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224 lines (200 loc) · 9.85 KB
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# coding=utf-8
import math
import torch
import numpy as np
from torch.nn import functional as F
from torch._jit_internal import Optional
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch import nn
from torch.nn import init
class DOConv2d(Module):
"""
DOConv2d can be used as an alternative for torch.nn.Conv2d.
The interface is similar to that of Conv2d, with one exception:
1. D_mul: the depth multiplier for the over-parameterization.
Note that the groups parameter switchs between DO-Conv (groups=1),
DO-DConv (groups=in_channels), DO-GConv (otherwise).
"""
__constants__ = ['stride', 'padding', 'dilation', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size', 'D_mul']
__annotations__ = {'bias': Optional[torch.Tensor]}
def __init__(self, in_channels, out_channels, kernel_size=3, D_mul=None, stride=1,
padding=1, dilation=1, groups=1, bias=False, padding_mode='zeros', simam=False):
super(DOConv2d, self).__init__()
kernel_size = (kernel_size, kernel_size)
stride = (stride, stride)
padding = (padding, padding)
dilation = (dilation, dilation)
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}
if padding_mode not in valid_padding_modes:
raise ValueError("padding_mode must be one of {}, but got padding_mode='{}'".format(
valid_padding_modes, padding_mode))
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.padding_mode = padding_mode
self._padding_repeated_twice = tuple(x for x in self.padding for _ in range(2))
self.simam = simam
#################################### Initailization of D & W ###################################
M = self.kernel_size[0]
N = self.kernel_size[1]
self.D_mul = M * N if D_mul is None or M * N <= 1 else D_mul
self.W = Parameter(torch.Tensor(out_channels, in_channels // groups, self.D_mul))
init.kaiming_uniform_(self.W, a=math.sqrt(5))
if M * N > 1:
self.D = Parameter(torch.Tensor(in_channels, M * N, self.D_mul))
init_zero = np.zeros([in_channels, M * N, self.D_mul], dtype=np.float32)
self.D.data = torch.from_numpy(init_zero)
eye = torch.reshape(torch.eye(M * N, dtype=torch.float32), (1, M * N, M * N))
D_diag = eye.repeat((in_channels, 1, self.D_mul // (M * N)))
if self.D_mul % (M * N) != 0: # the cases when D_mul > M * N
zeros = torch.zeros([in_channels, M * N, self.D_mul % (M * N)])
self.D_diag = Parameter(torch.cat([D_diag, zeros], dim=2), requires_grad=False)
else: # the case when D_mul = M * N
self.D_diag = Parameter(D_diag, requires_grad=False)
##################################################################################################
if simam:
self.simam_block = simam_module()
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
fan_in, _ = init._calculate_fan_in_and_fan_out(self.W)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
else:
self.register_parameter('bias', None)
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation={dilation}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
if self.padding_mode != 'zeros':
s += ', padding_mode={padding_mode}'
return s.format(**self.__dict__)
def __setstate__(self, state):
super(DOConv2d, self).__setstate__(state)
if not hasattr(self, 'padding_mode'):
self.padding_mode = 'zeros'
def _conv_forward(self, input, weight):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
weight, self.bias, self.stride,
(0, 0), self.dilation, self.groups)
return F.conv2d(input, weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
def forward(self, input):
M = self.kernel_size[0]
N = self.kernel_size[1]
DoW_shape = (self.out_channels, self.in_channels // self.groups, M, N)
if M * N > 1:
######################### Compute DoW #################
# (input_channels, D_mul, M * N)
D = self.D + self.D_diag
W = torch.reshape(self.W, (self.out_channels // self.groups, self.in_channels, self.D_mul))
# einsum outputs (out_channels // groups, in_channels, M * N),
# which is reshaped to
# (out_channels, in_channels // groups, M, N)
DoW = torch.reshape(torch.einsum('ims,ois->oim', D, W), DoW_shape)
#######################################################
else:
DoW = torch.reshape(self.W, DoW_shape)
if self.simam:
DoW_h1, DoW_h2 = torch.chunk(DoW, 2, dim=2)
DoW = torch.cat([self.simam_block(DoW_h1), DoW_h2], dim=2)
return self._conv_forward(input, DoW)
class DOConv2d_eval(Module):
"""
DOConv2d can be used as an alternative for torch.nn.Conv2d.
The interface is similar to that of Conv2d, with one exception:
1. D_mul: the depth multiplier for the over-parameterization.
Note that the groups parameter switchs between DO-Conv (groups=1),
DO-DConv (groups=in_channels), DO-GConv (otherwise).
"""
__constants__ = ['stride', 'padding', 'dilation', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size', 'D_mul']
__annotations__ = {'bias': Optional[torch.Tensor]}
def __init__(self, in_channels, out_channels, kernel_size=3, D_mul=None, stride=1,
padding=1, dilation=1, groups=1, bias=False, padding_mode='zeros', simam=False):
super(DOConv2d_eval, self).__init__()
kernel_size = (kernel_size, kernel_size)
stride = (stride, stride)
padding = (padding, padding)
dilation = (dilation, dilation)
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}
if padding_mode not in valid_padding_modes:
raise ValueError("padding_mode must be one of {}, but got padding_mode='{}'".format(
valid_padding_modes, padding_mode))
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.padding_mode = padding_mode
self._padding_repeated_twice = tuple(x for x in self.padding for _ in range(2))
self.simam = simam
#################################### Initailization of D & W ###################################
M = self.kernel_size[0]
N = self.kernel_size[1]
self.W = Parameter(torch.Tensor(out_channels, in_channels // groups, M, N))
init.kaiming_uniform_(self.W, a=math.sqrt(5))
self.register_parameter('bias', None)
def extra_repr(self):
s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation={dilation}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
if self.padding_mode != 'zeros':
s += ', padding_mode={padding_mode}'
return s.format(**self.__dict__)
def __setstate__(self, state):
super(DOConv2d, self).__setstate__(state)
if not hasattr(self, 'padding_mode'):
self.padding_mode = 'zeros'
def _conv_forward(self, input, weight):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
weight, self.bias, self.stride,
(0, 0), self.dilation, self.groups)
return F.conv2d(input, weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
def forward(self, input):
return self._conv_forward(input, self.W)
class simam_module(torch.nn.Module):
def __init__(self, e_lambda=1e-4):
super(simam_module, self).__init__()
self.activaton = nn.Sigmoid()
self.e_lambda = e_lambda
def forward(self, x):
b, c, h, w = x.size()
n = w * h - 1
x_minus_mu_square = (x - x.mean(dim=[2, 3], keepdim=True)).pow(2)
y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2, 3], keepdim=True) / n + self.e_lambda)) + 0.5
return x * self.activaton(y)
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