Add test for evaluation

.gitignore

@@ -110,4 +110,5 @@ __pycache__
 pretrained_models
 docs/build
 .vscode
-spleeter-feedstock/
+spleeter-feedstock/
+*FAKE_MUSDB_DIR

spleeter/commands/evaluate.py

@@ -163,3 +163,5 @@ def entrypoint(arguments, params):
         get_logger().info('%s:', instrument)
         for metric, value in metric.items():
             get_logger().info('%s: %s', metric, f'{np.median(value):.3f}')
+
+    return metrics

tests/test_eval.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+# coding: utf8
+
+""" Unit testing for Separator class. """
+
+__email__ = 'research@deezer.com'
+__author__ = 'Deezer Research'
+__license__ = 'MIT License'
+
+import filecmp
+import itertools
+from os import makedirs
+from os.path import splitext, basename, exists, join
+from tempfile import TemporaryDirectory
+
+import pytest
+import numpy as np
+
+import tensorflow as tf
+
+from spleeter.audio.adapter import get_default_audio_adapter
+from spleeter.commands import create_argument_parser
+
+from spleeter.commands import evaluate
+
+from spleeter.utils.configuration import load_configuration
+
+res_4stems = {  "vocals": {
+                    "SDR": -0.009,
+                    "SAR": -19.044,
+                    "SIR": -4.072,
+                    "ISR": -0.000
+                },
+                "drums": {
+                    "SDR": -0.066,
+                    "SAR": -14.294,
+                    "SIR": -4.908,
+                    "ISR": 0.002
+                },
+                "bass":{
+                    "SDR": -0.000,
+                    "SAR": -6.364,
+                    "SIR": -9.396,
+                    "ISR": -0.001
+                },
+                "other":{
+                    "SDR": -1.464,
+                    "SAR": -14.893,
+                    "SIR": -4.762,
+                    "ISR": -0.027
+                }
+            }
+
+
+def generate_fake_eval_dataset(path):
+    aa = get_default_audio_adapter()
+    n_songs = 2
+    fs = 44100
+    duration = 3
+    n_channels = 2
+    for song in range(n_songs):
+        song_path = join(path, "test", f"song{song}")
+        makedirs(song_path, exist_ok=True)
+        rng = np.random.RandomState(seed=0)
+        for instr in ["mixture", "vocals", "bass", "drums", "other"]:
+            filename = join(song_path, f"{instr}.wav")
+            data = rng.rand(duration*fs, n_channels)-0.5
+            aa.save(filename, data, fs)
+
+
+def test_evaluate(path="FAKE_MUSDB_DIR"):
+    generate_fake_eval_dataset(path)
+    p = create_argument_parser()
+    arguments = p.parse_args(["evaluate", "-p", "spleeter:4stems", "--mus_dir", path])
+    params = load_configuration(arguments.configuration)
+    metrics = evaluate.entrypoint(arguments, params)
+    for instrument, metric in metrics.items():
+        for metric, value in metric.items():
+            assert np.allclose(np.median(value), res_4stems[instrument][metric], atol=1e-3)