MobileNetV2是一种轻量级的深度神经网络，设计用于移动和嵌入式设备。它的核心思想是通过深度可分离卷积（Depthwise Separable Convolutions）和倒残差结构（Inverted Residuals）来减少计算复杂度和模型参数量。其主要特点包括：

• 深度可分离卷积：将标准卷积分解为深度卷积（Depthwise Convolution）和逐点卷积（Pointwise Convolution），大大减少了计算量。
• 倒残差结构：通过先扩展特征维度再压缩的方式，使网络更容易训练和优化，同时保持较高的性能。
• 线性瓶颈：在每个倒残差块的最后使用线性激活函数，以避免信息丢失。

 MobileNetV2的具体架构

MobileNetV2由多个倒残差块组成，每个块包含：

• 逐点卷积：使用1x1卷积核扩展特征维度。
• 深度卷积：使用3x3卷积核对每个通道独立进行卷积操作。
• 逐点卷积：再次使用1x1卷积核压缩特征维度。
• 跳跃连接：将输入和输出相加（在特征维度相同的情况下），形成残差连接。
  
  这些块叠加在一起，形成了一个深度神经网络，可以有效地提取图像特征。

垃圾分类系统的工作流程

定义了损失函数后，可以得到损失函数关于权重的梯度。梯度用于指示优化器优化权重的方向，以提高模型性能。

在训练MobileNetV2之前对MobileNetV2Backbone层的参数进行了固定，使其在训练过程中对该模块的权重参数不进行更新；只对MobileNetV2Head模块的参数进行更新。

MindSpore支持的损失函数有SoftmaxCrossEntropyWithLogits、L1Loss、MSELoss等。这里使用SoftmaxCrossEntropyWithLogits损失函数。

训练测试过程中会打印loss值，loss值会波动，但总体来说loss值会逐步减小，精度逐步提高。每个人运行的loss值有一定随机性，不一定完全相同。

每打印一个epoch后模型都会在测试集上的计算测试精度，从打印的精度值分析MobileNetV2模型的预测能力在不断提升。

1. 数据预处理：

   • 加载图像数据，并将其调整为统一的大小（如224x224）。
   • 归一化图像像素值，使其在0到1之间。
   • 将标签转换为独热编码（one-hot encoding）。

     
     利用ImageFolderDataset方法读取垃圾分类数据集，并整体对数据集进行处理。

     读取数据集时指定训练集和测试集，首先对整个数据集进行归一化，修改图像频道等预处理操作。然后对训练集的数据依次进行RandomCropDecodeResize、RandomHorizontalFlip、RandomColorAdjust、shuffle操作，以增加训练数据的丰富度；对测试集进行Decode、Resize、CenterCrop等预处理操作；最后返回处理后的数据集

2. 模型构建：

   • 使用预训练的MobileNetV2模型作为特征提取器，去掉顶层的分类部分。
   • 添加全局平均池化层（Global Average Pooling Layer），将特征图转换为固定长度的特征向量。
   • 添加全连接层和输出层，根据具体的分类任务输出相应数量的类别。

     __all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']
     
     def _make_divisible(v, divisor, min_value=None):
         if min_value is None:
             min_value = divisor
         new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
         if new_v < 0.9 * v:
             new_v += divisor
         return new_v
     
     class GlobalAvgPooling(nn.Cell):
         """
         Global avg pooling definition.
     
         Args:
     
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> GlobalAvgPooling()
         """
     
         def __init__(self):
             super(GlobalAvgPooling, self).__init__()
     
         def construct(self, x):
             x = P.mean(x, (2, 3))
             return x
     
     class ConvBNReLU(nn.Cell):
         """
         Convolution/Depthwise fused with Batchnorm and ReLU block definition.
     
         Args:
             in_planes (int): Input channel.
             out_planes (int): Output channel.
             kernel_size (int): Input kernel size.
             stride (int): Stride size for the first convolutional layer. Default: 1.
             groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
     
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
         """
     
         def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
             super(ConvBNReLU, self).__init__()
             padding = (kernel_size - 1) // 2
             in_channels = in_planes
             out_channels = out_planes
             if groups == 1:
                 conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding)
             else:
                 out_channels = in_planes
                 conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad',
                                  padding=padding, group=in_channels)
     
             layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
             self.features = nn.SequentialCell(layers)
     
         def construct(self, x):
             output = self.features(x)
             return output
     
     class InvertedResidual(nn.Cell):
         """
         Mobilenetv2 residual block definition.
     
