我想在现有的ResNet模型中提供 3,320,320张图片。该模型实际上期望输入大小为3、32、32 。因为我担心信息丢失,所以我并不想简单地调整图片大小。 预处理图像以使其能够在ResNet34上运行的最佳方法是什么? 我应该在ResNet的正向方法中添加其他图层吗?如果可以,哪种情况适合我?
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_fitmodule import FitModule
from torch.autograd import Variable
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(FitModule):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(FitModule):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = conv3x3(3, 64)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x): # add additional layers here?
x = x.float()
out = F.relu(self.bn1(self.conv1(x).float()).float())
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNet34():
return ResNet(BasicBlock, [3, 4, 6, 3])
非常感谢!
关于, 法比安
答案 0 :(得分:0)
如果将avg_pool
操作更改为'AdaptiveAvgPool2d',则您的模型将适用于任何图像尺寸。
但是,在当前设置下,进入合并阶段的320x320图像将是40x40,这是要合并的大型功能图。考虑添加更多的转换层。