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import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualDenseBlock_4C(nn.Module):
def __init__(self, nf=64, gc = 32, bias=True):
super(ResidualDenseBlock_4C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
# mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
return x4 * 0.2 + x
class RRDB(nn.Module):
'''Residual in Residual Dense Block'''
def __init__(self, nf):
super(RRDB, self).__init__()
gc = nf // 2
self.RDB1 = ResidualDenseBlock_4C(nf, gc)
self.RDB2 = ResidualDenseBlock_4C(nf, gc)
self.RDB3 = ResidualDenseBlock_4C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
return out * 0.2 + x
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True, skip=True, scale=2, bn=True, motion=False):
super().__init__()
factor = scale
# if bilinear, use the normal convolutions to reduce the number of channels
if bilinear:
if skip:
self.up = nn.Upsample(scale_factor=factor, mode='bilinear', align_corners=True)
self.conv = ConvLayer(in_channels, out_channels, bn=bn)
else:
self.up = nn.Upsample(scale_factor=factor, mode='bilinear', align_corners=True)
self.conv = ConvLayer(in_channels, out_channels)
else:
if skip:
self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=factor, stride=factor)
self.conv = ConvLayer(out_channels*2, out_channels, bn=bn, motion=motion)
else:
self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=factor, stride=factor)
self.conv = ConvLayer(out_channels, out_channels, bn=bn, motion=motion)
def forward(self, x1, x2=None):
x1 = self.up(x1)
if x2 is None:
return self.conv(x1)
# input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels, downsample=False,
upsample=False, skip=False, factor=2, motion=False):
super().__init__()
self.upsample = upsample
self.maxpool= None
if downsample:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=2)
if factor == 4:
self.maxpool = nn.MaxPool2d(2)
elif upsample:
self.conv1 = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=factor, stride=factor)
if motion:
self.shortcut = nn.Sequential(nn.Upsample(scale_factor=factor,
mode='bilinear',
align_corners=True),
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels))
else:
self.shortcut = nn.Sequential(nn.Upsample(scale_factor=factor,
mode='bilinear',
align_corners=True),
nn.Conv2d(in_channels, out_channels,kernel_size=1, stride=1))
else:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.shortcut = nn.Sequential()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, input):
shortcut = self.shortcut(input)
input = nn.ReLU()(self.conv1(input))
input = nn.ReLU()(self.conv2(input))
input = input + shortcut
if self.maxpool is not None:
input = self.maxpool(input)
return nn.LeakyReLU()(input)
class ConvLayer(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None, bn=True, motion=False, dilation=1):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
) if motion else nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=dilation, bias=False, dilation=dilation),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.conv(x)
class Conv3D(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride, padding):
super(Conv3D, self).__init__()
self.conv3d = nn.Conv3d(in_channel, out_channel, kernel_size=kernel_size, stride=stride, padding=padding)
self.bn3d = nn.BatchNorm3d(out_channel)
def forward(self, x):
# input x: (batch, seq, c, h, w)
x = x.permute(0, 2, 1, 3, 4).contiguous() # (batch, c, seq_len, h, w)
x = F.leaky_relu(self.bn3d(self.conv3d(x)))
x = x.permute(0, 2, 1, 3, 4).contiguous() # (batch, seq_len, c, h, w)
return x
class MatrixPredictor3DConv(nn.Module):
def __init__(self, hidden_len=64):
super(MatrixPredictor3DConv, self).__init__()
self.unet_base = hidden_len #64
self.hidden_len = hidden_len #64
self.conv_pre_1 = nn.Conv2d(hidden_len,hidden_len, kernel_size=3, stride=1, padding=1)
self.conv_pre_2 = nn.Conv2d(hidden_len, hidden_len, kernel_size=3, stride=1, padding=1)
