import torch s = dir(torch) for i in s: print(i)
结果
AVG AggregationType AnyType Argument ArgumentSpec BFloat16Storage BFloat16Tensor BenchmarkConfig BenchmarkExecutionStats Block BoolStorage BoolTensor BoolType BufferDict ByteStorage ByteTensor CONV_BN_FUSION CallStack Capsule CharStorage CharTensor ClassType Code CompilationUnit CompleteArgumentSpec ComplexDoubleStorage ComplexFloatStorage ComplexType ConcreteModuleType ConcreteModuleTypeBuilder CudaBFloat16StorageBase CudaBoolStorageBase CudaByteStorageBase CudaCharStorageBase CudaComplexDoubleStorageBase CudaComplexFloatStorageBase CudaDoubleStorageBase CudaFloatStorageBase CudaHalfStorageBase CudaIntStorageBase CudaLongStorageBase CudaShortStorageBase DeepCopyMemoTable DeviceObjType DictType DisableTorchFunction DoubleStorage DoubleTensor EnumType ErrorReport ExecutionPlan FUSE_ADD_RELU FatalError FileCheck FloatStorage FloatTensor FloatType FunctionSchema Future FutureType Generator Gradient Graph GraphExecutorState HOIST_CONV_PACKED_PARAMS HalfStorage HalfStorageBase HalfTensor INSERT_FOLD_PREPACK_OPS IODescriptor InferredType IntStorage IntTensor IntType InterfaceType JITException ListType LiteScriptModule LockingLogger LoggerBase LongStorage LongTensor MobileOptimizerType ModuleDict Node NoneType NoopLogger NumberType OptionalType ParameterDict PyObjectType PyTorchFileReader PyTorchFileWriter QInt32Storage QInt32StorageBase QInt8Storage QInt8StorageBase QUInt4x2Storage QUInt8Storage REMOVE_DROPOUT RRefType SUM ScriptClass ScriptFunction ScriptMethod ScriptModule ScriptObject Set ShortStorage ShortTensor Size StaticRuntime Storage Stream StreamObjType StringType TYPE_CHECKING Tensor TensorType ThroughputBenchmark TracingState TupleType Type USE_GLOBAL_DEPS USE_RTLD_GLOBAL_WITH_LIBTORCH Use Value _C _StorageBase _VF __all__ __annotations__ __builtins__ __cached__ __config__ __doc__ __file__ __future__ __loader__ __name__ __package__ __path__ __spec__ __version__ _adaptive_avg_pool2d _add_batch_dim _add_relu _add_relu_ _addmv_impl_ _aminmax _amp_foreach_non_finite_check_and_unscale_ _amp_update_scale _assert _autograd_functions _baddbmm_mkl_ _batch_norm_impl_index _bmm _cast_Byte _cast_Char _cast_Double _cast_Float _cast_Half _cast_Int _cast_Long _cast_Short _cat _choose_qparams_per_tensor _classes _compute_linear_combination _conj _convolution _convolution_nogroup _copy_from _ctc_loss _cudnn_ctc_loss _cudnn_init_dropout_state _cudnn_rnn _cudnn_rnn_flatten_weight _cufft_clear_plan_cache _cufft_get_plan_cache_max_size _cufft_get_plan_cache_size _cufft_set_plan_cache_max_size _cummax_helper _cummin_helper _debug_has_internal_overlap _dim_arange _dirichlet_grad _embedding_bag _embedding_bag_forward_only _empty_affine_quantized _empty_per_channel_affine_quantized _euclidean_dist _fake_quantize_learnable_per_channel_affine _fake_quantize_learnable_per_tensor_affine _fft_c2c _fft_c2r _fft_r2c _foreach_abs _foreach_abs_ _foreach_acos _foreach_acos_ _foreach_add _foreach_add_ _foreach_addcdiv _foreach_addcdiv_ _foreach_addcmul _foreach_addcmul_ _foreach_asin _foreach_asin_ _foreach_atan _foreach_atan_ _foreach_ceil _foreach_ceil_ _foreach_cos _foreach_cos_ _foreach_cosh _foreach_cosh_ _foreach_div _foreach_div_ _foreach_erf _foreach_erf_ _foreach_erfc _foreach_erfc_ _foreach_exp _foreach_exp_ _foreach_expm1 _foreach_expm1_ _foreach_floor _foreach_floor_ _foreach_frac _foreach_frac_ _foreach_lgamma _foreach_lgamma_ _foreach_log _foreach_log10 _foreach_log10_ _foreach_log1p _foreach_log1p_ _foreach_log2 _foreach_log2_ _foreach_log_ _foreach_maximum _foreach_minimum _foreach_mul _foreach_mul_ _foreach_neg _foreach_neg_ _foreach_reciprocal _foreach_reciprocal_ _foreach_round _foreach_round_ _foreach_sigmoid _foreach_sigmoid_ _foreach_sin _foreach_sin_ _foreach_sinh _foreach_sinh_ _foreach_sqrt _foreach_sqrt_ _foreach_sub _foreach_sub_ _foreach_tan _foreach_tan_ _foreach_tanh _foreach_tanh_ _foreach_trunc _foreach_trunc_ _foreach_zero_ _fused_dropout _grid_sampler_2d_cpu_fallback _has_compatible_shallow_copy_type _import_dotted_name _index_copy_ _index_put_impl_ _initExtension _jit_internal _linalg_inv_out_helper_ _linalg_qr_helper _linalg_solve_out_helper_ _linalg_utils _load_global_deps _lobpcg _log_softmax _log_softmax_backward_data _logcumsumexp _lowrank _lu_solve_helper _lu_with_info _make_dual _make_per_channel_quantized_tensor _make_per_tensor_quantized_tensor _masked_scale _mkldnn _mkldnn_reshape _mkldnn_transpose _mkldnn_transpose_ _mode _namedtensor_internals _nnpack_available _nnpack_spatial_convolution _ops _pack_padded_sequence _pad_packed_sequence _remove_batch_dim _reshape_from_tensor _rowwise_prune _s_where _sample_dirichlet _saturate_weight_to_fp16 _shape_as_tensor _six _sobol_engine_draw _sobol_engine_ff_ _sobol_engine_initialize_state_ _sobol_engine_scramble_ _softmax _softmax_backward_data _sparse_addmm _sparse_coo_tensor_unsafe _sparse_log_softmax _sparse_log_softmax_backward_data _sparse_matrix_mask_helper _sparse_mm _sparse_softmax _sparse_softmax_backward_data _sparse_sparse_matmul _sparse_sum _stack _standard_gamma _standard_gamma_grad _std _storage_classes _string_classes _syevd_helper _tensor_classes _tensor_str _test_serialization_subcmul _trilinear _unique _unique2 _unpack_dual _use_cudnn_ctc_loss _use_cudnn_rnn_flatten_weight _utils _utils_internal _validate_sparse_coo_tensor_args _var _vmap_internals _weight_norm _weight_norm_cuda_interface abs abs_ absolute acos acos_ acosh acosh_ adaptive_avg_pool1d adaptive_max_pool1d add addbmm addcdiv addcmul addmm addmv addmv_ addr affine_grid_generator align_tensors all allclose alpha_dropout alpha_dropout_ amax amin angle any arange arccos arccos_ arccosh arccosh_ arcsin arcsin_ arcsinh arcsinh_ arctan arctan_ arctanh arctanh_ are_deterministic_algorithms_enabled argmax argmin argsort as_strided as_strided_ as_tensor asin asin_ asinh asinh_ atan atan2 atan_ atanh atanh_ atleast_1d atleast_2d atleast_3d autocast_decrement_nesting autocast_increment_nesting autograd avg_pool1d backends baddbmm bartlett_window base_py_dll_path batch_norm batch_norm_backward_elemt batch_norm_backward_reduce batch_norm_elemt batch_norm_gather_stats batch_norm_gather_stats_with_counts batch_norm_stats batch_norm_update_stats bernoulli bfloat16 bilinear binary_cross_entropy_with_logits bincount binomial bitwise_and bitwise_not bitwise_or bitwise_xor blackman_window block_diag bmm bool broadcast_shapes broadcast_tensors broadcast_to bucketize can_cast cartesian_prod cat cdist cdouble ceil ceil_ celu celu_ cfloat chain_matmul channel_shuffle channels_last channels_last_3d cholesky cholesky_inverse cholesky_solve choose_qparams_optimized chunk clamp clamp_ clamp_max clamp_max_ clamp_min clamp_min_ classes clear_autocast_cache clip clip_ clone column_stack combinations compiled_with_cxx11_abi complex complex128 complex32 complex64 conj constant_pad_nd contiguous_format conv1d conv2d conv3d conv_tbc conv_transpose1d conv_transpose2d conv_transpose3d convolution