in common/model.py [0:0]
def __init__(self, num_joints_in, in_features, num_joints_out,
filter_widths, causal=False, dropout=0.25, channels=1024, 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__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
self.expand_conv = nn.Conv1d(num_joints_in*in_features, channels, filter_widths[0], bias=False)
layers_conv = []
layers_bn = []
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.Conv1d(channels, channels,
filter_widths[i] if not dense else (2*self.pad[-1] + 1),
dilation=next_dilation if not dense else 1,
bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
next_dilation *= filter_widths[i]
self.layers_conv = nn.ModuleList(layers_conv)
self.layers_bn = nn.ModuleList(layers_bn)