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263 lines
10 KiB
Python
263 lines
10 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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from torch.optim import AdamW
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import torch.optim as optim
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import itertools
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from model.warplayer import warp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from model.IFNet import *
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import torch.nn.functional as F
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from model.loss import *
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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nn.PReLU(out_planes)
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)
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def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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return nn.Sequential(
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torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
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kernel_size=4, stride=2, padding=1, bias=True),
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nn.PReLU(out_planes)
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)
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def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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)
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class ResBlock(nn.Module):
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def __init__(self, in_planes, out_planes, stride=2):
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super(ResBlock, self).__init__()
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if in_planes == out_planes and stride == 1:
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self.conv0 = nn.Identity()
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else:
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self.conv0 = nn.Conv2d(in_planes, out_planes,
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3, stride, 1, bias=False)
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
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self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
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self.relu1 = nn.PReLU(1)
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self.relu2 = nn.PReLU(out_planes)
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self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
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self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
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def forward(self, x):
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y = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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w = x.mean(3, True).mean(2, True)
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w = self.relu1(self.fc1(w))
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w = torch.sigmoid(self.fc2(w))
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x = self.relu2(x * w + y)
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return x
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c = 16
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class ContextNet(nn.Module):
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def __init__(self, cFlag):
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super(ContextNet, self).__init__()
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self.conv1 = ResBlock(3, c)
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self.conv2 = ResBlock(c, 2 * c)
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self.conv3 = ResBlock(2 * c, 4 * c)
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self.conv4 = ResBlock(4 * c, 8 * c)
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self.cFlag = cFlag
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def forward(self, x, flow):
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x = self.conv1(x)
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f1 = warp(x, flow, self.cFlag)
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x = self.conv2(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
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align_corners=False) * 0.5
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f2 = warp(x, flow, self.cFlag)
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x = self.conv3(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
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align_corners=False) * 0.5
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f3 = warp(x, flow, self.cFlag)
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x = self.conv4(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
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align_corners=False) * 0.5
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f4 = warp(x, flow, self.cFlag)
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return [f1, f2, f3, f4]
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class FusionNet(nn.Module):
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def __init__(self, cFlag):
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super(FusionNet, self).__init__()
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self.down0 = ResBlock(8, 2 * c)
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self.down1 = ResBlock(4 * c, 4 * c)
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self.down2 = ResBlock(8 * c, 8 * c)
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self.down3 = ResBlock(16 * c, 16 * c)
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self.up0 = deconv(32 * c, 8 * c)
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self.up1 = deconv(16 * c, 4 * c)
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self.up2 = deconv(8 * c, 2 * c)
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self.up3 = deconv(4 * c, c)
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self.conv = nn.Conv2d(c, 4, 3, 1, 1)
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self.cFlag = cFlag
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def forward(self, img0, img1, flow, c0, c1, flow_gt):
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warped_img0 = warp(img0, flow, self.cFlag)
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warped_img1 = warp(img1, -flow, self.cFlag)
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if flow_gt == None:
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warped_img0_gt, warped_img1_gt = None, None
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else:
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warped_img0_gt = warp(img0, flow_gt[:, :2])
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warped_img1_gt = warp(img1, flow_gt[:, 2:4])
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s0 = self.down0(torch.cat((warped_img0, warped_img1, flow), 1))
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s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
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s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
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s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
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x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
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x = self.up1(torch.cat((x, s2), 1))
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x = self.up2(torch.cat((x, s1), 1))
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x = self.up3(torch.cat((x, s0), 1))
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x = self.conv(x)
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return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
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class Model:
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def __init__(self, c_flag, local_rank=-1):
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self.flownet = IFNet(c_flag)
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self.contextnet = ContextNet(c_flag)
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self.fusionnet = FusionNet(c_flag)
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self.device(c_flag)
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self.optimG = AdamW(itertools.chain(
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self.flownet.parameters(),
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self.contextnet.parameters(),
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self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
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self.schedulerG = optim.lr_scheduler.CyclicLR(
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self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
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self.epe = EPE()
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self.ter = Ternary(c_flag)
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self.sobel = SOBEL(c_flag)
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if local_rank != -1:
