Add the benchmark dataset for testing the model selection

examples/cnn_ms/data/mnist.py

@@ -0,0 +1,92 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+import numpy as np
+import os
+import sys
+import gzip
+import codecs
+
+
+def check_dataset_exist(dirpath):
+    if not os.path.exists(dirpath):
+        print(
+            'The MNIST dataset does not exist. Please download the mnist dataset using python data/download_mnist.py'
+        )
+        sys.exit(0)
+    return dirpath
+
+
+def load_dataset():
+    train_x_path = '/tmp/train-images-idx3-ubyte.gz'  # need to change to local disk
+    train_y_path = '/tmp/train-labels-idx1-ubyte.gz'  # need to change to local disk
+    valid_x_path = '/tmp/t10k-images-idx3-ubyte.gz'  # need to change to local disk
+    valid_y_path = '/tmp/t10k-labels-idx1-ubyte.gz'  # need to change to local disk
+
+    train_x = read_image_file(check_dataset_exist(train_x_path)).astype(
+        np.float32)
+    train_y = read_label_file(check_dataset_exist(train_y_path)).astype(
+        np.float32)
+    valid_x = read_image_file(check_dataset_exist(valid_x_path)).astype(
+        np.float32)
+    valid_y = read_label_file(check_dataset_exist(valid_y_path)).astype(
+        np.float32)
+    return train_x, train_y, valid_x, valid_y
+
+
+def read_label_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2049
+        length = get_int(data[4:8])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=8).reshape((length))
+        return parsed
+
+
+def get_int(b):
+    return int(codecs.encode(b, 'hex'), 16)
+
+
+def read_image_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2051
+        length = get_int(data[4:8])
+        num_rows = get_int(data[8:12])
+        num_cols = get_int(data[12:16])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape(
+            (length, 1, num_rows, num_cols))
+        return parsed
+
+
+def normalize(train_x, val_x):
+    train_x /= 255
+    val_x /= 255
+    return train_x, val_x
+
+
+def load():
+    train_x, train_y, val_x, val_y = load_dataset()
+    train_x, val_x = normalize(train_x, val_x)
+    train_x = train_x.astype(np.float32)
+    val_x = val_x.astype(np.float32)
+    train_y = train_y.astype(np.int32)
+    val_y = val_y.astype(np.int32)
+    return train_x, train_y, val_x, val_y
+