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from __future__ import division
import torch
import torch.nn.functional as F
import numpy as np
import scipy.sparse
import math
from pathlib import Path
data_path = Path(__file__).parent / "data"
from comfy.model_management import get_torch_device
from wrapper_for_mps import sparse_to_dense
device = get_torch_device()
class SparseMM(torch.autograd.Function):
"""Redefine sparse @ dense matrix multiplication to enable backpropagation.
The builtin matrix multiplication operation does not support backpropagation in some cases.
"""
@staticmethod
def forward(ctx, sparse, dense):
ctx.req_grad = dense.requires_grad
ctx.save_for_backward(sparse)
return torch.matmul(sparse, dense)
@staticmethod
def backward(ctx, grad_output):
grad_input = None
sparse, = ctx.saved_tensors
if ctx.req_grad:
grad_input = torch.matmul(sparse.t(), grad_output)
return None, grad_input
def spmm(sparse, dense):
sparse = sparse.to(device)
dense = dense.to(device)
return SparseMM.apply(sparse, dense)
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = torch.nn.Parameter(torch.ones(hidden_size))
self.bias = torch.nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class GraphResBlock(torch.nn.Module):
"""
Graph Residual Block similar to the Bottleneck Residual Block in ResNet
"""
def __init__(self, in_channels, out_channels, mesh_type='body'):
super(GraphResBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.lin1 = GraphLinear(in_channels, out_channels // 2)
self.conv = GraphConvolution(out_channels // 2, out_channels // 2, mesh_type)
self.lin2 = GraphLinear(out_channels // 2, out_channels)
self.skip_conv = GraphLinear(in_channels, out_channels)
# print('Use BertLayerNorm in GraphResBlock')
self.pre_norm = BertLayerNorm(in_channels)
self.norm1 = BertLayerNorm(out_channels // 2)
self.norm2 = BertLayerNorm(out_channels // 2)
def forward(self, x):
trans_y = F.relu(self.pre_norm(x)).transpose(1,2)
y = self.lin1(trans_y).transpose(1,2)
y = F.relu(self.norm1(y))
y = self.conv(y)
trans_y = F.relu(self.norm2(y)).transpose(1,2)
y = self.lin2(trans_y).transpose(1,2)
z = x+y
return z
# class GraphResBlock(torch.nn.Module):
# """
# Graph Residual Block similar to the Bottleneck Residual Block in ResNet
# """
# def __init__(self, in_channels, out_channels, mesh_type='body'):
# super(GraphResBlock, self).__init__()
# self.in_channels = in_channels
# self.out_channels = out_channels
# self.conv = GraphConvolution(self.in_channels, self.out_channels, mesh_type)
# print('Use BertLayerNorm and GeLU in GraphResBlock')
# self.norm = BertLayerNorm(self.out_channels)
# def forward(self, x):
# y = self.conv(x)
# y = self.norm(y)
# y = gelu(y)
# z = x+y
# return z
class GraphLinear(torch.nn.Module):
"""
Generalization of 1x1 convolutions on Graphs
"""
def __init__(self, in_channels, out_channels):
super(GraphLinear, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.W = torch.nn.Parameter(torch.FloatTensor(out_channels, in_channels))
self.b = torch.nn.Parameter(torch.FloatTensor(out_channels))
self.reset_parameters()
def reset_parameters(self):
w_stdv = 1 / (self.in_channels * self.out_channels)
self.W.data.uniform_(-w_stdv, w_stdv)
self.b.data.uniform_(-w_stdv, w_stdv)
def forward(self, x):
return torch.matmul(self.W[None, :], x) + self.b[None, :, None]
class GraphConvolution(torch.nn.Module):
"""Simple GCN layer, similar to https://arxiv.org/abs/1609.02907."""
def __init__(self, in_features, out_features, mesh='body', bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
if mesh=='body':
adj_indices = torch.load(data_path / 'smpl_431_adjmat_indices.pt')
adj_mat_value = torch.load(data_path / 'smpl_431_adjmat_values.pt')
adj_mat_size = torch.load(data_path / 'smpl_431_adjmat_size.pt')
elif mesh=='hand':
adj_indices = torch.load(data_path / 'mano_195_adjmat_indices.pt')
adj_mat_value = torch.load(data_path / 'mano_195_adjmat_values.pt')
adj_mat_size = torch.load(data_path / 'mano_195_adjmat_size.pt')
self.adjmat = sparse_to_dense(torch.sparse_coo_tensor(adj_indices, adj_mat_value, size=adj_mat_size)).to(device)
self.weight = torch.nn.Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = torch.nn.Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
# stdv = 1. / math.sqrt(self.weight.size(1))
stdv = 6. / math.sqrt(self.weight.size(0) + 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, x):
if x.ndimension() == 2:
support = torch.matmul(x, self.weight)
output = torch.matmul(self.adjmat, support)
if self.bias is not None:
output = output + self.bias
return output
else:
output = []
for i in range(x.shape[0]):
support = torch.matmul(x[i], self.weight)
# output.append(torch.matmul(self.adjmat, support))
output.append(spmm(self.adjmat, support))
output = torch.stack(output, dim=0)
if self.bias is not None:
output = output + self.bias
return output
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')' |