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# https://github.com/sxyu/pixel-nerf/blob/master/src/model/resnetfc.py
from torch import nn
import torch
from vit.vision_transformer import Mlp, DropPath
# Resnet Blocks
class ResnetBlockFC(nn.Module):
"""
Fully connected ResNet Block class.
Taken from DVR code.
:param size_in (int): input dimension
:param size_out (int): output dimension
:param size_h (int): hidden dimension
"""
def __init__(self, size_in, size_out=None, size_h=None, beta=0.0, init_as_zero=False):
super().__init__()
# Attributes
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_in
self.size_h = size_h
self.size_out = size_out
# Submodules
self.fc_0 = nn.Linear(size_in, size_h)
self.fc_1 = nn.Linear(size_h, size_out)
# Init
nn.init.constant_(self.fc_0.bias, 0.0)
if init_as_zero:
nn.init.zeros_(self.fc_0.weight)
else:
nn.init.kaiming_normal_(self.fc_0.weight, a=0, mode="fan_in")
nn.init.constant_(self.fc_1.bias, 0.0)
nn.init.zeros_(self.fc_1.weight)
if beta > 0:
self.activation = nn.Softplus(beta=beta)
else:
self.activation = nn.ReLU()
if size_in == size_out:
self.shortcut = None
else:
self.shortcut = nn.Linear(size_in, size_out, bias=False)
# nn.init.constant_(self.shortcut.bias, 0.0)
nn.init.kaiming_normal_(self.shortcut.weight, a=0, mode="fan_in")
def forward(self, x):
# with profiler.record_function("resblock"):
net = self.fc_0(self.activation(x))
dx = self.fc_1(self.activation(net))
if self.shortcut is not None:
x_s = self.shortcut(x)
else:
x_s = x
return x_s + dx
# Resnet Blocks
class ResnetBlockFCViT(nn.Module):
"""
Fully connected ResNet Block class.
Taken from DVR code.
:param size_in (int): input dimension
:param size_out (int): output dimension
:param size_h (int): hidden dimension
"""
def __init__(self, size_in, size_out=None, size_h=None, beta=0.0, init_as_zero=False):
super().__init__()
# Attributes
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_in
self.size_h = size_h
self.size_out = size_out
# Submodules
self.fc_0 = nn.Linear(size_in, size_h)
self.fc_1 = nn.Linear(size_h, size_out)
# Init
nn.init.constant_(self.fc_0.bias, 0.0)
if init_as_zero:
nn.init.zeros_(self.fc_0.weight)
else:
nn.init.kaiming_normal_(self.fc_0.weight, a=0, mode="fan_in")
nn.init.constant_(self.fc_1.bias, 0.0)
nn.init.zeros_(self.fc_1.weight)
if beta > 0:
self.activation = nn.Softplus(beta=beta)
else:
self.activation = nn.ReLU()
if size_in == size_out:
self.shortcut = None
else:
self.shortcut = nn.Linear(size_in, size_out, bias=False)
# nn.init.constant_(self.shortcut.bias, 0.0)
nn.init.kaiming_normal_(self.shortcut.weight, a=0, mode="fan_in")
def forward(self, x):
# with profiler.record_function("resblock"):
net = self.fc_0(self.activation(x))
dx = self.fc_1(self.activation(net))
if self.shortcut is not None:
x_s = self.shortcut(x)
else:
x_s = x
return x_s + dx
# class Block(nn.Module):
# def __init__(self,
# dim,
# num_heads,
# mlp_ratio=4.,
# qkv_bias=False,
# qk_scale=None,
# drop=0.,
# attn_drop=0.,
# drop_path=0.,
# act_layer=nn.GELU,
# norm_layer=nn.LayerNorm):
# super().__init__()
# self.norm1 = norm_layer(dim)
# self.attn = Attention(dim,
# num_heads=num_heads,
# qkv_bias=qkv_bias,
# qk_scale=qk_scale,
# attn_drop=attn_drop,
# proj_drop=drop)
# self.drop_path = DropPath(
# drop_path) if drop_path > 0. else nn.Identity()
# self.norm2 = norm_layer(dim)
# mlp_hidden_dim = int(dim * mlp_ratio)
# self.mlp = Mlp(in_features=dim,
# hidden_features=mlp_hidden_dim,
# act_layer=act_layer,
# drop=drop)
# def forward(self, x, return_attention=False):
# y, attn = self.attn(self.norm1(x))
# if return_attention:
# return attn
# x = x + self.drop_path(y)
# x = x + self.drop_path(self.mlp(self.norm2(x)))
# return x
class ResMlp(nn.Module):
def __init__(self,
size_in,
size_out=None,
size_h=None,
drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
# Attributes
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_in
self.size_h = size_h
self.size_out = size_out
# Submodules
self.norm1 = norm_layer(size_in) # ? how to use
self.mlp = Mlp(in_features=size_in,
out_features=size_out,
act_layer=act_layer,
drop=drop)
# Residual shortcuts
if size_in == size_out:
self.shortcut = None
else:
self.shortcut = nn.Linear(size_in, size_out, bias=False)
self.norm2 = norm_layer(size_in)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
dx = self.mlp(self.norm1(x))
if self.shortcut is not None:
x_s = self.shortcut(self.norm2(x))
else:
x_s = x
return x_s + self.drop_path(dx) |