与其它框架 API 进行对比¶

警告

MegEngine 的 API 设计遵循 MEP 3 – Tensor API 设计规范 , 向《数组 API 标准》靠齐。

在同其它框架进行对比时，同样的命名不意味着用法也完全一致；
如果有新的 API 支持需求，可在 GitHub 创建相应的 Issue 或 Pull Request.

注解

你可以利用浏览器的查找功能在当前页面查询对应的 API.
当前页面并非自动生成，如发现有缺失/过时内容，欢迎编辑当前页面。

参见

当前页面更多用于检索，具有其它框架使用经验的用户还可以参考用户迁移指南。

Data Structure¶

NumPy	Pytorch	MegEngine	Comment
`ndarray`	`Tensor`	`Tensor`	深入理解 Tensor 数据结构

General tensor operations¶

Creation Functions¶

NumPy	Pytorch	MegEngine
`arange`	`arange`	`arange`
`linspace`	`linspace`	`linspace`
`eye`	`eye`	`eye`
`zeros`	`zeros`	`zeros`
`zeros_like`	`zeros_like`	`zeros_like`
`ones`	`ones`	`ones`
`ones_like`	`ones_like`	`ones_like`
`full`	`full`	`full`
`full_like`	`full_like`	`full_like`

Manipulation Functions¶

NumPy	Pytorch	MegEngine
`reshape`	`reshape`	`reshape`
`flatten`	`flatten`	`flatten`
`broadcast_to`	`broadcast_to` / `expand`	`broadcast_to`
`expand_dims`	`unsqueeze`	`expand_dims`
`squeeze`	`squeeze`	`squeeze`
`concatenate`	`cat`	`concat`
`stack`	`stack`	`stack`
`split`	`split`	`split`
`tile`	`tile`	`tile`
`repeat`	`repeat_interleave`	`repeat`
`roll`	`roll`	`roll`

Arithmetic operations¶

NumPy	Pytorch	MegEngine	Comment
`add`	`add`	`add`	`+` operator
`subtract`	`sub`	`sub`	`-` operator
`numpy.multiply`	`mul`	`mul`	`*` operator
`divide`	`div`	`div`	`/` operator
`floor_divide`	`floor_divide`	`floor_div`	`//` operator
`negative`	`neg`	`neg`
`absolute`	`abs`	`abs`
`power`	`pow`	`pow`	`**` operator
`mod`	`remainder`	`mod`	`%` operator
`sqrt`	`sqrt`	`sqrt`
`square`	`square`	`square`
`sign`	`sign`	`sign`
`maximum`	`maximum`	`maximum`
`minimum`	`minimum`	`minimum`
`round`	`round`	`round`
`ceil`	`ceil`	`ceil`
`floor`	`floor`	`floor`
`clip`	`clamp`	`clip`
`exp`	`exp`	`exp`
`expm1`	`expm1`	`expm1`
`log`	`log`	`log`
`log1p`	`log1p`	`log1p`

Trigonometric functions¶

NumPy	Pytorch	MegEngine
`sin`	`sin`	`sin`
`cos`	`cos`	`cos`
`tan`	`tan`	`tan`
`arcsin`	`asin`	`asin`
`arccos`	`acos`	`acos`
`arctan`	`atan`	`atan`

Hyperbolic functions¶

NumPy	Pytorch	MegEngine
`sinh`	`sinh`	`sinh`
`cosh`	`cosh`	`cosh`
`tanh`	`tanh`	`tanh`
`arcsinh`	`asinh`	`asinh`
`arccosh`	`acosh`	`acosh`
`arctanh`	`atanh`	`atanh`

Bit operations¶

NumPy	Pytorch	MegEngine	Comment
`left_shift`	Not Found	`left_shift`	`<<` operator
`right_shift`	Not Found	`right_shift`	`>>` operator

