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IamZLT
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model/Temporal_encoder.py
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model/Temporal_encoder.py
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## Our model was revised from https://github.com/zczcwh/PoseFormer/blob/main/common/model_poseformer.py
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import torch
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import torch.nn as nn
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from functools import partial
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from einops import rearrange
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from timm.models.layers import DropPath
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from common.opt import opts
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opt = opts().parse()
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#######################################################################################################################
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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#######################################################################################################################
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class Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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#######################################################################################################################
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class CVA_Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim ** -0.5
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self.Qnorm = nn.LayerNorm(dim)
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self.Knorm = nn.LayerNorm(dim)
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self.Vnorm = nn.LayerNorm(dim)
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self.QLinear = nn.Linear(dim, dim)
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self.KLinear = nn.Linear(dim, dim)
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self.VLinear = nn.Linear(dim, dim)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, CVA_input):
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B, N, C = x.shape
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q = self.QLinear(self.Qnorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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k = self.KLinear(self.Knorm(CVA_input)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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v = self.VLinear(self.Vnorm(x)).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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#######################################################################################################################
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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x = x + self.drop_path(self.attn(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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#######################################################################################################################
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class Temporal__features(nn.Module):
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def __init__(self, num_frame=9, num_joints=17, in_chans=2, embed_dim_ratio=32, depth=4,
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num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, norm_layer=None):
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super().__init__()
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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embed_dim = embed_dim_ratio * num_joints #### temporal embed_dim is num_joints * spatial embedding dim ratio
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out_dim = num_joints * 3 #### output dimension is num_joints * 3
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### Temporal patch embedding
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self.Temporal_pos_embed = nn.Parameter(torch.zeros(1, num_frame, embed_dim))
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
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for i in range(depth)])
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self.Temporal_norm = norm_layer(embed_dim)
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####### A easy way to implement weighted mean
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self.weighted_mean = torch.nn.Conv1d(in_channels=num_frame, out_channels=1, kernel_size=1)
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def forward(self, x):
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b = x.shape[0]
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x += self.Temporal_pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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x = self.Temporal_norm(x)
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##### x size [b, f, emb_dim], then take weighted mean on frame dimension, we only predict 3D pose of the center frame
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# x = self.weighted_mean(x)
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x = x.view(b, opt.frames, -1)
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return x
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