RAFT/core/raft.py
2020-03-26 23:19:08 -04:00

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