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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from einops import rearrange
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.core.attention import LoFTREncoderLayer
# -- Added by Chu King on 16th November 2025 for debugging purposes.
import os, signal
import logging
import torch.distributed as dist
# Ref: https://github.com/princeton-vl/RAFT/blob/master/core/update.py
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.conv2(self.relu(self.conv1(x)))
class SepConvGRU(nn.Module):
def __init__(self, hidden_dim=128, input_dim=192 + 128):
super(SepConvGRU, self).__init__()
self.convz1 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
)
self.convr1 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
)
self.convq1 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
)
self.convz2 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
)
self.convr2 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
)
self.convq2 = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
)
def forward(self, h, x):
# horizontal
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz1(hx))
r = torch.sigmoid(self.convr1(hx))
q = torch.tanh(self.convq1(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
# vertical
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz2(hx))
r = torch.sigmoid(self.convr2(hx))
q = torch.tanh(self.convq2(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
return h
class ConvGRU(nn.Module):
def __init__(self, hidden_dim, input_dim, kernel_size=3):
super(ConvGRU, self).__init__()
self.convz = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, kernel_size, padding=kernel_size // 2
)
self.convr = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, kernel_size, padding=kernel_size // 2
)
self.convq = nn.Conv2d(
hidden_dim + input_dim, hidden_dim, kernel_size, padding=kernel_size // 2
)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx))
r = torch.sigmoid(self.convr(hx))
q = torch.tanh(self.convq(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
return h
class SepConvGRU3D(nn.Module):
def __init__(self, hidden_dim=128, input_dim=192 + 128):
super(SepConvGRU3D, self).__init__()
self.convz1 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 1, 5), padding=(0, 0, 2)
)
self.convr1 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 1, 5), padding=(0, 0, 2)
)
self.convq1 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 1, 5), padding=(0, 0, 2)
)
self.convz2 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 5, 1), padding=(0, 2, 0)
)
self.convr2 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 5, 1), padding=(0, 2, 0)
)
self.convq2 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (1, 5, 1), padding=(0, 2, 0)
)
self.convz3 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (5, 1, 1), padding=(2, 0, 0)
)
self.convr3 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (5, 1, 1), padding=(2, 0, 0)
)
self.convq3 = nn.Conv3d(
hidden_dim + input_dim, hidden_dim, (5, 1, 1), padding=(2, 0, 0)
)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz1(hx))
r = torch.sigmoid(self.convr1(hx))
q = torch.tanh(self.convq1(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
# vertical
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz2(hx))
r = torch.sigmoid(self.convr2(hx))
q = torch.tanh(self.convq2(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
# time
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz3(hx))
r = torch.sigmoid(self.convr3(hx))
q = torch.tanh(self.convq3(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
return h
class BasicMotionEncoder(nn.Module):
def __init__(self, cor_planes):
super(BasicMotionEncoder, self).__init__()
self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
self.conv = nn.Conv2d(64 + 192, 128 - 2, 3, padding=1)
def forward(self, flow, corr):
cor = F.relu(self.convc1(corr))
cor = F.relu(self.convc2(cor))
flo = F.relu(self.convf1(flow))
flo = F.relu(self.convf2(flo))
cor_flo = torch.cat([cor, flo], dim=1)
out = F.relu(self.conv(cor_flo))
return torch.cat([out, flow], dim=1)
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x):
B, N, C = x.shape
# -- Bug fixed by Chu King on 22nd November 2025
qkv = self.qkv(x)
# -- qkv = x.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
qkv = qkv.view(B, N, 3, self.num_heads, C // self.num_heads)
qkv = qkv.permute(0, 3, 1, 2, 4) # -- (B, H, N, 3, -1)
# -- q, k, v = qkv, qkv, qkv
q, k, v = qkv.unbind(dim=3)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, C).contiguous()
x = self.proj(x)
return x
