Nested metrics dictionaries now can be passed to the loggers (#1582)

* now func merge_dicts works with nested dictionaries

* CHANGELOG.md upd

CHANGELOG.md

@@ -50,6 +50,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Added the possibility to pass nested metrics dictionaries to loggers ([#1582](https://github.com/PyTorchLightning/pytorch-lightning/pull/1582))
+
 - Fixed memory leak from opt return ([#1528](https://github.com/PyTorchLightning/pytorch-lightning/pull/1528))
 
 - Fixed saving checkpoint before deleting old ones ([#1453](https://github.com/PyTorchLightning/pytorch-lightning/pull/1453))

pytorch_lightning/loggers/base.py

@@ -280,6 +280,7 @@ class LoggerCollection(LightningLoggerBase):
     Args:
         logger_iterable: An iterable collection of loggers
     """
+
     def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
         super().__init__()
         self._logger_iterable = logger_iterable
@@ -347,20 +348,28 @@ def merge_dicts(
 
     Examples:
         >>> import pprint
-        >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1}
-        >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1}
-        >>> d3 = {'a': 1.1, 'v': 2.3}
+        >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}}
+        >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}}
+        >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}}
         >>> dflt_func = min
-        >>> agg_funcs = {'a': np.mean, 'v': max}
+        >>> agg_funcs = {'a': np.mean, 'v': max, 'd': {'d1': sum}}
         >>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func))
-        {'a': 1.3, 'b': 2.0, 'c': 1, 'v': 2.3}
+        {'a': 1.3,
+         'b': 2.0,
+         'c': 1,
+         'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}},
+         'v': 2.3}
     """
-
+    agg_key_funcs = agg_key_funcs or dict()
     keys = list(functools.reduce(operator.or_, [set(d.keys()) for d in dicts]))
     d_out = {}
     for k in keys:
-        fn = agg_key_funcs.get(k, default_func) if agg_key_funcs else default_func
-        agg_val = fn([v for v in [d_in.get(k) for d_in in dicts] if v is not None])
-        d_out[k] = agg_val
+        fn = agg_key_funcs.get(k)
+        values_to_agg = [v for v in [d_in.get(k) for d_in in dicts] if v is not None]
+
+        if isinstance(values_to_agg[0], dict):
+            d_out[k] = merge_dicts(values_to_agg, fn, default_func)
+        else:
+            d_out[k] = (fn or default_func)(values_to_agg)
 
     return d_out