self.conv3d_1 = Conv3D(self.unet_base, self.unet_base, kernel_size=(3, 3, 3), stride=1, padding=(1, 1, 1))
self.conv3d_2 = Conv3D(self.unet_base*2, self.unet_base*2, kernel_size=(3 , 3, 3), stride=1, padding=(0, 1, 1))
self.conv1_1 = nn.Conv2d(hidden_len, self.unet_base, kernel_size=3, stride=2, padding=1)
self.conv2_1 = nn.Conv2d(self.unet_base, self.unet_base * 2, kernel_size=3, stride=2, padding=1)
self.conv3_1 = nn.Conv2d(self.unet_base * 3, self.unet_base, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(self.unet_base, self.hidden_len, kernel_size=3, stride=1, padding=1)
self.bn_pre_1 = nn.BatchNorm2d(hidden_len)
self.bn_pre_2 = nn.BatchNorm2d(hidden_len)
self.bn1_1 = nn.BatchNorm2d(self.unet_base)
self.bn2_1 = nn.BatchNorm2d(self.unet_base * 2)
self.bn3_1 = nn.BatchNorm2d(self.unet_base)
self.bn4_1 = nn.BatchNorm2d(self.hidden_len)
def forward(self,x):
# x [B,T,C,32,32]
# out: [B,C,32,32]
batch, seq, z, h, w = x.size()
x = x.reshape(-1, x.size(-3), x.size(-2), x.size(-1))
x = F.leaky_relu(self.bn_pre_1(self.conv_pre_1(x)))
x = F.leaky_relu(self.bn_pre_2(self.conv_pre_2(x)))
x_1 = F.leaky_relu(self.bn1_1(self.conv1_1(x)))
x_1 = x_1.view(batch, -1, x_1.size(1), x_1.size(2), x_1.size(3)).contiguous() # (batch, seq, c, h, w)
x_1 = self.conv3d_1(x_1) # (batch, seq, c, h, w), 1st temporal conv
x_1 = x_1.view(-1, x_1.size(2), x_1.size(3), x_1.size(4)).contiguous() # (batch * seq, c, h, w)
x_2 = F.leaky_relu(self.bn2_1(self.conv2_1(x_1))) # (batch * seq, c, h // 2, w // 2)
x_2 = x_2.view(batch, -1, x_2.size(1), x_2.size(2), x_2.size(3)).contiguous() # (batch, seq, c, h, w)
x_2 = self.conv3d_2(x_2) # (batch, seq=1, c, h // 2, w // 2), 2nd temporal conv
x_2 = x_2.view(-1, x_2.size(2), x_2.size(3), x_2.size(4)).contiguous() # (batch * seq, c, h//2, w//2), seq = 1
x_1 = x_1.view(batch, -1, x_1.size(1), x_1.size(2), x_1.size(3)) # (batch, seq, c, h, w)
x_1 = x_1.permute(0, 2, 1, 3, 4).contiguous() # (batch, c, seq, h, w)
x_1 = F.adaptive_max_pool3d(x_1, (1, None, None)) # (batch, c, 1, h, w)
x_1 = x_1.permute(0, 2, 1, 3, 4).contiguous() # (batch, 1, c, h, w)
x_1 = x_1.view(-1, x_1.size(2), x_1.size(3), x_1.size(4)).contiguous() # (batch*1, c, h, w)
x_3 = F.leaky_relu(self.bn3_1(self.conv3_1(torch.cat((F.interpolate(x_2, scale_factor=(2, 2)), x_1), dim=1))))
x = x.view(batch, -1, x.size(1), x.size(2), x.size(3)) # (batch, seq, 1, h, w)
x = F.leaky_relu(self.bn4_1(self.conv4_1(F.interpolate(x_3, scale_factor=(2, 2)))))
return x
class SimpleMatrixPredictor3DConv_direct(nn.Module):
def __init__(self, T, hidden_len=64, image_pred=False, aft_seq_length=10):
super(SimpleMatrixPredictor3DConv_direct, self).__init__()
self.unet_base = hidden_len #64
self.hidden_len = hidden_len #64
self.conv_pre_1 = nn.Conv2d(hidden_len,hidden_len, kernel_size=3, stride=1, padding=1)
self.conv_pre_2 = nn.Conv2d(hidden_len, hidden_len, kernel_size=3, stride=1, padding=1)
self.fut_len = aft_seq_length
self.conv3d_1 = Conv3D(self.unet_base, self.unet_base, kernel_size=(3, 3, 3), stride=1, padding=(1, 1, 1))
if self.fut_len > 1 :
self.temporal_layer = Conv3D(self.unet_base*2, self.unet_base*2, kernel_size=(3, 3, 3), stride=1, padding=(1, 1, 1))
else:
self.temporal_layer = nn.Sequential(
nn.Conv2d(self.unet_base *2, self.unet_base * 2, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU())
input_len = T if image_pred else T - 1
self.conv_translate = nn.Sequential(
nn.Conv2d(self.unet_base * input_len , self.unet_base * self.fut_len, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU())
self.conv1_1 = nn.Conv2d(hidden_len, self.unet_base, kernel_size=3, stride=2, padding=1)
self.conv2_1 = nn.Conv2d(self.unet_base, self.unet_base * 2, kernel_size=3, stride=2, padding=1)
self.conv3_1 = nn.Conv2d(self.unet_base * 3, self.unet_base, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(self.unet_base, self.hidden_len, kernel_size=3, stride=1, padding=1)
self.bn_pre_1 = nn.BatchNorm2d(hidden_len)
self.bn_pre_2 = nn.BatchNorm2d(hidden_len)
