(train_dataset.get_col_names()) def datapipe(dataset, batch_size): image_transforms = [ vision.Rescale(1.0 / 255.0, 0), vision.Normalize(mean=(0.1307,), std=(0.3081,)), vision.HWC2CHW() ] label_transform = transforms.TypeCast(mindspore.int32) dataset = dataset.map(image_transforms, '...
in __init__ offload_model = offload.GetOffloadModel(consumer, self.__ori_dataset.get_col_names()) File "/Users/kaierlong/Pyenvs/env_ms_1.7.0/lib/python3.9/site-packages/mindspore/dataset/engine/datasets.py", line 1559, in get_col_names self._col_names = runtime_getter[0].GetColumnN...
"notebook/datasets/MNIST_Data.zip"path = download(url,"./",kind="zip",replace=True)# 区分训练数据集和测试数据集train_dataset = MnistDataset('MNIST_Data/train') test_dataset = MnistDataset('MNIST_Data/test')print(train_dataset.get_col_names()) defdatapipe(dataset, batch_size): image_tra...
print(train_dataset.get_col_names()) ['image', 'label'] 复制 MindSpore的dataset使用数据处理流水线(Data Processing Pipeline),需指定map、batch、shuffle等操作。使用map对图像数据及标签进行变换处理,将输入的图像缩放为1/255,根据均值0.1307和标准差值0.3081进行归一化处理。 def datapipe(dataset, batch_size...
错误分析/修正方法:错误提示size of column_names is:2 and number of returned NumPy array is:1。显然,2不等于1对吧,再对比一下代码,__getitem__返回的是1个元素,可是GeneratorDataset定义的input_columns参数却是[“col1”, “col2”],显然这里会不匹配。生成器(python generator)式自定义数据集先看一个例...
错误提示size of column_names is:2 and number of returned NumPy array is:1。显然,2不等于1对吧,再对比一下代码,__getitem__返回的是1个元素,可是GeneratorDataset定义的input_columns参数却是[“col1”, “col2”],显然这里会不匹配。 生成器(python generator)式自定义数据集 ...
Add new api dataset.get_col_names(!5384) Remove useless API MindRecord finish(!5580) MindSpore Lite Converter Add 6 TFLite op, 7 Caffe op, 1 ONNX op. Add support for Windows. Support parallel inference of multiple sessions to adapt to more scenarios Support 8bits only weight-quantization,...
mindspore.dataset.Dataset.get_batch_size","api_python/dataset/dataset_method/attribute/mindspore.dataset.Dataset.get_class_indexing","api_python/dataset/dataset_method/attribute/mindspore.dataset.Dataset.get_col_names","api_python/dataset/dataset_method/attribute/mindspore.dataset.Dataset.get_dataset_size...
GetParam(new_h, new_w) #一张图片分为row行和col列分块推理 preds = np.zeros([1, dataset.num_classes, new_h, new_w]).astype(np.float32)#初始化 count = np.zeros([1, 1, new_h, new_w]).astype(np.float32)#记录像素点推理次数 for rows,cols: h0 , w0, h1, w1 = GetIndex(...
rows, cols = GetParam(new_h, new_w) #一张图片分为row行和col列分块推理 preds = np.zeros([1, dataset.num_classes, new_h, new_w]).astype(np.float32)#初始化 count = np.zeros([1, 1, new_h, new_w]).astype(np.float32)#记录像素点推理次数 ...