Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
residual_block.py 2.91 KB
Newer Older
Houwen Peng's avatar
Request for Integrating the new NAS algorithm: Cream (#2705)  
Houwen Peng committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105 
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Written by Hao Du and Houwen Peng
# email: haodu8-c@my.cityu.edu.hk and houwen.peng@microsoft.com

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


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=True)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):

    def __init__(self, inplanes, planes, stride=1, expansion=4):
        super(Bottleneck, self).__init__()
        planes = int(planes / expansion)
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=True)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(
            planes,
            planes * expansion,
            kernel_size=1,
            bias=True)
        self.bn3 = nn.BatchNorm2d(planes * expansion)
        self.relu = nn.ReLU(inplace=True)
        self.stride = stride
        self.expansion = expansion
        if inplanes != planes * self.expansion:
            self.downsample = nn.Sequential(
                nn.Conv2d(inplanes, planes * self.expansion,
                          kernel_size=1, stride=stride, bias=True),
                nn.BatchNorm2d(planes * self.expansion),
            )
        else:
            self.downsample = None

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


def get_Bottleneck(in_c, out_c, stride):
    return Bottleneck(in_c, out_c, stride=stride)


def get_BasicBlock(in_c, out_c, stride):
    return BasicBlock(in_c, out_c, stride=stride)