remove dlipr dependency on mnist

MNIST_GAN/mnist.py

+"""
+Module to load the MNIST database of handwritten digits
+See http://yann.lecun.com/exdb/mnist/
+
+The images are 28x28 pixels (grayscale) showing a single handwritten digit from
+0 to 9. The dataset contains 60000 training and 10000 test images.
+"""
+
+from dlipr.utils import get_datapath, Dataset
+import numpy as np
+import gzip
+
+
+def load_data():
+    """Load the MNIST dataset.
+
+    Returns:
+        Dataset: MNIST data
+    """
+
+    def _read32(bytestream):
+        dt = np.dtype(np.uint32).newbyteorder('>')
+        return np.frombuffer(bytestream.read(4), dtype=dt)[0]
+
+    def _extract_images(fname):
+        with gzip.GzipFile(fileobj=open(fname, 'rb')) as bytestream:
+            _read32(bytestream)
+            num_images = _read32(bytestream)
+            rows = _read32(bytestream)
+            cols = _read32(bytestream)
+            buf = bytestream.read(rows * cols * num_images)
+            data = np.frombuffer(buf, dtype=np.uint8)
+            return data.reshape(num_images, rows, cols)
+
+    def _extract_labels(fname):
+        with gzip.GzipFile(fileobj=open(fname, 'rb')) as bytestream:
+            _read32(bytestream)
+            num_items = _read32(bytestream)
+            buf = bytestream.read(num_items)
+            return np.frombuffer(buf, dtype=np.uint8)
+
+    data = Dataset()
+    data.train_images = _extract_images(get_datapath('MNIST/train-images-idx3-ubyte.gz'))
+    data.train_labels = _extract_labels(get_datapath('MNIST/train-labels-idx1-ubyte.gz'))
+    data.test_images = _extract_images(get_datapath('MNIST/t10k-images-idx3-ubyte.gz'))
+    data.test_labels = _extract_labels(get_datapath('MNIST/t10k-labels-idx1-ubyte.gz'))
+    data.classes = np.arange(10)
+    return data