wordpress做外贸网站,临沂学做网站,做网站哪家好 青岛,怎么制作链接带图片和文字编辑内容介绍#xff1a;
MobileNet网络是由Google团队于2017年提出的专注于移动端、嵌入式或IoT设备的轻量级CNN网络#xff0c;相比于传统的卷积神经网络#xff0c;MobileNet网络使用深度可分离卷积#xff08;Depthwise Separable Convolution#xff09;的思想在准确率小…内容介绍
MobileNet网络是由Google团队于2017年提出的专注于移动端、嵌入式或IoT设备的轻量级CNN网络相比于传统的卷积神经网络MobileNet网络使用深度可分离卷积Depthwise Separable Convolution的思想在准确率小幅度降低的前提下大大减小了模型参数与运算量。并引入宽度系数 α和分辨率系数 β使模型满足不同应用场景的需求。
由于MobileNet网络中Relu激活函数处理低维特征信息时会存在大量的丢失所以MobileNetV2网络提出使用倒残差结构Inverted residual block和Linear Bottlenecks来设计网络以提高模型的准确率且优化后的模型更小。
具体内容
1. 导包
from download import download
import math
import numpy as np
import os
import randomfrom 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
from mindspore.amp import FixedLossScaleManager
import time
2. 下载数据集
url https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MindStudio-pc/data_en.zip
path download(url, ./, kindzip, replaceTrue)
3. 下载预训练权重
url https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/ComputerVision/mobilenetV2-200_1067.zip
path download(url, ./, kindzip, replaceTrue)
4. 数据加载
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(modems.GRAPH_MODE, device_targetCPU, device_id0) # 设置采用图模式执行设备为Ascend#
5. 标签及超参
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
})
6. 数据预处理
def create_dataset(dataset_path, config, trainingTrue, buffer_size1000):create a train or eval datasetArgs:dataset_path(string): the path of dataset.config(struct): the config of train and eval in diffirent platform.Returns:train_dataset, val_datasetdata_path os.path.join(dataset_path, train if training else test)ds de.ImageFolderDataset(data_path, num_parallel_workers4, class_indexingconfig.class_index)resize_height config.image_heightresize_width config.image_widthnormalize_op C.Normalize(mean[0.485*255, 0.456*255, 0.406*255], std[0.229*255, 0.224*255, 0.225*255])change_swap_op C.HWC2CHW()type_cast_op C2.TypeCast(mstype.int32)if training:crop_decode_resize C.RandomCropDecodeResize(resize_height, scale(0.08, 1.0), ratio(0.75, 1.333))horizontal_flip_op C.RandomHorizontalFlip(prob0.5)color_adjust C.RandomColorAdjust(brightness0.4, contrast0.4, saturation0.4)train_trans [crop_decode_resize, horizontal_flip_op, color_adjust, normalize_op, change_swap_op]train_ds ds.map(input_columnsimage, operationstrain_trans, num_parallel_workers4)train_ds train_ds.map(input_columnslabel, operationstype_cast_op, num_parallel_workers4)train_ds train_ds.shuffle(buffer_sizebuffer_size)ds train_ds.batch(config.batch_size, drop_remainderTrue)else:decode_op C.Decode()resize_op C.Resize((int(resize_width/0.875), int(resize_width/0.875)))center_crop C.CenterCrop(resize_width)eval_trans [decode_op, resize_op, center_crop, normalize_op, change_swap_op]eval_ds ds.map(input_columnsimage, operationseval_trans, num_parallel_workers4)eval_ds eval_ds.map(input_columnslabel, operationstype_cast_op, num_parallel_workers4)ds eval_ds.batch(config.eval_batch_size, drop_remainderTrue)return ds
7. 数据展示
ds create_dataset(dataset_pathconfig.dataset_path, configconfig, trainingFalse)
print(ds.get_dataset_size())
data ds.create_dict_iterator(output_numpyTrue)._get_next()
images data[image]
labels data[label]for i in range(1, 5):plt.subplot(2, 2, i)plt.imshow(np.transpose(images[i], (1,2,0)))plt.title(label: %s % class_en[labels[i]])plt.xticks([])
plt.show() 8. 模型搭建
__all__ [MobileNetV2, MobileNetV2Backbone, MobileNetV2Head, mobilenet_v2]def _make_divisible(v, divisor, min_valueNone):if min_value is None:min_value divisornew_v max(min_value, int(v divisor / 2) // divisor * divisor)if new_v 0.9 * v:new_v divisorreturn new_vclass 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 xclass 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_size1, stride1, groups1)def __init__(self, in_planes, out_planes, kernel_size3, stride1, groups1):super(ConvBNReLU, self).__init__()padding (kernel_size - 1) // 2in_channels in_planesout_channels out_planesif groups 1:conv nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_modepad, paddingpadding)else:out_channels in_planesconv nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_modepad,paddingpadding, groupin_channels)layers [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]self.features nn.SequentialCell(layers)def construct(self, x):output self.features(x)return outputclass 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 channelReturns: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 ouplayers []if expand_ratio ! 