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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.oldmodel.IFNet_HDv2 import * |
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import torch.nn.functional as F |
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from model.loss import * |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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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 Conv2(nn.Module): |
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def __init__(self, in_planes, out_planes, stride=2): |
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super(Conv2, self).__init__() |
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1) |
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self.conv2 = conv(out_planes, out_planes, 3, 1, 1) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.conv2(x) |
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return x |
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c = 32 |
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class ContextNet(nn.Module): |
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def __init__(self): |
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super(ContextNet, self).__init__() |
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self.conv0 = Conv2(3, c) |
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self.conv1 = Conv2(c, c) |
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self.conv2 = Conv2(c, 2*c) |
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self.conv3 = Conv2(2*c, 4*c) |
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self.conv4 = Conv2(4*c, 8*c) |
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def forward(self, x, flow): |
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x = self.conv0(x) |
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x = self.conv1(x) |
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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f1 = warp(x, flow) |
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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) |
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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) |
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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) |
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return [f1, f2, f3, f4] |
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class FusionNet(nn.Module): |
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def __init__(self): |
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super(FusionNet, self).__init__() |
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self.conv0 = Conv2(10, c) |
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self.down0 = Conv2(c, 2*c) |
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self.down1 = Conv2(4*c, 4*c) |
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self.down2 = Conv2(8*c, 8*c) |
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self.down3 = Conv2(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.ConvTranspose2d(c, 4, 4, 2, 1) |
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def forward(self, img0, img1, flow, c0, c1, flow_gt): |
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warped_img0 = warp(img0, flow[:, :2]) |
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warped_img1 = warp(img1, flow[:, 2:4]) |
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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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x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1)) |
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s0 = self.down0(x) |
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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, local_rank=-1): |
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self.flownet = IFNet() |
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self.contextnet = ContextNet() |
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self.fusionnet = FusionNet() |
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self.device() |
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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-4) |
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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() |
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self.sobel = SOBEL() |
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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): |
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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): |
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def convert(param): |
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if rank == -1: |
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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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else: |
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return param |
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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=device))) |
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self.contextnet.load_state_dict( |
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convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device))) |
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self.fusionnet.load_state_dict( |
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convert(torch.load('{}/unet.pkl'.format(path), map_location=device))) |
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def save_model(self, path, rank): |
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if rank == 0: |
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torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path)) |
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torch.save(self.contextnet.state_dict(), '{}/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[:, :2]) |
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c1 = self.contextnet(img1, flow[:, 2:4]) |
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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, scale=1.0): |
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imgs = torch.cat((img0, img1), 1) |
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flow, _ = self.flownet(imgs, scale) |
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return self.predict(imgs, flow, training=False) |
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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(4): |
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loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1) |
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loss_cons += self.epe(flow_list[i][:, 2:4], 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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