pytorch直接转ncnn 读了一部分ncnn的源码,确保对 *.bin 和 *.param 文件充分了解之后,封装了1个工具ncnn_utils,源码位于miemiedetection的mmdet/models/ncnn_utils.py,它支持写一次前向传播就能导出ncnn使用的 *.bin 和 *.param 文件,你只需给每个pytorch层增加1个export_ncnn()方法,export_ncnn()方法几乎只...
from keras.modelsimportSequential from keras.utilsimportnp_utilsasutils from keras.layersimportDropout,Dense,Flatten from keras.layers.convolutionalimportConv2D,MaxPooling2D 加载cifar10数据: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 (X,y),(X_test,y_test)=cifar10.load_data()# 规范化数据X...
你只要照着farward()方法写,在export_ncnn()方法里用ncnn_utils的api写一次前向传播就能把pytorch模型导出到ncnn。 如何导出 导出ncnn非常简单,只要在miemiedetection项目根目录下输入 python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -c ppyoloe_crn_s_300e_coco.pth --n...
y_train=keras.utils.to_categorical(y_train,num_classes)y_test=keras.utils.to_categorical(y_test,num_classes)print(y_train.shape,'ytrain')# 图像数据归一化 x_train=x_train.astype('float32')x_test=x_test.astype('float32')x_train/=255x_test/=255 构造卷积神经网络: 输入层->多组卷积及...
num_classes = 10 #图像数据有10个实际标签类别y_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)print(y_train.shape, 'ytrain')# 图像数据归一化x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_...
import torch.utils.data as Data import torch.nn.functional as F import sys import d2lzh_pytorch as d2l os.environ["CUDA_VISIBLE_DEVICES"] = "0" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') DATA_ROOT = "./Datasets" ...
train_ds, val_ds = tf.keras.utils.image_dataset_from_directory( "PetImages", validation_split=0.2,subset="both", seed=1337, image_size=image_size, batch_size=batch_size, ) 可视化数据 这是训练数据集中的前 9 张图片。如你所见,标签1 是“狗”,标签 0 是“猫”。
fromkeras.utils.vis_utilsimportplot_model fromkeras.layersimportConv2D, MaxPool2D, Flatten,Dense,Dropout,BatchNormalization,MaxPooling2D,Activation,Input fromsklearn.model_selectionimporttrain_test_split fromsklearn.preprocessingimportStandardScaler
from keras.utils import to_categoricalimport tensorflow as tfimport matplotlib.pyplot as plt```接下来,我们将构建一个简单的CNN模型。该模型将包含两个卷积层、一个池化层和两个全连接层。我们将使用Keras的Sequential API来创建模型。```pythonmodel = Sequential()model.add(Conv2D(32, kernel_size=(3, ...
from torch.utils.data.dataset import Dataset # 定义模型 class SVHN_Model1(nn.Module): def __init__(self): super(SVHN_Model1, self).__init__() # CNN提取特征模块 self.cnn = nn.Sequential( nn.Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2)), ...