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FlowNetC.py
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FlowNetC.py
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import torch
import torch.nn as nn
from torch.nn.init import kaiming_normal_, constant_
from .util import conv, predict_flow, deconv, crop_like, correlate
__all__ = ["flownetc", "flownetc_bn"]
class FlowNetC(nn.Module):
expansion = 1
def __init__(self, batchNorm=True):
super(FlowNetC, self).__init__()
self.batchNorm = batchNorm
self.conv1 = conv(self.batchNorm, 3, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
self.conv_redir = conv(self.batchNorm, 256, 32, kernel_size=1, stride=1)
self.conv3_1 = conv(self.batchNorm, 473, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm, 1024, 1024)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
kaiming_normal_(m.weight, 0.1)
if m.bias is not None:
constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
constant_(m.weight, 1)
constant_(m.bias, 0)
def forward(self, x):
x1 = x[:, :3]
x2 = x[:, 3:]
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
out_conv_redir = self.conv_redir(out_conv3a)
out_correlation = correlate(out_conv3a, out_conv3b)
in_conv3_1 = torch.cat([out_conv_redir, out_correlation], dim=1)
out_conv3 = self.conv3_1(in_conv3_1)
out_conv4 = self.conv4_1(self.conv4(out_conv3))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = crop_like(self.upsampled_flow6_to_5(flow6), out_conv5)
out_deconv5 = crop_like(self.deconv5(out_conv6), out_conv5)
concat5 = torch.cat((out_conv5, out_deconv5, flow6_up), 1)
flow5 = self.predict_flow5(concat5)
flow5_up = crop_like(self.upsampled_flow5_to_4(flow5), out_conv4)
out_deconv4 = crop_like(self.deconv4(concat5), out_conv4)
concat4 = torch.cat((out_conv4, out_deconv4, flow5_up), 1)
flow4 = self.predict_flow4(concat4)
flow4_up = crop_like(self.upsampled_flow4_to_3(flow4), out_conv3)
out_deconv3 = crop_like(self.deconv3(concat4), out_conv3)
concat3 = torch.cat((out_conv3, out_deconv3, flow4_up), 1)
flow3 = self.predict_flow3(concat3)
flow3_up = crop_like(self.upsampled_flow3_to_2(flow3), out_conv2a)
out_deconv2 = crop_like(self.deconv2(concat3), out_conv2a)
concat2 = torch.cat((out_conv2a, out_deconv2, flow3_up), 1)
flow2 = self.predict_flow2(concat2)
if self.training:
return flow2, flow3, flow4, flow5, flow6
else:
return flow2
def weight_parameters(self):
return [param for name, param in self.named_parameters() if "weight" in name]
def bias_parameters(self):
return [param for name, param in self.named_parameters() if "bias" in name]
def flownetc(data=None):
"""FlowNetS model architecture from the
"Learning Optical Flow with Convolutional Networks" paper (https://arxiv.org/abs/1504.06852)
Args:
data : pretrained weights of the network. will create a new one if not set
"""
model = FlowNetC(batchNorm=False)
if data is not None:
model.load_state_dict(data["state_dict"])
return model
def flownetc_bn(data=None):
"""FlowNetS model architecture from the
"Learning Optical Flow with Convolutional Networks" paper (https://arxiv.org/abs/1504.06852)
Args:
data : pretrained weights of the network. will create a new one if not set
"""
model = FlowNetC(batchNorm=True)
if data is not None:
model.load_state_dict(data["state_dict"])
return model