add timers and fix filehandle leak (#2989)

ml-agents-envs/mlagents/envs/brain.py

         return s
 
     @staticmethod
+    @timed
     def from_agent_proto(
         worker_id: int,
         agent_info_list: List[AgentInfoProto],

ml-agents/mlagents/trainers/demo_loader.py

 from mlagents.envs.communicator_objects.demonstration_meta_pb2 import (
     DemonstrationMetaProto,
 )
+from mlagents.envs.timers import timed, hierarchical_timer
 from google.protobuf.internal.decoder import _DecodeVarint32  # type: ignore
 
 
+@timed
 def make_demo_buffer(
     pair_infos: List[AgentInfoActionPairProto],
     brain_params: BrainParameters,
     return demo_buffer
 
 
+@timed
 def demo_to_buffer(
     file_path: str, sequence_length: int
 ) -> Tuple[BrainParameters, Buffer]:
     return brain_params, demo_buffer
 
 
+@timed
 def load_demonstration(
     file_path: str
 ) -> Tuple[BrainParameters, List[AgentInfoActionPairProto], int]:
     info_action_pairs = []
     total_expected = 0
     for _file_path in file_paths:
-        data = open(_file_path, "rb").read()
-        next_pos, pos, obs_decoded = 0, 0, 0
-        while pos < len(data):
-            next_pos, pos = _DecodeVarint32(data, pos)
-            if obs_decoded == 0:
-                meta_data_proto = DemonstrationMetaProto()
-                meta_data_proto.ParseFromString(data[pos : pos + next_pos])
-                total_expected += meta_data_proto.number_steps
-                pos = INITIAL_POS
-            if obs_decoded == 1:
-                brain_param_proto = BrainParametersProto()
-                brain_param_proto.ParseFromString(data[pos : pos + next_pos])
-                pos += next_pos
-            if obs_decoded > 1:
-                agent_info_action = AgentInfoActionPairProto()
-                agent_info_action.ParseFromString(data[pos : pos + next_pos])
-                if brain_params is None:
-                    brain_params = BrainParameters.from_proto(
-                        brain_param_proto, agent_info_action.agent_info
-                    )
-                info_action_pairs.append(agent_info_action)
-                if len(info_action_pairs) == total_expected:
-                    break
-                pos += next_pos
-            obs_decoded += 1
+        with open(_file_path, "rb") as fp:
+            with hierarchical_timer("read_file"):
+                data = fp.read()
+            next_pos, pos, obs_decoded = 0, 0, 0
+            while pos < len(data):
+                next_pos, pos = _DecodeVarint32(data, pos)
+                if obs_decoded == 0:
+                    meta_data_proto = DemonstrationMetaProto()
+                    meta_data_proto.ParseFromString(data[pos : pos + next_pos])
+                    total_expected += meta_data_proto.number_steps
+                    pos = INITIAL_POS
+                if obs_decoded == 1:
+                    brain_param_proto = BrainParametersProto()
+                    brain_param_proto.ParseFromString(data[pos : pos + next_pos])
+                    pos += next_pos
+                if obs_decoded > 1:
+                    agent_info_action = AgentInfoActionPairProto()
+                    agent_info_action.ParseFromString(data[pos : pos + next_pos])
+                    if brain_params is None:
+                        brain_params = BrainParameters.from_proto(
+                            brain_param_proto, agent_info_action.agent_info
+                        )
+                    info_action_pairs.append(agent_info_action)
+                    if len(info_action_pairs) == total_expected:
+                        break
+                    pos += next_pos
+                obs_decoded += 1
     return brain_params, info_action_pairs, total_expected