Spaces:
Runtime error
Runtime error
File size: 7,284 Bytes
35188e4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import torch.nn as nn
from networks.resnet_models import *
class NormalResnetBackbone(nn.Module):
def __init__(self, orig_resnet):
super(NormalResnetBackbone, self).__init__()
self.num_features = 2048
# take pretrained resnet, except AvgPool and FC
self.prefix = orig_resnet.prefix
self.maxpool = orig_resnet.maxpool
self.layer1 = orig_resnet.layer1
self.layer2 = orig_resnet.layer2
self.layer3 = orig_resnet.layer3
self.layer4 = orig_resnet.layer4
def get_num_features(self):
return self.num_features
def forward(self, x):
tuple_features = list()
x = self.prefix(x)
x = self.maxpool(x)
x = self.layer1(x)
tuple_features.append(x)
x = self.layer2(x)
tuple_features.append(x)
x = self.layer3(x)
tuple_features.append(x)
x = self.layer4(x)
tuple_features.append(x)
return tuple_features
class DilatedResnetBackbone(nn.Module):
def __init__(self, orig_resnet, dilate_scale=8, multi_grid=(1, 2, 4)):
super(DilatedResnetBackbone, self).__init__()
self.num_features = 2048
from functools import partial
if dilate_scale == 8:
orig_resnet.layer3.apply(partial(self._nostride_dilate, dilate=2))
if multi_grid is None:
orig_resnet.layer4.apply(partial(self._nostride_dilate, dilate=4))
else:
for i, r in enumerate(multi_grid):
orig_resnet.layer4[i].apply(partial(self._nostride_dilate, dilate=int(4 * r)))
elif dilate_scale == 16:
if multi_grid is None:
orig_resnet.layer4.apply(partial(self._nostride_dilate, dilate=2))
else:
for i, r in enumerate(multi_grid):
orig_resnet.layer4[i].apply(partial(self._nostride_dilate, dilate=int(2 * r)))
# Take pretrained resnet, except AvgPool and FC
self.prefix = orig_resnet.prefix
self.maxpool = orig_resnet.maxpool
self.layer1 = orig_resnet.layer1
self.layer2 = orig_resnet.layer2
self.layer3 = orig_resnet.layer3
self.layer4 = orig_resnet.layer4
def _nostride_dilate(self, m, dilate):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
# the convolution with stride
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate // 2, dilate // 2)
m.padding = (dilate // 2, dilate // 2)
# other convoluions
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
def get_num_features(self):
return self.num_features
def forward(self, x):
tuple_features = list()
x = self.prefix(x)
x = self.maxpool(x)
x = self.layer1(x)
tuple_features.append(x)
x = self.layer2(x)
tuple_features.append(x)
x = self.layer3(x)
tuple_features.append(x)
x = self.layer4(x)
tuple_features.append(x)
return tuple_features
def ResNetBackbone(backbone=None, width_multiplier=1.0, pretrained=None, multi_grid=None, norm_type='batchnorm'):
arch = backbone
if arch == 'resnet18':
orig_resnet = resnet18(pretrained=pretrained)
arch_net = NormalResnetBackbone(orig_resnet)
arch_net.num_features = 512
elif arch == 'resnet18_dilated8':
orig_resnet = resnet18(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
arch_net.num_features = 512
elif arch == 'resnet34':
orig_resnet = resnet34(pretrained=pretrained)
arch_net = NormalResnetBackbone(orig_resnet)
arch_net.num_features = 512
elif arch == 'resnet34_dilated8':
orig_resnet = resnet34(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
arch_net.num_features = 512
elif arch == 'resnet34_dilated16':
orig_resnet = resnet34(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=16, multi_grid=multi_grid)
arch_net.num_features = 512
elif arch == 'resnet50':
orig_resnet = resnet50(pretrained=pretrained, width_multiplier=width_multiplier)
arch_net = NormalResnetBackbone(orig_resnet)
elif arch == 'resnet50_dilated8':
orig_resnet = resnet50(pretrained=pretrained, width_multiplier=width_multiplier)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
elif arch == 'resnet50_dilated16':
orig_resnet = resnet50(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=16, multi_grid=multi_grid)
elif arch == 'deepbase_resnet50':
if pretrained:
pretrained = 'models/backbones/pretrained/3x3resnet50-imagenet.pth'
orig_resnet = deepbase_resnet50(pretrained=pretrained)
arch_net = NormalResnetBackbone(orig_resnet)
elif arch == 'deepbase_resnet50_dilated8':
if pretrained:
pretrained = 'models/backbones/pretrained/3x3resnet50-imagenet.pth'
# pretrained = "/home/gishin/Projects/DeepLearning/Oxford/cct/models/backbones/pretrained/3x3resnet50-imagenet.pth"
orig_resnet = deepbase_resnet50(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
elif arch == 'deepbase_resnet50_dilated16':
orig_resnet = deepbase_resnet50(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=16, multi_grid=multi_grid)
elif arch == 'resnet101':
orig_resnet = resnet101(pretrained=pretrained)
arch_net = NormalResnetBackbone(orig_resnet)
elif arch == 'resnet101_dilated8':
orig_resnet = resnet101(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
elif arch == 'resnet101_dilated16':
orig_resnet = resnet101(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=16, multi_grid=multi_grid)
elif arch == 'deepbase_resnet101':
orig_resnet = deepbase_resnet101(pretrained=pretrained)
arch_net = NormalResnetBackbone(orig_resnet)
elif arch == 'deepbase_resnet101_dilated8':
if pretrained:
pretrained = 'backbones/backbones/pretrained/3x3resnet101-imagenet.pth'
orig_resnet = deepbase_resnet101(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=8, multi_grid=multi_grid)
elif arch == 'deepbase_resnet101_dilated16':
orig_resnet = deepbase_resnet101(pretrained=pretrained)
arch_net = DilatedResnetBackbone(orig_resnet, dilate_scale=16, multi_grid=multi_grid)
else:
raise Exception('Architecture undefined!')
return arch_net
|