# Write a DataFlow¶

There are several existing DataFlow, e.g. ImageFromFile, DataFromList, which you can use if your data format is simple. However in general, you will probably need to write a new DataFlow to produce data for your task.

Usually, you just need to implement the get_data() method which yields a datapoint every time.

class MyDataFlow(DataFlow):
def get_data(self):
for k in range(100):
digit = np.random.rand(28, 28)
label = np.random.randint(10)
yield [digit, label]


Optionally, DataFlow can implement the following two methods:

• size(). Return the number of elements the generator can produce. Certain tensorpack features might require this.

• reset_state(). It is guaranteed that the actual process which runs a DataFlow will invoke this method before using it. So if this DataFlow needs to do something after a fork(), you should put it here.

A typical situation is when your DataFlow uses random number generator (RNG). Then you would need to reset the RNG here. Otherwise, child processes will have the same random seed. The RNGDataFlow base class does this for you.

With a “low-level” DataFlow defined like above, you can then compose it with existing modules (e.g. batching, prefetching, ...).

DataFlow implementations for several well-known datasets are provided in the dataflow.dataset module, you can take them as a reference.