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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from update import BasicUpdateBlock, SmallUpdateBlock, BasicUpdateBlock2
from extractor import BasicEncoder, SmallEncoder, ResNetFPN
from corr import CorrBlock, AlternateCorrBlock, CorrBlock2
from utils.utils import bilinear_sampler, coords_grid, upflow8, InputPadder, coords_grid2
from layer import conv3x3
import math
try:
autocast = torch.cuda.amp.autocast
except:
# dummy autocast for PyTorch < 1.6
class autocast:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
class RAFT(nn.Module):
def __init__(self, args):
super(RAFT, self).__init__()
self.args = args
if args.small:
self.hidden_dim = hdim = 96
self.context_dim = cdim = 64
args.corr_levels = 4
args.corr_radius = 3
else:
self.hidden_dim = hdim = 128
self.context_dim = cdim = 128
args.corr_levels = 4
args.corr_radius = 4
if 'dropout' not in self.args:
self.args.dropout = 0
if 'alternate_corr' not in self.args:
self.args.alternate_corr = False
# feature network, context network, and update block
if args.small:
self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
else:
self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def initialize_flow(self, img):
""" Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
N, C, H, W = img.shape
coords0 = coords_grid(N, H//8, W//8).to(img.device)
coords1 = coords_grid(N, H//8, W//8).to(img.device)
# optical flow computed as difference: flow = coords1 - coords0
return coords0, coords1
def upsample_flow(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, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(8 * flow, [3,3], padding=1)
up_flow = up_flow.view(N, 2, 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, 2, 8*H, 8*W)
def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
""" Estimate optical flow between pair of frames """
image1 = 2 * (image1 / 255.0) - 1.0
image2 = 2 * (image2 / 255.0) - 1.0
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
# run the feature network
with autocast(enabled=self.args.mixed_precision):
fmap1, fmap2 = self.fnet([image1, image2])
fmap1 = fmap1.float()
fmap2 = fmap2.float()
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
else:
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
# run the context network
with autocast(enabled=self.args.mixed_precision):
cnet = self.cnet(image1)
net, inp = torch.split(cnet, [hdim, cdim], dim=1)
net = torch.tanh(net)
inp = torch.relu(inp)
coords0, coords1 = self.initialize_flow(image1)
if flow_init is not None:
coords1 = coords1 + flow_init
flow_predictions = []
for itr in range(iters):
coords1 = coords1.detach()
corr = corr_fn(coords1) # index correlation volume
flow = coords1 - coords0
with autocast(enabled=self.args.mixed_precision):
net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
# F(t+1) = F(t) + \Delta(t)
coords1 = coords1 + delta_flow
# upsample predictions
if up_mask is None:
flow_up = upflow8(coords1 - coords0)
else:
flow_up = self.upsample_flow(coords1 - coords0, up_mask)
flow_predictions.append(flow_up)
if test_mode:
return coords1 - coords0, flow_up
return flow_predictions
##
# given depth, warp according to camera params.
# given flow+depth, warp in 2D
class RAFT2(nn.Module):
def __init__(self, args):
super(RAFT2, self).__init__()
self.args = args
self.output_dim = args.dim * 2
self.args.corr_levels = 4
self.args.corr_radius = args.radius
self.args.corr_channel = args.corr_levels * (args.radius * 2 + 1) ** 2
self.cnet = ResNetFPN(args, input_dim=6, output_dim=2 * self.args.dim, norm_layer=nn.BatchNorm2d, init_weight=True)
# conv for iter 0 results
self.init_conv = conv3x3(2 * args.dim, 2 * args.dim)
self.upsample_weight = nn.Sequential(
# convex combination of 3x3 patches
nn.Conv2d(args.dim, args.dim * 2, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(args.dim * 2, 64 * 9, 1, padding=0)
)
self.flow_head = nn.Sequential(
# flow(2) + weight(2) + log_b(2)
nn.Conv2d(args.dim, 2 * args.dim, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(2 * args.dim, 6, 3, padding=1)
)
if args.iters > 0:
self.fnet = ResNetFPN(args, input_dim=3, output_dim=self.output_dim, norm_layer=nn.BatchNorm2d, init_weight=True)
self.update_block = BasicUpdateBlock2(args, hdim=args.dim, cdim=args.dim)
def initialize_flow(self, img):
""" Flow is represented as difference between two coordinate grids flow = coords2 - coords1"""
N, C, H, W = img.shape
coords1 = coords_grid(N, H//8, W//8, device=img.device)
coords2 = coords_grid(N, H//8, W//8, device=img.device)
return coords1, coords2
def upsample_data(self, flow, info, mask):
""" Upsample [H/8, W/8, C] -> [H, W, C] using convex combination """
N, C, H, W = info.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(8 * flow, [3,3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_info = F.unfold(info, [3, 3], padding=1)
up_info = up_info.view(N, 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(N, 2, 8*H, 8*W), up_info.reshape(N, C, 8*H, 8*W)
def forward(self, image1, image2, iters=None, flow_gt=None, test_mode=False):
""" Estimate optical flow between pair of frames """
N, _, H, W = image1.shape
if iters is None:
iters = self.args.iters
if flow_gt is None:
flow_gt = torch.zeros(N, 2, H, W, device=image1.device)
image1 = 2 * (image1 / 255.0) - 1.0
image2 = 2 * (image2 / 255.0) - 1.0
image1 = image1.contiguous()
image2 = image2.contiguous()
flow_predictions = []
info_predictions = []
# padding
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
N, _, H, W = image1.shape
dilation = torch.ones(N, 1, H//8, W//8, device=image1.device)
# run the context network
cnet = self.cnet(torch.cat([image1, image2], dim=1))
cnet = self.init_conv(cnet)
net, context = torch.split(cnet, [self.args.dim, self.args.dim], dim=1)
# init flow
flow_update = self.flow_head(net)
weight_update = .25 * self.upsample_weight(net)
flow_8x = flow_update[:, :2]
info_8x = flow_update[:, 2:]
flow_up, info_up = self.upsample_data(flow_8x, info_8x, weight_update)
flow_predictions.append(flow_up)
info_predictions.append(info_up)
if self.args.iters > 0:
# run the feature network
fmap1_8x = self.fnet(image1)
fmap2_8x = self.fnet(image2)
corr_fn = CorrBlock2(fmap1_8x, fmap2_8x, self.args)
for itr in range(iters):
N, _, H, W = flow_8x.shape
flow_8x = flow_8x.detach()
coords2 = (coords_grid2(N, H, W, device=image1.device) + flow_8x).detach()
corr = corr_fn(coords2, dilation=dilation)
net = self.update_block(net, context, corr, flow_8x)
flow_update = self.flow_head(net)
weight_update = .25 * self.upsample_weight(net)
flow_8x = flow_8x + flow_update[:, :2]
info_8x = flow_update[:, 2:]
# upsample predictions
flow_up, info_up = self.upsample_data(flow_8x, info_8x, weight_update)
flow_predictions.append(flow_up)
info_predictions.append(info_up)
for i in range(len(info_predictions)):
flow_predictions[i] = padder.unpad(flow_predictions[i])
info_predictions[i] = padder.unpad(info_predictions[i])
if test_mode == False:
# exlude invalid pixels and extremely large diplacements
nf_predictions = []
for i in range(len(info_predictions)):
if not self.args.use_var:
var_max = var_min = 0
else:
var_max = self.args.var_max
var_min = self.args.var_min
raw_b = info_predictions[i][:, 2:]
log_b = torch.zeros_like(raw_b)
weight = info_predictions[i][:, :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_gt - flow_predictions[i]).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_predictions.append(nf_loss)
return {'final': flow_predictions[-1], 'flow': flow_predictions, 'info': info_predictions, 'nf': nf_predictions}
else:
return [flow_predictions,flow_predictions[-1]] |