checked all steps

README.md

@@ -23,13 +23,14 @@ cd ../..
 # set-up TF enviroment and compile cpp
 conda create -n OctSurf_env tensorflow-gpu==1.14.0
 conda activate OctSurf_env
+conda install -c conda-forge openbabel
 conda install -c conda-forge yacs tqdm
 pip install sklearn
 pip install matplotlib
 pip install seaborn
-# uncomment if want to visualize
+# uncomment if want to visualize in Paraview
 # pip install vtk
-conda install -c conda-forge openbabel
+
 
 cd octree/external && git clone --recursive https://github.com/wang-ps/octree-ext.git
 cd .. && mkdir build && cd build
@@ -53,7 +54,7 @@ cd octree_folder
 ls -lh
 cd ../../../../
 
-# parse by python
+# parse by python, and visualization(optional)
 cd python
 python octree_parse.py
 cd ../

pdbbind/python/Elements.java

@@ -1,3 +1,8 @@
+/*
+From CDK
+For Atom Radius Reference
+*/
+
 /* 
  * Copyright (C) 2006-2012  Egon Willighagen <egonw@users.sf.net>
  *                    2014  Mark B Vine (orcid:0000-0002-7794-0426)

pdbbind/python/clean_index_error.py

@@ -5,6 +5,8 @@ def clean_index_for_file(folder):
     contents = folder.split('/')
     id = contents[-1]
     # ligand_file = folder + '/{}_ligand.mol2'.format(id)
+    if id == 'index' or id == 'readme':
+        return None
     file = folder + '/{}_pocket.pdb'.format(id)
 
     rewrite = False

pdbbind/python/generate_pdb_list.py

@@ -225,4 +225,4 @@ def write_points_list(general_dic, core_folders, refined_folders, mode = 'reg',
     # check_dic_id(general_dic, general_folders, refined_folders, core_folders)
 
     # write_octree_list(general_dic, core_folders, refined_folders, depth = 5, mode  = mode, view_num = 24)
-    write_points_list(general_dic, core_folders, refined_folders, mode=mode, density = 3)
+    write_points_list(general_dic, core_folders, refined_folders, mode=mode, density = None)

pdbbind/python/utils.py

@@ -1,4 +1,4 @@
-# utils for adaptive_octree_opt.py.
+# utils for visualization
 
 
 import numpy as np

tensorflow/script/configs/test_resnet_depth6.yaml

@@ -0,0 +1,34 @@
+SOLVER:
+  gpu: 0,
+  run: test
+  ckpt: ./logs/pdbbind/resnet_points_reg_6/model/iter_188000.ckpt
+  test_iter: 11400 
+  task: reg
+
+DATA:
+  test:
+    dtype: points
+    distort: True
+    depth: 6
+    full_depth: 2
+    axis: xyz
+    angle: (180, 180, 180)
+    interval: (1, 1, 1)
+    scale: 0.0
+    jitter: 0.125
+    location: ../../pdbbind/data_folder/tfrecords/train_reg_points_den3.tfrecords
+    shuffle: 1
+    batch_size: 1
+    x_alias: data
+
+MODEL:
+  name: resnet  #resnet or vgg
+  channel: 24
+  nout: 1
+  depth: 6
+  resblock_num: 3
+  dropout: (0.0, )
+
+LOSS:
+  num_class: 1
+  weight_decay: 0.001

tensorflow/script/configs/train_resnet_depth6.yaml

@@ -41,7 +41,7 @@ DATA:
     x_alias: data
 
 MODEL:
-  name: resnet #ocnn or resnet
+  name: resnet #vgg or resnet
   channel: 24
   nout: 1
   depth: 6

tensorflow/script/network_factory.py

@@ -1,11 +1,8 @@
 import tensorflow as tf
 from network_cls import network_ocnn, network_resnet, network_cnn_grids, network_resnet_grids
-from network_unet import network_unet
-from network_hrnet import HRNet
-# from network_unet_scannet import network_unet34
 
 def cls_network(octree, flags, training, reuse=False):
-  if flags.name.lower() == 'ocnn':
+  if flags.name.lower() == 'vgg':
     return network_ocnn(octree, flags, training, reuse)
   elif flags.name.lower() == 'resnet':
     return network_resnet(octree, flags, training, reuse)