Pytorch_预训练模型
目录
1. 模型下载
import re
import os
import glob
import torch
from torch.hub import download_url_to_file
from torch.hub import urlparse
import torchvision.models as models
def download_model(url, dst_path):
parts = urlparse(url)
filename = os.path.basename(parts.path)
HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')
hash_prefix = HASH_REGEX.search(filename).group(1)
if os.path.exists(os.path.join(dst_path, filename)):
return filename
download_url_to_file(url, os.path.join(dst_path, filename), hash_prefix, True)
return filename
def saveToFolder(path):
#其他各种模型可以在这个目录下进行搜索查看 https://github.com/pytorch/vision/tree/master/torchvision/models
# model_urls = {
# 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
# 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
# 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
# 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
# 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
# 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
# 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
# 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
# }
model_urls={
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
}
if not (os.path.exists(path)):
os.makedirs(path)
for url in model_urls.values():
download_model(url, path)
def load_model(model_name, model_dir):
model = eval('models.%s(init_weights=False)' % model_name)
path_format = os.path.join(model_dir, '%s-[a-z0-9]*.pth' % model_name)
model_path = glob.glob(path_format)[0]
model.load_state_dict(torch.load(model_path))
return model
def main():
path = '/home/iot/jupyter/root_dir/liudongdong/pytorch_demo/pretainedpth/vgg16'
saveToFolder(path)
model = load_model('vgg16', path)
print(model)
if __name__ == "__main__":
main()
2. 模型查看
resnet.fc = torch.nn.Linear(resnet.fc.in_features, 100)
print(resnet) #将会输出网络每一层结构, print顺序可能不是最终网络的数据
# 或者采用torchviz模块，对网络结构进行可视化， 将会生成一个pdf 网络结构图
import torch
import torchvision
from torchviz import make_dot
x = torch.randn(10, 3, 224, 224).requires_grad_(True)
model50 = torchvision.models.resnet50()
y = model50(x)
vis_graph = make_dot(y, params=dict(list(resnet.named_parameters()) + [('x', x)]))
vise_graph.view()
#保存成文件形式
vise_graph.render(filename='resnet50', view=False, format='pdf')
- 方式二：以列表的形式
from torchsummary import summary
summary(model50, (3, 224, 224)) #模型参数，输入数据的格式
3. 模型初始化
适当的权值初始化可以加速模型的训练和模型的收敛，而错误的权值初始化会导致梯度消失/爆炸，从而无法完成网络的训练，因此需要控制网络输出值的尺度范围。torch.nn.init中提供了常用的初始化方法函数，1. Xavier，kaiming系列；2. 其他方法分布
从上图中的公式可以看出，*每传播一层，输出值数据的方差就会扩大n* *倍*，要想控制输出H的尺度范围，只需要控制H的方差为1，则无论经过多少层都可以维持在初始输入X的方差附近，因此*权重w需要初始化方差为1/n*（n为神经元的个数）
.1. Xavier 均匀分布
import os
import torch
import random
import numpy as np
import torch.nn as nn
def set_seed(seed=1):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(1) # 设置随机种子
class MLP(nn.Module):
def __init__(self, neural_num, layers):
super(MLP, self).__init__()
self.linears = nn.ModuleList([nn.Linear(neural_num, neural_num, bias=False) for i in range(layers)])
self.neural_num = neural_num
def forward(self, x):
for (i, linear) in enumerate(self.linears):
x = linear(x)
x = torch.tanh(x)
print("layer:{}, std:{}".format(i, x.std()))
if torch.isnan(x.std()):
print("output is nan in {} layers".format(i))
break
return x
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Linear):
#xavier手动计算
a = np.sqrt(6 / (self.neural_num + self.neural_num))
tanh_gain = nn.init.calculate_gain('tanh') #计算增益
a *= tanh_gain
nn.init.uniform_(m.weight.data, -a, a)
#调用pytorch实现xavier初始化，适用于饱和激活函数
