Add support for MNIST

dataset_tool.py

@@ -13,6 +13,7 @@
 import pickle
 import sys
 import tarfile
+import gzip
 import zipfile
 from pathlib import Path
 from typing import Callable, Optional, Tuple, Union
@@ -165,6 +166,36 @@ def iterate_images():
 
 #----------------------------------------------------------------------------
 
+def open_mnist(images_gz: str, *, max_images: Optional[int]):
+    labels_gz = images_gz.replace('-images-idx3-ubyte.gz', '-labels-idx1-ubyte.gz')
+    assert labels_gz != images_gz
+    images = []
+    labels = []
+
+    with gzip.open(images_gz, 'rb') as f:
+        images = np.frombuffer(f.read(), np.uint8, offset=16)
+    with gzip.open(labels_gz, 'rb') as f:
+        labels = np.frombuffer(f.read(), np.uint8, offset=8)
+
+    images = images.reshape(-1, 28, 28)
+    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
+    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (60000,) and labels.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+
+    max_idx = maybe_min(len(images), max_images)
+
+    def iterate_images():
+        for idx, img in enumerate(images):
+            yield dict(img=img, label=int(labels[idx]))
+            if idx >= max_idx-1:
+                break
+
+    return max_idx, iterate_images()
+
+#----------------------------------------------------------------------------
+
 def make_transform(
     transform: Optional[str],
     output_width: Optional[int],
@@ -225,10 +256,11 @@ def open_dataset(source, *, max_images: Optional[int]):
         else:
             return open_image_folder(source, max_images=max_images)
     elif os.path.isfile(source):
-        if source.endswith('cifar-10-python.tar.gz'):
+        if os.path.basename(source) == 'cifar-10-python.tar.gz':
             return open_cifar10(source, max_images=max_images)
-        ext = file_ext(source)
-        if ext == 'zip':
+        elif os.path.basename(source) == 'train-images-idx3-ubyte.gz':
+            return open_mnist(source, max_images=max_images)
+        elif file_ext(source) == 'zip':
             return open_image_zip(source, max_images=max_images)
         else:
             assert False, 'unknown archive type'
@@ -293,17 +325,18 @@ def convert_dataset(
     The input dataset format is guessed from the --source argument:
 
     \b
-    --source *_lmdb/                - Load LSUN dataset
-    --source cifar-10-python.tar.gz - Load CIFAR-10 dataset
-    --source path/                  - Recursively load all images from path/
-    --source dataset.zip            - Recursively load all images from dataset.zip
+    --source *_lmdb/                    Load LSUN dataset
+    --source cifar-10-python.tar.gz     Load CIFAR-10 dataset
+    --source train-images-idx3-ubyte.gz Load MNIST dataset
+    --source path/                      Recursively load all images from path/
+    --source dataset.zip                Recursively load all images from dataset.zip
 
-    The output dataset format can be either an image folder or a zip archive.  Specifying
-    the output format and path:
+    The output dataset format can be either an image folder or a zip archive.
+    Specifying the output format and path:
 
     \b
-    --dest /path/to/dir             - Save output files under /path/to/dir
-    --dest /path/to/dataset.zip     - Save output files into /path/to/dataset.zip archive
+    --dest /path/to/dir                 Save output files under /path/to/dir
+    --dest /path/to/dataset.zip         Save output files into /path/to/dataset.zip
 
     Images within the dataset archive will be stored as uncompressed PNG.