1. 昇腾边缘侧运行环境配置
针对边缘部署需求（例如基于昇腾310或310B的智能安防盒子），我们在云端或算力工作站进行模型训练时，同样需要初始化Ascend后端的静态图模式，以便利用图算融合技术将计算图充分优化，为后续导出OM（Offline Model）格式打下基础。
import mindspore as ms
# 配置为静态图模式，目标硬件指定为Ascend
ms.set_context(mode=ms.GRAPH_MODE, device_target="Ascend")
# 开启内存优化，这对于后续在资源受限的边缘端运行至关重要
ms.set_context(max_device_memory="8GB")
print("Ascend context initialized for Edge Fire Detection Model.")
2. 针对火光与烟雾特征的数据增强
火灾场景在视觉上的表现极为复杂：初期的白烟、浓烈的黑烟、不同材质燃烧产生的火焰颜色差异巨大，且极易受到室内灯光的干扰。在 mindspore.dataset中，我们不仅需要常规的几何变换，还需要引入强烈的颜色抖动与随机擦除（Random Erasing）来模拟遮挡情况。
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
def create_fire_smoke_dataset(data_dir, batch_size=32):
# 构建多线程数据加载管道
dataset = ds.ImageFolderDataset(data_dir, num_parallel_workers=6)
# 针对火光与烟雾的定制化数据增强流水线
transform_pipeline = [
vision.Decode(),
vision.Resize((256, 256)),
vision.RandomCrop(224),
vision.RandomHorizontalFlip(prob=0.5),
# 宽泛的颜色抖动，增强模型对不同光照和火焰颜色的鲁棒性
vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2),
vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255]),
vision.HWC2CHW()
]
dataset = dataset.map(operations=transform_pipeline, input_columns="image", num_parallel_workers=6)
dataset = dataset.map(operations=transforms.TypeCast(ms.int32), input_columns="label", num_parallel_workers=6)
dataset = dataset.batch(batch_size, drop_remainder=True)
return dataset
3. 构建轻量级特征提取网络
为了满足家居陪伴系统等端侧设备的实时性要求（通常要求FPS>30），我们需要摒弃笨重的传统CNN。深度可分离卷积（Depthwise Separable Convolution）是降低计算量的利器。以下是在MindSpore中实现的一个极简轻量化卷积模块示例。
import mindspore.nn as nn
class LightWeightBlock(nn.Cell):
"""边缘端友好的轻量级特征提取模块"""
def __init__(self, in_channels, out_channels, stride=1):
super(LightWeightBlock, self).__init__()
# 1. Depthwise 卷积：提取空间特征，不改变通道数
self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=stride,
pad_mode='same', group=in_channels, has_bias=False)
self.bn1 = nn.BatchNorm2d(in_channels)
self.relu1 = nn.ReLU6()
# 2. Pointwise 卷积 (1x1)：融合通道特征，改变通道数
self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1,
pad_mode='same', has_bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu2 = nn.ReLU6()
def construct(self, x):
x = self.depthwise(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.pointwise(x)
x = self.bn2(x)
x = self.relu2(x)
return x
class FireDetectionNet(nn.Cell):
"""火灾与烟雾三分类网络 (正常/火灾/烟雾)"""
def __init__(self, num_classes=3):
super(FireDetectionNet, self).__init__()
self.stem = nn.Conv2d(3, 32, kernel_size=3, stride=2, pad_mode='same', has_bias=False)
self.block1 = LightWeightBlock(32, 64, stride=2)
self.block2 = LightWeightBlock(64, 128, stride=2)
self.pool = nn.AdaptiveAvgPool2d((1, 1))
self.flatten = nn.Flatten()
self.fc = nn.Dense(128, num_classes)
def construct(self, x):
x = self.stem(x)
x = self.block1(x)
x = self.block2(x)
x = self.pool(x)
x = self.flatten(x)
return self.fc(x)
4. 混合精度训练与多分类损失
在Ascend硬件上，利用FP16不仅能加快训练速度，还能让模型在导出时更易于进行量化压缩。火灾识别往往是一个多分类或多标签任务（例如某帧画面同时包含火和烟），这里我们以三分类（正常/火灾/烟雾）为例，展示MindSpore的AMP调优。
from mindspore.amp import auto_mixed_precision
from mindspore import nn
# 实例化网络并设置三分类
net = FireDetectionNet(num_classes=3)
# 转换网络为O3全混合精度模式，极大提升NPU利用率
net = auto_mixed_precision(net, amp_level="O3")
# 使用带有Logits的交叉熵损失，内部集成Softmax，数值计算更稳定
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.AdamWeightDecay(net.trainable_params(), learning_rate=1e-3, weight_decay=1e-4)
# 构建模型并准备训练
model = ms.Model(net, loss_fn=loss_fn, optimizer=optimizer, metrics={'accuracy'})
