From cd3bd49d186e6c3e435ccfbc7e3deea233a957a3 Mon Sep 17 00:00:00 2001
From: samuelbroscheit <samuel.broscheit@gmail.com>
Date: Sat, 16 May 2020 23:30:48 +0200
Subject: [PATCH] Changes from master

---
 kge/job/eval.py  | 14 +++++---------
 kge/job/train.py | 11 ++---------
 2 files changed, 7 insertions(+), 18 deletions(-)

diff --git a/kge/job/eval.py b/kge/job/eval.py
index 0787bebd7..491f4fb12 100644
--- a/kge/job/eval.py
+++ b/kge/job/eval.py
@@ -1,14 +1,10 @@
 import time
-from typing import Any, Dict
+from typing import Any, Optional, Dict
 
 import torch
 from kge import Config, Dataset
 
-from kge import Config, Dataset
-from kge.job import Job
-from kge.model import KgeModel
-
-from typing import Dict, Union, Optional
+from kge.job import Job, TrainingJob
 
 
 class EvaluationJob(Job):
@@ -213,6 +209,7 @@ def __init__(self, config: Config, dataset: Dataset, parent_job, model):
 
         train_job_on_eval_split_config = config.clone()
         train_job_on_eval_split_config.set("train.split", self.eval_split)
+        train_job_on_eval_split_config.set("verbose", False)
         train_job_on_eval_split_config.set(
             "negative_sampling.filtering.splits",
             [self.config.get("train.split"), self.eval_split] + ["valid"]
@@ -229,9 +226,9 @@ def __init__(self, config: Config, dataset: Dataset, parent_job, model):
             config=train_job_on_eval_split_config,
             parent_job=self,
             dataset=dataset,
+            model=model,
             initialize_for_forward_only=True,
         )
-        self._train_job.model = model
         self._train_job_verbose = False
 
         if self.__class__ == TrainingLossEvaluationJob:
@@ -246,7 +243,7 @@ def _run(self) -> Dict[str, Any]:
         self.epoch = self.parent_job.epoch
         epoch_time += time.time()
 
-        train_trace_entry = self._train_job.run_epoch(verbose=self._train_job_verbose)
+        train_trace_entry = self._train_job.run_epoch()
         # compute trace
         trace_entry = dict(
             type="training_loss",
@@ -263,7 +260,6 @@ def _run(self) -> Dict[str, Any]:
 
 # HISTOGRAM COMPUTATION ###############################################################
 
-
 def __initialize_hist(hists, key, job):
     """If there is no histogram with given `key` in `hists`, add an empty one."""
     if key not in hists:
diff --git a/kge/job/train.py b/kge/job/train.py
index fa21ca942..6a0532b7a 100644
--- a/kge/job/train.py
+++ b/kge/job/train.py
@@ -1,8 +1,6 @@
-import itertools
 import os
 import math
 import time
-import sys
 from collections import defaultdict
 
 from dataclasses import dataclass
@@ -17,7 +15,7 @@
 from kge.model import KgeModel
 
 from kge.util import KgeLoss, KgeOptimizer, KgeSampler, KgeLRScheduler
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional
 import kge.job.util
 
 SLOTS = [0, 1, 2]
@@ -67,8 +65,6 @@ def __init__(
             self.model: KgeModel = KgeModel.create(config, dataset)
         else:
             self.model: KgeModel = model
-        self.optimizer = KgeOptimizer.create(config, self.model)
-        self.kge_lr_scheduler = KgeLRScheduler(config, self.optimizer)
         self.loss = KgeLoss.create(config)
         self.abort_on_nan: bool = config.get("train.abort_on_nan")
         self.batch_size: int = config.get("train.batch_size")
@@ -130,8 +126,6 @@ def __init__(
             for f in Job.job_created_hooks:
                 f(self)
 
-        self.model.train()
-
     @staticmethod
     def create(
         config: Config,
@@ -823,8 +817,7 @@ def _process_batch(self, batch_index, batch) -> TrainingJob._ProcessBatchResult:
 
 class TrainingJobNegativeSampling(TrainingJob):
     def __init__(
-            self, config, dataset, parent_job=None, model=None, initialize_for_forward_only=False
-    ):
+            self, config, dataset, parent_job=None, model=None, initialize_for_forward_only=False):
         super().__init__(config, dataset, parent_job, model, initialize_for_forward_only)
         self._sampler = KgeSampler.create(config, "negative_sampling", dataset)
         self._implementation = self.config.check(