From 9d726ebb10e031ac75557dac8cd8c78baa193603 Mon Sep 17 00:00:00 2001
From: Jim Martens <github@2martens.de>
Date: Thu, 16 May 2019 14:07:20 +0200
Subject: [PATCH] Finished vanilla SSD evaluation code

Signed-off-by: Jim Martens <github@2martens.de>
---
 src/twomartens/masterthesis/main.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/src/twomartens/masterthesis/main.py b/src/twomartens/masterthesis/main.py
index 922efd7..4f0d582 100644
--- a/src/twomartens/masterthesis/main.py
+++ b/src/twomartens/masterthesis/main.py
@@ -174,7 +174,11 @@ def _ssd_test(args: argparse.Namespace) -> None:
     batch_size = 16
     use_dropout = False if args.network == "ssd" else True
     output_path = f"{args.output_path}/val/{args.network}/{args.iteration}"
+    evaluation_path = f"{args.evaluation_path}/{args.network}"
+    result_file = f"{evaluation_path}/results-{args.iteration}.bin"
     label_file = f"{output_path}/labels.bin"
+    predictions_file = f"{output_path}/predictions.bin"
+    predictions_per_class_file = f"{output_path}/predictions_class.bin"
     
     # retrieve labels and un-batch them
     files = glob.glob(f"{output_path}/*ssd_labels*")
@@ -205,9 +209,15 @@ def _ssd_test(args: argparse.Namespace) -> None:
     del _predictions
     
     # prepare predictions for further use
+    with open(predictions_file, "wb") as file:
+        pickle.dump(predictions, file)
+    
     predictions_per_class = evaluate.prepare_predictions(predictions, ssd.N_CLASSES)
     del predictions
     
+    with open(predictions_per_class_file, "wb") as file:
+        pickle.dump(predictions_per_class, file)
+    
     # compute matches between predictions and ground truth
     true_positives, false_positives, \
         cum_true_positives, cum_false_positives = evaluate.match_predictions(predictions_per_class,
@@ -221,7 +231,19 @@ def _ssd_test(args: argparse.Namespace) -> None:
     average_precisions = evaluate.get_mean_average_precisions(cum_precisions, cum_recalls, ssd.N_CLASSES)
     mean_average_precision = evaluate.get_mean_average_precision(average_precisions)
     
-    # TODO store result of evaluation
+    results = {
+        "true_positives": true_positives,
+        "false_positives": false_positives,
+        "cumulative_true_positives": cum_true_positives,
+        "cumulative_false_positives": cum_false_positives,
+        "cumulative_precisions": cum_precisions,
+        "cumulative_recalls": cum_recalls,
+        "average precisions": average_precisions,
+        "mean_average_precision": mean_average_precision
+    }
+    
+    with open(result_file, "wb") as file:
+        pickle.dump(results, file)
     
 
 def _val(args: argparse.Namespace) -> None: