100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from modules.update import BasicUpdateBlock, SmallUpdateBlock
|
|
from modules.extractor import BasicEncoder, SmallEncoder
|
|
from modules.corr import CorrBlock
|
|
from utils.utils import bilinear_sampler, coords_grid, upflow8
|
|
|
|
|
|
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 args._get_kwargs():
|
|
args.dropout = 0
|
|
|
|
# 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 forward(self, image1, image2, iters=12, flow_init=None, upsample=True):
|
|
""" Estimate optical flow between pair of frames """
|
|
|
|
image1 = 2 * (image1 / 255.0) - 1.0
|
|
image2 = 2 * (image2 / 255.0) - 1.0
|
|
|
|
hdim = self.hidden_dim
|
|
cdim = self.context_dim
|
|
|
|
# run the feature network
|
|
fmap1, fmap2 = self.fnet([image1, image2])
|
|
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
|
|
|
|
# run the context network
|
|
cnet = self.cnet(image1)
|
|
net, inp = torch.split(cnet, [hdim, cdim], dim=1)
|
|
net, inp = torch.tanh(net), torch.relu(inp)
|
|
|
|
# if dropout is being used reset mask
|
|
self.update_block.reset_mask(net, inp)
|
|
coords0, coords1 = self.initialize_flow(image1)
|
|
|
|
flow_predictions = []
|
|
for itr in range(iters):
|
|
coords1 = coords1.detach()
|
|
corr = corr_fn(coords1) # index correlation volume
|
|
|
|
flow = coords1 - coords0
|
|
net, delta_flow = self.update_block(net, inp, corr, flow)
|
|
|
|
# F(t+1) = F(t) + \Delta(t)
|
|
coords1 = coords1 + delta_flow
|
|
|
|
if upsample:
|
|
flow_up = upflow8(coords1 - coords0)
|
|
flow_predictions.append(flow_up)
|
|
|
|
else:
|
|
flow_predictions.append(coords1 - coords0)
|
|
|
|
return flow_predictions
|
|
|
|
|