python|基于PyTorch搭建CNN实现视频动作分类任务 cnn|分类|pytorch

基于PyTorch搭建CNN实现视频动作分类任务2022年10月6日项目介绍简单实训项目：datafountain.cn
里面有具体的项目说明，并且可以下载数据集。
项目路径
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datasets是在官网下载的数据包，

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代码 BasketballDataset.py

继承dataset，适配pytorch的dataloader

import torch.nn as nn from PIL import Image import os from torch.utils.data import DataLoader, sampler, Dataset# 继承Module类，使用pytorch中设计好的Dataloader作为我们的数据的加载器。 # # 该加载器能够根据设定在每一次请求时自动加载一批训练数据， # # 能够自主实现多线程加载，能够在快速加载的同时尽可能的节省内存开销。# 而Dataloader类所加载的数据必须是pytorch中定义好的Dataset类， # # 所以我们的第一步，就是将我们的数据封装成一个Dataset类。class BasketballDataset(Dataset): def __init__(self, root_dir, labels=[], transform=None): super(BasketballDataset, self).__init__() self.root_dir = root_dir self.labels = labels self.transform = transform self.length = len(os.listdir(self.root_dir))def __len__(self): return self.length * 3# 因为此数据集一个视频有三张图片def __getitem__(self, idx): folder = idx // 3 + 1# 得到文件夹名非0部分 imidex = idx % 3 + 1# 得到每个文件夹下图片的编号1，2，3 # 文件夹，命名的文件夹名为5为数字，如00001，所以要进行格式转变 folder = format(folder, '05d') imgname = str(imidex) + '.jpg' img_path = os.path.join(self.root_dir, folder, imgname) image = Image.open(img_path)Label = 0 # test集没有标签 if len(self.labels) != 0: Label = self.labels[idx // 3][0] - 1 if self.transform: image = self.transform(image) if len(self.labels) != 0: sample = {'image': image, 'img_path': img_path, "Label": Label} else: sample = {'image': image, 'img_path': img_path} return sample

pre.py

【python|基于PyTorch搭建CNN实现视频动作分类任务】? 数据读取

from torch.utils.data import DataLoader import torchvision.transforms as T import scipy.iofrom basketball.basketballDataset import BasketballDatasetdef get_dataloader(name):# 保存的label的标签 label_mat = scipy.io.loadmat('../datasets/q3_2_data.mat') label_train = label_mat['trLb'] print('train_len:', len(label_train)) label_val = label_mat['valLb'] print('val_len:', len(label_val))if name == 'train': dataset_train = BasketballDataset(root_dir='../datasets/trainClips', labels=label_train, transform=T.ToTensor())dataloader_train = DataLoader(dataset_train, batch_size=32, shuffle=True, num_workers=4) return dataloader_trainif name == 'val': dataset_val = BasketballDataset(root_dir='../datasets/valClips', labels=label_val, transform=T.ToTensor())dataloader_val = DataLoader(dataset_val, batch_size=32, shuffle=True, num_workers=4) return dataloader_val if name == 'test': dataset_test = BasketballDataset(root_dir='../datasets/testClips', transform=T.ToTensor())dataloader_test = DataLoader(dataset_test, batch_size=32, shuffle=True, num_workers=4) return dataloader_testif __name__ == '__main__':# 保存的label的标签 label_mat = scipy.io.loadmat('../datasets/q3_2_data.mat') label_train = label_mat['trLb'] print('train_len:', len(label_train)) label_val = label_mat['valLb'] print('val_len:', len(label_val))# Dataloader类所加载的数据必须是pytorch中定义好的Dataset类， # 所以我们的第一步，就是将我们的数据封装成一个Dataset类。 dataset = BasketballDataset(root_dir='../datasets/trainClips', labels=label_train, transform=T.ToTensor())# Dataloader dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)for i in range(3): sample = dataset[i] print(sample['image'].shape) print(sample['Label']) print(sample['img_path'])for i, sample in enumerate(dataloader): print(i, sample['image'].shape, sample['img_path'], sample['Label']) if i > 5: break

model.py

? 继承nn.Module,编写网络

import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable from torch.utils.data import DataLoader, sampler, Dataset import torchvision.datasets as dset import torchvision.transforms as T import timeit from PIL import Image import os import numpy as np import scipy.io import torchvision.models.inception as inceptionclass Cnn(nn.Module): def __init__(self, channel=3): super(Cnn, self).__init__() self.sequential = nn.Sequential( nn.Conv2d(channel, 8, kernel_size=7, stride=1), nn.BatchNorm2d(8), nn.ReLU(inplace=True), nn.MaxPool2d(2, stride=2), nn.Conv2d(8, 16, 7, 1), nn.BatchNorm2d(16), nn.ReLU(inplace=True), nn.MaxPool2d(2, stride=2), nn.Flatten(), nn.ReLU(inplace=True), nn.Linear(16 * 11 * 11, 10) )def forward(self, x): return self.sequential(x)if __name__ == '__main__': model = Cnn() model = model x = torch.randn(32, 3, 64, 64) x_var = Variable(x)# 需要将其封装为Variable类型。 outputs = model(x_var) print(np.array(outputs.size()))# 检查模型输出。 np.array_equal(np.array(outputs.size()), np.array([32, 10]))

train.py

? 模型训练

import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variablefrom basketball.pre import get_dataloader from basketball.model import Cnndtype = torch.FloatTensor# 这是pytorch所支持的cpu数据类型中的浮点数类型。print_every = 100# 这个参数用于控制loss的打印频率，因为我们需要在训练过程中不断的对loss进行检测。model = Cnn() loss_fun = nn.CrossEntropyLoss() opt_fun = optim.RMSprop(model.parameters(), lr=0.0001)def train(epoch): dataloader_train = get_dataloader('train') dataloader_val = get_dataloader('val') model.train() for i in range(epoch): for idx, sample in enumerate(dataloader_train): img = Variable(sample['image']) labels = Variable(sample['Label'].long())output = model(img) loss = loss_fun(output, labels)opt_fun.zero_grad() loss.backward() opt_fun.step()if idx % 100 == 0: model.eval() acc_count = 0.0 all_count = 0 with torch.no_grad(): for idx, sample in enumerate(dataloader_val): img = Variable(sample['image']) labels = Variable(sample['Label'].long()) output = model(img)_, pre = torch.max(output, dim=1) acc_count += (pre == labels).sum().item() all_count += labels.size()[0]print('第{}轮，第{}次，loss为: {},验证集准确率为：{}'.format(i + 1, idx + 1, loss.item(), acc_count / all_count))if __name__ == '__main__': train(1) torch.save(model.state_dict(), '../model/basketball.pth')

predict.py

? 模型预测test数据集，并保存到result.csv文件中

import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable from torch.utils.data import DataLoader, sampler, Dataset import torchvision.datasets as dset import torchvision.transforms as T import timeit from PIL import Image import os import numpy as np import scipy.io import torchvision.models.inception as inceptionfrom basketball.model import Cnn from basketball.pre import get_dataloaderdef predict(): dataloader_test = get_dataloader('test') model = Cnn() model.load_state_dict(torch.load('../model/basketball.pth'))write_csv = open('../result.csv', 'w') count = 0 write_csv.write('Id' + ',' + 'Class' + '\n')model.eval() for t, sample, in enumerate(dataloader_test): img = Variable(sample['image']) output = model(img) _, pre = torch.max(output, dim=1) for i in range(len(pre)): write_csv.write(str(count) + ',' + str(pre[i].item()) + '\n') count += 1write_csv.close() return countif __name__ == '__main__': count = predict() print(count)

预测结果