         Args:
             inp (int): Input channel.
             oup (int): Output channel.
             stride (int): Stride size for the first convolutional layer. Default: 1.
             expand_ratio (int): expand ration of input channel
     
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> ResidualBlock(3, 256, 1, 1)
         """
     
         def __init__(self, inp, oup, stride, expand_ratio):
             super(InvertedResidual, self).__init__()
             assert stride in [1, 2]
     
             hidden_dim = int(round(inp * expand_ratio))
             self.use_res_connect = stride == 1 and inp == oup
     
             layers = []
             if expand_ratio != 1:
                 layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
             layers.extend([
                 ConvBNReLU(hidden_dim, hidden_dim,
                            stride=stride, groups=hidden_dim),
                 nn.Conv2d(hidden_dim, oup, kernel_size=1,
                           stride=1, has_bias=False),
                 nn.BatchNorm2d(oup),
             ])
             self.conv = nn.SequentialCell(layers)
             self.cast = P.Cast()
     
         def construct(self, x):
             identity = x
             x = self.conv(x)
             if self.use_res_connect:
                 return P.add(identity, x)
             return x
     
     class MobileNetV2Backbone(nn.Cell):
         """
         MobileNetV2 architecture.
     
         Args:
             class_num (int): number of classes.
             width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
             has_dropout (bool): Is dropout used. Default is false
             inverted_residual_setting (list): Inverted residual settings. Default is None
             round_nearest (list): Channel round to . Default is 8
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> MobileNetV2(num_classes=1000)
         """
     
         def __init__(self, width_mult=1., inverted_residual_setting=None, round_nearest=8,
                      input_channel=32, last_channel=1280):
             super(MobileNetV2Backbone, self).__init__()
             block = InvertedResidual
             # setting of inverted residual blocks
             self.cfgs = inverted_residual_setting
             if inverted_residual_setting is None:
                 self.cfgs = [
                     # t, c, n, s
                     [1, 16, 1, 1],
                     [6, 24, 2, 2],
                     [6, 32, 3, 2],
                     [6, 64, 4, 2],
                     [6, 96, 3, 1],
                     [6, 160, 3, 2],
                     [6, 320, 1, 1],
                 ]
     
             # building first layer
             input_channel = _make_divisible(input_channel * width_mult, round_nearest)
             self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
             features = [ConvBNReLU(3, input_channel, stride=2)]
             # building inverted residual blocks
             for t, c, n, s in self.cfgs:
                 output_channel = _make_divisible(c * width_mult, round_nearest)
                 for i in range(n):
                     stride = s if i == 0 else 1
                     features.append(block(input_channel, output_channel, stride, expand_ratio=t))
                     input_channel = output_channel
             features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
             self.features = nn.SequentialCell(features)
             self._initialize_weights()
     
         def construct(self, x):
             x = self.features(x)
             return x
     
         def _initialize_weights(self):
             """
             Initialize weights.
     
             Args:
     
             Returns:
                 None.
     
             Examples:
                 >>> _initialize_weights()
             """
             self.init_parameters_data()
             for _, m in self.cells_and_names():
                 if isinstance(m, nn.Conv2d):
                     n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                     m.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
                                                               m.weight.data.shape).astype("float32")))
                     if m.bias is not None:
                         m.bias.set_data(
                             Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
                 elif isinstance(m, nn.BatchNorm2d):
                     m.gamma.set_data(
                         Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
                     m.beta.set_data(
                         Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
     
         @property
         def get_features(self):
             return self.features
     
     class MobileNetV2Head(nn.Cell):
         """
         MobileNetV2 architecture.
     
         Args:
             class_num (int): Number of classes. Default is 1000.
             has_dropout (bool): Is dropout used. Default is false
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> MobileNetV2(num_classes=1000)
         """
     
         def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False, activation="None"):
             super(MobileNetV2Head, self).__init__()
             # mobilenet head
             head = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_bias=True)] if not has_dropout else
                     [GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_bias=True)])
             self.head = nn.SequentialCell(head)
             self.need_activation = True
             if activation == "Sigmoid":
                 self.activation = nn.Sigmoid()
             elif activation == "Softmax":
                 self.activation = nn.Softmax()
             else:
                 self.need_activation = False
             self._initialize_weights()
     
         def construct(self, x):
             x = self.head(x)
             if self.need_activation:
                 x = self.activation(x)
             return x
     
         def _initialize_weights(self):
             """
             Initialize weights.
     
             Args:
     
             Returns:
                 None.
     
             Examples:
                 >>> _initialize_weights()
             """
             self.init_parameters_data()
             for _, m in self.cells_and_names():
                 if isinstance(m, nn.Dense):
                     m.weight.set_data(Tensor(np.random.normal(
                         0, 0.01, m.weight.data.shape).astype("float32")))
                     if m.bias is not None:
                         m.bias.set_data(
                             Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
         @property
         def get_head(self):
             return self.head
     
     class MobileNetV2(nn.Cell):
         """
         MobileNetV2 architecture.
     