Logic functions¶

NumPy	Pytorch	MegEngine	Comment
`isnan`	`isnan`	`isnan`
`isinf`	`isinf`	`isinf`
`logical_and`	Not Found	`logical_and`	`&` operator
`logical_not`	Not Found	`logical_not`	`~` operator
`logical_or`	Not Found	`logical_or`	`\|` operator
`logical_xor`	Not Found	`logical_xor`	`^` operator
`equal`	`equal`	`equal`
`not_equal`	`not_equal`	`not_equal`
`less`	`less`	`less`
`less_equal`	`less_equal`	`less_equal`
`greater`	`greater`	`greater`
`greater_equal`	`greater_equal`	`greater_equal`

Statistical Functions¶

NumPy	Pytorch	MegEngine
`sum`	`sum`	`sum`
`prod`	`prod`	`prod`
`mean`	`mean`	`mean`
`min`	`min`	`min`
`max`	`max`	`max`
`var`	`var`	`var`
`std`	`std`	`std`
`cumsum`	`cumsum`	`cumsum`

Linear Algebra Functions¶

NumPy	Pytorch	MegEngine
`transpose`	`transpose`	`transpose`
`dot`	`dot`	`dot`
`inv`	`inv`	`matinv`
`matmul`	`matmul`	`matmul`
`svd`	`svd`	`svd`
`norm`	`norm`	`norm`

Indexing Functions¶

NumPy	Pytorch	MegEngine	Comment
`take_along_axis`	`gather`	`gather`
`put_along_axis`	`scatter`	`scatter`
`where`	`where`	`where` / `cond_take`	取决于传参情况

Searching Functions¶

NumPy	Pytorch	MegEngine	Comment
`argmin`	`argmin`	`argmin`
`argmax`	`argmax`	`argmax`

Sorting Functions¶

NumPy	Pytorch	MegEngine	Comment
`argsort`	`argsort`	`argsort`
`sort`	`sort`	`sort`

NN Funtional Operations¶

Convolution functions¶

Pytorch	MegEngine	Comment
`conv1d`	`conv1d`
`conv2d`	`conv2d`
`conv3d`	`conv3d`
`conv_transpose1d`	Not Implemeted
`conv_transpose2d`	`conv_transpose2d`
`conv_transpose3d`	`conv_transpose3d`
local_conv2d	`local_conv2d`
deformable_conv2d	`deformable_conv2d`
`unfold`	`sliding_window`
`fold`	`sliding_window_transpose`

Pooling functions¶

Pytorch	MegEngine	Comment
`avg_pool1d`	Not Implemeted
`avg_pool2d`	`avg_pool2d`
`avg_pool3d`	Not Implemeted
`max_pool1d`	Not Implemeted
`max_pool2d`	`max_pool2d`
`max_pool3d`	Not Implemeted
`max_unpool1d`	Not Implemeted
`max_unpool2d`	Not Implemeted
`max_unpool3d`	Not Implemeted
`lp_pool1d`	Not Implemeted
`lp_pool2d`	Not Implemeted
`adaptive_max_pool1d`	Not Implemeted
`adaptive_max_pool2d`	`adaptive_max_pool2d`
`adaptive_max_pool3d`	Not Implemeted
`adaptive_avg_pool1d`	Not Implemeted
`adaptive_avg_pool2d`	`adaptive_avg_pool2d`
`adaptive_avg_pool3d`	Not Implemeted

Non-linear activation functions¶

Pytorch	MegEngine	Comment
`threshold`	Not Implemeted
`relu`	`relu`
`hardtanh`	Not Implemeted
`hardswish`	`hswish`
`relu6`	`relu6`
`elu`	Not Implemeted
`selu`	Not Implemeted
`celu`	Not Implemeted
`leaky_relu`	`leaky_relu`
`prelu`	`prelu`
`rrelu`	Not Implemeted
`glu`	Not Implemeted
`gelu`	`gelu`
`logsigmoid`	`logsigmoid`
`hardshrink`	Not Implemeted
`tanhshrink`	Not Implemeted
`softsign`	Not Implemeted
`softplus`	Not Implemeted
`softmin`	Not Implemeted
`softmax`	`softmax`
`softshrink`	Not Implemeted
`gumbel_softmax`	Not Implemeted
`log_softmax`	`logsoftmax`
`sigmoid`	`sigmoid`
`hardsigmoid`	`hsigmoid`
`silu`	`silu`