1:layers.append(ConvBNReLU(inp, hidden_dim, kernel_size1))layers.extend([ConvBNReLU(hidden_dim, hidden_dim,stridestride, groupshidden_dim),nn.Conv2d(hidden_dim, oup, kernel_size1,stride1, has_biasFalse),nn.BatchNorm2d(oup),])self.conv nn.SequentialCell(layers)self.cast P.Cast()def construct(self, x):identity xx self.conv(x)if self.use_res_connect:return P.add(identity, x)return xclass 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 falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples: MobileNetV2(num_classes1000)def __init__(self, width_mult1., inverted_residual_settingNone, round_nearest8,input_channel32, last_channel1280):super(MobileNetV2Backbone, self).__init__()block InvertedResidual# setting of inverted residual blocksself.cfgs inverted_residual_settingif 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 layerinput_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, stride2)]# building inverted residual blocksfor 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 1features.append(block(input_channel, output_channel, stride, expand_ratiot))input_channel output_channelfeatures.append(ConvBNReLU(input_channel, self.out_channels, kernel_size1))self.features nn.SequentialCell(features)self._initialize_weights()def construct(self, x):x self.features(x)return xdef _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_channelsm.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, dtypefloat32)))elif isinstance(m, nn.BatchNorm2d):m.gamma.set_data(Tensor(np.ones(m.gamma.data.shape, dtypefloat32)))m.beta.set_data(Tensor(np.zeros(m.beta.data.shape, dtypefloat32)))propertydef get_features(self):return self.featuresclass MobileNetV2Head(nn.Cell):MobileNetV2 architecture.Args:class_num (int): Number of classes. Default is 1000.has_dropout (bool): Is dropout used. Default is falseReturns:Tensor, output tensor.Examples: MobileNetV2(num_classes1000)def __init__(self, input_channel1280, num_classes1000, has_dropoutFalse, activationNone):super(MobileNetV2Head, self).__init__()# mobilenet headhead ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_biasTrue)] if not has_dropout else[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_biasTrue)])self.head nn.SequentialCell(head)self.need_activation Trueif activation Sigmoid:self.activation nn.Sigmoid()elif activation Softmax:self.activation nn.Softmax()else:self.need_activation Falseself._initialize_weights()def construct(self, x):x self.head(x)if self.need_activation:x self.activation(x)return xdef _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, dtypefloat32)))propertydef get_head(self):return self.headclass 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 falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples: MobileNetV2(backbone, head)def __init__(self, num_classes1000, width_mult1., has_dropoutFalse, inverted_residual_settingNone, \round_nearest8, input_channel32, last_channel1280):super(MobileNetV2, self).__init__()self.backbone MobileNetV2Backbone(width_multwidth_mult, \inverted_residual_settinginverted_residual_setting, \round_nearestround_nearest, input_channelinput_channel, last_channellast_channel).get_featuresself.head MobileNetV2Head(input_channelself.backbone.out_channel, num_classesnum_classes, \has_dropouthas_dropout).get_headdef construct(self, x):x self.backbone(x)x self.head(x)return xclass 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_classes1000)def __init__(self, backbone, head):super(MobileNetV2Combine, self).__init__(auto_prefixFalse)self.backbone backboneself.head headdef construct(self, x):x self.backbone(x)x self.head(x)return xdef mobilenet_v2(backbone, head):return MobileNetV2Combine(backbone, head)
9. 训练与测试
def cosine_decay(total_steps, lr_init0.0, lr_end0.0, lr_max0.1, warmup_steps0):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 ratelr_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_stepslr_all_steps []inc_per_step (lr_max - lr_init) / warmup_steps if warmup_steps else 0for 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_endlr_all_steps.append(lr)return lr_all_steps
def switch_precision(net, data_type):if ms.get_context(device_target) Ascend:net.to_float(data_type)for _, cell in net.cells_and_names():if isinstance(cell, nn.Dense):cell.to_float(ms.float32)
LOSS_SCALE 1024train_dataset create_dataset(dataset_pathconfig.dataset_path, configconfig)
eval_dataset create_dataset(dataset_pathconfig.dataset_path, configconfig)
step_size train_dataset.get_dataset_size()backbone MobileNetV2Backbone() #last_channelconfig.backbone_out_channels
