pytorch实现对输入超过三通道的数据进行训练
时间：2021-04-07 10:12:16|栏目：Python代码|点击： 次
案例背景:视频识别
假设每次输入是8s的灰度视频,视频帧率为25fps,则视频由200帧图像序列构成.每帧是一副单通道的灰度图像,通过pythonb里面的np.stack(深度拼接)可将200帧拼接成200通道的深度数据.进而送到网络里面去训练.
如果输入图像200通道觉得多,可以对视频进行抽帧,针对具体场景可以随机抽帧或等间隔抽帧.比如这里等间隔抽取40帧.则最后输入视频相当于输入一个40通道的图像数据了.
pytorch对超过三通道数据的加载:
读取视频每一帧,转为array格式,然后依次将每一帧进行深度拼接,最后得到一个40通道的array格式的深度数据,保存到pickle里.
对每个视频都进行上述操作,保存到pickle里.
我这里将火的视频深度数据保存在一个.pkl文件中,一共2504个火的视频,即2504个火的深度数据.
将非火的视频深度数据保存在一个.pkl文件中,一共3985个非火的视频,即3985个非火的深度数据.
数据加载
import torch
from torch.utils import data
import os
from PIL import Image
import numpy as np
import pickle
class Fire_Unfire(data.Dataset):
def __init__(self,fire_path,unfire_path):
self.pickle_fire = open(fire_path,'rb')
self.pickle_unfire = open(unfire_path,'rb')
def __getitem__(self,index):
if index <2504:
fire = pickle.load(self.pickle_fire)#高*宽*通道
fire = fire.transpose(2,0,1)#通道*高*宽
data = torch.from_numpy(fire)
label = 1
return data,label
elif index>=2504 and index<6489:
unfire = pickle.load(self.pickle_unfire)
unfire = unfire.transpose(2,0,1)
data = torch.from_numpy(unfire)
label = 0
return data,label
def __len__(self):
return 6489
root_path = './datasets/train' dataset = Fire_Unfire(root_path +'/fire_train.pkl',root_path +'/unfire_train.pkl') #转换成pytorch网络输入的格式(批量大小,通道数,高,宽) from torch.utils.data import DataLoader fire_dataloader = DataLoader(dataset,batch_size=4,shuffle=True,drop_last = True)
模型训练
import torch
from torch.utils import data
from nets.mobilenet import mobilenet
from config.config import default_config
from torch.autograd import Variable as V
import numpy as np
import sys
import time
opt = default_config()
def train():
#模型定义
model = mobilenet().cuda()
if opt.pretrain_model:
model.load_state_dict(torch.load(opt.pretrain_model))
#损失函数
criterion = torch.nn.CrossEntropyLoss().cuda()
#学习率
lr = opt.lr
#优化器
optimizer = torch.optim.SGD(model.parameters(),lr = lr,weight_decay=opt.weight_decay)
pre_loss = 0.0
#训练
for epoch in range(opt.max_epoch):
#训练数据
train_data = Fire_Unfire(opt.root_path +'/fire_train.pkl',opt.root_path +'/unfire_train.pkl')
train_dataloader = data.DataLoader(train_data,batch_size=opt.batch_size,shuffle=True,drop_last = True)
loss_sum = 0.0
for i,(datas,labels) in enumerate(train_dataloader):
#print(i,datas.size(),labels)
#梯度清零
optimizer.zero_grad()
#输入
input = V(datas.cuda()).float()
#目标
target = V(labels.cuda()).long()
#输出
score = model(input).cuda()
#损失
loss = criterion(score,target)
loss_sum += loss
#反向传播
loss.backward()
#梯度更新
optimizer.step()
print('{}{}{}{}{}'.format('epoch:',epoch,',','loss:',loss))
torch.save(model.state_dict(),'models/mobilenet_%d.pth'%(epoch+370))
RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target'
解决方案：target = target.long()