# tanh_gain = nn.init.calculate_gain('tanh')
# nn.init.xavier_uniform_(m.weight.data, gain=tanh_gain)
# flag = 0
flag = 1
if flag:
layer_nums = 100
neural_nums = 256
batch_size = 16
net = MLP(neural_nums, layer_nums)
net.initialize()
inputs = torch.randn((batch_size, neural_nums)) # normal: mean=0, std=1
output = net(inputs)
print(output)
torch.nn.init.xavier_uniform_(tensor, gain=1)
xavier初始化方法中服从均匀分布U(−a,a) ，分布的参数a = gain * sqrt(6/fan_in+fan_out)，
这里有一个gain，增益的大小是依据激活函数类型来设定
eg：nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
.2. Xavier正态分布
torch.nn.init.xavier_normal_(tensor, gain=1)
xavier初始化方法中服从正态分布，
mean=0,std = gain * sqrt(2/fan_in + fan_out)
.3. kaiming均匀分布
torch.nn.init.kaiming_uniform_(tensor, a=0, mode=‘fan_in’, nonlinearity=‘leaky_relu’)
此为均匀分布，U～（-bound, bound）, bound = sqrt(6/(1+a^2)*fan_in)
其中，a为激活函数的负半轴的斜率，relu是0
mode- 可选为fan_in 或 fan_out, fan_in使正向传播时，方差一致; fan_out使反向传播时，方差一致
nonlinearity- 可选 relu 和 leaky_relu ，默认值为 。 leaky_relu
nn.init.kaiming_uniform_(w, mode=‘fan_in’, nonlinearity=‘relu’)
import os
import torch
import random
import numpy as np
import torch.nn as nn
def set_seed(seed=1):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(1) # 设置随机种子
class MLP(nn.Module):
def __init__(self, neural_num, layers):
super(MLP, self).__init__()
self.linears = nn.ModuleList([nn.Linear(neural_num, neural_num, bias=False) for i in range(layers)])
self.neural_num = neural_num
def forward(self, x):
for (i, linear) in enumerate(self.linears):
x = linear(x)
x = torch.relu(x)
print("layer:{}, std:{}".format(i, x.std()))
if torch.isnan(x.std()):
print("output is nan in {} layers".format(i))
break
return x
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Linear):
#kaiming初始化手动
nn.init.normal_(m.weight.data, std=np.sqrt(2 / self.neural_num))
#kaiming初始化
# nn.init.kaiming_normal_(m.weight.data)
# flag = 0
flag = 1
if flag:
layer_nums = 100
neural_nums = 256
batch_size = 16
net = MLP(neural_nums, layer_nums)
net.initialize()
inputs = torch.randn((batch_size, neural_nums)) # normal: mean=0, std=1
output = net(inputs)
print(output)
.4. kaiming 正态分布
torch.nn.init.kaiming_normal_(tensor, a=0, mode=‘fan_in’, nonlinearity=‘leaky_relu’)
此为0均值的正态分布，N～ (0,std)，其中std = sqrt(2/(1+a^2)*fan_in)
其中，a为激活函数的负半轴的斜率，relu是0
mode- 可选为fan_in 或 fan_out, fan_in使正向传播时，方差一致;fan_out使反向传播时，方差一致
nonlinearity- 可选 relu 和 leaky_relu ，默认值为 。 leaky_relu
nn.init.kaiming_normal_(w, mode=‘fan_out’, nonlinearity=‘relu’)
.5. 均匀初始化分布
torch.nn.init.uniform_(tensor, a=0, b=1)
使值服从均匀分布U(a,b)
.6. 正态初始化分布
torch.nn.init.normal_(tensor, mean=0, std=1)
使值服从正态分布N(mean, std)，默认值为0，1
.7. 常数初始化
torch.nn.init.constant_(tensor, val)
使值为常数val nn.init.constant_(w, 0.3)
.8. 单位矩阵初始化
torch.nn.init.eye_(tensor)
将二维tensor初始化为单位矩阵（the identity matrix）
.9. 正交初始化
torch.nn.init.orthogonal_(tensor, gain=1)
使得tensor是正交的，论文:Exact solutions to the nonlinear dynamics of learning in deep linear neural networks” - Saxe, A. et al. (2013)
.10. 稀疏初始化
torch.nn.init.sparse_(tensor, sparsity, std=0.01)
从正态分布N～（0. std）中进行稀疏化，使每一个column有一部分为0
sparsity- 每一个column稀疏的比例，即为0的比例
nn.init.sparse_(w, sparsity=0.1)
注意 model.modules()和 model.children()的区别：model.modules()会迭代地遍历模型的所有子层，而**model.children()**只会遍历模型下的一层。
- 对网络中某一层进行初始化
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
init.xavier_uniform(self.conv1.weight)
init.constant(self.conv1.bias, 0.1)
- 对网络整体进行初始化
def weights_init(m):
classname=m.__class__.__name__
if classname.find('Conv') != -1:
xavier(m.weight.data)
xavier(m.bias.data)
net = Net()#构建网络
net.apply(weights_init) #apply函数会递归地搜索网络内的所有module并把参数表示的函数应用到所有的module上。
#对所有的Conv层都初始化权重.
