mindspore.lazy_inline

mindspore.lazy_inline(fn=None, attrs=None, policy=None)

Make the cell to be reusable. The corresponding sub graph will not be inline at first and will be inline with the policy. Registering the decorator of the built-in function __init__ of a cell, the decorator will add the parameters of __init__ according to the attrs as the attributes of this cell.

Warning

This feature is only supported on Ascend and is not supported on other hardwares. The construct parameters must be positional or key word arguments and have not default values. The cell has not switch sub graph.

Parameters

• fn (function) – __init__ function of a cell.

• attrs (Union[list[string], string]) – The attributes list to add for the cell.

• policy (Union[None, "front"]) –

  The policy of inline. Default is None.

  • None: The cell will be compiled to sub graph and will not be inline.

  • "front": The cell will be compiled to sub graph first and will be inline at front end.

Returns

function, original function.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> import mindspore.nn as nn
>>> from mindspore import lazy_inline
>>> from mindspore import context
>>> from mindspore import ops
>>> def conv3x3(in_channels, out_channels, stride=1, padding=1, pad_mode='pad'):
...     return nn.Conv2d(in_channels, out_channels,
...                      kernel_size=3, stride=stride, padding=padding, pad_mode=pad_mode)
...
>>> def conv1x1(in_channels, out_channels, stride=1, padding=0, pad_mode='pad'):
...     return nn.Conv2d(in_channels, out_channels,
...                      kernel_size=1, stride=stride, padding=padding, pad_mode=pad_mode)
...
>>> class Block(nn.Cell):
...     expansion = 4
...
...     @lazy_inline
...     def __init__(self,
...                  in_channels,
...                  out_channels,
...                  stride=1,
...                  down_sample=False):
...         super(Block, self).__init__()
...
...         out_chls = out_channels
...         self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
...         self.bn1 = nn.BatchNorm2d(out_chls)
...
...         self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
...         self.bn2 = nn.BatchNorm2d(out_chls)
...
...         self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
...         self.bn3 = nn.BatchNorm2d(out_channels)
...
...         self.relu = nn.ReLU()
...         self.downsample = down_sample
...
...         self.conv_down_sample = conv1x1(in_channels, out_channels,
...                                         stride=stride, padding=0)
...         self.bn_down_sample = nn.BatchNorm2d(out_channels)
...         self.add = ops.Add()
...
...     def construct(self, x):
...         identity = x
...
...         out = self.conv1(x)
...         out = self.bn1(out)
...         out = self.relu(out)
...
...         out = self.conv2(out)
...         out = self.bn2(out)
...         out = self.relu(out)
...
...         out = self.conv3(out)
...         out = self.bn3(out)
...
...         if self.downsample:
...             identity = self.conv_down_sample(identity)
...             identity = self.bn_down_sample(identity)
...
...         out = self.add(out, identity)
...         out = self.relu(out)
...
...         return out
...
>>> class Net(nn.Cell):
...     def __init__(self, block, num_classes=100):
...         super(Net, self).__init__()
...
...         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')
...         self.bn1 = nn.BatchNorm2d(64)
...         self.relu = nn.ReLU()
...         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='valid')
...
...         self.layer = self.MakeLayer(
...             block, 50, in_channels=64, out_channels=2048, stride=2)
...         self.avgpool = nn.AvgPool2d(7, 1)
...         self.flatten = ops.Flatten()
...
...     def MakeLayer(self, block, layer_num, in_channels, out_channels, stride):
...         layers = []
...         resblk = block(in_channels, out_channels,
...                        stride=stride, down_sample=True)
...         layers.append(resblk)
...
...         for _ in range(1, layer_num):
...             resblk = block(out_channels, out_channels, stride=1)
...             layers.append(resblk)
...
...         return nn.SequentialCell(layers)
...
...     def construct(self, x):
...         x = self.conv1(x)
...         x = self.bn1(x)
...         x = self.relu(x)
...         x = self.maxpool(x)
...         x = self.layer(x)
...         x = self.avgpool(x)
...         x = self.flatten(x)
...         return x
...
>>> def test_compile():
...     net = Net(Block)
...     inp = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32))
...     net(inp)
...
>>> context.set_context(mode=context.GRAPH_MODE,
...                     save_graphs=True, save_graphs_path="./lazy")
...
>>> test_compile()