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import torch
from torchsummary import summary
import torch.nn as nn
from model.local_attention import LocalGraph
from model.global_attention import MultiGlobalGraph, SingleGlobalGraph
class GraphAttentionBlock(nn.Module):
def __init__(self, adj, input_dim, output_dim, p_dropout):
super(GraphAttentionBlock, self).__init__()
hid_dim = output_dim
self.relu = nn.ReLU(inplace=True)
self.local_graph_layer = LocalGraph(adj, input_dim, hid_dim, p_dropout)
self.global_graph_layer = MultiGlobalGraph(adj, input_dim, input_dim//4, dropout=p_dropout)
# self.global_graph_layer = SingleGlobalGraph(adj, input_dim, output_dim)
self.cat_conv = nn.Conv2d(3*output_dim, 2*output_dim, 1, bias=False)
self.cat_bn = nn.BatchNorm2d(2*output_dim, momentum=0.1)
def forward(self, x):
# x: (B, C, T, N) --> (B, T, N, C)
x = x.permute(0, 2, 3, 1)
residual = x
x_ = self.local_graph_layer(x)
y_ = self.global_graph_layer(x)
x = torch.cat((residual, x_, y_), dim=-1)
# x: (B, T, N, C) --> (B, C, T, N)
x = x.permute(0, 3, 1, 2)
x = self.relu(self.cat_bn(self.cat_conv(x)))
return x
class SpatioTemporalModelBase(nn.Module):
"""
Do not instantiate this class.
"""
def __init__(self, adj, num_joints_in, in_features, num_joints_out,
filter_widths, causal, dropout, channels):
super().__init__()
# Validate input
for fw in filter_widths:
assert fw % 2 != 0, 'Only odd filter widths are supported'
self.num_joints_in = num_joints_in
self.in_features = in_features
self.num_joints_out = num_joints_out
self.filter_widths = filter_widths
self.drop = nn.Dropout(dropout)
self.relu = nn.ReLU(inplace=True)
self.pad = [filter_widths[0] // 2]
self.init_bn = nn.BatchNorm2d(in_features, momentum=0.1)
self.expand_bn = nn.BatchNorm2d(channels, momentum=0.1)
self.shrink = nn.Conv2d(2**len(self.filter_widths)*channels, 3, 1, bias=False)
def receptive_field(self):
"""
Return the total receptive field of this model as # of frames.
"""
frames = 0
for f in self.pad:
frames += f
return 1 + 2 * frames
def total_causal_shift(self):
"""
Return the asymmetric offset for sequence padding.
The returned value is typically 0 if causal convolutions are disabled,
otherwise it is half the receptive field.
"""
frames = self.causal_shift[0]
next_dilation = self.filter_widths[0]
for i in range(1, len(self.filter_widths)):
frames += self.causal_shift[i] * next_dilation
next_dilation *= self.filter_widths[i]
return frames
def forward(self, x):
"""
X: (B, C, T, N)
B: batchsize
T: Temporal
N: The number of keypoints
C: The feature dimension of keypoints
"""
assert len(x.shape) == 4
assert x.shape[-2] == self.num_joints_in
assert x.shape[-1] == self.in_features
# X: (B, T, N, C)
x = self._forward_blocks(x)
x = self.shrink(x)
# x: (B, C, T, N) --> (B, T, N, C)
x = x.permute(0, 2, 3, 1)
return x
class SpatioTemporalModel(SpatioTemporalModelBase):
"""
Reference 3D pose estimation model with temporal convolutions.
This implementation can be used for all use-cases.
"""
def __init__(self, adj, num_joints_in, in_features, num_joints_out,
filter_widths, causal=False, dropout=0.25, channels=64, dense=False):
"""
Initialize this model.
