parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input ...
flatten(input, start_dim=0, end_dim=-1) → Tensor # Parameters:input (Tensor) – 输入为Tensor #start_dim (int) – 展平的开始维度 #end_dim (int) – 展平的最后维度 #example #一个3x2x2的三维张量 >>> t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9,...
importtorchfromtorch.utils.cpp_extensionimportloadcpp_ext=load(name="python_example",sources=["hxx_test.cpp"],extra_cflags=['-O2'],verbose=True,)print(cpp_ext.add(2,3)) 引用 PyTorch 添加 C++ 拓展 https://github.com/kwea123/pytorch-cppcuda-tutorial/tree/master Pytorch C Extension(C拓展)...
voidlaunch_add2(float*c,constfloat*a,constfloat*b,int n); 代码语言:javascript 代码运行次数:0 运行 AI代码解释 __global__voidadd2_kernel(float*c,constfloat*a,constfloat*b,int n){for(int i=blockIdx.x*blockDim.x+threadIdx.x;\ i<n;i+=gridDim.x*blockDim.x){c[i]=a[i]+b[i];...
接下来使用cmake来进行配置吧,我们首先自己创建一个文件夹,存放我们的主程序main.cpp还有CMakeLists.txt,然后我们再创建一个build的空文件夹,之后我们编译好的文件都存放在build文件夹里头。 目录结构大概就是这样,假设这个文件夹存放在D盘: example — build — main.cpp — CMakeLists.txt ...
target_link_libraries(example-app "${TORCH_LIBRARIES}") set_property(TARGET example-app PROPERTY CXX_STANDARD 14) 至此,就可以运行以下命令从example-app/文件夹中构建应用程序啦: mkdir build cd build cmake -DCMAKE_PREFIX_PATH=/path/to/libt...
If you'd like to contribute your own example or fix a bug please make sure to take a look atCONTRIBUTING.md. About A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. pytorch.org/examples Resources Readme
description="cpp pytorch example", long_description="cpp pytorch example", ext_modules=[ CppExtension( name="cppcuda_tutorial", sources=['interpolation.cpp'] ) ], cmdclass={ 'build_ext': BuildExtension } ) 1. 2. 3. 4. 5. 6.
(batch_size)))# Let's see what if the model identifiers the labels of those exampleoutputs = model(images)# We got the probability for every 10 labels. The highest (max) probability should be correct label_, predicted = torch.max(outputs,1)# Let's show the predicted labels on the ...
3. The output of the model will be a list of values for each class detected in the image (for example: if our model detects a class “car” it will return a list of values for all cars detected in the image, like car locations and probabilities) and so we will plot each of these...