应用实践之基于MobileNetv2的垃圾分类

import math
import numpy as np
import os
import random

from matplotlib import pyplot as plt
from easydict import EasyDict
from PIL import Image
import numpy as np
import mindspore.nn as nn
from mindspore import ops as P
from mindspore.ops import add
from mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.dataset as de
import mindspore.dataset.vision as C
import mindspore.dataset.transforms as C2
import mindspore as ms
from mindspore import set_context, nn, Tensor, load_checkpoint, save_checkpoint, export
from mindspore.train import Model
from mindspore.train import Callback, LossMonitor, ModelCheckpoint, CheckpointConfig

os.environ['GLOG_v'] = '3' # Log level includes 3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
os.environ['GLOG_logtostderr'] = '0' # 0：输出到文件，1：输出到屏幕
os.environ['GLOG_log_dir'] = '../../log' # 日志目录
os.environ['GLOG_stderrthreshold'] = '2' # 输出到目录也输出到屏幕：3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
set_context(mode=ms.GRAPH_MODE, device_target="CPU", device_id=0) # 设置采用图模式执行，设备为Ascend#

# 垃圾分类数据集标签，以及用于标签映射的字典。
garbage_classes = {
    '干垃圾': ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服'],
    '可回收物': ['报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张'],
    '湿垃圾': ['菜叶', '橙皮', '蛋壳', '香蕉皮'],
    '有害垃圾': ['电池', '药片胶囊', '荧光灯', '油漆桶']
}

class_cn = ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服',
            '报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张',
            '菜叶', '橙皮', '蛋壳', '香蕉皮',
            '电池', '药片胶囊', '荧光灯', '油漆桶']
class_en = ['Seashell', 'Lighter','Old Mirror', 'Broom','Ceramic Bowl', 'Toothbrush','Disposable Chopsticks','Dirty Cloth',
            'Newspaper', 'Glassware', 'Basketball', 'Plastic Bottle', 'Cardboard','Glass Bottle', 'Metalware', 'Hats', 'Cans', 'Paper',
            'Vegetable Leaf','Orange Peel', 'Eggshell','Banana Peel',
            'Battery', 'Tablet capsules','Fluorescent lamp', 'Paint bucket']

index_en = {'Seashell': 0, 'Lighter': 1, 'Old Mirror': 2, 'Broom': 3, 'Ceramic Bowl': 4, 'Toothbrush': 5, 'Disposable Chopsticks': 6, 'Dirty Cloth': 7,
            'Newspaper': 8, 'Glassware': 9, 'Basketball': 10, 'Plastic Bottle': 11, 'Cardboard': 12, 'Glass Bottle': 13, 'Metalware': 14, 'Hats': 15, 'Cans': 16, 'Paper': 17,
            'Vegetable Leaf': 18, 'Orange Peel': 19, 'Eggshell': 20, 'Banana Peel': 21,
            'Battery': 22, 'Tablet capsules': 23, 'Fluorescent lamp': 24, 'Paint bucket': 25}

# 训练超参
config = EasyDict({
    "num_classes": 26,
    "image_height": 224,
    "image_width": 224,
    #"data_split": [0.9, 0.1],
    "backbone_out_channels":1280,
    "batch_size": 16,
    "eval_batch_size": 8,
    "epochs": 10,
    "lr_max": 0.05,
    "momentum": 0.9,
    "weight_decay": 1e-4,
    "save_ckpt_epochs": 1,
    "dataset_path": "./data_en",
    "class_index": index_en,
    "pretrained_ckpt": "./mobilenetV2-200_1067.ckpt" # mobilenetV2-200_1067.ckpt 
})

__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)