cluttered_mnist_video.py

import numpy as np

from fuel.transformers import Mapping
from fuel.datasets import H5PYDataset

import tasks

class ClutteredMNISTVideo(H5PYDataset):
    def __init__(self, which_sets, **kwargs):
        kwargs.setdefault('load_in_memory', False)
        super(ClutteredMNISTVideo, self).__init__(
            "/data/lisatmp3/cooijmat/datasets/cluttered-mnist-video/cluttered-mnist-video.hdf5",
            which_sets, **kwargs)

class Task(tasks.Classification):
    name = "cluttered_mnist_video"

    def __init__(self, *args, **kwargs):
        super(Task, self).__init__(*args, **kwargs)
        self.n_channels = 1
        self.n_classes = 10

    def load_datasets(self):
        return dict(
            train=ClutteredMNISTVideo(which_sets=["train"]),
            valid=ClutteredMNISTVideo(which_sets=["valid"]),
            test=ClutteredMNISTVideo(which_sets=["test"]))

    def get_stream_num_examples(self, which_set, monitor):
        if monitor and which_set == "train":
            return 10000
        return super(Task, self).get_stream_num_examples(which_set, monitor)

    def preprocess(data):
        x, y = data
        # move channel just after batch axis
        x = np.rollaxis(x, x.ndim - 1, 1)
        x = np.float32(x) / 255.0
        x_shape = np.tile([x.shape[2:]], (x.shape[0], 1))
        return (x.astype(np.float32),
                x_shape.astype(np.float32),
                y.astype(np.uint8))