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import torch
import torch.nn as nn
from modules.components.amt_bilateral.blocks.raft import (
coords_grid,
SmallUpdateBlock, BidirCorrBlock, BasicUpdateBlock
)
from .blocks.feat_enc import (
SmallEncoder,
BasicEncoder,
LargeEncoder
)
from .blocks.ifrnet import (
resize,
Encoder,
InitDecoder,
IntermediateDecoder
)
from .blocks.multi_flow import (
multi_flow_combine,
MultiFlowDecoder
)
from .blocks.BilateralCorrelation_NN import bilateralcorrelation_nn
from ..components import register
from utils.padder import InputPadder
@register('amt_bilateral')
class Model(nn.Module):
def __init__(self,
model_size='S',
corr_radius=3,
corr_lvls=4,
num_flows=3,
channels=[20, 32, 44, 56],
skip_channels=20,
scale_factor=1):
super(Model, self).__init__()
self.model_size = model_size
self.radius = corr_radius
self.corr_levels = corr_lvls
self.num_flows = num_flows
self.channels = channels
self.skip_channels = skip_channels
self.scale_factor = scale_factor
if self.model_size == 'S':
self.feat_encoder = SmallEncoder(output_dim=84, norm_fn='instance', dropout=0.)
elif self.model_size == 'L':
self.feat_encoder = BasicEncoder(output_dim=128, norm_fn='instance', dropout=0.)
elif self.model_size == 'G':
self.feat_encoder = LargeEncoder(output_dim=128, norm_fn='instance', dropout=0.)
self.encoder = Encoder(channels, large=True)
self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
self.update4 = self._get_updateblock(channels[2])
self.update3 = self._get_updateblock(channels[1], 2)
self.update2 = self._get_updateblock(channels[0], 4)
if self.model_size == 'G':
self.update3_high = self._get_updateblock(channels[1], None)
self.update2_high = self._get_updateblock(channels[0], None)
self.comb_block = nn.Sequential(
nn.Conv2d(3 * self.num_flows, 6 * self.num_flows, 7, 1, 3),
nn.PReLU(6 * self.num_flows),
nn.Conv2d(6 * self.num_flows, 3, 7, 1, 3),
)
def _get_updateblock(self, cdim, scale_factor=None):
return BasicUpdateBlock(cdim=cdim, hidden_dim=192, flow_dim=64,
corr_dim=256, corr_dim2=192, fc_dim=188,
scale_factor=scale_factor, corr_levels=self.corr_levels,
radius=self.radius)
def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
# convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
# based on linear assumption
# t1_scale = 1. / (1. - embt)
# t0_scale = 1. / embt
if downsample != 1:
inv = 1 / downsample
flow0 = inv * resize(flow0, scale_factor=inv)
flow1 = inv * resize(flow1, scale_factor=inv)
corr0, corr1 = corr_fn(flow0, flow1, embt)
corr = torch.cat([corr0, corr1], dim=1)
flow = torch.cat([flow0, flow1], dim=1)
return corr, flow
def forward(self, img0, img1, time_step, scale_factor=1.0, eval=False, **kwargs):
scale_factor = self.scale_factor
padder = InputPadder(img0.shape, divisor=int(16 / scale_factor))
img0, img1 = padder.pad(img0, img1)
mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
img0 = img0 - mean_
img1 = img1 - mean_
img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
b, _, h, w = img0_.shape
coord = coords_grid(b, h // 8, w // 8, img0.device)
fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8]
corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
# f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
# f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
######################################### the 4th decoder #########################################
up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, time_step)
corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord,
up_flow0_4, up_flow1_4,
time_step, downsample=1)
# residue update with lookup corr
delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
up_flow0_4 = up_flow0_4 + delta_flow0_4
up_flow1_4 = up_flow1_4 + delta_flow1_4
ft_3_ = ft_3_ + delta_ft_3_
######################################### the 3rd decoder #########################################
up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
corr_3, flow_3 = self._corr_scale_lookup(corr_fn,
coord, up_flow0_3, up_flow1_3,
time_step, downsample=2)
# residue update with lookup corr
delta_ft_2_, delta_flow_3 = self.update3(ft_2_, flow_3, corr_3)
delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
up_flow0_3 = up_flow0_3 + delta_flow0_3
up_flow1_3 = up_flow1_3 + delta_flow1_3
ft_2_ = ft_2_ + delta_ft_2_
if self.model_size == 'G':
# residue update with lookup corr (hr)
corr_3 = resize(corr_3, scale_factor=2.0)
up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1)
delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3)
ft_2_ += delta_ft_2_
up_flow0_3 += delta_up_flow_3[:, 0:2]
up_flow1_3 += delta_up_flow_3[:, 2:4]
######################################### the 2nd decoder #########################################
up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
corr_2, flow_2 = self._corr_scale_lookup(corr_fn,
coord, up_flow0_2, up_flow1_2,
time_step, downsample=4)
# residue update with lookup corr
delta_ft_1_, delta_flow_2 = self.update2(ft_1_, flow_2, corr_2)
delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
up_flow0_2 = up_flow0_2 + delta_flow0_2
up_flow1_2 = up_flow1_2 + delta_flow1_2
ft_1_ = ft_1_ + delta_ft_1_
if self.model_size == 'G':
# residue update with lookup corr (hr)
corr_2 = resize(corr_2, scale_factor=4.0)
up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1)
delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2)
ft_1_ += delta_ft_1_
up_flow0_2 += delta_up_flow_2[:, 0:2]
up_flow1_2 += delta_up_flow_2[:, 2:4]
######################################### the 1st decoder #########################################
up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
if scale_factor != 1.0:
up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0/scale_factor)) * (1.0/scale_factor)
mask = resize(mask, scale_factor=(1.0/scale_factor))
img_res = resize(img_res, scale_factor=(1.0/scale_factor))
# up_flow0_1, up_flow1_1, mask, img_res = padder.unpad(up_flow0_1, up_flow1_1, mask, img_res)
# Merge multiple predictions
imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1,
mask, img_res, mean_)
imgt_pred = torch.clamp(imgt_pred, 0, 1)
imgt_pred = padder.unpad(imgt_pred)
if eval:
return { 'imgt_pred': imgt_pred, }
else:
up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, int(h/scale_factor), int(w/scale_factor))
up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, int(h/scale_factor), int(w/scale_factor))
return {
'imgt_pred': imgt_pred,
'flow0_pred': [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
'flow1_pred': [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
'flowfwd': up_flow0_1[:, 0],
'flowbwd': up_flow1_1[:, 0],
'ft_pred': [ft_1_, ft_2_, ft_3_],
}
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