copysign cos cos_ cosh cosh_ cosine_embedding_loss cosine_similarity count_nonzero cpp cross ctc_loss ctypes cuda cuda_path cuda_version cudnn_affine_grid_generator cudnn_batch_norm cudnn_convolution cudnn_convolution_transpose cudnn_grid_sampler cudnn_is_acceptable cummax cummin cumprod cumsum default_generator deg2rad deg2rad_ dequantize det detach detach_ device diag diag_embed diagflat diagonal diff digamma dist distributed distributions div divide dll dll_path dll_paths dlls dot double dropout dropout_ dsmm dstack dtype eig einsum embedding embedding_bag embedding_renorm_ empty empty_like empty_meta empty_quantized empty_strided enable_grad eq equal erf erf_ erfc erfc_ erfinv exp exp2 exp2_ exp_ expm1 expm1_ eye fake_quantize_per_channel_affine fake_quantize_per_tensor_affine fbgemm_linear_fp16_weight fbgemm_linear_fp16_weight_fp32_activation fbgemm_linear_int8_weight fbgemm_linear_int8_weight_fp32_activation fbgemm_linear_quantize_weight fbgemm_pack_gemm_matrix_fp16 fbgemm_pack_quantized_matrix feature_alpha_dropout feature_alpha_dropout_ feature_dropout feature_dropout_ fft fill_ finfo fix fix_ flatten flip fliplr flipud float float16 float32 float64 float_power floor floor_ floor_divide fmax fmin fmod fork frac frac_ frobenius_norm from_file from_numpy full full_like functional futures gather gcd gcd_ ge geqrf ger get_default_dtype get_device get_file_path get_num_interop_threads get_num_threads get_rng_state glob greater greater_equal grid_sampler grid_sampler_2d grid_sampler_3d group_norm gru gru_cell gt half hamming_window hann_window hardshrink has_cuda has_cudnn has_lapack has_mkl has_mkldnn has_openmp heaviside hinge_embedding_loss histc hsmm hspmm hstack hub hypot i0 i0_ igamma igammac iinfo imag import_ir_module import_ir_module_from_buffer index_add index_copy index_fill index_put index_put_ index_select init_num_threads initial_seed inner instance_norm int int16 int32 int64 int8 int_repr inverse is_anomaly_enabled is_autocast_enabled is_complex is_deterministic is_distributed is_floating_point is_grad_enabled is_loaded is_nonzero is_same_size is_signed is_storage is_tensor is_vulkan_available isclose isfinite isinf isnan isneginf isposinf isreal istft jit kaiser_window kernel32 kl_div kron kthvalue last_error layer_norm layout lcm lcm_ ldexp ldexp_ le legacy_contiguous_format lerp less less_equal lgamma linalg linspace load lobpcg log log10 log10_ log1p log1p_ log2 log2_ log_ log_softmax logaddexp logaddexp2 logcumsumexp logdet logical_and logical_not logical_or logical_xor logit logit_ logspace logsumexp long lstm lstm_cell lstsq lt lu lu_solve lu_unpack manual_seed margin_ranking_loss masked_fill masked_scatter masked_select matmul matrix_exp matrix_power matrix_rank max max_pool1d max_pool1d_with_indices max_pool2d max_pool3d maximum mean median memory_format merge_type_from_type_comment meshgrid min minimum miopen_batch_norm miopen_convolution miopen_convolution_transpose miopen_depthwise_convolution miopen_rnn mkldnn_adaptive_avg_pool2d mkldnn_convolution mkldnn_convolution_backward_weights mkldnn_linear_backward_weights mkldnn_max_pool2d mkldnn_max_pool3d mm mode moveaxis movedim msort mul multinomial multiply multiprocessing mv mvlgamma name nan_to_num nan_to_num_ nanmedian nanquantile nansum narrow narrow_copy native_batch_norm native_group_norm native_layer_norm native_norm ne neg neg_ negative negative_ nextafter nn no_grad nonzero norm norm_except_dim normal not_equal nuclear_norm numel