = x + self.drop_path(self.mlp(self.norm2(x)))
hops = hops + MSA
hops = hops + self.drop_path(self.mlp_hop(self.norm_hop2(hops)))
return x, hops, MSA
#######################################################################################################################
class Multi_In_Out_Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.cva_attn = CVA_Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
# self.max_pool = nn.MaxPool1d(3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
self.norm_hop1 = norm_layer(dim)
self.norm_hop2 = norm_layer(dim)
self.mlp_hop = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, hops, CVA_input, edge_embedding):
MSA = self.drop_path(self.cva_attn(x, CVA_input, edge_embedding))
MSA = self.norm_hop1(hops) * MSA
x = x + MSA
x = x + self.drop_path(self.mlp(self.norm2(x)))
hops = hops + MSA
hops = hops + self.drop_path(self.mlp_hop(self.norm_hop2(hops)))
return x, hops, MSA
#######################################################################################################################
class First_view_Spatial_features(nn.Module):
def __init__(self, num_frame=9, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, norm_layer=None):
super().__init__()
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
### spatial patch embedding
self.Spatial_patch_to_embedding = nn.Linear(in_chans, embed_dim_ratio)
self.Spatial_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
self.hop_to_embedding = nn.Linear(68, embed_dim_ratio)
self.hop_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.block1 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[0],
norm_layer=norm_layer)
self.block2 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[1],
norm_layer=norm_layer)
self.block3 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[2],
norm_layer=norm_layer)
self.block4 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[3],
norm_layer=norm_layer)
self.Spatial_norm = norm_layer(embed_dim_ratio)
self.hop_norm = norm_layer(embed_dim_ratio)
def forward(self, x, hops, edge_embedding):
b, _, f, p = x.shape ##### b is batch size, f is number of frames, p is number of joints
x = rearrange(x, 'b c f p -> (b f) p c', )
x = self.Spatial_patch_to_embedding(x)
x += self.Spatial_pos_embed
x = self.pos_drop(x)
hops = rearrange(hops, 'b c f p -> (b f) p c', )
hops = self.hop_to_embedding(hops)
hops += self.hop_pos_embed
hops = self.pos_drop(hops)
x, hops, MSA1 = self.block1(x, hops, edge_embedding)
x, hops, MSA2 = self.block2(x, hops, edge_embedding)
x, hops, MSA3 = self.block3(x, hops, edge_embedding)
x, hops, MSA4 = self.block4(x, hops, edge_embedding)
x = self.Spatial_norm(x)
x = rearrange(x, '(b f) w c -> b f (w c)', f=f)
hops = self.hop_norm(hops)
hops = rearrange(hops, '(b f) w c -> b f (w c)', f=f)
return x, hops, MSA1, MSA2, MSA3, MSA4
#######################################################################################################################
class Spatial_features(nn.Module):
def __init__(self, num_frame=9, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, norm_layer=None):
super().__init__()
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
### spatial patch embedding
self.Spatial_patch_to_embedding = nn.Linear(in_chans, embed_dim_ratio)
self.Spatial_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
self.hop_to_embedding = nn.Linear(68, embed_dim_ratio)
self.hop_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.block1 = Multi_In_Out_Block(
dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[0], norm_layer=norm_layer)
self.block2 = Multi_In_Out_Block(
dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[1], norm_layer=norm_layer)
self.block3 = Multi_In_Out_Block(
dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[2], norm_layer=norm_layer)
self.block4 = Multi_In_Out_Block(
dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[3], norm_layer=norm_layer)
self.Spatial_norm = norm_layer(embed_dim_ratio)
self.hop_norm = norm_layer(embed_dim_ratio)
def forward(self, x, hops, MSA1, MSA2, MSA3, MSA4, edge_embedding):
b, _, f, p = x.shape ##### b is batch size, f is number of frames, p is number of joints
x = rearrange(x, 'b c f p -> (b f) p c', )
x = self.Spatial_patch_to_embedding(x)
x += self.Spatial_pos_embed
x = self.pos_drop(x)
hops = rearrange(hops, 'b c f p -> (b f) p c', )
hops = self.hop_to_embedding(hops)
hops += self.hop_pos_embed
hops = self.pos_drop(hops)
x, hops, MSA1 = self.block1(x, hops, MSA1, edge_embedding)
x, hops, MSA2 = self.block2(x, hops, MSA2, edge_embedding)
x, hops, MSA3 = self.block3(x, hops, MSA3, edge_embedding)
x, hops, MSA4 = self.block4(x, hops, MSA4, edge_embedding)
x = self.Spatial_norm(x)
x = rearrange(x, '(b f) w c -> b f (w c)', f=f)
hops = self.hop_norm(hops)
hops = rearrange(hops, '(b f) w c -> b f (w c)', f=f)
return x, hops, MSA1, MSA2, MSA3, MSA4
model/Temporal_encoder.py
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## Our model was revised from https://github.com/zczcwh/PoseFormer/blob/main/common/model_poseformer.py
import torch
import torch.nn as nn
from functools import partial
from einops import rearrange
from timm.models.layers import DropPath
from common.opt import opts
opt = opts().parse()
#######################################################################################################################
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
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 Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#######################################################################################################################
class CVA_Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.Qnorm = nn.LayerNorm(dim)
self.Knorm = nn.LayerNorm(dim)
self.Vnorm = nn.LayerNorm(dim)
self.QLinear = nn.Linear(dim, dim)
self.KLinear = nn.Linear(dim, dim)
self.VLinear = nn.Linear(dim, dim)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, CVA_input):
B, N, C = x.shape
q = self.QLinear(self.Qnorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = self.KLinear(self.Knorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
v = self.VLinear(self.Vnorm(x)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#######################################################################################################################
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
#######################################################################################################################
class Temporal__features(nn.Module):
def __init__(self, num_frame=9, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, norm_layer=None):
super().__init__()
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
embed_dim = embed_dim_ratio * num_joints #### temporal embed_dim is num_joints * spatial embedding dim ratio
out_dim = num_joints * 3 #### output dimension is num_joints * 3
### Temporal patch embedding
self.Temporal_pos_embed = nn.Parameter(torch.zeros(1, num_frame, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.Temporal_norm = norm_layer(embed_dim)
####### A easy way to implement weighted mean
self.weighted_mean = torch.nn.Conv1d(in_channels=num_frame, out_channels=1, kernel_size=1)
def forward(self, x):
b = x.shape[0]
x += self.Temporal_pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.Temporal_norm(x)
##### x size [b, f, emb_dim], then take weighted mean on frame dimension, we only predict 3D pose of the center frame
# x = self.weighted_mean(x)
x = x.view(b, opt.frames, -1)
return x
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