fix training error

tensorflow/script/config.py

@@ -12,10 +12,10 @@
 _C.SOLVER.logdir          = 'logs'    # Directory where to write event logs
 _C.SOLVER.ckpt            = ''        # Restore weights from checkpoint file
 _C.SOLVER.run             = 'train'   # Choose from train or test
-_C.SOLVER.type            = 'adam'     # Choose from sgd or adam
+_C.SOLVER.type            = 'sgd'     # Choose from sgd or adam
 _C.SOLVER.max_iter        = 160000    # Maximum training iterations
 _C.SOLVER.test_iter       = 100       # Test steps in testing phase
-_C.SOLVER.test_every_iter = 4000      # Test model every n training steps
+_C.SOLVER.test_every_iter = 1000      # Test model every n training steps
 _C.SOLVER.lr_type         = 'step'    # Learning rate type: step or cos
 _C.SOLVER.learning_rate   = 0.01       # Initial learning rate
 _C.SOLVER.gamma           = 0.1       # Learning rate step-wise decay
@@ -24,11 +24,12 @@
 _C.SOLVER.var_name        = ('_name',)# Variable names used for finetuning
 _C.SOLVER.ignore_var_name = ('_name',)# Ignore variable names when loading ckpt
 _C.SOLVER.verbose         = False     # Whether to output some messages
-_C.SOLVER.task            = 'reg'
+_C.SOLVER.task            = 'class'
 
 
 # DATA related parameters
 _C.DATA = CN()
+_C.DATA.name = 'CASF'
 _C.DATA.train = CN()
 _C.DATA.train.dtype      = 'points'   # The data type: points or octree
 _C.DATA.train.x_alias    = 'data'     # The alias of the data
@@ -70,7 +71,7 @@
 _C.MODEL = CN()
 _C.MODEL.name            = ''         # The name of the model
 _C.MODEL.depth           = 5          # The input octree depth
-_C.MODEL.depth_out       = 2          # The output feature depth
+_C.MODEL.depth_out       = 5          # The output feature depth
 _C.MODEL.channel         = 3          # The input feature channel
 _C.MODEL.factor          = 1          # The factor used to widen the network
 _C.MODEL.nout            = 40         # The output feature channel

tensorflow/script/dataset.py

@@ -199,9 +199,9 @@ def merge_octrees(octrees, labels):
 
       dataset = tf.data.TFRecordDataset(record_names).take(take).repeat()
       if shuffle_size > 1: dataset = dataset.shuffle(shuffle_size)
-      itr = dataset.map(self.parse_example, num_parallel_calls=8) \
-                   .batch(batch_size).map(merge_octrees, num_parallel_calls=8) \
-                   .prefetch(8).make_one_shot_iterator() 
+      itr = dataset.map(self.parse_example, num_parallel_calls=36) \
+                   .batch(batch_size).map(merge_octrees, num_parallel_calls=36) \
+                   .prefetch(36).make_one_shot_iterator() 
     return itr if return_iter else itr.get_next()
 
 class GridDataset:

tensorflow/script/network_cls.py

@@ -5,7 +5,7 @@
 # octree-based resnet55
 def network_resnet(octree, flags, training=True, reuse=None):
   depth = flags.depth
-  channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8]
+  channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8, 8, 8]
   with tf.variable_scope("ocnn_resnet", reuse=reuse):
     data = octree_property(octree, property_name="feature", dtype=tf.float32, 
                            depth=depth, channel=flags.channel)
@@ -65,7 +65,7 @@ def network_resnet_grids(grids, flags, training=True, reuse=None):
 # the ocnn in the paper
 def network_ocnn(octree, flags, training=True, reuse=None):
   depth = flags.depth
-  channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8] #[512, 256, 128, 64, 32, 16, 8, 4, 2] #[2048, 1024, 512, 256, 128, 64, 32, 32, 32]
+  channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8, 8, 8] #[512, 256, 128, 64, 32, 16, 8, 4, 2] #[2048, 1024, 512, 256, 128, 64, 32, 32, 32]
   with tf.variable_scope("ocnn", reuse=reuse):
     data = octree_property(octree, property_name="feature", dtype=tf.float32,
                            depth=depth, channel=flags.channel)

