Spaces:
Runtime error
Runtime error
File size: 9,629 Bytes
c62dd62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
import torch
import torch.nn as nn
import numpy as np
from torch.optim import AdamW
import torch.optim as optim
import itertools
from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP
from model.oldmodel.IFNet_HDv2 import *
import torch.nn.functional as F
from model.loss import *
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=True),
nn.PReLU(out_planes)
)
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
return nn.Sequential(
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
kernel_size=4, stride=2, padding=1, bias=True),
nn.PReLU(out_planes)
)
def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=True),
)
class Conv2(nn.Module):
def __init__(self, in_planes, out_planes, stride=2):
super(Conv2, self).__init__()
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
return x
c = 32
class ContextNet(nn.Module):
def __init__(self):
super(ContextNet, self).__init__()
self.conv0 = Conv2(3, c)
self.conv1 = Conv2(c, c)
self.conv2 = Conv2(c, 2*c)
self.conv3 = Conv2(2*c, 4*c)
self.conv4 = Conv2(4*c, 8*c)
def forward(self, x, flow):
x = self.conv0(x)
x = self.conv1(x)
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
f1 = warp(x, flow)
x = self.conv2(x)
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
align_corners=False) * 0.5
f2 = warp(x, flow)
x = self.conv3(x)
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
align_corners=False) * 0.5
f3 = warp(x, flow)
x = self.conv4(x)
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
align_corners=False) * 0.5
f4 = warp(x, flow)
return [f1, f2, f3, f4]
class FusionNet(nn.Module):
def __init__(self):
super(FusionNet, self).__init__()
self.conv0 = Conv2(10, c)
self.down0 = Conv2(c, 2*c)
self.down1 = Conv2(4*c, 4*c)
self.down2 = Conv2(8*c, 8*c)
self.down3 = Conv2(16*c, 16*c)
self.up0 = deconv(32*c, 8*c)
self.up1 = deconv(16*c, 4*c)
self.up2 = deconv(8*c, 2*c)
self.up3 = deconv(4*c, c)
self.conv = nn.ConvTranspose2d(c, 4, 4, 2, 1)
def forward(self, img0, img1, flow, c0, c1, flow_gt):
warped_img0 = warp(img0, flow[:, :2])
warped_img1 = warp(img1, flow[:, 2:4])
if flow_gt == None:
warped_img0_gt, warped_img1_gt = None, None
else:
warped_img0_gt = warp(img0, flow_gt[:, :2])
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1))
s0 = self.down0(x)
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
x = self.up1(torch.cat((x, s2), 1))
x = self.up2(torch.cat((x, s1), 1))
x = self.up3(torch.cat((x, s0), 1))
x = self.conv(x)
return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
class Model:
def __init__(self, local_rank=-1):
self.flownet = IFNet()
self.contextnet = ContextNet()
self.fusionnet = FusionNet()
self.device()
self.optimG = AdamW(itertools.chain(
self.flownet.parameters(),
self.contextnet.parameters(),
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
self.schedulerG = optim.lr_scheduler.CyclicLR(
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
self.epe = EPE()
self.ter = Ternary()
self.sobel = SOBEL()
if local_rank != -1:
self.flownet = DDP(self.flownet, device_ids=[
local_rank], output_device=local_rank)
self.contextnet = DDP(self.contextnet, device_ids=[
local_rank], output_device=local_rank)
self.fusionnet = DDP(self.fusionnet, device_ids=[
local_rank], output_device=local_rank)
def train(self):
self.flownet.train()
self.contextnet.train()
self.fusionnet.train()
def eval(self):
self.flownet.eval()
self.contextnet.eval()
self.fusionnet.eval()
def device(self):
self.flownet.to(device)
self.contextnet.to(device)
self.fusionnet.to(device)
def load_model(self, path, rank):
def convert(param):
if rank == -1:
return {
k.replace("module.", ""): v
for k, v in param.items()
if "module." in k
}
else:
return param
if rank <= 0:
self.flownet.load_state_dict(
convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
self.contextnet.load_state_dict(
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
self.fusionnet.load_state_dict(
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
def save_model(self, path, rank):
if rank == 0:
torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
def predict(self, imgs, flow, training=True, flow_gt=None):
img0 = imgs[:, :3]
img1 = imgs[:, 3:]
c0 = self.contextnet(img0, flow[:, :2])
c1 = self.contextnet(img1, flow[:, 2:4])
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
align_corners=False) * 2.0
refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
img0, img1, flow, c0, c1, flow_gt)
res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
mask = torch.sigmoid(refine_output[:, 3:4])
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
pred = merged_img + res
pred = torch.clamp(pred, 0, 1)
if training:
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
else:
return pred
def inference(self, img0, img1, scale=1.0):
imgs = torch.cat((img0, img1), 1)
flow, _ = self.flownet(imgs, scale)
return self.predict(imgs, flow, training=False)
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
for param_group in self.optimG.param_groups:
param_group['lr'] = learning_rate
if training:
self.train()
else:
self.eval()
flow, flow_list = self.flownet(imgs)
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
imgs, flow, flow_gt=flow_gt)
loss_ter = self.ter(pred, gt).mean()
if training:
with torch.no_grad():
loss_flow = torch.abs(warped_img0_gt - gt).mean()
loss_mask = torch.abs(
merged_img - gt).sum(1, True).float().detach()
loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
align_corners=False).detach()
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
align_corners=False) * 0.5).detach()
loss_cons = 0
for i in range(4):
loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
loss_cons = loss_cons.mean() * 0.01
else:
loss_cons = torch.tensor([0])
loss_flow = torch.abs(warped_img0 - gt).mean()
loss_mask = 1
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
if training:
self.optimG.zero_grad()
loss_G = loss_l1 + loss_cons + loss_ter
loss_G.backward()
self.optimG.step()
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
if __name__ == '__main__':
img0 = torch.zeros(3, 3, 256, 256).float().to(device)
img1 = torch.tensor(np.random.normal(
0, 1, (3, 3, 256, 256))).float().to(device)
imgs = torch.cat((img0, img1), 1)
model = Model()
model.eval()
print(model.inference(imgs).shape)
|