Zzier
609aa0e9aa
* 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>
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.nn.parameter import Parameter
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import math
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class Normalize(nn.Module):
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def __init__(self, power=2):
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super(Normalize, self).__init__()
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self.power = power
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def forward(self, x):
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norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
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out = x.div(norm)
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return out
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class GraphConvolution(nn.Module):
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"""
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Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
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"""
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def __init__(self, in_features, out_features, adj_size=9, bias=True):
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super(GraphConvolution, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.adj_size = adj_size
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self.weight = Parameter(torch.FloatTensor(in_features, out_features))
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if bias:
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self.bias = Parameter(torch.FloatTensor(out_features))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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#self.bn = nn.BatchNorm2d(self.out_features)
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self.bn = nn.BatchNorm1d(out_features * adj_size)
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def reset_parameters(self):
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stdv = 1. / math.sqrt(self.weight.size(1))
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self.weight.data.uniform_(-stdv, stdv)
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if self.bias is not None:
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self.bias.data.uniform_(-stdv, stdv)
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def forward(self, input, adj):
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support = torch.matmul(input, self.weight)
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output_ = torch.bmm(adj, support)
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if self.bias is not None:
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output_ = output_ + self.bias
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output = output_.view(output_.size(0), output_.size(1)*output_.size(2))
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output = self.bn(output)
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output = output.view(output_.size(0), output_.size(1), output_.size(2))
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return output
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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+ str(self.in_features) + ' -> ' \
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+ str(self.out_features) + ')'
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class GCN(nn.Module):
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def __init__(self, adj_size, nfeat, nhid, isMeanPooling = True):
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super(GCN, self).__init__()
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self.adj_size = adj_size
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self.nhid = nhid
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self.isMeanPooling = isMeanPooling
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self.gc1 = GraphConvolution(nfeat, nhid ,adj_size)
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self.gc2 = GraphConvolution(nhid, nhid, adj_size)
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def forward(self, x, adj):
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x_ = F.dropout(x, 0.5, training=self.training)
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x_ = F.relu(self.gc1(x_, adj))
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x_ = F.dropout(x_, 0.5, training=self.training)
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x_ = F.relu(self.gc2(x_, adj))
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return x_
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