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from typing import Literal
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin
from torchvision.models import (
ResNet34_Weights,
WeightsEnum,
)
from core.update import GMAUpdateBlock
from core.corr import CorrBlock
from core.utils.utils import coords_grid, InputPadder
from core.extractor import ResNetFPN16x
from core.layer import conv3x3
from core.gma import Attention
class MEMFOF(
nn.Module,
PyTorchModelHubMixin,
# optionally, you can add metadata which gets pushed to the model card
pipeline_tag="optical-flow-estimation",
license="bsd-3-clause",
):
def __init__(
self,
backbone: Literal["resnet18", "resnet34", "resnet50"] = "resnet34",
backbone_weights: WeightsEnum = ResNet34_Weights.IMAGENET1K_V1,
dim: int = 512,
corr_levels: int = 4,
corr_radius: int = 4,
num_blocks: int = 2,
use_var: bool = True,
var_min: float = 0.0,
var_max: float = 10.0,
):
super().__init__()
self.dim = dim
self.corr_levels = corr_levels
self.corr_radius = corr_radius
self.use_var = use_var
self.var_min = var_min
self.var_max = var_max
self.cnet = ResNetFPN16x(9, dim * 2, backbone, backbone_weights)
self.init_conv = conv3x3(2 * dim, 2 * dim)
self.upsample_weight = nn.Sequential(
nn.Conv2d(dim, dim * 2, 3, padding=1),
nn.ReLU(),
nn.Conv2d(dim * 2, 2 * 16 * 16 * 9, 1, padding=0),
)
self.flow_head = nn.Sequential(
# flow(2) + weight(2) + log_b(2)
nn.Conv2d(dim, 2 * dim, 3, padding=1),
nn.ReLU(),
nn.Conv2d(2 * dim, 2 * 6, 3, padding=1),
)
self.fnet = ResNetFPN16x(3, dim * 2, backbone, backbone_weights)
corr_channel = corr_levels * (corr_radius * 2 + 1) ** 2
self.update_block = GMAUpdateBlock(num_blocks, corr_channel, hdim=dim, cdim=dim)
self.att = Attention(dim=dim, heads=1, dim_head=dim)
def forward(
self,
images: torch.Tensor,
iters: int = 8,
flow_gts: torch.Tensor | None = None,
fmap_cache: list[torch.Tensor | None] = [None, None, None],
) -> dict[str, torch.Tensor | list[torch.Tensor] | None]:
"""Forward pass of the MEMFOF model.
Parameters
----------
images : torch.Tensor
Tensor of shape [B, 3, 3, H, W].
Images should be in range [0, 255].
iters : int, optional
Number of iterations for flow refinement, by default 8
flow_gts : torch.Tensor | None, optional
Ground truth flow fields of shape [B, 2, 2, H, W], by default None
First dimension of size 2 represents backward and forward flows
Second dimension of size 2 represents x and y components
fmap_cache : list[torch.Tensor | None], optional
Cache for feature maps to be used in current forward pass, by default [None, None, None]
Returns
-------
dict[str, torch.Tensor | list[torch.Tensor] | None]
Dictionary containing:
- "flow": List of flow predictions of shape [B, 2, 2, H, W] at each iteration
- "info": List of additional information of shape [B, 2, 4, H, W] at each iteration
- "nf": List of negative free energy losses of shape [B, 2, 2, H, W] at each iteration (only during training)
- "fmap_cache": Feature map cache of this forward pass
"""
B, _, _, H, W = images.shape
if flow_gts is None:
flow_gts = torch.zeros(B, 2, 2, H, W, device=images.device)
images = 2 * (images / 255.0) - 1.0
images = images.contiguous()
flow_predictions = []
info_predictions = []
# padding
padder = InputPadder(images.shape)
images = padder.pad(images)
B, _, _, H, W = images.shape
dilation = torch.ones(B, 1, H // 16, W // 16, device=images.device)
# run the context network
cnet = self.cnet(torch.cat([images[:, 0], images[:, 1], images[:, 2]], dim=1))
cnet = self.init_conv(cnet)
net, context = torch.split(cnet, [self.dim, self.dim], dim=1)
attention = self.att(context)
# init flow
flow_update = self.flow_head(net)
weight_update = 0.25 * self.upsample_weight(net)
flow_16x_21 = flow_update[:, 0:2]
info_16x_21 = flow_update[:, 2:6]
flow_16x_23 = flow_update[:, 6:8]
info_16x_23 = flow_update[:, 8:12]
if self.training or iters == 0:
flow_up_21, info_up_21 = self._upsample_data(
flow_16x_21, info_16x_21, weight_update[:, : 16 * 16 * 9]
)
flow_up_23, info_up_23 = self._upsample_data(
flow_16x_23, info_16x_23, weight_update[:, 16 * 16 * 9 :]
)
flow_predictions.append(torch.stack([flow_up_21, flow_up_23], dim=1))
info_predictions.append(torch.stack([info_up_21, info_up_23], dim=1))
if iters > 0:
# run the feature network
fmap1_16x = (
self.fnet(images[:, 0])
if fmap_cache[0] is None
else fmap_cache[0].clone().to(cnet)
)
fmap2_16x = (
self.fnet(images[:, 1])
if fmap_cache[1] is None
else fmap_cache[1].clone().to(cnet)
)
fmap3_16x = (
self.fnet(images[:, 2])
if fmap_cache[2] is None
else fmap_cache[2].clone().to(cnet)
)
corr_fn_21 = CorrBlock(
fmap2_16x, fmap1_16x, self.corr_levels, self.corr_radius
)
corr_fn_23 = CorrBlock(
fmap2_16x, fmap3_16x, self.corr_levels, self.corr_radius
)
for itr in range(iters):
B, _, H, W = flow_16x_21.shape
flow_16x_21 = flow_16x_21.detach()
flow_16x_23 = flow_16x_23.detach()
coords21 = (
coords_grid(B, H, W, device=images.device) + flow_16x_21
).detach()
coords23 = (
coords_grid(B, H, W, device=images.device) + flow_16x_23
).detach()
corr_21 = corr_fn_21(coords21, dilation=dilation)
corr_23 = corr_fn_23(coords23, dilation=dilation)
corr = torch.cat([corr_21, corr_23], dim=1)
flow_16x = torch.cat([flow_16x_21, flow_16x_23], dim=1)
net = self.update_block(net, context, corr, flow_16x, attention)
flow_update = self.flow_head(net)
weight_update = 0.25 * self.upsample_weight(net)
flow_16x_21 = flow_16x_21 + flow_update[:, 0:2]
info_16x_21 = flow_update[:, 2:6]
flow_16x_23 = flow_16x_23 + flow_update[:, 6:8]
info_16x_23 = flow_update[:, 8:12]
if self.training or itr == iters - 1:
flow_up_21, info_up_21 = self._upsample_data(
flow_16x_21, info_16x_21, weight_update[:, : 16 * 16 * 9]
)
flow_up_23, info_up_23 = self._upsample_data(
flow_16x_23, info_16x_23, weight_update[:, 16 * 16 * 9 :]
)
flow_predictions.append(torch.stack([flow_up_21, flow_up_23], dim=1))
info_predictions.append(torch.stack([info_up_21, info_up_23], dim=1))
for i in range(len(info_predictions)):
flow_predictions[i] = padder.unpad(flow_predictions[i])
info_predictions[i] = padder.unpad(info_predictions[i])
new_fmap_cache = [None, None, None]
if iters > 0:
new_fmap_cache[0] = fmap1_16x.clone().cpu()
new_fmap_cache[1] = fmap2_16x.clone().cpu()
new_fmap_cache[2] = fmap3_16x.clone().cpu()
if not self.training:
return {
"flow": flow_predictions,
"info": info_predictions,
"nf": None,
"fmap_cache": new_fmap_cache,
}
else:
# exlude invalid pixels and extremely large diplacements
nf_predictions = []
for i in range(len(info_predictions)):
if not self.use_var:
var_max = var_min = 0
else:
var_max = self.var_max
var_min = self.var_min
nf_losses = []
for k in range(2):
raw_b = info_predictions[i][:, k, 2:]
log_b = torch.zeros_like(raw_b)
weight = info_predictions[i][:, k, :2]
# Large b Component
log_b[:, 0] = torch.clamp(raw_b[:, 0], min=0, max=var_max)
# Small b Component
log_b[:, 1] = torch.clamp(raw_b[:, 1], min=var_min, max=0)
# term2: [N, 2, m, H, W]
term2 = (
(flow_gts[:, k] - flow_predictions[i][:, k]).abs().unsqueeze(2)
) * (torch.exp(-log_b).unsqueeze(1))
# term1: [N, m, H, W]
term1 = weight - math.log(2) - log_b
nf_loss = torch.logsumexp(
weight, dim=1, keepdim=True
) - torch.logsumexp(term1.unsqueeze(1) - term2, dim=2)
nf_losses.append(nf_loss)
nf_predictions.append(torch.stack(nf_losses, dim=1))
return {
"flow": flow_predictions,
"info": info_predictions,
"nf": nf_predictions,
"fmap_cache": new_fmap_cache,
}
def _upsample_data(self, flow, info, mask):
"""Upsample [H/16, W/16, C] -> [H, W, C] using convex combination"""
"""Forward pass of the MEMFOF model.
Parameters
----------
flow : torch.Tensor
Tensor of shape [B, 2, H / 16, W / 16].
info : torch.Tensor
Tensor of shape [B, 4, H / 16, W / 16].
mask : torch.Tensor
Tensor of shape [B, 9 * 16 * 16, H / 16, W / 16]
Returns
-------
flow : torch.Tensor
Tensor of shape [B, 2, H, W]
info : torch.Tensor
Tensor of shape [B, 4, H, W]
"""
B, C, H, W = info.shape
mask = mask.view(B, 1, 9, 16, 16, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(16 * flow, [3, 3], padding=1)
up_flow = up_flow.view(B, 2, 9, 1, 1, H, W)
up_info = F.unfold(info, [3, 3], padding=1)
up_info = up_info.view(B, C, 9, 1, 1, H, W)
up_flow = torch.sum(mask * up_flow, dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
up_info = torch.sum(mask * up_info, dim=2)
up_info = up_info.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(B, 2, 16 * H, 16 * W), up_info.reshape(
B, C, 16 * H, 16 * W
)
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