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import torch
import math
import numpy
import torch.nn.functional as F
import torch.nn as nn
import torchvision.transforms.v2.functional as TF
import modules.components.upr_net_mod2.correlation as correlation
import modules.components.upr_net_mod2.softsplat as softsplat
from modules.components.upr_net_mod2.m2m import *
from modules.components.upr_net_mod2.backwarp import backwarp
from .costvol import costvol_func
from ..components import register
from utils.padder import InputPadder
from utils.vos.model.network import STCN
from utils.vos.model.inference_core import InferenceCore
# **************************************************************************************************#
# => Feature Pyramid
# **************************************************************************************************#
def photometric_consistency(img0, img1, flow01):
return (img0 - backwarp(img1, flow01)).abs().sum(dim=1, keepdims=True)
def flow_consistency(flow01, flow10):
return (flow01 + backwarp(flow10, flow01)).abs().sum(dim=1, keepdims=True)
def gaussian(x):
gaussian_kernel = torch.tensor([[1, 2, 1],
[2, 4, 2],
[1, 2, 1]]) / 16
gaussian_kernel = gaussian_kernel.repeat(2, 1, 1, 1)
gaussian_kernel = gaussian_kernel.to("cpu")#torch.cuda.current_device())
x = torch.nn.functional.pad(x, (1, 1, 1, 1), mode='reflect')
out = torch.nn.functional.conv2d(x, gaussian_kernel, groups=x.shape[1])
# out = TF.gaussian_blur(x, [3, 3], sigma=[2, 2])
return out
def variance_flow(flow):
flow = flow * torch.tensor(data=[2.0 / (flow.shape[3] - 1.0), 2.0 / (flow.shape[2] - 1.0)], dtype=flow.dtype,
device=flow.device).view(1, 2, 1, 1)
return (gaussian(flow ** 2) - gaussian(flow) ** 2 + 1e-4).sqrt().abs().sum(dim=1, keepdim=True)
class FeatPyramid(nn.Module):
"""A 3-level feature pyramid, which by default is shared by the motion
estimator and synthesis network.
"""
def __init__(self):
super(FeatPyramid, self).__init__()
self.conv_stage0 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=32),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=32),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=32),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3,
stride=1, padding=1))
self.conv_stage1 = nn.Sequential(
nn.InstanceNorm2d(num_features=32),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3,
stride=2, padding=1),
nn.InstanceNorm2d(num_features=64),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=64),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=64),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1), )
self.conv_stage2 = nn.Sequential(
nn.InstanceNorm2d(num_features=64),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3,
stride=2, padding=1),
nn.InstanceNorm2d(num_features=128),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=128),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1),
nn.InstanceNorm2d(num_features=128),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1), )
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, img):
C0 = self.conv_stage0(img)
C1 = self.conv_stage1(C0)
C2 = self.conv_stage2(C1)
return [C0, C1, C2]
# **************************************************************************************************#
# => Motion Estimation
# **************************************************************************************************#
class MotionEstimator(nn.Module):
"""Bi-directional optical flow estimator
1) construct partial cost volume with the CNN features from the stage 2 of
the feature pyramid;
2) estimate bi-directional flows, by feeding cost volume, CNN features for
both warped images, CNN feature and estimated flow from previous iteration.
"""
def __init__(self):
super(MotionEstimator, self).__init__()
# 64 + 256 + 128 * 2 + 128 = 704
self.conv_flow = nn.Sequential(
nn.Conv2d(4, 128, 7, padding=3),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(128, 64, 3, padding=1),
nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.conv_corr = nn.Sequential(
nn.Conv2d(81, 64, 1, padding=0),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(64, 128, 3, padding=1),
nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.conv_layer1 = nn.Sequential(
nn.Conv2d(in_channels=704, out_channels=320,
kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(inplace=False, negative_slope=0.1))
self.conv_layer2 = nn.Sequential(
nn.Conv2d(in_channels=320, out_channels=256,
kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(inplace=False, negative_slope=0.1))
self.conv_layer3 = nn.Sequential(
nn.Conv2d(in_channels=256, out_channels=224,
kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(inplace=False, negative_slope=0.1))
self.conv_layer4 = nn.Sequential(
nn.Conv2d(in_channels=224, out_channels=192,
kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(inplace=False, negative_slope=0.1))
self.conv_layer5 = nn.Sequential(
nn.Conv2d(in_channels=192, out_channels=128,
kernel_size=3, stride=1, padding=1))
self.conv_layer6 = nn.Sequential(
nn.LeakyReLU(inplace=False, negative_slope=0.1),
nn.Conv2d(in_channels=128, out_channels=4,
kernel_size=3, stride=1, padding=1, bias=False))
self.upsampler = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 16 * 9, 1, padding=0)
)
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
# elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
# if m.weight is not None:
# nn.init.constant_(m.weight, 1)
# if m.bias is not None:
# nn.init.constant_(m.bias, 0)
def upsample(self, flow, mask):
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
N, _, H, W = flow.shape
mask = mask.view(N, 1, 9, 4, 4, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(4 * flow, [3, 3], padding=1)
up_flow = up_flow.view(N, 4, 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)
return up_flow.reshape(N, 4, 4 * H, 4 * W)
def forward(self, feat0, feat1, last_feat, last_flow):
corr_fn = correlation.FunctionCorrelation
feat0_warp = backwarp(feat0, last_flow[:, :2])
feat1_warp = backwarp(feat1, last_flow[:, 2:])
volume0 = F.leaky_relu(
input=costvol_func.apply(feat0_warp, feat1_warp),
negative_slope=0.1, inplace=False)
volume1 = F.leaky_relu(
input=costvol_func.apply(feat1_warp, feat0_warp),
negative_slope=0.1, inplace=False)
corr0 = self.conv_corr(volume0)
corr1 = self.conv_corr(volume1)
flo = self.conv_flow(last_flow)
input_feat = torch.cat([corr0, corr1, feat0_warp, feat1_warp, last_feat, flo], 1)
feat = self.conv_layer1(input_feat)
feat = self.conv_layer2(feat)
feat = self.conv_layer3(feat)
feat = self.conv_layer4(feat)
feat = self.conv_layer5(feat)
flow_res = self.conv_layer6(feat)
flow = last_flow + flow_res
mask = self.upsampler(feat) * .25
flow = self.upsample(flow, mask)
return flow, feat
# **************************************************************************************************#
# => Frame Synthesis
# **************************************************************************************************#
class SynthesisNetwork(nn.Module):
def __init__(self, splat_mode='average'):
super(SynthesisNetwork, self).__init__()
input_channels = 9 + 4 + 6
self.encoder_conv = nn.Sequential(
nn.Conv2d(in_channels=input_channels, out_channels=64,
kernel_size=3, stride=1, padding=1),
nn.PReLU(num_parameters=64),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=64))
self.encoder_down1 = nn.Sequential(
nn.Conv2d(in_channels=64 + 32 + 32, out_channels=128,