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self.flownet = DDP(self.flownet, device_ids=[
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local_rank], output_device=local_rank)
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self.contextnet = DDP(self.contextnet, device_ids=[
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local_rank], output_device=local_rank)
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self.fusionnet = DDP(self.fusionnet, device_ids=[
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local_rank], output_device=local_rank)
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def train(self):
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self.flownet.train()
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self.contextnet.train()
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self.fusionnet.train()
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def eval(self):
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self.flownet.eval()
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self.contextnet.eval()
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self.fusionnet.eval()
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def device(self, c_flag):
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if torch.cuda.is_available() and not c_flag:
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device = torch.device("cuda")
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else:
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device = torch.device("cpu")
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self.flownet.to(device)
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self.contextnet.to(device)
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self.fusionnet.to(device)
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def load_model(self, path, rank=0):
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def convert(param):
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return {
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k.replace("module.", ""): v
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for k, v in param.items()
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if "module." in k
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}
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if rank == 0:
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self.flownet.load_state_dict(
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convert(torch.load('{}/flownet.pkl'.format(path), map_location=torch.device("cpu"))))
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self.contextnet.load_state_dict(
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convert(torch.load('{}/contextnet.pkl'.format(path), map_location=torch.device("cpu"))))
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self.fusionnet.load_state_dict(
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convert(torch.load('{}/unet.pkl'.format(path), map_location=torch.device("cpu"))))
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def save_model(self, path, rank=0):
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if rank == 0:
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torch.save(self.flownet.state_dict(),
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'{}/flownet.pkl'.format(path))
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torch.save(self.contextnet.state_dict(),
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'{}/contextnet.pkl'.format(path))
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torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
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def predict(self, imgs, flow, training=True, flow_gt=None):
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img0 = imgs[:, :3]
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img1 = imgs[:, 3:]
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c0 = self.contextnet(img0, flow)
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c1 = self.contextnet(img1, -flow)
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flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
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align_corners=False) * 2.0
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refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
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img0, img1, flow, c0, c1, flow_gt)
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res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
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mask = torch.sigmoid(refine_output[:, 3:4])
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merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
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pred = merged_img + res
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pred = torch.clamp(pred, 0, 1)
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if training:
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return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
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else:
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return pred
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def inference(self, img0, img1):
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imgs = torch.cat((img0, img1), 1)
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flow, _ = self.flownet(imgs)
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return self.predict(imgs, flow, training=False).detach()
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def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
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for param_group in self.optimG.param_groups:
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param_group['lr'] = learning_rate
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if training:
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self.train()
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else:
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self.eval()
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flow, flow_list = self.flownet(imgs)
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pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
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imgs, flow, flow_gt=flow_gt)
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loss_ter = self.ter(pred, gt).mean()
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if training:
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with torch.no_grad():
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loss_flow = torch.abs(warped_img0_gt - gt).mean()
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loss_mask = torch.abs(
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merged_img - gt).sum(1, True).float().detach()
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loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
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align_corners=False).detach()
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flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
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align_corners=False) * 0.5).detach()
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loss_cons = 0
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for i in range(3):
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loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
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loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
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loss_cons = loss_cons.mean() * 0.01
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else:
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loss_cons = torch.tensor([0])
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loss_flow = torch.abs(warped_img0 - gt).mean()
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loss_mask = 1
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loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
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if training:
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self.optimG.zero_grad()
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loss_G = loss_l1 + loss_cons + loss_ter
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loss_G.backward()
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self.optimG.step()
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return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
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if __name__ == '__main__':
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img0 = torch.zeros(3, 3, 256, 256).float().to(device)
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img1 = torch.tensor(np.random.normal(
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0, 1, (3, 3, 256, 256))).float().to(device)
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imgs = torch.cat((img0, img1), 1)
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model = Model()
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model.eval()
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print(model.inference(imgs).shape)
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