Gluon end to end tutorial

docs/tutorial_utils/data/oxford_102_flower_dataset.py

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+# 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.
+
+
+"""
+This scripts downloads and prepares the Oxford 102 Category Flower Dataset for training
+Dataset is from: http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
+Script is modified from: /~https://github.com/Arsey/keras-transfer-learning-for-oxford102
+"""
+
+import glob
+import os
+import tarfile
+from shutil import copyfile
+
+import numpy as np
+from mxnet import gluon
+from scipy.io import loadmat
+
+
+def download_data():
+    data_url = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/'
+    image_file_name = '102flowers.tgz'
+    label_file_name = 'imagelabels.mat'
+    setid_file_name = 'setid.mat'
+
+    global data_path, image_path, label_path, setid_path
+    image_path = os.path.join(data_path, image_file_name)
+    label_path = os.path.join(data_path, label_file_name)
+    setid_path = os.path.join(data_path, setid_file_name)
+    # download the dataset into current directory
+    if not os.path.exists(data_path):
+        os.mkdir(data_path)
+    if not os.path.isfile(image_path):
+        gluon.utils.download(url=data_url + image_file_name, path=data_path)
+        print("Extracting downloaded dataset...")
+        tarfile.open(image_path).extractall(path=data_path)
+    if not os.path.isfile(label_path):
+        gluon.utils.download(url=data_url + label_file_name, path=data_path)
+    if not os.path.isfile(setid_path):
+        gluon.utils.download(url=data_url + setid_file_name, path=data_path)
+
+
+def prepare_data():
+    # Read .mat file containing training, testing, and validation sets.
+    global data_path, image_path, label_path, setid_path
+    setid = loadmat(setid_path)
+
+    idx_train = setid['trnid'][0] - 1
+    idx_test = setid['tstid'][0] - 1
+    idx_valid = setid['valid'][0] - 1
+
+    # Read .mat file containing image labels.
+    image_labels = loadmat(label_path)['labels'][0]
+
+    # Subtract one to get 0-based labels
+    image_labels -= 1
+
+    # extracted images are stored in folder 'jpg'
+    files = sorted(glob.glob(os.path.join(data_path, 'jpg', '*.jpg')))
+    file_label_pairs = np.array([i for i in zip(files, image_labels)])
+
+    # move files from extracted folder to train, test, valid
+    move_files('train', file_label_pairs[idx_test, :])
+    move_files('test', file_label_pairs[idx_train, :])
+    move_files('valid', file_label_pairs[idx_valid, :])
+
+def move_files(dir_name, file_label_pairs):
+    data_segment_dir = os.path.join(data_path, dir_name)
+    if not os.path.exists(data_segment_dir):
+        os.mkdir(data_segment_dir)
+
+    for i in range(0, 102):
+        class_dir = os.path.join(data_segment_dir, str(i))
+        if not os.path.exists(class_dir):
+            os.mkdir(class_dir)
+
+    for file, label in file_label_pairs:
+        src = str(file)
+        dst = os.path.join(data_path, dir_name, label, src.split(os.sep)[-1])
+        copyfile(src, dst)
+
+
+def get_data(dir_name):
+    global data_path
+    data_path = dir_name
+    download_data()
+    prepare_data()