- 权重初始化
# Common practise for initialization.
for layer in model.modules():
if isinstance(layer, torch.nn.Conv2d):
torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',
nonlinearity='relu')
if layer.bias is not None:
torch.nn.init.constant_(layer.bias, val=0.0)
elif isinstance(layer, torch.nn.BatchNorm2d):
torch.nn.init.constant_(layer.weight, val=1.0)
torch.nn.init.constant_(layer.bias, val=0.0)
elif isinstance(layer, torch.nn.Linear):
torch.nn.init.xavier_normal_(layer.weight)
if layer.bias is not None:
torch.nn.init.constant_(layer.bias, val=0.0)
# Initialization with given tensor.
layer.weight = torch.nn.Parameter(tensor)
- 对指定层进行Finetune
count = 0
para_optim = []
for k in model.children():
count += 1
# 6 should be changed properly
if count > 6:
for param in k.parameters():
para_optim.append(param)
else:
for param in k.parameters():
param.requires_grad = False
optimizer = optim.RMSprop(para_optim, lr)
- 对固定部分参数训练
# 只有True的才训练
optimizer.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
#前面的参数就是False，而后面的不变
for p in self.parameters():
p.requires_grad=False
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
- 优化
optimizer = optim.Adam([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': args['lr'], 'weight_decay': args['weight_decay']}
], betas=(args['momentum'], 0.999))
- 加载部分权重
# 获得模型的键值
keys=[]
for k,v in desnet.state_dict().items():
if v.shape:
keys.append(k)
print(k,v.shape)
# 从预训练文件中加载权重
state={}
pretrained_dict = torch.load('/home/lulu/pytorch/Paper_Code/weights/densenet121-a639ec97.pth')
for i,(k,v) in enumerate(pretrained_dict.items()):
if 'classifier' not in k:
state[keys[i]] = v
# 保存权重
torch.save(state,'/home/lulu/pytorch/Paper_Code/weights/densenet121.pth')
4. 构建模型
- Sequential：
顺序性，各网络层之间严格按照顺序执行，常用语block构建- ModuleList：
迭代性，常用于大量重复网络构建，通过for循环实现重复构建- ModuleDict：
索引性，常用于可选择的网络层
.1. nn.Sequential
# ============================ Sequential
class LeNetSequential(nn.Module):
def __init__(self, classes):
super(LeNetSequential, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 6, 5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, 5),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),)
self.classifier = nn.Sequential(
nn.Linear(16*5*5, 120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84, classes),)
def forward(self, x):
x = self.features(x)
x = x.view(x.size()[0], -1)
x = self.classifier(x)
return x
.2. nn.ModuleList
功能：
像python的**list**一样包装多个网络层，以迭代的方式调用网络层
- append（）：在modulelist后面添加网络层
- extend（）：拼接两个modulelist
- insert（）：在modulelist中指定位置插入网络层
class ModuleList(nn.Module):
def __init__(self):
super(ModuleList, self).__init__()
self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(20)])
def forward(self, x):
for i, linear in enumerate(self.linears):
x = linear(x)
return x
net = ModuleList()
print(net)
fake_data = torch.ones((10, 10))
output = net(fake_data)
print(output)
.3. nn.ModuleDict
功能：
像python的dict一样包装多个网络层（每一个给一个key，可通过key索引网络层）
- clear（）：清空moduleDict
- items（）：返回可迭代的键值对（key-value pairs）
- keys（）：返回字典的key
- values（）：返回字典的value
- pop（）：返回一对键值，并从字典中删除
# ============================ ModuleDict
class ModuleDict(nn.Module):
