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# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import torch.nn as nn
import torch.nn.functional as F
class RPN(nn.Module):
def __init__(self):
super(RPN, self).__init__()
def forward(self, z_f, x_f):
raise NotImplementedError
def template(self, template):
raise NotImplementedError
def track(self, search):
raise NotImplementedError
def param_groups(self, start_lr, feature_mult=1, key=None):
if key is None:
params = filter(lambda x:x.requires_grad, self.parameters())
else:
params = [v for k, v in self.named_parameters() if (key in k) and v.requires_grad]
params = [{'params': params, 'lr': start_lr * feature_mult}]
return params
def conv2d_dw_group(x, kernel):
batch, channel = kernel.shape[:2]
x = x.view(1, batch*channel, x.size(2), x.size(3)) # 1 * (b*c) * k * k
kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3)) # (b*c) * 1 * H * W
out = F.conv2d(x, kernel, groups=batch*channel)
out = out.view(batch, channel, out.size(2), out.size(3))
return out
class DepthCorr(nn.Module):
def __init__(self, in_channels, hidden, out_channels, kernel_size=3):
super(DepthCorr, self).__init__()
# adjust layer for asymmetrical features
self.conv_kernel = nn.Sequential(
nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
)
self.conv_search = nn.Sequential(
nn.Conv2d(in_channels, hidden, kernel_size=kernel_size, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
)
self.head = nn.Sequential(
nn.Conv2d(hidden, hidden, kernel_size=1, bias=False),
nn.BatchNorm2d(hidden),
nn.ReLU(inplace=True),
nn.Conv2d(hidden, out_channels, kernel_size=1)
)
def forward_corr(self, kernel, input):
kernel = self.conv_kernel(kernel)
input = self.conv_search(input)
feature = conv2d_dw_group(input, kernel)
return feature
def forward(self, kernel, search):
feature = self.forward_corr(kernel, search)
out = self.head(feature)
return out
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