         Args:
             class_num (int): number of classes.
             width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
             has_dropout (bool): Is dropout used. Default is false
             inverted_residual_setting (list): Inverted residual settings. Default is None
             round_nearest (list): Channel round to . Default is 8
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> MobileNetV2(backbone, head)
         """
     
         def __init__(self, num_classes=1000, width_mult=1., has_dropout=False, inverted_residual_setting=None, \
             round_nearest=8, input_channel=32, last_channel=1280):
             super(MobileNetV2, self).__init__()
             self.backbone = MobileNetV2Backbone(width_mult=width_mult, \
                 inverted_residual_setting=inverted_residual_setting, \
                 round_nearest=round_nearest, input_channel=input_channel, last_channel=last_channel).get_features
             self.head = MobileNetV2Head(input_channel=self.backbone.out_channel, num_classes=num_classes, \
                 has_dropout=has_dropout).get_head
     
         def construct(self, x):
             x = self.backbone(x)
             x = self.head(x)
             return x
     
     class MobileNetV2Combine(nn.Cell):
         """
         MobileNetV2Combine architecture.
     
         Args:
             backbone (Cell): the features extract layers.
             head (Cell):  the fully connected layers.
         Returns:
             Tensor, output tensor.
     
         Examples:
             >>> MobileNetV2(num_classes=1000)
         """
     
         def __init__(self, backbone, head):
             super(MobileNetV2Combine, self).__init__(auto_prefix=False)
             self.backbone = backbone
             self.head = head
     
         def construct(self, x):
             x = self.backbone(x)
             x = self.head(x)
             return x
     
     def mobilenet_v2(backbone, head):
         return MobileNetV2Combine(backbone, head)

3. 模型训练：

   • 冻结MobileNetV2的卷积层，只训练新增的全连接层。
   • 使用交叉熵损失函数和Adam优化器进行模型训练。
   • 在验证集上监控模型表现，并使用早停（Early Stopping）和模型检查点（Model Checkpoint）保存最优模型。

     def cosine_decay(total_steps, lr_init=0.0, lr_end=0.0, lr_max=0.1, warmup_steps=0):
         """
         Applies cosine decay to generate learning rate array.
     
         Args:
            total_steps(int): all steps in training.
            lr_init(float): init learning rate.
            lr_end(float): end learning rate
            lr_max(float): max learning rate.
            warmup_steps(int): all steps in warmup epochs.
     
         Returns:
            list, learning rate array.
         """
         lr_init, lr_end, lr_max = float(lr_init), float(lr_end), float(lr_max)
         decay_steps = total_steps - warmup_steps
         lr_all_steps = []
         inc_per_step = (lr_max - lr_init) / warmup_steps if warmup_steps else 0
         for i in range(total_steps):
             if i < warmup_steps:
                 lr = lr_init + inc_per_step * (i + 1)
             else:
                 cosine_decay = 0.5 * (1 + math.cos(math.pi * (i - warmup_steps) / decay_steps))
                 lr = (lr_max - lr_end) * cosine_decay + lr_end
             lr_all_steps.append(lr)
     
         return lr_all_steps

4. 模型训练与测试

   在进行正式的训练之前，定义训练函数，读取数据并对模型进行实例化，定义优化器和损失函数。

   首先简单介绍损失函数及优化器的概念：
   
   损失函数：又叫目标函数，用于衡量预测值与实际值差异的程度。深度学习通过不停地迭代来缩小损失函数的值。定义一个好的损失函数，可以有效提高模型的性能。
   
   优化器：用于最小化损失函数，从而在训练过程中改进模型。

5. 模型推理
   加载模型Checkpoint进行推理，使用load_checkpoint接口加载数据时，需要把数据传入给原始网络，而不能传递给带有优化器和损失函数的训练网络。

   CKPT="save_mobilenetV2_model.ckpt"
   
   def image_process(image):
       """Precess one image per time.
       
       Args:
           image: shape (H, W, C)
       """
       mean=[0.485*255, 0.456*255, 0.406*255]
       std=[0.229*255, 0.224*255, 0.225*255]
       image = (np.array(image) - mean) / std
       image = image.transpose((2,0,1))
       img_tensor = Tensor(np.array([image], np.float32))
       return img_tensor
   
   def infer_one(network, image_path):
       image = Image.open(image_path).resize((config.image_height, config.image_width))
       logits = network(image_process(image))
       pred = np.argmax(logits.asnumpy(), axis=1)[0]
       print(image_path, class_en[pred])
   
   def infer():
       backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)
       head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)
       network = mobilenet_v2(backbone, head)
       load_checkpoint(CKPT, network)
       for i in range(91, 100):
           infer_one(network, f'data_en/test/Cardboard/000{i}.jpg')
   infer()