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class TimeAttnBlock(nn.Module):
def __init__(self, dim=256, num_heads=8):
super(TimeAttnBlock, self).__init__()
self.temporal_attn = Attention(dim, num_heads=8, qkv_bias=False, qk_scale=None)
self.temporal_fc = nn.Linear(dim, dim)
self.temporal_norm1 = nn.LayerNorm(dim)
nn.init.constant_(self.temporal_fc.weight, 0)
nn.init.constant_(self.temporal_fc.bias, 0)
def forward(self, x, T=1):
_, _, h, w = x.shape
x = rearrange(x, "(b t) m h w -> (b h w) t m", h=h, w=w, t=T)
res_temporal1 = self.temporal_attn(self.temporal_norm1(x))
res_temporal1 = rearrange(
res_temporal1, "(b h w) t m -> b (h w t) m", h=h, w=w, t=T
)
res_temporal1 = self.temporal_fc(res_temporal1)
res_temporal1 = rearrange(
res_temporal1, " b (h w t) m -> b t m h w", h=h, w=w, t=T
)
x = rearrange(x, "(b h w) t m -> b t m h w", h=h, w=w, t=T)
x = x + res_temporal1
x = rearrange(x, "b t m h w -> (b t) m h w", h=h, w=w, t=T)
return x
class SpaceAttnBlock(nn.Module):
def __init__(self, dim=256, num_heads=8):
super(SpaceAttnBlock, self).__init__()
self.encoder_layer = LoFTREncoderLayer(dim, nhead=num_heads, attention="linear")
def forward(self, x, T=1):
_, _, h, w = x.shape
x = rearrange(x, "(b t) m h w -> (b t) (h w) m", h=h, w=w, t=T)
x = self.encoder_layer(x, x)
x = rearrange(x, "(b t) (h w) m -> (b t) m h w", h=h, w=w, t=T)
return x
class BasicUpdateBlock(nn.Module):
def __init__(self, hidden_dim, cor_planes, mask_size=8, attention_type=None):
super(BasicUpdateBlock, self).__init__()
self.attention_type = attention_type
if attention_type is not None:
if "update_time" in attention_type:
self.time_attn = TimeAttnBlock(dim=256, num_heads=8)
if "update_space" in attention_type:
self.space_attn = SpaceAttnBlock(dim=256, num_heads=8)
self.encoder = BasicMotionEncoder(cor_planes)
self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128 + hidden_dim)
self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
self.mask = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, mask_size ** 2 * 9, 1, padding=0),
)
def forward(self, net, inp, corr, flow, upsample=True, t=1):
motion_features = self.encoder(flow, corr)
inp = torch.cat((inp, motion_features), dim=1)
if self.attention_type is not None:
if "update_time" in self.attention_type:
inp = self.time_attn(inp, T=t)
if "update_space" in self.attention_type:
inp = self.space_attn(inp, T=t)
net = self.gru(net, inp)
delta_flow = self.flow_head(net)
# scale mask to balence gradients
mask = 0.25 * self.mask(net)
return net, mask, delta_flow
class FlowHead3D(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead3D, self).__init__()
self.conv1 = nn.Conv3d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv3d(hidden_dim, 2, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.conv2(self.relu(self.conv1(x)))
class SequenceUpdateBlock3D(nn.Module):
def __init__(self, hidden_dim, cor_planes, mask_size=8, attention_type=None):
super(SequenceUpdateBlock3D, self).__init__()
# -- Extracts motion-related features from:
# * current flow estimate
# * correlation volume
self.encoder = BasicMotionEncoder(cor_planes)
# -- 3D separable convolution GRU enables temporal reasoning with 3D convolutions.
self.gru = SepConvGRU3D(hidden_dim=hidden_dim, input_dim=128 + hidden_dim)
self.flow_head = FlowHead3D(hidden_dim, hidden_dim=256)
self.mask = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim + 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim + 128, (mask_size ** 2) * 9, 1, padding=0),
)
self.attention_type = attention_type
if attention_type is not None:
if "update_time" in attention_type:
self.time_attn = TimeAttnBlock(dim=256, num_heads=8)
if "update_space" in attention_type:
self.space_attn = SpaceAttnBlock(dim=256, num_heads=8)
def forward(self, net, inp, corrs, flows, t, upsample=True):
inp_tensor = []
motion_features = self.encoder(flows, corrs)
inp_tensor = torch.cat([inp, motion_features], dim=1)
if self.attention_type is not None:
if "update_time" in self.attention_type:
inp_tensor = self.time_attn(inp_tensor, T=t)
if "update_space" in self.attention_type:
inp_tensor = self.space_attn(inp_tensor, T=t)
net = rearrange(net, "(b t) c h w -> b c t h w", t=t)
inp_tensor = rearrange(inp_tensor, "(b t) c h w -> b c t h w", t=t)
net = self.gru(net, inp_tensor)
delta_flow = self.flow_head(net)
# scale mask to balance gradients
net = rearrange(net, " b c t h w -> (b t) c h w")
mask = 0.25 * self.mask(net)
delta_flow = rearrange(delta_flow, " b c t h w -> (b t) c h w")
return net, mask, delta_flow
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