def __init__(self):
super(ModuleDict, self).__init__()
self.choices = nn.ModuleDict({
'conv': nn.Conv2d(10, 10, 3),
'pool': nn.MaxPool2d(3)
})
self.activations = nn.ModuleDict({
'relu': nn.ReLU(),
'prelu': nn.PReLU()
})
def forward(self, x, choice, act):
x = self.choices[choice](x)
x = self.activations[act](x)
return x
net = ModuleDict()
fake_img = torch.randn((4, 10, 32, 32))
output = net(fake_img, 'conv', 'relu')
#prelu输出结果有负值，改为relu后输出没有负数，可以检查是不是按照我们的想法运行的
print(output)
5. 使用预训练模型
.0. AlexNet 预训练模型修改
1. 直接使用AlexNet，并添加可视化
import os
import torch
import torch.nn
import torchvision.models as models
import torchvision.transforms as transforms
import torch.nn.functional as F
import torchvision.utils as utils
import cv2
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import argparse
"""
input commands
"""
paser = argparse.ArgumentParser()
paser.add_argument("--test_img", type=str, default='whippet.jpg', help="testing image")
opt = paser.parse_args()
# function for visualizing the feature maps
def visualize_activation_maps(input, model):
I = utils.make_grid(input, nrow=1, normalize=True, scale_each=True)
img = I.permute((1, 2, 0)).cpu().numpy()
conv_results = []
x = input
for idx, operation in enumerate(model.features):
x = operation(x)
if idx in {1, 4, 7, 9, 11}:
conv_results.append(x)
for i in range(5):
conv_result = conv_results[i]
N, C, H, W = conv_result.size()
mean_acti_map = torch.mean(conv_result, 1, True)
mean_acti_map = F.interpolate(mean_acti_map, size=[224,224], mode='bilinear', align_corners=False)
map_grid = utils.make_grid(mean_acti_map, nrow=1, normalize=True, scale_each=True)
map_grid = map_grid.permute((1, 2, 0)).mul(255).byte().cpu().numpy()
map_grid = cv2.applyColorMap(map_grid, cv2.COLORMAP_JET)
map_grid = cv2.cvtColor(map_grid, cv2.COLOR_BGR2RGB)
map_grid = np.float32(map_grid) / 255
visual_acti_map = 0.6 * img + 0.4 * map_grid
tensor_visual_acti_map = torch.from_numpy(visual_acti_map).permute(2, 0, 1)
file_name_visual_acti_map = 'conv{}_activation_map.jpg'.format(i+1)
utils.save_image(tensor_visual_acti_map, file_name_visual_acti_map)
return 0
# main
if __name__ == "__main__":
"""
data transforms, for pre-processing the input testing image before feeding into the net
"""
data_transforms = transforms.Compose([
transforms.Resize((224,224)), # resize the input to 224x224
transforms.ToTensor(), # put the input to tensor format
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # normalize the input
# the normalization is based on images from ImageNet
])
# obtain the file path of the testing image
test_image_dir = './alexnet_images'
test_image_filepath = os.path.join(test_image_dir, opt.test_img)
#print(test_image_filepath)
# open the testing image
img = Image.open(test_image_filepath)
print("original image's shape: " + str(img.size))
# pre-process the input
transformed_img = data_transforms(img)
print("transformed image's shape: " + str(transformed_img.shape))
# form a batch with only one image
batch_img = torch.unsqueeze(transformed_img, 0) #Returns a new tensor with a dimension of size one inserted at the specified position.