kernel_size=3, stride=2, padding=1),
nn.PReLU(num_parameters=128),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=128),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=128))
self.encoder_down2 = nn.Sequential(
nn.Conv2d(in_channels=128 + 64 + 64, out_channels=256,
kernel_size=3, stride=2, padding=1),
nn.PReLU(num_parameters=256),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=256),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=256))
self.decoder_up1 = nn.Sequential(
torch.nn.ConvTranspose2d(in_channels=256 + 128 + 128,
out_channels=128, kernel_size=4, stride=2,
padding=1, bias=True),
nn.PReLU(num_parameters=128),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=128))
self.decoder_up2 = nn.Sequential(
torch.nn.ConvTranspose2d(in_channels=128 + 128,
out_channels=64, kernel_size=4, stride=2,
padding=1, bias=True),
nn.PReLU(num_parameters=64),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=64))
self.decoder_conv = nn.Sequential(
nn.Conv2d(in_channels=64 + 64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=64),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3,
stride=1, padding=1),
nn.PReLU(num_parameters=64))
self.pred = nn.Conv2d(in_channels=64, out_channels=4, kernel_size=3,
stride=1, padding=1)
self.splat_mode = splat_mode
if self.splat_mode == 'softmax':
# New params for splatting mask generation
self.alpha = torch.nn.Parameter(torch.ones(1, 1, 1, 1))
self.alpha_splat_photo_consistency = torch.nn.Parameter(torch.ones(1, 1, 1, 1))
self.alpha_splat_flow_consistency = torch.nn.Parameter(torch.ones(1, 1, 1, 1))
self.alpha_splat_variation_flow = torch.nn.Parameter(torch.ones(1, 1, 1, 1))
def get_splat_weight(self, img0, img1, flow01, flow10):
if self.splat_mode == 'softmax':
M_splat = 1 / (
1 + self.alpha_splat_photo_consistency * photometric_consistency(img0, img1, flow01).detach()) + \
1 / (1 + self.alpha_splat_flow_consistency * flow_consistency(flow01, flow10).detach()) + \
1 / (1 + self.alpha_splat_variation_flow * variance_flow(flow01).detach())
return M_splat * self.alpha
else:
return None
def get_warped_representations(self, bi_flow, c0, c1, m_splat_0, m_splat_1, i0=None, i1=None, time_period=0.5):
flow_t0 = bi_flow[:, :2] * time_period * 2
flow_t1 = bi_flow[:, 2:4] * (1 - time_period) * 2
warped_c0 = backwarp(c0, flow_t0)
warped_c1 = backwarp(c1, flow_t1)
if (i0 is None) and (i1 is None):
return warped_c0, warped_c1
else:
warped_img0 = backwarp(i0, flow_t0)
warped_img1 = backwarp(i1, flow_t1)
scaler = torch.Tensor([i0.shape[3], i0.shape[2]]).view(1, 2, 1, 1)#.cuda()
flow_t0_t1 = torch.cat((flow_t0 / scaler, flow_t1 / scaler), 1)
return warped_img0, warped_img1, warped_c0, warped_c1, flow_t0_t1
def forward(self, last_i, i0, i1, c0_pyr, c1_pyr, bi_flow_pyr, time_period=0.5, multi_flow=False):
m_splat_0_0 = self.get_splat_weight(i0, i1, bi_flow_pyr[0][:, :2], bi_flow_pyr[0][:, 2:4])
m_splat_1_0 = self.get_splat_weight(i1, i0, bi_flow_pyr[0][:, 2:4], bi_flow_pyr[0][:, :2])
warped_img0, warped_img1, warped_c0, warped_c1, flow_0t_1t = \
self.get_warped_representations(
bi_flow_pyr[0], c0_pyr[0], c1_pyr[0], m_splat_0_0, m_splat_1_0, i0, i1,
time_period=time_period)
input_feat = torch.cat(
(last_i, warped_img0, warped_img1, i0, i1, flow_0t_1t), 1)
s0 = self.encoder_conv(input_feat)
s1 = self.encoder_down1(torch.cat((s0, warped_c0, warped_c1), 1))
warped_c0, warped_c1 = self.get_warped_representations(
bi_flow_pyr[1], c0_pyr[1], c1_pyr[1], None, None,
time_period=time_period)
s2 = self.encoder_down2(torch.cat((s1, warped_c0, warped_c1), 1))
warped_c0, warped_c1 = self.get_warped_representations(
bi_flow_pyr[2], c0_pyr[2], c1_pyr[2], None, None,
time_period=time_period)
