-
Notifications
You must be signed in to change notification settings - Fork 1
/
overtones_schubert_softdtw_W2.txt
140 lines (135 loc) · 11.1 KB
/
overtones_schubert_softdtw_W2.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
2022-09-02 09:02:12 | INFO : Logging experiment overtones_schubert_softdtw_mss
2022-09-02 09:02:12 | INFO : Experiment config: do training = False
2022-09-02 09:02:12 | INFO : Experiment config: do validation = False
2022-09-02 09:02:12 | INFO : Experiment config: do testing = True
2022-09-02 09:02:12 | INFO : Training set parameters: {'context': 75, 'seglength': 500, 'stride': 200, 'compression': 10}
2022-09-02 09:02:12 | INFO : Validation set parameters: {'context': 75, 'seglength': 500, 'stride': 500, 'compression': 10}
2022-09-02 09:02:12 | INFO : Test set parameters: {'context': 75, 'seglength': 500, 'stride': 500, 'compression': 10}
2022-09-02 09:02:12 | INFO : Test parameters: {'batch_size': 16, 'shuffle': False, 'num_workers': 8}
2022-09-02 09:02:12 | INFO : Save filewise results = True, in folder /home/[email protected]/Repos/multipitch_softdtw/experiments/results_filewise/overtones_schubert_softdtw_mss.csv
2022-09-02 09:02:12 | INFO : Save model predictions = True, in folder /home/[email protected]/Repos/multipitch_softdtw/predictions/overtones_schubert_softdtw_mss
2022-09-02 09:02:12 | INFO : CUDA use_cuda: True
2022-09-02 09:02:12 | INFO : CUDA device: cuda:0
2022-09-02 09:02:13 | INFO : --- Model config: --------------------------------------------
2022-09-02 09:02:13 | INFO : Model: basic_cnn_segm_sigmoid
2022-09-02 09:02:13 | INFO : Model parameters: {'n_chan_input': 6, 'n_chan_layers': [20, 20, 10, 1], 'n_bins_in': 216, 'n_bins_out': 72, 'a_lrelu': 0.3, 'p_dropout': 0.2}
2022-09-02 09:02:16 | INFO :
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
basic_cnn_segm_sigmoid [1, 1, 100, 72] --
├─LayerNorm: 1-1 [1, 174, 6, 216] 2,592
├─Sequential: 1-2 [1, 20, 174, 216] --
│ └─Conv2d: 2-1 [1, 20, 174, 216] 27,020
│ └─LeakyReLU: 2-2 [1, 20, 174, 216] --
│ └─MaxPool2d: 2-3 [1, 20, 174, 216] --
│ └─Dropout: 2-4 [1, 20, 174, 216] --
├─Sequential: 1-3 [1, 20, 174, 72] --
│ └─Conv2d: 2-5 [1, 20, 174, 72] 3,620
│ └─LeakyReLU: 2-6 [1, 20, 174, 72] --
│ └─MaxPool2d: 2-7 [1, 20, 174, 72] --
│ └─Dropout: 2-8 [1, 20, 174, 72] --
├─Sequential: 1-4 [1, 10, 100, 72] --
│ └─Conv2d: 2-9 [1, 10, 100, 72] 15,010
│ └─LeakyReLU: 2-10 [1, 10, 100, 72] --
│ └─Dropout: 2-11 [1, 10, 100, 72] --
├─Sequential: 1-5 [1, 1, 100, 72] --
│ └─Conv2d: 2-12 [1, 1, 100, 72] 11
│ └─LeakyReLU: 2-13 [1, 1, 100, 72] --
│ └─Dropout: 2-14 [1, 1, 100, 72] --
│ └─Conv2d: 2-15 [1, 1, 100, 72] 2
│ └─Sigmoid: 2-16 [1, 1, 100, 72] --
==========================================================================================
Total params: 48,255
Trainable params: 48,255
Non-trainable params: 0
Total mult-adds (G): 1.17
==========================================================================================
Input size (MB): 0.90
Forward/backward pass size (MB): 10.51
Params size (MB): 0.19
Estimated Total Size (MB): 11.61
