listify | [Educational/Workshop Repo] | Learning library

It seems that some issues with torch that is used in colab

FastAI Forum Try to install specific version of torch in your colab before run fastAI python code

split should be faster

The comments above raise some valid points. Still, I do believe there is a way to achieve what you're after (whether or not this is a particularly good idea is less clear) by leveraging three functions from the purrr package in combination:

1. purrr::vec_depth allows us to get the (nesting) depth of a given list,
2. purrr::modify_depth allows us to apply a function to an list at the specified level of depth, and
3. purrr::reduce allows us to iteratively apply a function and have the result of each iteration be passed as the input to the subsequent iteration.

In essence, we want to convert any data.frame found at any level in the list to a tibble. This can easily be achieved using several rounds of purrr::modify_depth where we simply alter the depth depending on the level of the list we wish to target. Crucially, however, we want to do this in a way so that changes to level 1, for example, are retained when we move on to targeting level 2; changes to level 1 and 2 are retained when we move on to level 3; and so on. This is where purrr::reduce comes in: each time we apply purrr::modify_depth to convert a data.frame to a tibble, we'll ensure that the resultant output gets passed as the input to the next iteration. This is illustrated in the MWE below

Start with the basic setup of data structures and libraries

Using the json object that you list above, the following should work:

The below custom classes are necessary to deal with the binary image segmentation datasets which use 0 and 255 to encode the mask

I have faced this problem and the solution is very simple. Just call each transform function that you want to apply each inside a separate list enclosed by a list and pass it into get_transforms function's xtra_tfms parameter. (It could even be a tuple of tuples or any collection)

Every widget has a configure method which will return all of the configuration options for a widget.

When you call configure you get back a dictionary. The keys are the option names, the value is a tuple. The tuple will either have two values or five values.

Two values

If the dictionary element has two values, it represents an alias to another option. The values are as follows:

1. the name of the option (eg: "bd" for borderwidth)
2. the tcl/tk option that it is aliased to (e.g. "-borderwidth")

For example, for widgets with a borderwidth attribute, the result of configure() will have a key "bd" with the value ('bd', '-borderwidth').

Five values

If the dictionary element has five values, it is a normal option. The values are as follows:

1. The name of the option (e.g. "borderwidth")
2. The option database¹ name (e.g. "borderWidth")
3. The option database class name (e.g. "BorderWidth")
4. The default value of the option
5. The currently configured value of the option

Printing all options and their values

You can iterate over this dictionary to print out the values by examing all values that have five elements. For example:

Per the documents, you need to get the npm create react class package. From the command line of your project's file path, you need to do npm install create-react-class --save, and then modify the code like this:

I can't reproduce your reported problem. When I load up your code I see this: