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Add our model
This commit is contained in:
hzwer
101
Flownet.py
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101
Flownet.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from warplayer import warp
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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, kernel_size=4, stride=2, padding=1),
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nn.BatchNorm2d(out_planes),
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nn.PReLU(out_planes)
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)
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def conv_wo_act(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=False),
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nn.BatchNorm2d(out_planes),
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)
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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=False),
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nn.BatchNorm2d(out_planes),
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nn.PReLU(out_planes)
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)
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class ResBlock(nn.Module):
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def __init__(self, in_planes, out_planes, stride=1):
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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, 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_wo_act(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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class Flownet(nn.Module):
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def __init__(self, in_planes, scale=1, c=64):
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super(Flownet, self).__init__()
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self.scale = scale
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self.conv0 = conv(in_planes, c, 3, 2, 1)
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self.res0 = ResBlock(c, c)
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self.res1 = ResBlock(c, c)
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self.res2 = ResBlock(c, c)
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self.res3 = ResBlock(c, c)
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self.res4 = ResBlock(c, c)
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self.res5 = ResBlock(c, c)
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self.conv1 = nn.Conv2d(c, 8, 3, 1, 1)
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self.up = nn.PixelShuffle(2)
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def forward(self, x):
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if self.scale != 1:
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x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
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x = self.conv0(x)
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x = self.res0(x)
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x = self.res1(x)
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x = self.res2(x)
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x = self.res3(x)
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x = self.res4(x)
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x = self.res5(x)
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x = self.conv1(x)
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flow = self.up(x)
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
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return flow
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class FlownetCas(nn.Module):
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def __init__(self):
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super(FlownetCas, self).__init__()
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self.block0 = Flownet(6, scale=4, c=192)
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self.block1 = Flownet(8, scale=2, c=128)
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self.block2 = Flownet(8, scale=1, c=64)
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def forward(self, x):
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x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
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flow0 = self.block0(x)
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F1 = flow0
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warped_img0 = warp(x[:, :3], F1)
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warped_img1 = warp(x[:, 3:], -F1)
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
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F2 = (flow0 + flow1)
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warped_img0 = warp(x[:, :3], F2)
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warped_img1 = warp(x[:, 3:], -F2)
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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83
loss.py
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83
loss.py
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import torch
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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grid = None
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Grid = {}
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class EPE(nn.Module):
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def __init__(self):
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super(EPE, self).__init__()
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def forward(self, flow, gt, loss_mask):
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loss_map = (flow - gt.detach()) ** 2
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loss_map = (loss_map.sum(1, True) + 1e-6) ** 0.5
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return (loss_map * loss_mask)
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class Ternary(nn.Module):
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def __init__(self):
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super(Ternary, self).__init__()
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patch_size = 7
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out_channels = patch_size * patch_size
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self.w = np.eye(out_channels).reshape((patch_size, patch_size, 1, out_channels))
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self.w = np.transpose(self.w, (3, 2, 0, 1))
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self.w = torch.tensor(self.w).float().to(device)
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def transform(self, img):
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patches = F.conv2d(img, self.w, padding=3, bias=None)
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transf = patches - img
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transf_norm = transf / torch.sqrt(0.81 + transf**2)
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return transf_norm
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def rgb2gray(self, rgb):
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r, g, b = rgb[:, 0:1,:,:], rgb[:, 1:2,:,:], rgb[:, 2:3,:,:]
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gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
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return gray
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def hamming(self, t1, t2):
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dist = (t1 - t2) ** 2
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dist_norm = torch.mean(dist / (0.1 + dist), 1, True)
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return dist_norm
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def valid_mask(self, t, padding):
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n, _, h, w = t.size()
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inner = torch.ones(n, 1, h - 2 * padding, w - 2 * padding).type_as(t)
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mask = F.pad(inner, [padding] * 4)
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return mask