Normalization functions¶

Pytorch	MegEngine	Comment
`batch_norm`	`batch_norm`
`instance_norm`	Not Implemeted
`layer_norm`	Not Implemeted
`local_response_norm`	Not Implemeted
`normalize`	`normalize`

Linear functions¶

Pytorch	MegEngine	Comment
`linear`	`linear`
`bilinear`	Not Implemeted

Dropout functions¶

Pytorch	MegEngine	Comment
`dropout`	`dropout`
`alpha_dropout`	Not Implemeted
`feature_alpha_dropout`	Not Implemeted
`dropout2d`	Not Implemeted
`dropout3d`	Not Implemeted

Sparse functions¶

Pytorch	MegEngine	Comment
`embedding`	`embedding`
`embedding_bag`	Not Implemeted
`one_hot`	`one_hot`

Metric functions¶

Pytorch	MegEngine	Comment
`pairwise_distance`	Not Implemeted
`cosine_similarity`	Not Implemeted
`pdist`	Not Implemeted

Loss functions¶

Pytorch	MegEngine	Comment
`binary_cross_entropy_with_logits`	`binary_cross_entropy`
`poisson_nll_loss`	Not Implemeted
`cosine_embedding_loss`	Not Implemeted
`cross_entropy`	`cross_entropy`
`ctc_loss`	Not Implemeted
`hinge_embedding_loss`	Not Implemeted
`kl_div`	Not Implemeted
`l1_loss`	`l1_loss`
`mse_loss`	`square_loss`
`margin_ranking_loss`	Not Implemeted
`multilabel_margin_loss`	Not Implemeted
`multilabel_soft_margin_loss`	Not Implemeted
`multi_margin_loss`	`hinge_loss`
`nll_loss`	Not Implemeted
`smooth_l1_loss`	Not Implemeted
`soft_margin_loss`	Not Implemeted
`triplet_margin_loss`	Not Implemeted
`triplet_margin_with_distance_loss`	Not Implemeted

NN Module¶

Pytorch	MegEngine	Comment
`Parameter`	`Parameter`

Containers¶

Pytorch	MegEngine	Comment
`Module`	`Module`
`Sequential`	`Sequential`
`ModuleList`	MegEngine 原生支持
`ModuleDict`	MegEngine 原生支持
`ParameterList`	MegEngine 原生支持
`ParameterDict`	MegEngine 原生支持

Initialization¶

Pytorch	MegEngine	Comment
`calculate_gain`	`calculate_gain`
_calculate_fan_in_and_fan_out	`calculate_fan_in_and_fan_out`
_calculate_correct_fan	`calculate_correct_fan`
`uniform_`	`uniform_`
`normal_`	`normal_`
`constant_`	`fill_`
`ones_`	`ones_`
`zeros_`	`zeros_`
`eye_`	Not Implemeted
`dirac_`	Not Implemeted
`xavier_uniform_`	`xavier_uniform_`
`xavier_normal_`	`xavier_normal_`
`kaiming_uniform_`	`msra_uniform_`
`kaiming_normal_`	`msra_normal_`
`orthogonal_`	Not Implemeted
`sparse_`	Not Implemeted

Convolution Layers¶

Pytorch	MegEngine	Comment
`Conv1d`	`Conv1d`
`Conv2d`	`Conv2d`
`Conv3d`	`Conv3d`
`ConvTranspose1d`	Not Implemeted
`ConvTranspose2d`	`ConvTranspose2d`
`ConvTranspose3d`	`ConvTranspose3d`
LocalConv2d	`LocalConv2d`
DeformableConv2d	`DeformableConv2d`
`Conv1d`	`Conv1d`
`Unfold`	`SlidingWindowTranspose`
`Fold`	`SlidingWindow`

