mindspore.lazy_inline
- mindspore.lazy_inline(fn=None, attrs=None)[源代码]
指定一个cell是可复用的。该cell在前端编译为可复用的子图,后端根据策略内联。 注册此装饰器到cell的内置函数 __init__ 时,此装饰器会按照 attrs 的值去添加 __init__ 函数对应的入参作为cell的属性。
警告
该特性仅支持Ascend,其它硬件不支持。
- 参数:
fn (function) - cell的 __init__ 函数。
attrs (Union[list[string], string]) - cell需要添加的属性列表。
- 返回:
function,原始函数。
- 支持平台:
Ascend
样例:
>>> 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()