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# --------------------------------------------------------
# Borrow from unofficial MLPMixer (https://github.com/920232796/MlpMixer-pytorch)
# Borrow from ResNet
# Modified by Zigang Geng ([email protected])
# --------------------------------------------------------
import torch
import torch.nn as nn
class FCBlock(nn.Module):
def __init__(self, dim, out_dim):
super().__init__()
self.ff = nn.Sequential(
nn.Linear(dim, out_dim),
nn.LayerNorm(out_dim),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.ff(x)
class MLPBlock(nn.Module):
def __init__(self, dim, inter_dim, dropout_ratio):
super().__init__()
self.ff = nn.Sequential(
nn.Linear(dim, inter_dim),
nn.GELU(),
nn.Dropout(dropout_ratio),
nn.Linear(inter_dim, dim),
nn.Dropout(dropout_ratio)
)
def forward(self, x):
return self.ff(x)
class MixerLayer(nn.Module):
def __init__(self,
hidden_dim,
hidden_inter_dim,
token_dim,
token_inter_dim,
dropout_ratio):
super().__init__()
self.layernorm1 = nn.LayerNorm(hidden_dim)
self.MLP_token = MLPBlock(token_dim, token_inter_dim, dropout_ratio)
self.layernorm2 = nn.LayerNorm(hidden_dim)
self.MLP_channel = MLPBlock(hidden_dim, hidden_inter_dim, dropout_ratio)
def forward(self, x):
y = self.layernorm1(x)
y = y.transpose(2, 1)
y = self.MLP_token(y)
y = y.transpose(2, 1)
z = self.layernorm2(x + y)
z = self.MLP_channel(z)
out = x + y + z
return out
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1,
downsample=None, dilation=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=dilation, bias=False, dilation=dilation)
self.bn1 = nn.BatchNorm2d(planes, momentum=0.1)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
padding=dilation, bias=False, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes, momentum=0.1)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def make_conv_layers(feat_dims, kernel=3, stride=1, padding=1, bnrelu_final=True):
layers = []
for i in range(len(feat_dims)-1):
layers.append(
nn.Conv2d(
in_channels=feat_dims[i],
out_channels=feat_dims[i+1],
kernel_size=kernel,
stride=stride,
padding=padding
))
# Do not use BN and ReLU for final estimation
if i < len(feat_dims)-2 or (i == len(feat_dims)-2 and bnrelu_final):
layers.append(nn.BatchNorm2d(feat_dims[i+1]))
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers) |