Pooling layers¶

Pytorch	MegEngine	Comment
`MaxPool1d`	Not Implemeted
`MaxPool2d`	`MaxPool2d`
`MaxPool3d`	Not Implemeted
`MaxUnpool1d`	Not Implemeted
`MaxUnpool2d`	Not Implemeted
`MaxUnpool3d`	Not Implemeted
`AvgPool1d`	Not Implemeted
`AvgPool2d`	`AvgPool2d`
`AvgPool3d`	Not Implemeted
`FractionalMaxPool2d`	Not Implemeted
`LPPool1d`	Not Implemeted
`LPPool2d`	Not Implemeted
`AdaptiveMaxPool1d`	Not Implemeted
`AdaptiveMaxPool2d`	`AdaptiveMaxPool2d`
`AdaptiveMaxPool3d`	Not Implemeted
`AdaptiveAvgPool1d`	Not Implemeted
`AdaptiveAvgPool2d`	`AdaptiveAvgPool2d`
`AdaptiveAvgPool3d`	Not Implemeted

Padding Layers¶

Pytorch	MegEngine	Comment
`ReflectionPad1d`	`Pad`	mode = REFLECT
`ReflectionPad2d`	`Pad`	mode = REFLECT
`ReflectionPad3d`	`Pad`	mode = REFLECT
`ReplicationPad1d`	`Pad`	mode = EDGE
`ReplicationPad2d`	`Pad`	mode = EDGE
`ReplicationPad3d`	`Pad`	mode = EDGE
`ZeroPad2d`	`Pad`	mode = CONSTANT
`ConstantPad1d`	`Pad`	mode = CONSTANT
`ConstantPad2d`	`Pad`	mode = CONSTANT
`ConstantPad3d`	`Pad`	mode = CONSTANT

Non-linear Activations¶

Pytorch	MegEngine	Comment
`ELU`	Not Implemeted
`Hardshrink`	Not Implemeted
`Hardsigmoid`	Not Implemeted
`Hardtanh`	Not Implemeted
`Hardswish`	Not Implemeted
`LeakyReLU`	`LeakyReLU`
`LogSigmoid`	Not Implemeted
`MultiheadAttention`	Not Implemeted
`PReLU`	`PReLU`
`ReLU`	`ReLU`
`ReLU6`	Not Implemeted
`RReLU`	Not Implemeted
`SELU`	Not Implemeted
`CELU`	Not Implemeted
`GELU`	`GELU`
`Sigmoid`	`Sigmoid`
`SiLU`	`SiLU`
`Softplus`	Not Implemeted
`Softshrink`	Not Implemeted
`Softsign`	Not Implemeted
`Tanh`	Not Implemeted
`Tanhshrink`	Not Implemeted
`Threshold`	Not Implemeted
`Softmin`	Not Implemeted
`Softmax`	`Softmax`
`Softmax2d`	Not Implemeted
`LogSoftmax`	Not Implemeted
`AdaptiveLogSoftmaxWithLoss`	Not Implemeted

Normalization Layers¶

Pytorch	MegEngine	Comment
`BatchNorm1d`	`BatchNorm1d`
`BatchNorm2d`	`BatchNorm2d`
`BatchNorm3d`	Not Implemeted
`GroupNorm`	`GroupNorm`
`SyncBatchNorm`	`SyncBatchNorm`
`InstanceNorm1d`	Not Implemeted
`InstanceNorm2d`	`InstanceNorm`
`InstanceNorm3d`	Not Implemeted
`LayerNorm`	`LayerNorm`
`LocalResponseNorm`	Not Implemeted

Recurrent Layers¶

Pytorch	MegEngine	Comment
`RNNBase`	Not Implemeted
`RNN`	Not Implemeted
`LSTM`	Not Implemeted
`GRU`	Not Implemeted
`RNNCell`	Not Implemeted
`LSTMCell`	Not Implemeted
`GRUCell`	Not Implemeted

Transformer Layers¶

Pytorch	MegEngine	Comment
`Transformer`	Not Implemeted
`TransformerEncoder`	Not Implemeted
`TransformerDecoder`	Not Implemeted
`TransformerEncoderLayer`	Not Implemeted
`TransformerDecoderLayer`	Not Implemeted

Linear Layers¶

Pytorch	MegEngine	Comment
`Identity`	`Identity`
`Linear`	`Linear`
`Bilinear`	Not Implemeted

