a simple tensor in google sheets showing day of week, steak sales and almond butter sales 查看TENSOR的维度: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 TENSOR.ndim>>>3 查看TENSOR的形状: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Check shapeofTENSORTENSOR.shape>>>torch.Size(...
ifget_tensor_model_parallel_rank()==0:#rank0才压缩 #Checkthatallkeyshavethesamedatatype. _check_data_types(keys,data,datatype) #Flattenthedataassociatedwiththekeys flatten_data=torch.cat( [data[key].contiguous().view(-1)forkeyinkeys],dim=0).cuda() else: flatten_data=torch.empty(total_n...
defplot_predictions(train_data=X_train,train_labels=y_train,test_data=X_test,test_labels=y_test,predictions=None):""" Plots training data,test data and compares predictions.""" plt.figure(figsize=(10,7))# Plot training datainblue plt.scatter(train_data,train_labels,c="b",s=4,label="...
size_in_bytes; } // (...) - omitted for brevity void* data_ptr = PyArray_DATA(array); auto& type = CPU(dtype_to_aten(PyArray_TYPE(array))); Py_INCREF(obj); return type.tensorFromBlob(data_ptr, sizes, strides, [obj](void* data) { AutoGIL gil; Py_DECREF(obj)...
vector = torch.tensor([7, 7]) vector >>> tensor([7, 7]) 你认为它有多少个维度? # Check the number of dimensions of vector vector.ndim >>> 1 这很奇怪,vector包含两个数字,但只有一个维度。 有一个判断维度的小技巧: 您可以通过外部方括号 ([) 的数量来判断 PyTorch 中张量的维数,并且只需...
获取tensor 信息 最常用的3个属性 shape - 形状 dtype - datatype device - 存储的设备? (usually GPU or CPU) # Create a tensorsome_tensor=torch.rand(3,4)# Find out details about itprint(some_tensor)print(f"Shape of tensor:{some_tensor.shape}")print(f"Datatype of tensor:{some_tensor.dt...
void* data_ptr = PyArray_DATA(array); auto& type = CPU(dtype_to_aten(PyArray_TYPE(array))); Py_INCREF(obj); returntype.tensorFromBlob(data_ptr, sizes, strides, [obj](void* data) { AutoGIL gil; Py_DECREF(obj); }); } 代码摘自(tensor_numpy.cpp:https://github.com/pytorch/pytorch...
github地址:https://github.com/xiezhongzhao/pytorch_extension 1. 任务定义 在人体检测的过程中,大部分新的检测算法模型都是采用pytorch框架进行训练,模型部署采用tflite方式, 由于pytorch中upsample算子实现方式和开发板
void* data_ptr = PyArray_DATA(array); auto& type = CPU(dtype_to_aten(PyArray_TYPE(array))); Py_INCREF(obj); return type.tensorFromBlob(data_ptr, sizes, strides, [obj](void* data) { AutoGIL gil; Py_DECREF(obj); }); }
template<typename DataType, typename OutputType> void cutlass_gemm_unpack(torch::Tensor A, torch::Tensor B, torch::Tensor C) { // Get data sizes const int M = A.sizes()[0]; const int K = B.sizes()[0]; const int N = B.sizes()[1]; // Casting to the data type of the inp...