x = self.decoder_up1(torch.cat((s2, warped_c0, warped_c1), 1))
x = self.decoder_up2(torch.cat((x, s1), 1))
x = self.decoder_conv(torch.cat((x, s0), 1))
# prediction
refine = self.pred(x)
refine_res = torch.sigmoid(refine[:, :3]) * 2 - 1
refine_mask = torch.sigmoid(refine[:, 3:])
merged_img = (warped_img0 * refine_mask +
warped_img1 * (1 - refine_mask))
interp_img = merged_img + refine_res
# interp_img = torch.clamp(interp_img, 0, 1)
extra_dict = {}
extra_dict["refine_res"] = refine_res
extra_dict["refine_mask"] = refine_mask
extra_dict["warped_img0"] = warped_img0
extra_dict["warped_img1"] = warped_img1
extra_dict["merged_img"] = merged_img
return interp_img, extra_dict
# **************************************************************************************************#
# => Unified model
# **************************************************************************************************#
@register('upr_net_mod2')
class Model(nn.Module):
def __init__(self, pyr_level=3, nr_lvl_skipped=0, splat_mode='average'):
super(Model, self).__init__()
print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@UPR-back exp43@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@')
self.pyr_level = pyr_level
self.feat_pyramid = FeatPyramid()
self.nr_lvl_skipped = nr_lvl_skipped
self.motion_estimator = MotionEstimator()
self.synthesis_network = SynthesisNetwork(splat_mode)
self.splat_mode = splat_mode
def forward_one_lvl(self,
img0, img1, last_feat, last_flow, last_interp=None,
time_period=0.5, skip_me=False):
# context feature extraction
feat0_pyr = self.feat_pyramid(img0)
feat1_pyr = self.feat_pyramid(img1)
# bi-directional flow estimation
if not skip_me:
last_flow = F.interpolate(
input=last_flow, scale_factor=0.25,
mode="nearest") * 0.25
flow, feat = self.motion_estimator(
feat0_pyr[-1], feat1_pyr[-1],
last_feat, last_flow)
else:
flow = last_flow
feat = last_feat
# frame synthesis
## optical flow is estimated at 1/4 resolution
ori_resolution_flow = flow
## consturct 3-level flow pyramid for synthesis network
bi_flow_pyr = []
tmp_flow = ori_resolution_flow
bi_flow_pyr.append(tmp_flow)
for i in range(2):
tmp_flow = F.interpolate(
input=tmp_flow, scale_factor=0.5,
mode="nearest") * 0.5
bi_flow_pyr.append(tmp_flow)
## merge warped frames as initial interpolation for frame synthesis
if last_interp is None:
flow_t0 = ori_resolution_flow[:, :2] * time_period * 2
flow_t1 = ori_resolution_flow[:, 2:4] * (1 - time_period) * 2
warped_img0 = backwarp(img0, flow_t0)
warped_img1 = backwarp(img1, flow_t1)
last_interp = warped_img0 * (1 - time_period) + warped_img1 * time_period
## do synthesis
interp_img, extra_dict = self.synthesis_network(
last_interp, img0, img1, feat0_pyr, feat1_pyr, bi_flow_pyr,
time_period=time_period)
return flow, feat, interp_img, extra_dict
def forward(self, img0, img1, time_step, seg0=None, segt=None, seg1=None,
pyr_level=None, nr_lvl_skipped=None, imgt=None, **kwargs):
if pyr_level is None: pyr_level = self.pyr_level
if nr_lvl_skipped is None: nr_lvl_skipped = self.nr_lvl_skipped
N, _, H, W = img0.shape
flow0_pred = []
flow1_pred = []
interp_imgs = []
skipped_levels = [] if nr_lvl_skipped == 0 else \
list(range(pyr_level))[::-1][-nr_lvl_skipped:]
with torch.set_grad_enabled(False):
tenStats = [img0, img1]
tenMean_ = sum([tenIn.mean([1, 2, 3], True) for tenIn in tenStats]) / len(tenStats)
tenStd_ = (sum([tenIn.std([1, 2, 3], False, True).square() + (
tenMean_ - tenIn.mean([1, 2, 3], True)).square() for tenIn in tenStats]) / len(tenStats)).sqrt()
img0 = (img0 - tenMean_) / (tenStd_ + 0.0000001)
img1 = (img1 - tenMean_) / (tenStd_ + 0.0000001)
padder = InputPadder(img0.shape, divisor=int(4 * 2 ** pyr_level))
img0, img1 = padder.pad(img0, img1)
