Update ParsingGait (#160)

* Update ParsingGait * Clear up the confusion Clear up the confusion about gait3d and gait3d-parsing. * Update 0.get_started.md * Add BaseParsingCuttingTransform * Update gcn.py * Create gaitbase_gait3d_parsing_btz32x2_fixed.yaml * Add gait3d_parsing config file * Update 1.model_zoo.md Update Gait3D-Parsing checkpoints * Update 1.model_zoo.md add configuration * Update 1.model_zoo.md center text --------- Co-authored-by: Junhao Liang <43094337+darkliang@users.noreply.github.com>
2023-10-29 21:53:02 +08:00
parent 44fb2414f2
commit 609aa0e9aa
17 changed files with 1422 additions and 3 deletions
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.parameter import Parameter
+import math
+
+
+class Normalize(nn.Module):
+
+    def __init__(self, power=2):
+        super(Normalize, self).__init__()
+        self.power = power
+
+    def forward(self, x):
+        norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
+        out = x.div(norm)
+        return out
+
+
+class GraphConvolution(nn.Module):
+    """
+    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
+    """
+
+    def __init__(self, in_features, out_features, adj_size=9, bias=True):
+        super(GraphConvolution, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.adj_size = adj_size
+
+        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
+        
+        if bias:
+            self.bias = Parameter(torch.FloatTensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+        #self.bn = nn.BatchNorm2d(self.out_features)
+        self.bn = nn.BatchNorm1d(out_features * adj_size)
+
+    def reset_parameters(self):
+        stdv = 1. / math.sqrt(self.weight.size(1))
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.uniform_(-stdv, stdv)
+
+    def forward(self, input, adj):
+        support = torch.matmul(input, self.weight)
+        output_ = torch.bmm(adj, support)
+        if self.bias is not None:
+            output_ =  output_ + self.bias
+        output = output_.view(output_.size(0), output_.size(1)*output_.size(2))
+        output = self.bn(output)
+        output = output.view(output_.size(0), output_.size(1), output_.size(2))
+
+        return output
+
+    def __repr__(self):
+        return self.__class__.__name__ + ' (' \
+               + str(self.in_features) + ' -> ' \
+               + str(self.out_features) + ')'
+
+
+class GCN(nn.Module):
+    def __init__(self, adj_size, nfeat, nhid, isMeanPooling = True):
+        super(GCN, self).__init__()
+
+        self.adj_size = adj_size
+        self.nhid = nhid
+        self.isMeanPooling = isMeanPooling
+        self.gc1 = GraphConvolution(nfeat, nhid ,adj_size)
+        self.gc2 = GraphConvolution(nhid, nhid, adj_size)
+
+    def forward(self, x, adj):
+        x_ = F.dropout(x, 0.5, training=self.training) 
+        x_ = F.relu(self.gc1(x_, adj))
+        x_ = F.dropout(x_, 0.5, training=self.training)
+        x_ = F.relu(self.gc2(x_, adj))
+        return x_
+