mindspore.common.lazy_inline 源代码

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"""lazy_inline"""
from __future__ import absolute_import
import inspect
from functools import wraps


[文档]def lazy_inline(fn=None, attrs=None): """ Make the cell to be reusable. The corresponding sub graph will not be inline at first. 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. Args: fn (function): `__init__` function of a cell. attrs (Union[list[string], string]): The attributes list to add for the cell. 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() """ def wrap_cell(fn): @wraps(fn) def deco(self, *args, **kwargs): arguments = [] if attrs is None: bound_args = inspect.signature(fn).bind(self, *args, **kwargs) arguments = bound_args.arguments del arguments['self'] arguments = arguments.values() fn(self, *args, **kwargs) if attrs is None: self.cell_init_args = "lazy_inline_" + type(self).__name__ + str(arguments) return if isinstance(attrs, list): for item in attrs: if not isinstance(item, str): raise ValueError(f"attr must be a string") if hasattr(self, item): arguments.append(getattr(self, item)) elif isinstance(attrs, str): if hasattr(self, attrs): arguments = getattr(self, attrs) else: raise ValueError(f"attrs must be list or string") self.cell_init_args = "lazy_inline_" + type(self).__name__ + str(arguments) return deco if fn is not None: return wrap_cell(fn) return wrap_cell