# Freeze parameters of backbone. You can comment these two lines.
for param in backbone.get_parameters():param.requires_grad False
# load parameters from pretrained model
load_checkpoint(config.pretrained_ckpt, backbone)head MobileNetV2Head(input_channelbackbone.out_channels, num_classesconfig.num_classes)
network mobilenet_v2(backbone, head)# define loss, optimizer, and model
loss nn.SoftmaxCrossEntropyWithLogits(sparseTrue, reductionmean)
loss_scale FixedLossScaleManager(LOSS_SCALE, drop_overflow_updateFalse)
lrs cosine_decay(config.epochs * step_size, lr_maxconfig.lr_max)
opt nn.Momentum(network.trainable_params(), lrs, config.momentum, config.weight_decay, loss_scaleLOSS_SCALE)# 定义用于训练的train_loop函数。
def train_loop(model, dataset, loss_fn, optimizer):# 定义正向计算函数def forward_fn(data, label):logits model(data)loss loss_fn(logits, label)return loss# 定义微分函数使用mindspore.value_and_grad获得微分函数grad_fn,输出loss和梯度。# 由于是对模型参数求导,grad_position 配置为None传入可训练参数。grad_fn ms.value_and_grad(forward_fn, None, optimizer.parameters)# 定义 one-step training函数def train_step(data, label):loss, grads grad_fn(data, label)optimizer(grads)return losssize dataset.get_dataset_size()model.set_train()for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):loss train_step(data, label)if batch % 10 0:loss, current loss.asnumpy(), batchprint(floss: {loss:7f} [{current:3d}/{size:3d}])# 定义用于测试的test_loop函数。
def test_loop(model, dataset, loss_fn):num_batches dataset.get_dataset_size()model.set_train(False)total, test_loss, correct 0, 0, 0for data, label in dataset.create_tuple_iterator():pred model(data)total len(data)test_loss loss_fn(pred, label).asnumpy()correct (pred.argmax(1) label).asnumpy().sum()test_loss / num_batchescorrect / totalprint(fTest: \n Accuracy: {(100*correct):0.1f}%, Avg loss: {test_loss:8f} \n)print( Starting Training )
# 由于时间问题训练过程只进行了2个epoch 可以根据需求调整。
epoch_begin_time time.time()
epochs 2
for t in range(epochs):begin_time time.time()print(fEpoch {t1}\n-------------------------------)train_loop(network, train_dataset, loss, opt)ms.save_checkpoint(network, save_mobilenetV2_model.ckpt)end_time time.time()times end_time - begin_timeprint(fper epoch time: {times}s)test_loop(network, eval_dataset, loss)
epoch_end_time time.time()
times epoch_end_time - epoch_begin_time
print(ftotal time: {times}s)
print( Training Success )
10. 模型推理
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) / stdimage image.transpose((2,0,1))img_tensor Tensor(np.array([image], np.float32))return img_tensordef 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(), axis1)[0]print(image_path, class_en[pred])def infer():backbone MobileNetV2Backbone(last_channelconfig.backbone_out_channels)head MobileNetV2Head(input_channelbackbone.out_channels, num_classesconfig.num_classes)network mobilenet_v2(backbone, head)load_checkpoint(CKPT, network)for i in range(91, 100):infer_one(network, fdata_en/test/Cardboard/000{i}.jpg)
infer()
MobileNetV2是MobileNet系列的一个优化版本由Google团队提出并特别针对移动端、嵌入式和IoT设备进行了优化。相比于传统的卷积神经网络CNNMobileNetV2通过引入深度可分离卷积Depthwise Separable Convolution以及倒置残差结构Inverted Residuals和线性瓶颈Linear Bottleneck等技术显著减少了模型参数和运算量同时保持了较高的准确率。这种轻量级的设计使得MobileNetV2在资源受限的设备上也能高效运行非常适合垃圾分类这类需要快速响应和实时处理的应用场景。