self.bn1_1 = nn.BatchNorm2d(self.unet_base)
self.bn2_1 = nn.BatchNorm2d(self.unet_base * 2)
self.bn3_1 = nn.BatchNorm2d(self.unet_base)
self.bn4_1 = nn.BatchNorm2d(self.hidden_len)
self.bn_translate = nn.BatchNorm2d(self.unet_base * self.fut_len)
def forward(self,x):
# x [B,T,C,32,32]
# out: [B,C,32,32]
batch, seq, z, h, w = x.size()
x = x.reshape(-1, x.size(-3), x.size(-2), x.size(-1))
x = F.leaky_relu(self.bn_pre_1(self.conv_pre_1(x)))
x = F.leaky_relu(self.bn_pre_2(self.conv_pre_2(x)))
x_1 = F.leaky_relu(self.bn1_1(self.conv1_1(x)))
x_1 = x_1.view(batch, -1, x_1.size(1), x_1.size(2), x_1.size(3)).contiguous() # (batch, seq, c, h, w)
x_1 = self.conv3d_1(x_1) # (batch, seq, c, h, w), 1st temporal conv
batch, seq, c, h, w = x_1.shape
x_tmp = x_1.reshape(batch,-1,h,w)
x_tmp = self.bn_translate(self.conv_translate(x_tmp))
x_1 = x_tmp.reshape(batch,self.fut_len,c,h,w)
x_1 = x_1.view(-1, x_1.size(2), x_1.size(3), x_1.size(4)).contiguous() # (batch * seq, c, h, w)
x_2 = F.leaky_relu(self.bn2_1(self.conv2_1(x_1))) # (batch * seq, c, h // 2, w // 2)
if self.fut_len > 1:
x_2 = x_2.view(batch, -1, x_2.size(1), x_2.size(2), x_2.size(3)).contiguous() # (batch, seq, c, h, w)
x_2 = self.temporal_layer(x_2) # (batch, seq=10, c, h // 2, w // 2)
x_2 = x_2.view(-1, x_2.size(2), x_2.size(3), x_2.size(4)).contiguous() # (batch * seq, c, h//2, w//2), seq = 1
else:
x_2 = self.temporal_layer(x_2) # (batch * seq,c, h // 2, w // 2)
x_1 = x_1.view(batch, -1, x_1.size(1), x_1.size(2), x_1.size(3)) # (batch, seq, c, h, w)
x_1 = x_1.reshape(-1, x_1.size(2), x_1.size(3), x_1.size(4))
x_3 = F.leaky_relu(self.bn3_1(self.conv3_1(torch.cat((F.interpolate(x_2, size=x_1.shape[2:]), x_1), dim=1))))
x = x.view(batch, -1, x.size(1), x.size(2), x.size(3)) # (batch, seq, 1, h, w)
x = F.leaky_relu(self.bn4_1(self.conv4_1(F.interpolate(x_3, size = x.shape[3:]))))
return x
class PredictModel(nn.Module):
def __init__(self, T, hidden_len=32, aft_seq_length=10, mx_h=32, mx_w=32, use_direct_predictor=True):
super(PredictModel, self).__init__()
self.mx_h = mx_h
self.mx_w = mx_w
self.hidden_len = hidden_len
self.fut_len = aft_seq_length
self.conv1 = nn.Conv2d( 1, hidden_len, kernel_size=3, padding=1, bias=False)
self.fuse_conv = nn.Conv2d(hidden_len*2, hidden_len, kernel_size=3, padding=1, bias=False)
if use_direct_predictor:
self.predictor = SimpleMatrixPredictor3DConv_direct(T=T, hidden_len=hidden_len, aft_seq_length=aft_seq_length)
else:
self.predictor = MatrixPredictor3DConv(hidden_len)
self.out_conv = nn.Conv2d(hidden_len, 1, kernel_size=3, padding=1, bias=False)
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
def res_interpolate(self,in_tensor,template_tensor):
'''
in_tensor: batch,c,h'w',H'W'
tempolate_tensor: batch,c,hw,HW
out_tensor: batch,c,hw,HW
'''
out_tensor = F.interpolate(in_tensor,template_tensor.shape[-2:]) # (BThw,target_h,target_w)
return out_tensor
def forward(self,matrix_seq, softmax=False, res=None):
B,T,hw,window_size = matrix_seq.size()
matrix_seq = matrix_seq.reshape(-1,hw,self.mx_h,self.mx_w) # (BT,hw,hw)
matrix_seq = matrix_seq.reshape(B*T*hw,self.mx_h,self.mx_w).unsqueeze(1) # (BThw,1,h,w)
x = self.conv1(matrix_seq)
x = x.reshape(B,T,hw,-1,self.mx_h,self.mx_w)
x = x.permute(0,2,1,3,4,5).reshape(B*hw,T,-1,self.mx_h,self.mx_w)
emb = self.predictor(x)
emb = emb.reshape(B*hw*self.fut_len,-1,self.mx_h,self.mx_w)
res_emb = emb.clone()
if res is not None:
template = emb.clone().reshape(B,hw,emb.shape[1],-1).permute(0,2,1,3)
in_tensor = res.clone().reshape(B,hw//4,emb.shape[1],-1).permute(0,2,1,3)
res_tensor = self.res_interpolate(in_tensor,template).permute(0,2,1,3).reshape(emb.shape)
emb = self.fuse_conv(torch.cat([emb,res_tensor],dim=1))
out = self.out_conv(emb) #(Bhwt,16,h//4,w//4)
out = out.reshape(B,hw,-1,self.mx_h,self.mx_w)
out = out.permute(0,2,1,3,4)
out = out.reshape(B,-1,hw,window_size)
if softmax:
out = out.view(B,out.shape[1],-1)
out = self.softmax(out)
out = out.reshape(B,-1,hw,window_size)
return out,res_emb |