Update DLA license, fix typos, and improve logs for FAQs

NOTICE

@@ -115,6 +115,40 @@ modification, are permitted provided that the following conditions are met:
    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.
+
+==============================================================================
+DLA licence
+==============================================================================
+
+BSD 3-Clause License
+
+Copyright (c) 2018, Fisher Yu
+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

readme/DATA.md

@@ -18,10 +18,9 @@ If you want to reproduce the results in the paper for benchmark evaluation and t
           |   |-- person_keypoints_train2017.json
           |   |-- person_keypoints_val2017.json
           |   |-- image_info_test-dev2017.json
-          `-- images
-              |-- train2017
-              |-- val2017
-              |-- test2017
+          |---|-- train2017
+          |---|-- val2017
+          `---|-- test2017
   ~~~
 
 - [Optional] If you want to train ExtremeNet, generate extreme point annotation from segmentation:

readme/INSTALL.md

@@ -64,7 +64,7 @@ After install Anaconda:
     cd $CenterNet_ROOT/src/lib/models/networks/DCNv2
     ./make.sh
     ~~~
-6. [Optional] Compile NMS if your want to use multi-scale testing or test ExtremeNet.
+6. [Optional, only required if you are using extremenet or multi-scale testing] Compile NMS if your want to use multi-scale testing or test ExtremeNet.
 
     ~~~
     cd $CenterNet_ROOT/src/lib/external

src/lib/detectors/ctdet.py

@@ -8,7 +8,11 @@ from progress.bar import Bar
 import time
 import torch
 
-from external.nms import soft_nms
+try:
+  from external.nms import soft_nms
+except:
+  print('NMS not imported! If you need it,'
+        ' do \n cd $CenterNet_ROOT/src/lib/external \n make')
 from models.decode import ctdet_decode
 from models.utils import flip_tensor
 from utils.image import get_affine_transform

src/lib/detectors/ddd.py

@@ -8,7 +8,7 @@ from progress.bar import Bar
 import time
 import torch
 
-from external.nms import soft_nms
+
 from models.decode import ddd_decode
 from models.utils import flip_tensor
 from utils.image import get_affine_transform

src/lib/detectors/exdet.py

@@ -12,7 +12,6 @@ from progress.bar import Bar
 import time
 import torch
 
-from external.nms import soft_nms
 from models.decode import exct_decode, agnex_ct_decode
 from models.utils import flip_tensor
 from utils.image import get_affine_transform, transform_preds

src/lib/detectors/multi_pose.py

@@ -8,7 +8,11 @@ from progress.bar import Bar
 import time
 import torch
 
-from external.nms import soft_nms_39
+try:
+  from external.nms import soft_nms_39
+except:
+  print('NMS not imported! If you need it,'
+        ' do \n cd $CenterNet_ROOT/src/lib/external \n make')
 from models.decode import multi_pose_decode
 from models.utils import flip_tensor, flip_lr_off, flip_lr
 from utils.image import get_affine_transform

src/lib/models/model.py

@@ -45,18 +45,22 @@ def load_model(model, model_path, optimizer=None, resume=False,
   model_state_dict = model.state_dict()
 
   # check loaded parameters and created model parameters
+  msg = 'If you see this, your model does not fully load the ' + \
+        'pre-trained weight. Please make sure ' + \
+        'you have correctly specified --arch xxx ' + \
+        'or set the correct --num_classes for your own dataset.'
   for k in state_dict:
     if k in model_state_dict:
       if state_dict[k].shape != model_state_dict[k].shape:
         print('Skip loading parameter {}, required shape{}, '\
-              'loaded shape{}.'.format(
-          k, model_state_dict[k].shape, state_dict[k].shape))
+              'loaded shape{}. {}'.format(
+          k, model_state_dict[k].shape, state_dict[k].shape) + msg)
         state_dict[k] = model_state_dict[k]
     else:
-      print('Drop parameter {}.'.format(k))
+      print('Drop parameter {}.'.format(k) + msg)
   for k in model_state_dict:
     if not (k in state_dict):
-      print('No param {}.'.format(k))
+      print('No param {}.'.format(k) + msg)
       state_dict[k] = model_state_dict[k]
   model.load_state_dict(state_dict, strict=False)