343 lines
15 KiB
Python
343 lines
15 KiB
Python
## 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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#######################################################################################################################
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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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self.edge_embedding = nn.Linear(17*17, 17*17)
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def forward(self, x, edge_embedding):
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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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edge_embedding = self.edge_embedding(edge_embedding)
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edge_embedding = edge_embedding.reshape(1, 17, 17).unsqueeze(0).repeat(B, self.num_heads, 1, 1)
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# print(edge_embedding.shape)
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attn = attn + edge_embedding
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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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self.edge_embedding = nn.Linear(17*17, 17*17)
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def forward(self, x, CVA_input, edge_embedding):
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B, N, C = x.shape
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# CVA_input = self.max_pool(CVA_input)
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# print(CVA_input.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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edge_embedding = self.edge_embedding(edge_embedding)
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edge_embedding = edge_embedding.reshape(1, 17, 17).unsqueeze(0).repeat(B, self.num_heads, 1, 1)
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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), edge_embedding))
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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 Multi_Out_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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self.norm_hop1 = norm_layer(dim)
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self.norm_hop2 = norm_layer(dim)
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self.mlp_hop = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x, hops, edge_embedding):
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MSA = self.drop_path(self.attn(self.norm1(x), edge_embedding))
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MSA = self.norm_hop1(hops) * MSA
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x = x + MSA
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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hops = hops + MSA
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hops = hops + self.drop_path(self.mlp_hop(self.norm_hop2(hops)))
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return x, hops, MSA
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#######################################################################################################################
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class Multi_In_Out_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.cva_attn = CVA_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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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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# self.max_pool = nn.MaxPool1d(3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
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self.norm_hop1 = norm_layer(dim)
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self.norm_hop2 = norm_layer(dim)
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self.mlp_hop = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x, hops, CVA_input, edge_embedding):
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MSA = self.drop_path(self.cva_attn(x, CVA_input, edge_embedding))
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MSA = self.norm_hop1(hops) * MSA
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x = x + MSA
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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hops = hops + MSA
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hops = hops + self.drop_path(self.mlp_hop(self.norm_hop2(hops)))
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return x, hops, MSA
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#######################################################################################################################
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class First_view_Spatial_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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### spatial patch embedding
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self.Spatial_patch_to_embedding = nn.Linear(in_chans, embed_dim_ratio)
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self.Spatial_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
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self.hop_to_embedding = nn.Linear(68, embed_dim_ratio)
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self.hop_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
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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.block1 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[0],
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norm_layer=norm_layer)
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self.block2 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[1],
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norm_layer=norm_layer)
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self.block3 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[2],
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norm_layer=norm_layer)
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self.block4 = Multi_Out_Block(dim=embed_dim_ratio, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[3],
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norm_layer=norm_layer)
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self.Spatial_norm = norm_layer(embed_dim_ratio)
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self.hop_norm = norm_layer(embed_dim_ratio)
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def forward(self, x, hops, edge_embedding):
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b, _, f, p = x.shape ##### b is batch size, f is number of frames, p is number of joints
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x = rearrange(x, 'b c f p -> (b f) p c', )
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x = self.Spatial_patch_to_embedding(x)
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x += self.Spatial_pos_embed
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x = self.pos_drop(x)
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hops = rearrange(hops, 'b c f p -> (b f) p c', )
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hops = self.hop_to_embedding(hops)
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hops += self.hop_pos_embed
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hops = self.pos_drop(hops)
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x, hops, MSA1 = self.block1(x, hops, edge_embedding)
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x, hops, MSA2 = self.block2(x, hops, edge_embedding)
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x, hops, MSA3 = self.block3(x, hops, edge_embedding)
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x, hops, MSA4 = self.block4(x, hops, edge_embedding)
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x = self.Spatial_norm(x)
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x = rearrange(x, '(b f) w c -> b f (w c)', f=f)
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hops = self.hop_norm(hops)
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hops = rearrange(hops, '(b f) w c -> b f (w c)', f=f)
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return x, hops, MSA1, MSA2, MSA3, MSA4
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#######################################################################################################################
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class Spatial_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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### spatial patch embedding
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self.Spatial_patch_to_embedding = nn.Linear(in_chans, embed_dim_ratio)
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self.Spatial_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
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self.hop_to_embedding = nn.Linear(68, embed_dim_ratio)
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self.hop_pos_embed = nn.Parameter(torch.zeros(1, num_joints, embed_dim_ratio))
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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.block1 = Multi_In_Out_Block(
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dim=embed_dim_ratio, 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[0], norm_layer=norm_layer)
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self.block2 = Multi_In_Out_Block(
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dim=embed_dim_ratio, 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[1], norm_layer=norm_layer)
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self.block3 = Multi_In_Out_Block(
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dim=embed_dim_ratio, 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[2], norm_layer=norm_layer)
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self.block4 = Multi_In_Out_Block(
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dim=embed_dim_ratio, 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[3], norm_layer=norm_layer)
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self.Spatial_norm = norm_layer(embed_dim_ratio)
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self.hop_norm = norm_layer(embed_dim_ratio)
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def forward(self, x, hops, MSA1, MSA2, MSA3, MSA4, edge_embedding):
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b, _, f, p = x.shape ##### b is batch size, f is number of frames, p is number of joints
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x = rearrange(x, 'b c f p -> (b f) p c', )
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x = self.Spatial_patch_to_embedding(x)
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x += self.Spatial_pos_embed
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x = self.pos_drop(x)
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hops = rearrange(hops, 'b c f p -> (b f) p c', )
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hops = self.hop_to_embedding(hops)
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hops += self.hop_pos_embed
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hops = self.pos_drop(hops)
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x, hops, MSA1 = self.block1(x, hops, MSA1, edge_embedding)
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x, hops, MSA2 = self.block2(x, hops, MSA2, edge_embedding)
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x, hops, MSA3 = self.block3(x, hops, MSA3, edge_embedding)
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x, hops, MSA4 = self.block4(x, hops, MSA4, edge_embedding)
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x = self.Spatial_norm(x)
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x = rearrange(x, '(b f) w c -> b f (w c)', f=f)
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hops = self.hop_norm(hops)
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hops = rearrange(hops, '(b f) w c -> b f (w c)', f=f)
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return x, hops, MSA1, MSA2, MSA3, MSA4 |