trw.arch.darts_ops

Module Contents

Classes

ReLUConvBN2d	Stack of relu-conv-bn
ReduceChannels2d	Base class for all neural network modules.
Identity	Identity module
Zero2d	zero by stride
DilConv2d	relu-dilated conv-bn
SepConv2d	implemented separate convolution via pytorch groups parameters

Attributes

DARTS_PRIMITIVES_2D

trw.arch.darts_ops.DARTS_PRIMITIVES_2D

class trw.arch.darts_ops.ReLUConvBN2d(C_in, C_out, kernel_size, stride, padding, affine=True)

Bases: torch.nn.Module

Stack of relu-conv-bn

forward(self, x)

class trw.arch.darts_ops.ReduceChannels2d(C_in, C_out, affine=True)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Variables

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(self, x)

class trw.arch.darts_ops.Identity

Bases: torch.nn.Module

Identity module

forward(self, x)

class trw.arch.darts_ops.Zero2d(stride)

Bases: torch.nn.Module

zero by stride

forward(self, x)

class trw.arch.darts_ops.DilConv2d(C_in, C_out, kernel_size, stride, padding, dilation, affine=True)

Bases: torch.nn.Module

relu-dilated conv-bn

forward(self, x)

class trw.arch.darts_ops.SepConv2d(C_in, C_out, kernel_size, stride, padding, affine=True)

Bases: torch.nn.Module

implemented separate convolution via pytorch groups parameters

forward(self, x)