drn&drn_seg代码
问题：
drn文件
import pdb
#pdb是 ThePythonDebugger 的缩写，为Python标准库的一个模块。pdb模块规定了一个Python程序交互式源代码调试器，支持在设置断点（包括条件断点），也支持源码级单步调试，支持栈帧监视，支持源代码列出，支持任意栈帧上下文的随机Python代码估值。它还支持事后调试（post-mortem debugging），并且能在程序控制下被调用。
import torch
import torch.nn as nn #class torch.nn.Module与class torch.nn.Parameter() https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/
import math
import torch.utils.model_zoo as model_zoo
# 在给定URL上加载Torch序列化对象。https://pytorch-cn.readthedocs.io/zh/latest/package_references/model_zoo/
torch.backends.cudnn.benchmark = True
# 与GPU 相关的 flag，提速https://mp.weixin.qq.com/s?src=11×tamp=1606096921&ver=2723&signature=jfMPYOAtEseWFpFchs1vKCGBrrOtigSR6fyj1lQz0MK4BeT3sFRxvQXi2Y5*EGXiS*v-V1n39jNzzWYtt93OzrK8c*TxDQBc0OLbVOQtXCJ8zsWA*l3LmTbBE5skkCVj&new=1
BatchNorm = nn.BatchNorm2d
#对小批量(mini-batch)3d数据组成的4d输入进行批标准化(Batch Normalization)操作https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/#class-torchnnbatchnorm2dnum_features-eps1e-05-momentum01-affinetruesource
# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
'drn-d-105': webroot + 'drn_d_105-12b40979.pth'
}
def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=padding, bias=False, dilation=dilation)#二维卷积层, 输入的尺度是(N, C_in,H,W)，输出尺度（N,C_out,H_out,W_out）的计算方式：
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
dilation=(1, 1), residual=True):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride,
padding=dilation[0], dilation=dilation[0])
self.bn1 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes,
padding=dilation[1], dilation=dilation[1])
self.bn2 = BatchNorm(planes)
self.downsample = downsample
self.stride = stride
self.residual = residual
def forward(self, x)://前向传播：将上一层的输出作为下一层的输入，并计算下一层的输出，一直到运算到输出层为止。
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)
if self.residual:
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
dilation=(1, 1), residual=True):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation[1], bias=False,
dilation=dilation[1])
self.bn2 = BatchNorm(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = BatchNorm(planes * 4)
self.relu = nn.ReLU(inplace=True)
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)
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
class DRN(nn.Module):
def __init__(self, block, layers, num_classes=1000,
channels=(16, 32, 64, 128, 256, 512, 512, 512),
out_map=False, out_middle=False, pool_size=28, arch='D'):
super(DRN, self).__init__()
self.inplanes = channels[0]
self.out_map = out_map
self.out_dim = channels[-1]
self.out_middle = out_middle
self.arch = arch
if arch == 'C':
self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
padding=3, bias=False)
self.bn1 = BatchNorm(channels[0])
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(
BasicBlock, channels[0], layers[0], stride=1)
self.layer2 = self._make_layer(
BasicBlock, channels[1], layers[1], stride=2)
elif arch == 'D':
self.layer0 = nn.Sequential(
nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
bias=False),
BatchNorm(channels[0]),
nn.ReLU(inplace=True)
)
self.layer1 = self._make_conv_layers(
channels[0], layers[0], stride=1)
self.layer2 = self._make_conv_layers(
channels[1], layers[1], stride=2)
self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2)
self.layer5 = self._make_layer(block, channels[4], layers[4],
dilation=2, new_level=False)
self.layer6 = None if layers[5] == 0 else \
self._make_layer(block, channels[5], layers[5], dilation=4,
new_level=False)
if arch == 'C':
self.layer7 = None if layers[6] == 0 else \
self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
new_level=False, residual=False)
self.layer8 = None if layers[7] == 0 else \
self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
new_level=False, residual=False)
elif arch == 'D':
self.layer7 = None if layers[6] == 0 else \
self._make_conv_layers(channels[6], layers[6], dilation=2)
self.layer8 = None if layers[7] == 0 else \
self._make_conv_layers(channels[7], layers[7], dilation=1)
if num_classes > 0:
self.avgpool = nn.AvgPool2d(pool_size)
self.fc = nn.Conv2d(self.out_dim, num_classes, kernel_size=1,
stride=1, padding=0, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, BatchNorm):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
new_level=True, residual=True):
assert dilation == 1 or dilation % 2 == 0
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
BatchNorm(planes * block.expansion),
)
layers = list()
layers.append(block(
self.inplanes, planes, stride, downsample,
dilation=(1, 1) if dilation == 1 else (
dilation // 2 if new_level else dilation, dilation),
residual=residual))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, residual=residual,
dilation=(dilation, dilation)))
return nn.Sequential(*layers)
def _make_conv_layers(self, channels, convs, stride=1, dilation=1):
