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from torch import nn
import torch
import torch.nn.functional as F
from motion.model.layer_norm_fp16 import RMSNorm, LayerNorm
class ResConv1DBlock(nn.Module):
def __init__(self, n_in, n_state, bias, norm_type, activate_type):
super().__init__()
if activate_type.lower() == "silu":
activate = nn.SiLU()
elif activate_type.lower() == "relu":
activate = nn.ReLU()
elif activate_type.lower() == "gelu":
activate = nn.GELU()
elif activate_type.lower() == "mish":
activate = nn.Mish()
if norm_type.lower() == "rmsnorm":
norm = RMSNorm
elif norm_type.lower() == "layernorm":
norm = LayerNorm
self.norm1 = norm(n_state)
self.norm2 = norm(n_in)
self.relu1 = activate
self.relu2 = activate
self.conv1 = nn.Conv1d(n_in, n_state, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv1d(n_state, n_in, 1, 1, 0, bias=bias)
def forward(self, x):
x_orig = x
x = self.conv1(x)
x = self.norm1(x.transpose(-2, -1))
x = self.relu1(x.transpose(-2, -1))
x = self.conv2(x)
x = self.norm2(x.transpose(-2, -1))
x = self.relu2(x.transpose(-2, -1))
x = x + x_orig
return x
class Encoder_Block(nn.Module):
def __init__(self, begin_channel=263, latent_dim=512, num_layers=6, TN=1, bias=False, norm_type="rmsnorm", activate_type="silu"):
super(Encoder_Block, self).__init__()
self.layers = []
begin_channel = begin_channel
target_channel = latent_dim
if activate_type.lower() == "silu":
activate = nn.SiLU()
elif activate_type.lower() == "relu":
activate = nn.ReLU()
elif activate_type.lower() == "gelu":
activate = nn.GELU()
elif activate_type.lower() == "mish":
activate = nn.Mish()
self.layers.append(nn.Conv1d(begin_channel, target_channel, 3, 2, 1, bias=bias))
self.layers.append(activate)
for _ in range(num_layers): ### 196 -> 98 -> 49 -> 24 -> 12 -> 6 -> 3
self.layers.append(nn.Conv1d(target_channel, target_channel, 3, 2, 1, bias=bias))
self.layers.append(activate)
self.layers.append(ResConv1DBlock(target_channel, target_channel, bias, norm_type, activate_type))
self.layers = nn.Sequential(*self.layers)
self.maxpool = nn.AdaptiveMaxPool1d(TN)
def forward(self, x):
bs, njoints, nfeats, nframes = x.shape
reshaped_x = x.reshape(bs, njoints * nfeats, nframes) ### [bs, 263, seq]
res1 = self.layers(reshaped_x) #### [bs, 512, 1]
res2 = self.maxpool(res1)
res3 = res2.permute(2, 0, 1)
return res3 |