Merge pull request #820 from apache/master

Update dev branch from master

cmake/Thirdparty/FindGlog.cmake

@@ -21,7 +21,7 @@ FIND_PATH(GLOG_INCLUDE_DIR NAMES glog/logging.h PATHS "$ENV{GLOG_DIR}/include")
 FIND_LIBRARY(GLOG_LIBRARIES NAMES glog)
 
 INCLUDE(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARIES)
+find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARIES)
 
 IF(GLOG_FOUND)
     #    MESSAGE(STATUS "Found glog at ${GLOG_INCLUDE_DIR}")

examples/onnx/superresolution.py

@@ -0,0 +1,104 @@
+#
+# 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 th
+
+import os
+import numpy as np
+from PIL import Image
+from resizeimage import resizeimage
+
+from singa import device
+from singa import tensor
+from singa import sonnx
+import onnx
+from utils import download_model, check_exist_or_download
+
+import logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(message)s')
+
+
+def preprocess(img):
+    img = resizeimage.resize_cover(img, [224, 224], validate=False)
+    img_ycbcr = img.convert('YCbCr')
+    img_y_0, img_cb, img_cr = img_ycbcr.split()
+    img_ndarray = np.asarray(img_y_0)
+    img_4 = np.expand_dims(np.expand_dims(img_ndarray, axis=0), axis=0)
+    img_5 = img_4.astype(np.float32) / 255.0
+    return img_5, img_cb, img_cr
+
+
+def get_image():
+    # download image
+    image_url = 'https://s3.amazonaws.com/model-server/inputs/kitten.jpg'
+    img = Image.open(check_exist_or_download(image_url))
+    return img
+
+
+class MyModel(sonnx.SONNXModel):
+
+    def __init__(self, onnx_model):
+        super(MyModel, self).__init__(onnx_model)
+
+    def forward(self, *x):
+        y = super(MyModel, self).forward(*x)
+        return y[0]
+
+    def train_one_batch(self, x, y):
+        pass
+
+
+if __name__ == "__main__":
+
+    url = 'https://github.com/onnx/models/raw/master/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.tar.gz'
+    download_dir = '/tmp/'
+    model_path = os.path.join(download_dir, 'super_resolution',
+                              'super_resolution.onnx')
+
+    logging.info("onnx load model...")
+    download_model(url)
+    onnx_model = onnx.load(model_path)
+
+    # preprocess
+    logging.info("preprocessing...")
+    img = get_image()
+    img_y, img_cb, img_cr = preprocess(img)
+    # sg_ir = sonnx.prepare(onnx_model) # run without graph
+    # y = sg_ir.run([img])
+
+    logging.info("model compling...")
+    dev = device.create_cuda_gpu()
+    x = tensor.PlaceHolder(img_y.shape, device=dev)
+    model = MyModel(onnx_model)
+    model.compile([x], is_train=False, use_graph=True, sequential=True)
+
+    # inference
+    logging.info("model running...")
+    x_batch = tensor.Tensor(device=dev, data=img_y)
+    img_y = model.forward(x_batch)
+    array_img_y = tensor.to_numpy(img_y)
+    img_out_y = Image.fromarray(np.uint8((array_img_y[0] * 255.0).clip(0,
+                                                                       255)[0]),
+                                mode='L')
+
+    # postprocess
+    logging.info("postprocessing...")
+    final_img = Image.merge("YCbCr", [
+        img_out_y,
+        img_cb.resize(img_out_y.size, Image.BICUBIC),
+        img_cr.resize(img_out_y.size, Image.BICUBIC),
+    ]).convert("RGB")
+    final_img.show()