Merge pull request #820 from apache/master
Update dev branch from master
diff --git a/cmake/Thirdparty/FindGlog.cmake b/cmake/Thirdparty/FindGlog.cmake
index e18c602..35caefd 100644
--- a/cmake/Thirdparty/FindGlog.cmake
+++ b/cmake/Thirdparty/FindGlog.cmake
@@ -21,7 +21,7 @@
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}")
diff --git a/examples/onnx/159008.jpg b/examples/onnx/159008.jpg
new file mode 100644
index 0000000..2b90845
--- /dev/null
+++ b/examples/onnx/159008.jpg
Binary files differ
diff --git a/examples/onnx/superresolution.py b/examples/onnx/superresolution.py
new file mode 100644
index 0000000..caaae19
--- /dev/null
+++ b/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()
