[jacinto-ai/pytorch-jacinto-ai-devkit.git] / modules / pytorch_jacinto_ai / xvision / datasets / pixel2pixel / cityscapes_plus.py
diff --git a/modules/pytorch_jacinto_ai/xvision/datasets/pixel2pixel/cityscapes_plus.py b/modules/pytorch_jacinto_ai/xvision/datasets/pixel2pixel/cityscapes_plus.py
index 0eae1e7fd418a62a4a7642b05bc26a1313d198b7..dec7afe04866c849b523f98dcada6e06c18e334e 100755 (executable)
-'''
-Dataset loader for The Cityscapes dataset:
+#################################################################################
+# Copyright (c) 2018-2021, Texas Instruments Incorporated - http://www.ti.com
+# All Rights Reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################
+# Also includes parts from: https://github.com/pytorch/vision
+# License: License: https://github.com/pytorch/vision/blob/master/LICENSE
+
+# BSD 3-Clause License
+#
+# Copyright (c) Soumith Chintala 2016,
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# ==============================================================================
+
+# Some parts of the code are borrowed from: https://github.com/ansleliu/LightNet
+# with the following license:
+#
+# MIT License
+#
+# Copyright (c) 2018 Huijun Liu
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+Reference:
+
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele,
-“The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Proc. of the IEEE Conference on Computer
-Vision and Pattern Recognition (CVPR), 2016.
+“The Cityscapes Dataset for Semantic Urban Scene Understanding,”
+in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
https://www.cityscapes-dataset.com/
-'''
+"""
+
import os
import numpy as np
void_classes = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1]
valid_classes = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]
- class_names = ['unlabelled', 'road', 'sidewalk', 'building', 'wall', 'fence',
- 'pole', 'traffic_light', 'traffic_sign', 'vegetation', 'terrain',
- 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train',
- 'motorcycle', 'bicycle']
+ # class_names = ['unlabelled', 'road', 'sidewalk', 'building', 'wall', 'fence',
+ # 'pole', 'traffic_light', 'traffic_sign', 'vegetation', 'terrain',
+ # 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train',
+ # 'motorcycle', 'bicycle']
ignore_index = 255
class_map = dict(zip(valid_classes, range(19)))
label_colours = dict(zip(range(5), colors))
void_classes = [-1, 255]
valid_classes = [0, 1, 2, 3, 4]
- class_names = ['road', 'sky', 'pedestrian', 'vehicle', 'background']
+ # class_names = ['road', 'sky', 'pedestrian', 'vehicle', 'background']
ignore_index = 255
class_map = dict(zip(valid_classes, range(5)))
void_classes = []
valid_classes = [0, 255]
- class_names = ['static', 'moving']
+ # class_names = ['static', 'moving']
ignore_index = 255
class_map = dict(zip(valid_classes, range(2)))
num_classes_ = 2