print("image batch's shape: " + str(batch_img.shape))
# load pre-trained AlexNet model
print("\nfeed the input into the pre-trained alexnet to get the output")
alexnet = models.alexnet(pretrained=True)
# put the model to eval mode for testing
alexnet.eval()
# obtain the output of the model
output = alexnet(batch_img)
print("output vector's shape: " + str(output.shape))
# obtain the activation maps
visualize_activation_maps(batch_img, alexnet)
# map the class no. to the corresponding label
with open('class_names_ImageNet.txt') as labels:
classes = [i.strip() for i in labels.readlines()]
# print the first 5 classes to see the labels
print("\nprint the first 5 classes to see the lables")
for i in range(5):
print("class " + str(i) + ": " + str(classes[i]))
# sort the probability vector in descending order
sorted, indices = torch.sort(output, descending=True)
percentage = F.softmax(output, dim=1)[0] * 100.0
# obtain the first 5 classes (with the highest probability) the input belongs to
results = [(classes[i], percentage[i].item()) for i in indices[0][:5]]
print("\nprint the first 5 classes the testing image belongs to")
for i in range(5):
print('{}: {:.4f}%'.format(results[i][0], results[i][1]))
2. 修改Alexnet最后一层
import torchvision.models as models
model = models.AlexNet()
print(model)
#修改网络的第一个卷积层的输入为4通道，输出的结果预测为10个类别
model.features[0]=nn.Conv2d(4, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
model.classifier[6] = nn.Linear(4096,10)
print(model)
model = cifar10_cnn.CIFAR10_Nettest()
pretrained_dict = torch.load('models/cifar10_statedict.pkl')
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print(model)
new_model_dict = model.state_dict()
dict_name = list(new_model_dict)
for i, p in enumerate(dict_name):
print(i, p)
print('before change:\n',new_model_dict['classifier.5.bias'])
model.classifier[5]=nn.Linear(1024,17)
change_model_dict = model.state_dict()
new_dict_name = list(change_model_dict)
print('after change:\n',change_model_dict['classifier.5.bias'])
import torch.nn as nn
from torchvision import models
class BuildAlexNet(nn.Module):
def __init__(self, model_type, n_output):
super(BuildAlexNet, self).__init__()
self.model_type = model_type
if model_type == 'pre':
model = models.alexnet(pretrained=True)
self.features = model.features
fc1 = nn.Linear(9216, 4096)
fc1.bias = model.classifier[1].bias
fc1.weight = model.classifier[1].weight
fc2 = nn.Linear(4096, 4096)
fc2.bias = model.classifier[4].bias
fc2.weight = model.classifier[4].weight
self.classifier = nn.Sequential(
nn.Dropout(),
fc1,
nn.ReLU(inplace=True),
nn.Dropout(),
fc2,
nn.ReLU(inplace=True),
nn.Linear(4096, n_output))
#
# model.classifier[6]==nn.Linear(4096,n_output)
# self.classifier = model.classifier
if model_type == 'new':
self.features = nn.Sequential(
nn.Conv2d(3, 64, 11, 4, 2),
nn.ReLU(inplace = True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(64, 192, 5, 1, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(192, 384, 3, 1, 1),
nn.ReLU(inplace = True),
nn.Conv2d(384, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(9216, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, n_output))
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
out = self.classifier(x)
return out
import numpy as np
from torch.autograd import Variable
import torch
if __name__ == '__main__':
model_type = 'pre'
n_output = 10
alexnet = BuildAlexNet(model_type, n_output)
print(alexnet)
x = np.random.rand(1,3,224,224)
x = x.astype(np.float32)
x_ts = torch.from_numpy(x)
x_in = Variable(x_ts)
y = alexnet(x_in)
.1. ResNet参数修改
resnet网络最后一层分类层fc是对1000种类型进行划分，对于自己的数据集，如果只有9类
# coding=UTF-8
import torchvision.models as models
#调用模型
model = models.resnet50(pretrained=True)
#提取fc层中固定的参数
fc_features = model.fc.in_features
#修改类别为9
model.fc = nn.Linear(fc_features, 9)
.2. 增减卷积层
1、先建立好自己的网络（与预训练的模型类似，要不谈何fine-tune）
2、然后将预训练模型参数与自己搭建的网络不一致的部分参数去掉
3、将保留的合适的参数读入网络初始化，实现fine-tune的效果
# -*- coding:utf-8 -*-
#####################
#建立自己的网络模型net
#####################
###然后读出预训练模型参数以resnet152为例，我不是利用程序下载的，我是习惯了下载好存储在文件夹中
pretrained_dict = torch.load(save_path)
model_dict = net.state_dict() #(读出搭建的网络的参数，以便后边更新之后初始化）
####去除不属于model_dict的键值
pretrained_dict={ k : v for k, v in pretrained_dict.items() if k in model_dict}
###更新现有的model_dict的值
model_dict.update(pretrained_dict)
##加载模型需要的参数
net.load_state_dict(model_dict)
.3. ImageNet计算多层卷积特征
class FeatureExtractor(torch.nn.Module):
"""Helper class to extract several convolution features from the given
pre-trained model.