tensorflow/script/network_factory.py

@@ -1,14 +1,31 @@
 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() == 'vgg':
+  if flags.name.lower() == 'ocnn':
     return network_ocnn(octree, flags, training, reuse)
   elif flags.name.lower() == 'resnet':
     return network_resnet(octree, flags, training, reuse)
+  elif flags.name.lower() == 'hrnet':
+    return HRNet(flags).network_cls(octree, training, reuse)
   elif flags.name.lower() == 'cnn_grids':
     return network_cnn_grids(octree, flags, training, reuse)
   elif flags.name.lower() == 'resnet_grids':
     return network_resnet_grids(octree, flags, training, reuse)
   else:
     print('Error, no network: ' + flags.name)
+
+def seg_network(octree, flags, training, reuse=False, pts=None, mask=None):
+  if flags.name.lower() == 'unet':
+    return network_unet(octree, flags, training, reuse)
+  elif flags.name.lower() == 'hrnet':
+    return HRNet(flags).network_seg(octree, training, reuse, pts, mask)
+  # elif flags.name.lower() == 'unet_scannet':
+  #   return network_unet34(octree, flags, training, reuse, pts, mask)
+  else:
+    print('Error, no network: ' + flags.name)
+
+

tensorflow/script/test_reg_model.py

@@ -27,6 +27,9 @@ def check_input(dataset='test', training=False, reuse=False, task = 'class'):
 if __name__ == '__main__':
   # solver = TFSolver(FLAGS.SOLVER, check_input)
   # solver.check_grids()
-
+  #print(FLAGS.SOLVER)
   solver = TFSolver(FLAGS, get_output)
-  solver.test_ave()
+  test_size_dic = {'CASF': 285, 'general_2019': 1146, 'refined_2019': 394, 'decoy':1460, 'training_15241':15241, 'training_15235': 15235}
+  solver.test_ave(test_size=test_size_dic[FLAGS.DATA.name])
+
+  #solver.test_ave()

tensorflow/script/tfsolver.py

@@ -96,7 +96,7 @@ def train(self):
     self.build_train_graph()
 
     # qq: add
-    self.qq_set_update_after_k_round()
+    # self.qq_set_update_after_k_round()
 
     # checkpoint
     start_iter = 1
@@ -124,16 +124,16 @@ def train(self):
         # training
         # qq: revise the training, to update gradients after multiple iterations
         # first 2 lines are original code.
-        # summary, _ = sess.run([self.summ_train, self.train_op])
-        # summary_writer.add_summary(summary, i)
-        if i == 0:
-          sess.run(self.zero_ops)
-        if i % 10 !=0 or i ==0:
-          sess.run(self.accum_ops)
-        else:
-          sess.run(self.accum_ops)
-          sess.run(self.train_step)
-          sess.run(self.zero_ops)
+        summary, _ = sess.run([self.summ_train, self.train_op])
+        summary_writer.add_summary(summary, i)
+        #if i == 0:
+        #  sess.run(self.zero_ops)
+        #if i % 10 !=0 or i ==0:
+        #  sess.run(self.accum_ops)
+        #else:
+        #  sess.run(self.accum_ops)
+        #  sess.run(self.train_step)
+        #  sess.run(self.zero_ops)
         # qq: end revise
 
         # testing
@@ -205,7 +205,7 @@ def param_stats(self):
   def test(self):
     # build graph
     self.build_test_graph()
-    self.qq_set_update_after_k_round()
+    #self.qq_set_update_after_k_round()
 
     # checkpoint
     assert(self.flags.ckpt)   # the self.flags.ckpt should be provided
@@ -276,6 +276,7 @@ def test_ave(self, test_size = 285):
         iter_test_result = sess.run(outputs)
         test_logits.append(iter_test_result[0])
         test_labels.append(iter_test_result[1])
+       # print(iter_test_result[0], iter_test_result[1])
 
     all_preds = np.array(test_logits).reshape(test_size, -1)
     all_labels = np.array(test_labels).reshape(test_size, -1)
@@ -286,10 +287,20 @@ def test_ave(self, test_size = 285):
     all_labels_mean = all_labels.mean(axis=0)
     all_preds_mean = all_preds.mean(axis=0)
 
+    #all_labels = all_labels.reshape(test_size,-1)
+    #all_preds = all_preds.reshape(test_size, -1)
+    #all_labels_mean = all_labels.mean(axis=1)
+    #all_preds_mean = all_preds.mean(axis=1)
     # if abs(all_labels.std(axis=0).sum()) < 1e-4:
     #   print(all_labels.std(axis=0))
     #   print(all_labels)
 
+    print(all_labels_mean)
+    #print(all_preds_mean)
+    import pandas as pd
+    df = pd.DataFrame({'label': all_labels_mean, 'pred': all_preds_mean})
+    df.to_csv('pred_label.csv')
+
     def report_reg_metrics(all_labels, all_preds):
       from scipy.stats import pearsonr, spearmanr, kendalltau
       from sklearn.metrics import roc_curve, auc, mean_squared_error, mean_absolute_error, r2_score
@@ -311,8 +322,86 @@ def report_scatter(all_labels, all_probs):
       sns.regplot(all_labels, all_probs)
       plt.show()
 