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def forward(self, img0, img1):
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img0 = self.transform(self.rgb2gray(img0))
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img1 = self.transform(self.rgb2gray(img1))
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return self.hamming(img0, img1) * self.valid_mask(img0, 1)
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class SOBEL(nn.Module):
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def __init__(self):
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super(SOBEL, self).__init__()
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self.kernelX = torch.tensor([
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[1, 0, -1],
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[2, 0, -2],
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[1, 0, -1],
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]).float()
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self.kernelY = self.kernelX.clone().T
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self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device)
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self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(device)
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def forward(self, pred, gt):
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N, C, H, W = pred.shape[0], pred.shape[1], pred.shape[2], pred.shape[3]
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img_stack = torch.cat([pred.reshape(N*C, 1, H, W), gt.reshape(N*C, 1, H, W)], 0)
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sobel_stack_x = F.conv2d(img_stack, self.kernelX, padding=1)
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sobel_stack_y = F.conv2d(img_stack, self.kernelY, padding=1)
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pred_X, gt_X = sobel_stack_x[:N*C], sobel_stack_x[N*C:]
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pred_Y, gt_Y = sobel_stack_y[:N*C], sobel_stack_y[N*C:]
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L1X, L1Y = torch.abs(pred_X-gt_X), torch.abs(pred_Y-gt_Y)
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loss = (L1X+L1Y)
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return loss
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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(0, 1, (3, 3, 256, 256))).float().to(device)
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ternary_loss = Ternary()
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print(ternary_loss(img0, img1).shape)
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221
model.py
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221
model.py
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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 warplayer import warp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from Flownet import *
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import torch.nn.functional as F
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from 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 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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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, 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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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, 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):
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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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def forward(self, x, flow):
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x = self.conv1(x)
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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", align_corners=False, recompute_scale_factor=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", align_corners=False, recompute_scale_factor=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", align_corners=False, recompute_scale_factor=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 Unet(nn.Module):
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def __init__(self):
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super(Unet, 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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def forward(self, img0, img1, flow, c0, c1, flow_gt):
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warped_img0 = warp(img0, flow)
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warped_img1 = warp(img1, -flow)
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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, local_rank=-1):
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self.flownet = FlownetCas()
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self.contextnet = Contextnet()
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self.unet = Unet()
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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.unet.parameters()), lr=1e-6, weight_decay=1e-5)
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self.schedulerG = optim.lr_scheduler.CyclicLR(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=[local_rank], output_device=local_rank)
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self.contextnet = DDP(self.contextnet, device_ids=[local_rank], output_device=local_rank)
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self.unet = DDP(self.unet, device_ids=[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.unet.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.unet.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.unet.to(device)
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def load_model(self, path, rank=0):
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if rank == 0:
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self.flownet.load_state_dict(torch.load('{}/flownet.pkl'.format(path)))
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self.contextnet.load_state_dict(torch.load('{}/contextnet.pkl'.format(path)))
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self.unet.load_state_dict(torch.load('{}/unet.pkl'.format(path)))
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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(),'{}/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.unet.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", align_corners=False, recompute_scale_factor=False) * 2.0
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refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.unet(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, imgs):
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with torch.no_grad():
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flow, _ = self.flownet(imgs)
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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()
|
||||
# with torch.no_grad():
|
||||
# flow_gt = estimate(gt, img0)
|
||||
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, recompute_scale_factor=False).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(-flow_list[i], 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)
|
||||
17
warplayer.py
Normal file
17
warplayer.py
Normal file
@@ -0,0 +1,17 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
backwarp_tenGrid = {}
|
||||
|
||||
def warp(tenInput, tenFlow):
|
||||
k = (str(tenFlow.device), str(tenFlow.size()))
|
||||
if k not in backwarp_tenGrid:
|
||||
tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3]).view(1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
|
||||
tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2]).view(1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
|
||||
backwarp_tenGrid[k] = torch.cat([ tenHorizontal, tenVertical ], 1).to(device)
|
||||
|
||||
tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0) ], 1)
|
||||
|
||||
g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
|
||||
return torch.nn.functional.grid_sample(input=tenInput, grid=torch.clamp(g, -1, 1), mode='bilinear', padding_mode='zeros', align_corners=True)
|
||||
Reference in New Issue
Block a user