Merge pull request #258 from deepmodeling/r1.2

merge R1.2 bug fixings to master

Author: amcadmus

setup.py

@@ -4,17 +4,16 @@
 from setuptools_scm import get_version
 from packaging.version import LegacyVersion
 from os import path, makedirs
-import imp, sys, platform
+import os, imp, sys, platform, sysconfig
 
 def get_dp_install_path() :
-    site_packages_path = path.join(path.dirname(path.__file__), 'site-packages')
-    dp_scm_version     = get_version(root="./", relative_to=__file__)
+    site_packages_path = sysconfig.get_paths()['purelib']
+    dp_scm_version     = get_version(root=".", relative_to=__file__)
     python_version     = 'py' + str(sys.version_info.major + sys.version_info.minor * 0.1)
     os_info            = sys.platform
     machine_info       = platform.machine()
-    dp_pip_install_path    = site_packages_path + '/deepmd'
-    dp_setup_install_path    = site_packages_path + '/deepmd_kit-' + dp_scm_version + '-' + python_version + '-' + os_info + '-' + machine_info + '.egg/deepmd'
-    
+    dp_pip_install_path         = os.path.join(site_packages_path, 'deepmd')
+    dp_setup_install_path       = os.path.join(site_packages_path, 'deepmd_kit-' + dp_scm_version + '-' + python_version + '-' + os_info + '-' + machine_info + '.egg', 'deepmd')
     return dp_pip_install_path, dp_setup_install_path
 
 readme_file = path.join(path.dirname(path.abspath(__file__)), 'README.md')

source/train/Loss.py

@@ -301,11 +301,12 @@ def build (self,
         polar_hat = label_dict[self.label_name]
         polar = model_dict[self.tensor_name]
         l2_loss = tf.reduce_mean( tf.square(self.scale*(polar - polar_hat)), name='l2_'+suffix)
+        more_loss = {'nonorm': l2_loss}
         if not self.atomic :
             atom_norm  = 1./ global_cvt_2_tf_float(natoms[0]) 
             l2_loss = l2_loss * atom_norm
         self.l2_l = l2_loss
-        more_loss = {}
+        self.l2_more = more_loss['nonorm']
 
         return l2_loss, more_loss
 
@@ -321,10 +322,10 @@ def print_on_training(self,
                           feed_dict_test,
                           feed_dict_batch) :
         error_test\
-            = sess.run([self.l2_l], \
+            = sess.run([self.l2_more], \
                        feed_dict=feed_dict_test)
         error_train\
-            = sess.run([self.l2_l], \
+            = sess.run([self.l2_more], \
                        feed_dict=feed_dict_batch)
         print_str = ""
         prop_fmt = "   %9.2e %9.2e"

source/train/test.py

@@ -12,6 +12,7 @@
 from deepmd import DeepPot
 from deepmd import DeepDipole
 from deepmd import DeepPolar
+from deepmd import DeepGlobalPolar
 from deepmd import DeepWFC
 from tensorflow.python.framework import ops
 
@@ -28,6 +29,8 @@ def test (args):
         dp = DeepDipole(args.model)    
     elif de.model_type == 'polar':
         dp = DeepPolar(args.model)    
+    elif de.model_type == 'global_polar':
+        dp = DeepGlobalPolar(args.model)    
     elif de.model_type == 'wfc':
         dp = DeepWFC(args.model)    
     else :
@@ -41,7 +44,9 @@ def test (args):
         elif de.model_type == 'dipole':
             err, siz = test_dipole(dp, args)
         elif de.model_type == 'polar':
-            err, siz = test_polar(dp, args)
+            err, siz = test_polar(dp, args, global_polar=False)
+        elif de.model_type == 'global_polar':
+            err, siz = test_polar(dp, args, global_polar=True)
         elif de.model_type == 'wfc':
             err, siz = test_wfc(dp, args)
         else :
@@ -61,6 +66,8 @@ def test (args):
             print_dipole_sys_avg(avg_err)
         elif de.model_type == 'polar':
             print_polar_sys_avg(avg_err)
+        elif de.model_type == 'global_polar':
+            print_polar_sys_avg(avg_err)
         elif de.model_type == 'wfc':
             print_wfc_sys_avg(avg_err)
         else :
@@ -223,12 +230,15 @@ def print_wfc_sys_avg(avg):
     print ("WFC  L2err : %e eV/A" % avg[0])
 
 
-def test_polar (dp, args) :
+def test_polar (dp, args, global_polar = False) :
     if args.rand_seed is not None :
         np.random.seed(args.rand_seed % (2**32))
 
     data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
-    data.add('polarizability', 9, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
+    if not global_polar:
+        data.add('polarizability', 9, atomic=True,  must=True, high_prec=False, type_sel = dp.get_sel_type())
+    else:
+        data.add('polarizability', 9, atomic=False, must=True, high_prec=False, type_sel = dp.get_sel_type())
     test_data = data.get_test ()
     numb_test = args.numb_test
     natoms = len(test_data["type"][0])
@@ -239,12 +249,21 @@ def test_polar (dp, args) :
     box = test_data["box"][:numb_test]
     atype = test_data["type"][0]
     polar = dp.eval(coord, box, atype)
+    sel_type = dp.get_sel_type()
+    sel_natoms = 0
+    for ii in sel_type:
+        sel_natoms += sum(atype == ii)
 
     polar = polar.reshape([numb_test,-1])
     l2f = (l2err (polar  - test_data["polarizability"] [:numb_test]))
+    l2fs = l2f/np.sqrt(sel_natoms)
+    l2fa = l2f/sel_natoms
 
     print ("# number of test data : %d " % numb_test)
-    print ("Polarizability  L2err : %e eV/A" % l2f)
+    print ("Polarizability  L2err       : %e eV/A" % l2f)
+    if global_polar:
+        print ("Polarizability  L2err/sqrtN : %e eV/A" % l2fs)
+        print ("Polarizability  L2err/N     : %e eV/A" % l2fa)
 
     detail_file = args.detail_file
     if detail_file is not None :