使用 ImageFolder 处理过后就可以进行如class_names = train_dataset.classes这样方便的操作了。 importtorchfromtorchvisionimporttransforms,datasetsfromtorch.utils.dataimportDataLoader# 定义数据预处理的转换器transform=transforms.Compose([transforms.Resize((224,224)),# 调整图像大小transforms.ToTensor(),# 将图像转换...
img_folder = root + '/train_cifar10/' else: file_annotation = root + '/annotations/cifar10_test.json' img_folder = root + '/test_cifar10/' fp = open(file_annotation,'r') data_dict = json.load(fp) assert len(data_dict['images'])==len(data_dict['categories']) num_data = le...
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],output_names=['classes', 'boxes'] if y is None else ['output']) # Checks onnx_model = onnx.load(f) # load onnx model onnx.checker.check_model(onnx_model) # check onnx model print(onn...
(self.train_set)''' load_data也是我们自定义的函数,用途:读取数据集中的数据 ( 图片数据+标签label '''defload_data(self,data_folder,data_name,label_name):withgzip.open(os.path.join(data_folder,label_name),'rb')aslbpath:# rb表示的是读取二进制数据 y_train=np.frombuffer(lbpath.read(),...
python main.py --gpu0-a vgg16 /path/to/images/folder 由于vgg系列只测试model执行语句数据比较...
test_lmdb(lmdb_path, index=1)defgeneral_image_folder(opt):""" Create lmdb for general image folders If all the images have the same resolution, it will only store one copy of resolution info. Otherwise, it will store every resolution info. ...
A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs) Learning rate schedulers Id...
transforms.ToTensor()#thisalso converts all pixel values from0to255to be between0.0and1.0]) 在一张图片上验证我们的变换: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defplot_transformed_images(image_paths,transform,n=3,seed=42):"""Plots a seriesofrandom images from image_paths.Will ope...
Load the new images as an image datastore. The imageDatastore function automatically labels the images based on folder names and stores the data as an ImageDatastore object. Divide the data into training and validation data sets. Use 70% of the images for training and 30% for validation. Get...
raw_images/- the folder with Zurich RAW to RGB dataset results/- visual image results saved while training results/full-resolution/- visual results for full-resolution RAW image data saved during the testing load_dataset.py- python script that loads training data ...