+    def report_cluster_corr(all_labels, all_probs):
+      import pandas as pd
+      clusters = pd.read_excel(r'./predicted clusters.xlsx', engine='openpyxl')
+      clusters = clusters[:285]
+      clusters.at[15, 'PDB code'] = "1E66"
+      clusters.at[171, 'PDB code'] = "3E92"
+      clusters.at[172, 'PDB code'] = "3E93"
+
+      with open(r'./points_list_test_reg.txt', "r") as fid:
+        pred_list = []
+        for line in fid.readlines():
+          pred_list.append(line.strip('\n'))
+
+      #pred_values = []
+      #for i in clusters["PDB code"]:  # loops through each protein for the respective PDB codes
+      #  for j,item in enumerate(pred_list):  # loops through each line of the prediction value text file
+      #    item = item.upper()  # changes each line to uppercase because the txt file PDB codes are lowercase and we need them in uppercase
+      #    if item[18:22] == i:  # j[18:22] is the PDB code for the pred value. This matches the PDB codes of the prediction and true values of proteins
+            # x = item[44:]  # j[44:] is the prediction value
+            # x = float(x)  # turns predicion value from string to float
+      #      x = all_probs[j]
+      #      pred_values.append(x)
+      #clusters["pred"] = pred_values  # adds a column in the cluster dataframe for predicted values
+      #print(clusters)
+
+      #corr = clusters.groupby('Cluster ID')[['Binding constant','pred']].corr().iloc[0::2,-1]
+      import matplotlib.pyplot as plt
+      import seaborn as sns
+      #plt.figure()
+      #sns.distplot(corr, kde=False)
+      #plt.xlabel('Correlation')
+      #plt.ylabel('Count')
+      #plt.savefig('./cluster.png')
+
+      #mean_df = clusters.groupby('Cluster ID').mean()
+      #plt.figure()
+      #sns.regplot(mean_df['Binding constant'], mean_df['pred'])
+      #plt.xlabel('Cluster Mean Label')
+      #plt.ylabel('Cluster Mean Pred')
+      #plt.savefig('./cluster_mean.png')
+      #print('Inter cluster corr: {}'.format(np.corrcoef(mean_df['Binding constant'], mean_df['pred'])[0,1]))
+
+      print("Double Verify")
+      cluster_list = []
+      id_list = []
+      clusters = clusters.set_index('PDB code')
+      for j, item in enumerate(pred_list):
+        item = item.upper()
+        id = item[18:22]
+        cluster_list.append(clusters.loc[id, 'Cluster ID'])
+        id_list.append(id)
+        print(id, all_labels[j], all_probs[j], clusters.loc[id, 'Binding constant'])        
+
+      new_df = pd.DataFrame({"pred": all_probs, "label": all_labels, "cluster": cluster_list, "id": id_list})
+      corr = new_df.groupby('cluster')[['label', 'pred']].corr().iloc[0::2, -1]
+      plt.figure()
+      sns.distplot(corr, kde=False)
+      plt.xlabel('Correlation')
+      plt.ylabel('Count')
+      plt.savefig('./cluster.png')
+      print('Corr: {}'.format(list(np.array(corr))))
+      #print(new_df)
+      new_df.to_csv('result.csv')
+
+      mean_df = new_df.groupby('cluster').mean()
+      plt.figure()
+      sns.regplot(mean_df['label'], mean_df['pred'])
+      plt.xlabel('Cluster Mean Label')
+      plt.ylabel('Cluster Mean Pred')
+      plt.savefig('./cluster_mean.png')
+      print('Inter cluster corr: {}'.format(np.corrcoef(mean_df['label'], mean_df['pred'])[0,1]))
+
+      print("<0: ", (corr<0).sum())
+      print(">0.8: ", (corr>=0.8).sum())
+      print(">0.9: ", (corr>=0.9).sum())
+      print('min; ', corr.min())
+
     report_reg_metrics(all_labels_mean, all_preds_mean)
     report_scatter(all_labels_mean, all_preds_mean)
+    report_cluster_corr(all_labels_mean, all_preds_mean)
 
   def check_grids(self, test_size = 285):
     # build graph
@@ -341,3 +430,4 @@ def check_grids(self, test_size = 285):
 
   def run(self):
     eval('self.{}()'.format(self.flags.run))
+