modules = []
for i in range(convs):
modules.extend([
nn.Conv2d(self.inplanes, channels, kernel_size=3,
stride=stride if i == 0 else 1,
padding=dilation, bias=False, dilation=dilation),
BatchNorm(channels),
nn.ReLU(inplace=True)])
self.inplanes = channels
return nn.Sequential(*modules)
def forward(self, x):
y = list()
if self.arch == 'C':
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
elif self.arch == 'D':
x = self.layer0(x)
x = self.layer1(x)
y.append(x)
x = self.layer2(x)
y.append(x)
x = self.layer3(x)
y.append(x)
x = self.layer4(x)
y.append(x)
x = self.layer5(x)
y.append(x)
if self.layer6 is not None:
x = self.layer6(x)
y.append(x)
if self.layer7 is not None:
x = self.layer7(x)
y.append(x)
if self.layer8 is not None:
x = self.layer8(x)
y.append(x)
if self.out_map:
x = self.fc(x)
else:
x = self.avgpool(x)
x = self.fc(x)
x = x.view(x.size(0), -1)
if self.out_middle:
return x, y
else:
return x
class DRN_A(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(DRN_A, self).__init__()
self.out_dim = 512 * block.expansion
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=4)
self.avgpool = nn.AvgPool2d(28, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, BatchNorm):
m.weight.data.fill_(1)
m.bias.data.zero_()
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
dilation=(dilation, dilation)))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def drn_a_50(pretrained=False, **kwargs):
model = DRN_A(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def drn_c_26(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-26']))
return model
def drn_c_42(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-42']))
return model
def drn_c_58(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-58']))
return model
def drn_d_22(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-22']))
return model
def drn_d_24(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-24']))
return model
def drn_d_38(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-38']))
return model
def drn_d_40(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-40']))
return model
def drn_d_54(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-54']))
return model
def drn_d_56(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-56']))
return model
def drn_d_105(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-105']))
return model
def drn_d_107(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-107']))
return model
drn_seg
import math
import torch
import torch.nn as nn
from networks.drn import drn_c_26
def fill_up_weights(up):
w = up.weight.data
f = math.ceil(w.size(2) / 2)
c = (2 * f - 1 - f % 2) / (2. * f)
for i in range(w.size(2)):
for j in range(w.size(3)):
w[0, 0, i, j] = \
(1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
for c in range(1, w.size(0)):
w[c, 0, :, :] = w[0, 0, :, :]
class DRNSeg(nn.Module):
def __init__(self, classes, pretrained_drn=False,
pretrained_model=None, use_torch_up=False):
super(DRNSeg, self).__init__()
model = drn_c_26(pretrained=pretrained_drn)
self.base = nn.Sequential(*list(model.children())[:-2])
if pretrained_model:
self.load_pretrained(pretrained_model)
self.seg = nn.Conv2d(model.out_dim, classes,
kernel_size=1, bias=True)
m = self.seg
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
if use_torch_up:
self.up = nn.UpsamplingBilinear2d(scale_factor=8)
else:
up = nn.ConvTranspose2d(classes, classes, 16, stride=8, padding=4,
output_padding=0, groups=classes,
bias=False)
fill_up_weights(up)
up.weight.requires_grad = False
self.up = up
def forward(self, x):
x = self.base(x)
x = self.seg(x)
y = self.up(x)
return y
def optim_parameters(self, memo=None):
for param in self.base.parameters():
yield param
for param in self.seg.parameters():
yield param
def load_pretrained(self, pretrained_model):
print("loading the pretrained drn model from %s" % pretrained_model)
state_dict = torch.load(pretrained_model, map_location='cpu')
if hasattr(state_dict, '_metadata'):
del state_dict._metadata
# filter out unnecessary keys
pretrained_dict = state_dict['model']
pretrained_dict = {k[5:]: v for k, v in pretrained_dict.items() if k.split('.')[0] == 'base'}
# load the pretrained state dict
self.base.load_state_dict(pretrained_dict)
class DRNSub(nn.Module):
def __init__(self, num_classes, pretrained_model=None, fix_base=False):
super(DRNSub, self).__init__()
drnseg = DRNSeg(2)
if pretrained_model:
print("loading the pretrained drn model from %s" % pretrained_model)
state_dict = torch.load(pretrained_model, map_location='cpu')
drnseg.load_state_dict(state_dict['model'])
self.base = drnseg.base
if fix_base:
for param in self.base.parameters():
param.requires_grad = False
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.base(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x