Arguments:
num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
dropout -- dropout probability
channels -- number of convolution channels
dense -- use regular dense convolutions instead of dilated convolutions (ablation experiment)
"""
super().__init__(adj, num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
self.expand_conv = nn.Conv2d(in_features, channels, (filter_widths[0], 1), bias=False)
nn.init.kaiming_normal_(self.expand_conv.weight)
layers_conv = []
layers_graph_conv = []
layers_bn = []
layers_graph_conv.append(GraphAttentionBlock(adj, channels, channels, p_dropout=dropout))
self.causal_shift = [(filter_widths[0]) // 2 if causal else 0]
next_dilation = filter_widths[0]
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
self.causal_shift.append((filter_widths[i] // 2 * next_dilation) if causal else 0)
layers_conv.append(nn.Conv2d(2**i*channels, 2**i*channels, (filter_widths[i], 1) if not dense else (2*self.pad[-1]+1, 1),
dilation=(next_dilation, 1) if not dense else (1, 1), bias=False))
layers_bn.append(nn.BatchNorm2d(2**i*channels, momentum=0.1))
layers_conv.append(nn.Conv2d(2**i*channels, 2**i*channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm2d(2**i*channels, momentum=0.1))
layers_graph_conv.append(GraphAttentionBlock(adj, 2**i*channels, 2**i*channels, p_dropout=dropout))
next_dilation *= filter_widths[i]
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
self.layers_graph_conv = nn.ModuleList(layers_graph_conv)
def _forward_blocks(self, x):
# x: (B, T, N, C) --> (B, C, T, N)
x = x.permute(0, 3, 1, 2)
x = self.init_bn(x)
x = self.relu(self.expand_bn(self.expand_conv(x)))
x = self.layers_graph_conv[0](x)
for i in range(len(self.pad) - 1):
pad = self.pad[i + 1]
shift = self.causal_shift[i + 1]
res = x[:, :, pad + shift: x.shape[2] - pad + shift]
# x: (B, C, T, N)
x = self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x)))
x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
x = self.layers_graph_conv[i + 1](x)
return x
class SpatioTemporalModelOptimized1f(SpatioTemporalModelBase):
"""
3D pose estimation model optimized for single-frame batching, i.e.
where batches have input length = receptive field, and output length = 1.
This scenario is only used for training when stride == 1.
This implementation replaces dilated convolutions with strided convolutions
to avoid generating unused intermediate results. The weights are interchangeable
with the reference implementation.
"""
def __init__(self, adj, num_joints_in, in_features, num_joints_out,
filter_widths, causal=False, dropout=0.25, channels=64):
"""
Initialize this model.
Arguments:
num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
in_features -- number of input features for each joint (typically 2 for 2D input)
num_joints_out -- number of output joints (can be different than input)
filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
dropout -- dropout probability
channels -- number of convolution channels
"""
super().__init__(adj, num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
self.expand_conv = nn.Conv2d(in_features, channels, (filter_widths[0], 1), stride=(filter_widths[0], 1), bias=False)
nn.init.kaiming_normal_(self.expand_conv.weight)
layers_conv = []
layers_graph_conv = []
layers_bn = []
layers_graph_conv.append(GraphAttentionBlock(adj, channels, channels, p_dropout=dropout))
self.causal_shift = [(filter_widths[0] // 2) if causal else 0]
next_dilation = filter_widths[0]
for i in range(1, len(filter_widths)):
self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
self.causal_shift.append((filter_widths[i] // 2) if causal else 0)
layers_conv.append(nn.Conv2d(2**i*channels, 2**i*channels, (filter_widths[i], 1), stride=(filter_widths[i], 1), bias=False))
layers_bn.append(nn.BatchNorm2d(2**i*channels, momentum=0.1))
layers_conv.append(nn.Conv2d(2**i*channels, 2**i*channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm2d(2**i*channels, momentum=0.1))
layers_graph_conv.append(GraphAttentionBlock(adj, 2**i*channels, 2**i*channels, p_dropout=dropout))
next_dilation *= filter_widths[i]
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)
self.layers_graph_conv = nn.ModuleList(layers_graph_conv)
def _forward_blocks(self, x):
# x: (B, T, N, C) --> (B, C, T, N)
x = x.permute(0, 3, 1, 2)
x = self.init_bn(x)
x = self.relu(self.expand_bn(self.expand_conv(x)))
x = self.layers_graph_conv[0](x)
for i in range(len(self.pad) - 1):
res = x[:, :, self.causal_shift[i+1] + self.filter_widths[i+1]//2 :: self.filter_widths[i+1]]
# x: (B, C, T, N)
x = self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x)))
x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
x = self.layers_graph_conv[i+1](x)
return x
if __name__ == "__main__":
import torch
import numpy as np
import torchsummary
from common.skeleton import Skeleton
from common.graph_utils import adj_mx_from_skeleton
h36m_skeleton = Skeleton(parents=[-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15],
joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31])
humaneva_skeleton = Skeleton(parents=[-1, 0, 1, 2, 3, 1, 5, 6, 0, 8, 9, 0, 11, 12, 1],
joints_left=[2, 3, 4, 8, 9, 10],
joints_right=[5, 6, 7, 11, 12, 13])
adj = adj_mx_from_skeleton(h36m_skeleton)
model = SpatioTemporalModel(adj, num_joints_in=17, in_features=2, num_joints_out=17,
filter_widths=[3, 3, 3], channels=128)
model = model.cuda()
model_params = 0
for parameter in model.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
input = torch.randn(2, 27, 17, 2)
input = input.cuda()
# summary(model, (27, 15, 2))
output = model(input)
print(output.shape)
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