Attributes:
_model, torch.nn.Module.
_layers_to_extract, list<str> or set<str>
Example:
>>> model = torchvision.models.resnet152(pretrained=True)
>>> model = torch.nn.Sequential(collections.OrderedDict(
list(model.named_children())[:-1]))
>>> conv_representation = FeatureExtractor(
pretrained_model=model,
layers_to_extract={'layer1', 'layer2', 'layer3', 'layer4'})(image)
"""
def __init__(self, pretrained_model, layers_to_extract):
torch.nn.Module.__init__(self)
self._model = pretrained_model
self._model.eval()
self._layers_to_extract = set(layers_to_extract)
def forward(self, x):
with torch.no_grad():
conv_representation = []
for name, layer in self._model.named_children():
x = layer(x)
if name in self._layers_to_extract:
conv_representation.append(x)
return conv_representation
4. 训练特定层，冻结其它层
将模型起始的一些层的权重保持不变，重新训练后面的层，得到新的权重。在这个过程中，可多次进行尝试，从而能够依据结果找到 frozen layers 和 retrain layers 之间的最佳搭配。 如何使用预训练模型，是由数据集大小和新旧数据集(预训练的数据集和自己要解决的数据集)之间数据的相似度来决定的。
requires_grad为False来冻结网络参数- **filter(lambda p: p.requires_grad, model.parameters())**过滤掉requires_grad=false的层
#首先自己新定义一个网络
class CNN(nn.Module):
def __init__(self, block, layers, num_classes=9):
#自己新定义的CNN与继承的ResNet网络结构大体相同，即除了新增层，其他层的层名与ResNet的相同。
self.inplanes = 64
super(ResNet, self).__init__() #继承ResNet网络结构
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=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
#新增一个反卷积层
self.convtranspose1 = nn.ConvTranspose2d(2048, 2048, kernel_size=3, stride=1, padding=1, output_padding=0, groups=1, bias=False, dilation=1)
#新增一个最大池化层
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
#将原来的fc层改成fclass层
self.fclass = nn.Linear(2048, num_classes) #原来的fc层：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, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=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))
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)
#3个新加层的forward
x = x.view(x.size(0), -1)
#因为接下来的self.convtranspose1层的输入通道是2048
x = self.convtranspose1(x)
x = self.maxpool2(x)
x = x.view(x.size(0), -1)
#因为接下来的self.fclass层的输入通道是2048
x = self.fclass(x)
return x
#加载model
resnet50 = models.resnet50(pretrained=True)
cnn = CNN(Bottleneck, [3, 4, 6, 3]) #创建一个自己新定义的网络对象cnn。
#微调全连接层
model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
model.fc = nn.Linear(512, 100) # Replace the last fc layer
optimizer = torch.optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)
#以较大的学习率微调全连接层，较小的学习率微调卷积层
model = torchvision.models.resnet18(pretrained=True)
finetuned_parameters = list(map(id, model.fc.parameters()))
conv_parameters = (p for p in model.parameters() if id(p) not in finetuned_parameters)
parameters = [{'params': conv_parameters, 'lr': 1e-3},
{'params': model.fc.parameters()}]
optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)
.5. keypointrcnn_resnet50_fpn 模型使用
import torch
import torchvision
import torch.nn as nn
def get_model(num_kpts,train_kptHead=False,train_fpn=True):
is_available = torch.cuda.is_available()
device =torch.device('cuda:0' if is_available else 'cpu')
dtype = torch.cuda.FloatTensor if is_available else torch.FloatTensor
model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=True)
for i,param in enumerate(model.parameters()):
param.requires_grad = False
if train_kptHead!=False:
for i, param in enumerate(model.roi_heads.keypoint_head.parameters()):
if i/2>=model.roi_heads.keypoint_head.__len__()/2-train_kptHead:
param.requires_grad = True
if train_fpn==True:
for param in model.backbone.fpn.parameters():
param.requires_grad = True
out = nn.ConvTranspose2d(512, num_kpts, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
model.roi_heads.keypoint_predictor.kps_score_lowres = out
return model, device, dtype
#model, device, dtype=get_model(2)
