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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> retnet
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 22/02/2024 15:23
=================================================='''
# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File glretrieve -> retnet
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 15/02/2024 10:55
=================================================='''
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class ResBlock(nn.Module):
def __init__(self, inplanes, outplanes, stride=1, groups=32, dilation=1, norm_layer=None, ac_fn=None):
super(ResBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self.conv1 = conv1x1(inplanes, outplanes)
self.bn1 = norm_layer(outplanes)
self.conv2 = conv3x3(outplanes, outplanes, stride, groups, dilation)
self.bn2 = norm_layer(outplanes)
self.conv3 = conv1x1(outplanes, outplanes)
self.bn3 = norm_layer(outplanes)
if ac_fn is None:
self.ac_fn = nn.ReLU(inplace=True)
else:
self.ac_fn = ac_fn
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.ac_fn(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.ac_fn(out)
out = self.conv3(out)
out = self.bn3(out)
out += identity
out = self.ac_fn(out)
return out
class GeneralizedMeanPooling(nn.Module):
r"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Pooling
- At p = 1, one gets Average Pooling
The output is of size H x W, for any input size.
The number of output features is equal to the number of input planes.
Args:
output_size: the target output size of the image of the form H x W.
Can be a tuple (H, W) or a single H for a square image H x H
H and W can be either a ``int``, or ``None`` which means the size will
be the same as that of the input.
"""
def __init__(self, norm, output_size=1, eps=1e-6):
super(GeneralizedMeanPooling, self).__init__()
assert norm > 0
self.p = float(norm)
self.output_size = output_size
self.eps = eps
def forward(self, x):
x = x.clamp(min=self.eps).pow(self.p)
return torch.nn.functional.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.p) + ', ' \
+ 'output_size=' + str(self.output_size) + ')'
class GeneralizedMeanPoolingP(GeneralizedMeanPooling):
""" Same, but norm is trainable
"""
def __init__(self, norm=3, output_size=1, eps=1e-6):
super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps)
self.p = nn.Parameter(torch.ones(1) * norm)
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input):
return F.normalize(input, p=2, dim=self.dim)
class RetNet(nn.Module):
def __init__(self, indim=256, outdim=1024):
super().__init__()
ac_fn = nn.GELU()
self.convs = nn.Sequential(
# no batch normalization
nn.Conv2d(in_channels=indim, out_channels=512, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(512),
# nn.ReLU(),
ResBlock(512, 512, groups=32, stride=1, ac_fn=ac_fn),
ResBlock(512, 512, groups=32, stride=1, ac_fn=ac_fn),
nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(1024),
# nn.ReLU(),
ResBlock(inplanes=1024, outplanes=1024, groups=32, stride=1, ac_fn=ac_fn),
ResBlock(inplanes=1024, outplanes=1024, groups=32, stride=1, ac_fn=ac_fn),
)
self.pool = GeneralizedMeanPoolingP()
self.fc = nn.Linear(1024, out_features=outdim)
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def forward(self, x):
out = self.convs(x)
out = self.pool(out).reshape(x.shape[0], -1)
out = self.fc(out)
out = F.normalize(out, p=2, dim=1)
return out
if __name__ == '__main__':
mode = RetNet(indim=256, outdim=1024)
state_dict = mode.state_dict()
keys = state_dict.keys()
print(keys)
shapes = [state_dict[v].shape for v in keys]
print(shapes)
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