update dpmd test

tests/dpmdargs.py

@@ -316,7 +316,7 @@ def model_args ():
     return ca
 
 
-def learning_rate_args():
+def learning_rate_exp():
     doc_start_lr = 'The learning rate the start of the training.'
     doc_stop_lr = 'The desired learning rate at the end of the training.'
     doc_decay_steps = 'The learning rate is decaying every this number of training steps.'
@@ -326,9 +326,24 @@ def learning_rate_args():
         Argument("stop_lr", float, optional = True, default = 1e-8, doc = doc_stop_lr),
         Argument("decay_steps", int, optional = True, default = 5000, doc = doc_decay_steps)
     ]
+    return args
+
+
+def learning_rate_variant_type_args():
+    doc_lr = 'The type of the learning rate. Current type `exp`, the exponentially decaying learning rate is supported.'
+
+    return Variant("type", 
+                   [Argument("exp", dict, learning_rate_exp())],
+                   optional = True,
+                   default_tag = 'exp',
+                   doc = doc_lr)
+
 
-    doc_lr = "The learning rate options" 
-    return Argument("learning_rate", dict, args, [], doc = doc_lr)
+def learning_rate_args():
+    doc_lr = "The definitio of learning rate" 
+    return Argument("learning_rate", dict, [], 
+                    [learning_rate_variant_type_args()],
+                    doc = doc_lr)
 
 
 def start_pref(item):
@@ -378,15 +393,16 @@ def loss_args():
     return ca
 
 def training_args():
+    link_sys = make_link("systems", "training/systems")
     doc_systems = 'The data systems. This key can be provided with a listthat specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.'
-    doc_set_prefix = 'The prefix of the sets in the systems.'
+    doc_set_prefix = f'The prefix of the sets in the {link_sys}.'
     doc_stop_batch = 'Number of training batch. Each training uses one batch of data.'
-    doc_batch_size = 'This key can be \n\n\
-- list: the length of which is the same as the `systems`. The batch size of each system is given by the elements of the list.\n\n\
-- int: all `systems` uses the same batch size.\n\n\
-- string "auto": automatically determines the batch size os that the batch_size times the number of atoms in the system is no less than 32.\n\n\
-- string "auto:N": automatically determines the batch size os that the batch_size times the number of atoms in the system is no less than N.'
-    doc_seed = 'The random seed for training.'
+    doc_batch_size = f'This key can be \n\n\
+- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\
+- int: all {link_sys} use the same batch size.\n\n\
+- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\
+- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.'
+    doc_seed = 'The random seed for getting frames from the training data set.'
     doc_disp_file = 'The file for printing learning curve.'
     doc_disp_freq = 'The frequency of printing learning curve.'
     doc_numb_test = 'Number of frames used for the test during training.'
@@ -396,12 +412,21 @@ def training_args():
     doc_time_training = 'Timing durining training.'
     doc_profiling = 'Profiling during training.'
     doc_profiling_file = 'Output file for profiling.'
+    doc_train_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\
+- "prob_uniform"  : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\
+- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\
+- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.'
+    doc_train_sys_probs = "A list of float, should be of the same length as `train_systems`, specifying the probability of each system."
+    doc_tensorboard = 'Enable tensorboard'
+    doc_tensorboard_log_dir = 'The log directory of tensorboard outputs'
 
     args = [
-        Argument("systems", [list,str], optional = False, doc = doc_systems),
+        Argument("systems", [list,str], optional = False, doc = doc_systems, alias = ["trn_systems"]),
         Argument("set_prefix", str, optional = True, default = 'set', doc = doc_set_prefix),
-        Argument("stop_batch", int, optional = False, doc = doc_stop_batch),
-        Argument("batch_size", [list,int,str], optional = True, default = 'auto', doc = doc_batch_size),
+        Argument("auto_prob", str, optional = True, default = "prob_sys_size", doc = doc_train_auto_prob_style, alias = ["trn_auto_prob", "auto_prob_style"]),
+        Argument("sys_probs", list, optional = True, default = None, doc = doc_train_sys_probs, alias = ["trn_sys_probs"]),
+        Argument("batch_size", [list,int,str], optional = True, default = 'auto', doc = doc_batch_size, alias = ["trn_batch_size"]),
+        Argument("numb_steps", int, optional = False, doc = doc_stop_batch, alias = ["stop_batch"]),
         Argument("seed", [int,None], optional = True, doc = doc_seed),
         Argument("disp_file", str, optional = True, default = 'lcueve.out', doc = doc_disp_file),
         Argument("disp_freq", int, optional = True, default = 1000, doc = doc_disp_freq),
@@ -411,7 +436,9 @@ def training_args():
         Argument("disp_training", bool, optional = True, default = True, doc = doc_disp_training),
         Argument("time_training", bool, optional = True, default = True, doc = doc_time_training),
         Argument("profiling", bool, optional = True, default = False, doc = doc_profiling),
-        Argument("profiling_file", str, optional = True, default = 'timeline.json', doc = doc_profiling_file)
+        Argument("profiling_file", str, optional = True, default = 'timeline.json', doc = doc_profiling_file),
+        Argument("tensorboard", bool, optional = True, default = False, doc = doc_tensorboard),
+        Argument("tensorboard_log_dir", str, optional = True, default = 'log', doc = doc_tensorboard_log_dir),
     ]
 
     doc_training = 'The training options'
@@ -493,14 +520,14 @@ def normalize(data):
     },
 
     "learning_rate" :{
-        "_type":		"exp",
+        "type":		"exp",
         "decay_steps":	5000,
         "start_lr":	0.001,	
         "stop_lr":	3.51e-8,
         "_comment":	"that's all"
-    },
+        },
 
-    "loss" :{
+        "loss" :{
         "start_pref_e":	0.02,
         "limit_pref_e":	1,
         "start_pref_f":	1000,
@@ -526,11 +553,11 @@ def normalize(data):
         "numb_test":	10,
         "save_freq":	1000,
         "save_ckpt":	"model.ckpt",
-        "_load_ckpt":	"model.ckpt",
+        "load_ckpt":	"model.ckpt",
         "disp_training":true,
         "time_training":true,
-        "_tensorboard":	false,
-        "_tensorboard_log_dir":"log",
+        "tensorboard":	false,
+        "tensorboard_log_dir":"log",
         "profiling":	false,
         "profiling_file":"timeline.json",
         "_comment":	"that's all"

tests/test_checker.py

@@ -258,13 +258,6 @@ def test_sub_variants(self):
                     Argument("type2", dict)])
             ])
 
-    def test_dpmd(self):
-        import json
-        from dpmdargs import check, example_json_str
-        data = json.loads(example_json_str)
-        check(data)
-        # print("\n\n"+docstr)
-
 
 if __name__ == "__main__":
     unittest.main()