Add the implementation of the utils functions for the generative model

examples/singa_peft/examples/model/gan_utils.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+#
+# 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 gzip
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import pickle
+import sys
+import time
+
+try:
+	import urllib.request as ul_request
+except ImportError:
+	import urllib as ul_request
+
+def print_log(s):
+    t = time.ctime()
+    print('[{}]{}'.format(t, s))
+
+def load_data(filepath):
+	with gzip.open(filepath, 'rb') as f:
+		train_set, valid_set, test_set = pickle.load(f, encoding='bytes')
+		traindata = train_set[0].astype(np.float32)
+		validdata = valid_set[0].astype(np.float32)
+		testdata = test_set[0].astype(np.float32)
+		trainlabel = train_set[1].astype(np.float32)
+		validlabel = valid_set[1].astype(np.float32)
+		testlabel = test_set[1].astype(np.float32)
+		return traindata, trainlabel, validdata, validlabel, testdata, testlabel
+
+def download_data(gzfile, url):
+	if os.path.exists(gzfile):
+		print('Downloaded already!')
+		sys.exit(0)
+	print('Downloading data %s' % (url))
+	ul_request.urlretrieve(url, gzfile)
+	print('Finished!')
+
+def show_images(filepath):
+	with open(filepath, 'rb') as f:
+		imgs = pickle.load(f)
+		r, c = 5, 5
+		fig, axs = plt.subplots(5, 5)
+		cnt = 0
+		for i in range(r):
+			for j in range(c):
+				axs[i, j].imshow(imgs[cnt, :, :, 0], cmap='gray')
+				axs[i, j].axis('off')
+				cnt += 1
+		plt.show()