nvtoolsext_dll_path ones ones_like onnx ops optim orgqr ormqr os outer overrides pairwise_distance parse_ir parse_schema parse_type_comment path_patched pca_lowrank pdist per_channel_affine per_channel_affine_float_qparams per_channel_symmetric per_tensor_affine per_tensor_symmetric pfiles_path pinverse pixel_shuffle pixel_unshuffle platform poisson poisson_nll_loss polar polygamma pow prelu prepare_multiprocessing_environment preserve_format prev_error_mode prod profiler promote_types py_dll_path q_per_channel_axis q_per_channel_scales q_per_channel_zero_points q_scale q_zero_point qint32 qint8 qr qscheme quantile quantization quantize_per_channel quantize_per_tensor quantized_batch_norm quantized_gru quantized_gru_cell quantized_lstm quantized_lstm_cell quantized_max_pool1d quantized_max_pool2d quantized_rnn_relu_cell quantized_rnn_tanh_cell quasirandom quint4x2 quint8 rad2deg rad2deg_ rand rand_like randint randint_like randn randn_like random randperm range ravel real reciprocal reciprocal_ relu relu_ remainder renorm repeat_interleave res reshape resize_as_ result_type rnn_relu rnn_relu_cell rnn_tanh rnn_tanh_cell roll rot90 round round_ row_stack rrelu rrelu_ rsqrt rsqrt_ rsub saddmm save scalar_tensor scatter scatter_add searchsorted seed select selu selu_ serialization set_anomaly_enabled set_autocast_enabled set_default_dtype set_default_tensor_type set_deterministic set_flush_denormal set_grad_enabled set_num_interop_threads set_num_threads set_printoptions set_rng_state sgn short sigmoid sigmoid_ sign signbit sin sin_ sinc sinc_ sinh sinh_ slogdet smm softmax solve sort sparse sparse_coo sparse_coo_tensor split split_with_sizes spmm sqrt sqrt_ square square_ squeeze sspaddmm stack std std_mean stft storage strided sub subtract sum svd svd_lowrank swapaxes swapdims symeig sys t take tan tan_ tanh tanh_ tensor tensor_split tensordot testing textwrap th_dll_path threshold threshold_ tile topk torch trace transpose trapz triangular_solve tril tril_indices triplet_margin_loss triu triu_indices true_divide trunc trunc_ typename types uint8 unbind unify_type_list unique unique_consecutive unsafe_chunk unsafe_split unsafe_split_with_sizes unsqueeze use_deterministic_algorithms utils vander var var_mean vdot version view_as_complex view_as_real vstack wait warnings where with_load_library_flags xlogy xlogy_ zero_ zeros zeros_like
要查看 PyTorch 中特定函数的用法，可以使用 help() 函数，如下所示：
import torch # 查看torch中add函数的用法 help(torch.add)
这将打印出 torch.add 函数的帮助文档，包括函数的参数、返回值、用法示例等。您也可以在 Jupyter Notebook 或 IPython 中使用 ? 符号来查看帮助文档，如下所示：
import torch # 查看torch中add函数的用法 torch.add?
这将显示与上面 help(torch.add) 相同的帮助文档。
Output exceeds the size limit. Open the full output data in a text editor Docstring: add(input, other, *, out=None) Adds the scalar :attr:`other` to each element of the input :attr:`input` and returns a new resulting tensor. .. math:: \text{out} = \text{input} + \text{other} If :attr:`input` is of type FloatTensor or DoubleTensor, :attr:`other` must be a real number, otherwise it should be an integer. Args: input (Tensor): the input tensor. value (Number): the number to be added to each element of :attr:`input` Keyword arguments: out (Tensor, optional): the output tensor. Example:: >>> a = torch.randn(4) >>> a tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) >>> torch.add(a, 20) ... [-18.6971, -18.0736, -17.0994, -17.3216], [ -6.7845, -6.1610, -5.1868, -5.4090], [ -8.9902, -8.3667, -7.3925, -7.6147]]) Type: builtin_function_or_method