N, _, H, W = img0.shape
# The original input resolution corresponds to level 0.
for level in list(range(pyr_level))[::-1]:
if level != 0:
scale_factor = 1 / 2 ** level
img0_this_lvl = F.interpolate(
input=img0, scale_factor=scale_factor,
mode="bilinear", align_corners=False)
img1_this_lvl = F.interpolate(
input=img1, scale_factor=scale_factor,
mode="bilinear", align_corners=False)
else:
img0_this_lvl = img0
img1_this_lvl = img1
# skip motion estimation, directly use up-sampled optical flow
skip_me = False
# the lowest-resolution pyramid level
if level == pyr_level - 1:
last_flow = torch.zeros(
(N, 4, H // (2 ** (level)), W // (2 ** (level)))
).to(img0.device)
last_feat = torch.zeros(
(N, 128, H // (2 ** (level + 2)), W // (2 ** (level + 2)))
).to(img0.device)
last_interp = None
# skip some levels for both motion estimation and frame synthesis
elif level in skipped_levels[:-1]:
continue
# last level (original input resolution), only skip motion estimation
elif (level == 0) and len(skipped_levels) > 0:
if len(skipped_levels) == pyr_level:
last_flow = torch.zeros(
(N, 4, H, W)).to(img0.device)
last_interp = None
else:
resize_factor = 2 ** len(skipped_levels)
last_flow = F.interpolate(
input=flow, scale_factor=resize_factor,
mode="bilinear", align_corners=False) * resize_factor
last_interp = F.interpolate(
input=interp_img, scale_factor=resize_factor,
mode="bilinear", align_corners=False)
skip_me = True
# last level (original input resolution), motion estimation + frame
# synthesis
else:
last_flow = F.interpolate(input=flow, scale_factor=2.0,
mode="bilinear", align_corners=False) * 2
last_feat = F.interpolate(input=feat, scale_factor=2.0,
mode="bilinear", align_corners=False) * 2
last_interp = F.interpolate(
input=interp_img, scale_factor=2.0,
mode="bilinear", align_corners=False)
flow, feat, interp_img, extra_dict = self.forward_one_lvl(
img0_this_lvl, img1_this_lvl,
last_feat, last_flow, last_interp,
time_step, skip_me=skip_me)
flow0_pred.append(
padder.unpad(flow[:, :2]))
flow1_pred.append(
padder.unpad(flow[:, 2:]))
interp_imgs.append(padder.unpad(F.interpolate(interp_img, scale_factor=2 ** level)) * tenStd_ + tenMean_)
# directly up-sample estimated flow to full resolution with bi-linear
# interpolation
refine_res = padder.unpad(extra_dict["refine_res"])
refine_mask = padder.unpad(extra_dict["refine_mask"])
warped_img0 = padder.unpad(extra_dict["warped_img0"]) * tenStd_ + tenMean_
warped_img1 = padder.unpad(extra_dict["warped_img1"]) * tenStd_ + tenMean_
merged_img = padder.unpad(extra_dict["merged_img"]) * tenStd_ + tenMean_
result_dict = {
"imgt_preds": interp_imgs, "flow0_pred": flow0_pred[::-1], "flow1_pred": flow1_pred[::-1],
'imgt_pred': interp_imgs[-1].contiguous(), "flowfwd": flow0_pred[-1], "flowbwd": flow1_pred[-1],
'refine_res': refine_res, 'refine_mask': refine_mask, 'warped_img0': warped_img0,
'warped_img1': warped_img1, 'merged_img': merged_img,
}
return result_dict
if __name__ == "__main__":
pass |