Dropout Layers¶

Pytorch	MegEngine	Comment
`Dropout`	`Dropout`
`Dropout2d`	Not Implemeted
`Dropout3d`	Not Implemeted
`AlphaDropout`	Not Implemeted

Sparse Layers¶

Pytorch	MegEngine	Comment
`Embedding`	`Embedding`
`EmbeddingBag`	Not Implemeted

Distance Functions¶

Pytorch	MegEngine	Comment
`CosineSimilarity`	Not Implemeted
`PairwiseDistance`	Not Implemeted

Loss Functions¶

参见

请参考 loss function 的 functional 实现。

Pytorch	MegEngine	Comment
`L1Loss`	Not Implemeted
`MSELoss`	Not Implemeted
`CrossEntropyLoss`	Not Implemeted
`CTCLoss`	Not Implemeted
`NLLLoss`	Not Implemeted
`PoissonNLLLoss`	Not Implemeted
`KLDivLoss`	Not Implemeted
`BCELoss`	Not Implemeted
`BCEWithLogitsLoss`	Not Implemeted
`MarginRankingLoss`	Not Implemeted
`HingeEmbeddingLoss`	Not Implemeted
`MultiLabelMarginLoss`	Not Implemeted
`SmoothL1Loss`	Not Implemeted
`SoftMarginLoss`	Not Implemeted
`MultiLabelSoftMarginLoss`	Not Implemeted
`CosineEmbeddingLoss`	Not Implemeted
`MultiMarginLoss`	Not Implemeted
`TripletMarginLoss`	Not Implemeted
`TripletMarginWithDistanceLoss`	Not Implemeted

Vision functions¶

Pytorch	MegEngine	Comment
`pixel_shuffle`	Not Implemeted
`pad`	Not Implemeted
`interpolate`	`interpolate`
`upsample`	`interpolate`
`upsample_nearest`	`interpolate`
`upsample_bilinear`	`interpolate`
`grid_sample`	`remap`
`affine_grid`	`warp_affine`
`nms`	`nms`
`roi_align`	`roi_align`
`roi_pool`	`roi_pooling`

OpenCV Python Package¶

Pytorch	MegEngine	Comment
cvtColor	`cvt_color`
resize	`interpolate`
remap	`remap`
warpAffine	`warp_affine`
warpPerspective	`warp_perspective`

NVIDIA¶

Pytorch	MegEngine	Comment
correlation	`correlation`
nvof	`nvof`

Not Implemeted¶

注解

一些 API 在 MegEngine 中可能还没有实现，但所有的 API 并不是一开始就被设计出来的。我们可以像搭积木一样，利用已经存在的基础 API 来组合出 MegEngine 中尚未提供的接口。

比如 “如何实现 roll ” 这个问题，可以使用 split 和 concat 拼接出来：

import megengine.functional as F

def roll(x, shifts, axis):
    shp = x.shape
    dim = len(shp)
    if isinstance(shifts, int):
        assert isinstance(axis, int)
        shifts = [shifts]
        axis = [axis]
    assert len(shifts) == len(axis)
    y = x
    for i in range(len(shifts)):
        axis_ = axis[i]
        shift_ = shifts[i]
        axis_t_ = axis_ + dim if axis_ < 0 else axis_
        assert (
            dim > axis_t_ >= 0
        ), "axis out of range (expected to be in range of [{}, {}], but got {})".format(
            -dim, dim - 1, axis_
        )
        if shift_ == 0:
            continue
            size = shp[axis_t_]
        if shift_ > 0:
            a, b = F.split(y, [size - shift_,], axis=axis_t_)
        else:
            a, b = F.split(y, [-shift_,], axis=axis_t_)
        y = F.concat((b, a), axis=axis_t_)
      return y

除此之外，你可以尝试在 GitHub Issues 或论坛中针对 API 问题发起求助。

我们也欢迎你将自己实现的 API 以 Pull Request 的形式提交到 MegEngine 代码库中来～

注解

对于缺失的 Loss Funtions 算子，大都可自行设计实现。

megengine.utils.profiler.Profiler.stop

Deprecated APIs