import preprocess as prep start_time = datetime.datetime.now() input_tensor = prep.transform(image) input_batch = input_tensor.unsqueeze(0).to(device) 为什么报错AttributeError: module 'preprocess' has no attribute 'transform'
时间: 2024-03-25 09:40:53 浏览: 15
这个错误通常是由于导入的"preprocess"模块中确实没有名为"transform"的函数或方法。可以尝试以下几个方法:
1. 检查一下你的"preprocess"模块中是否真的有名为"transform"的函数或方法。如果没有,你可以尝试添加一个名为"transform"的函数或方法,或者修改你的代码,使用"preprocess"模块中已有的函数或方法。
2. 如果你确定"preprocess"模块中有名为"transform"的函数或方法,那么可能是因为你的模块导入方式不正确。你可以尝试使用以下方式导入模块:
```python
from preprocess import transform
```
然后在你的代码中使用"transform"函数。
3. 如果以上方法都不行,可以尝试重命名你的"preprocess"模块,比如改为"mypreprocess",然后修改你的代码中的导入语句和函数调用。
如果你还有其他问题,可以提供更多的上下文或代码片段,这样我才能更准确地帮助你。
相关问题
def yolo_meminout(frame_in,img_w,img_h,frame_out): ## image preprocess start start_time = time.time() start_time_total = start_time img_boxed = letterbox_image(frame_in,416,416) # img_boxed.save("./pictures/pictrue_boxed.jpg") img_array_3_416_416 = image_to_array_1dim(img_boxed,416,416) input_tmp_img = float32_int(img_array_3_416_416) end_time = time.time() image_preprocess = end_time - start_time # image preprocess end ## load image to memory(DRAM) start start_time = time.time() np.copyto(img_base_buffer[0:259584],input_tmp_img) end_time = time.time() load_image_to_memory = end_time - start_time
这段代码是用来进行图像预处理和将图像加载到内存中的,其中使用了一些自定义的函数,如letterbox_image和image_to_array_1dim。可以看出,图像被缩放到了416x416的大小,并且被转换为了一维的float32类型数组。然后,这个数组被拷贝到了内存中。这个函数的返回值不清楚,可能是预处理和加载所用的时间。
x_train = scaler.fit_transform(x_train)
This line of code is using the `fit_transform` method of the `scaler` object to scale the `x_train` data.
The `fit_transform` method is a convenient way to first fit the scaler to the data (i.e. calculate the mean and standard deviation of the data) and then transform the data using the calculated parameters.
The `scaler` object is typically an instance of a class from the `sklearn.preprocessing` module, such as `StandardScaler`, `MinMaxScaler`, or `RobustScaler`. These scalers are commonly used to preprocess data for machine learning algorithms by scaling features to have zero mean and unit variance or scaling features to a specific range.
In this case, `scaler.fit_transform(x_train)` is scaling the `x_train` data using the `fit_transform` method of the `scaler` object. The scaled data is then assigned back to `x_train`.