==========================================================================================
2022-09-02 09:02:16 | INFO :
###################### START TESTING ######################
2022-09-02 09:02:16 | INFO : ### trained model loaded from /home/[email protected]/Repos/multipitch_softdtw/models/overtones_schubert_softdtw_mss.pt
2022-09-02 09:02:21 | INFO : file Schubert_D911-18_SC06.npy tested. Cosine sim: 0.6921997368307716
2022-09-02 09:02:22 | INFO : file Schubert_D911-22_SC06.npy tested. Cosine sim: 0.7871825016351666
2022-09-02 09:02:25 | INFO : file Schubert_D911-01_HU33.npy tested. Cosine sim: 0.8464748019690623
2022-09-02 09:02:26 | INFO : file Schubert_D911-03_HU33.npy tested. Cosine sim: 0.8117562654231811
2022-09-02 09:02:28 | INFO : file Schubert_D911-14_HU33.npy tested. Cosine sim: 0.8097481916844471
2022-09-02 09:02:31 | INFO : file Schubert_D911-01_SC06.npy tested. Cosine sim: 0.867031353201525
2022-09-02 09:02:33 | INFO : file Schubert_D911-24_SC06.npy tested. Cosine sim: 0.8558320699309546
2022-09-02 09:02:34 | INFO : file Schubert_D911-14_SC06.npy tested. Cosine sim: 0.8048732260438054
2022-09-02 09:02:35 | INFO : file Schubert_D911-02_SC06.npy tested. Cosine sim: 0.6469466947547912
2022-09-02 09:02:37 | INFO : file Schubert_D911-13_SC06.npy tested. Cosine sim: 0.8368302571280637
2022-09-02 09:02:39 | INFO : file Schubert_D911-11_HU33.npy tested. Cosine sim: 0.7422100475174066
2022-09-02 09:02:41 | INFO : file Schubert_D911-05_HU33.npy tested. Cosine sim: 0.7253389027630919
2022-09-02 09:02:43 | INFO : file Schubert_D911-04_HU33.npy tested. Cosine sim: 0.6308357885307959
2022-09-02 09:02:44 | INFO : file Schubert_D911-03_SC06.npy tested. Cosine sim: 0.7956565758634074
2022-09-02 09:02:45 | INFO : file Schubert_D911-09_SC06.npy tested. Cosine sim: 0.807085279746677
2022-09-02 09:02:46 | INFO : file Schubert_D911-19_HU33.npy tested. Cosine sim: 0.6804813067367935
2022-09-02 09:02:48 | INFO : file Schubert_D911-20_SC06.npy tested. Cosine sim: 0.8312519754309391
2022-09-02 09:02:50 | INFO : file Schubert_D911-23_HU33.npy tested. Cosine sim: 0.8316601469836218
2022-09-02 09:02:52 | INFO : file Schubert_D911-12_SC06.npy tested. Cosine sim: 0.7637631761661744
2022-09-02 09:02:53 | INFO : file Schubert_D911-18_HU33.npy tested. Cosine sim: 0.7355022239328929
2022-09-02 09:02:54 | INFO : file Schubert_D911-02_HU33.npy tested. Cosine sim: 0.6705923363182172
2022-09-02 09:02:55 | INFO : file Schubert_D911-12_HU33.npy tested. Cosine sim: 0.7497602122865522
2022-09-02 09:02:58 | INFO : file Schubert_D911-05_SC06.npy tested. Cosine sim: 0.7532633648526317
2022-09-02 09:02:59 | INFO : file Schubert_D911-16_SC06.npy tested. Cosine sim: 0.6966358406135684
2022-09-02 09:03:01 | INFO : file Schubert_D911-21_HU33.npy tested. Cosine sim: 0.8420100317428327
2022-09-02 09:03:02 | INFO : file Schubert_D911-13_HU33.npy tested. Cosine sim: 0.8395230862390314
2022-09-02 09:03:03 | INFO : file Schubert_D911-19_SC06.npy tested. Cosine sim: 0.7626963465353223
2022-09-02 09:03:05 | INFO : file Schubert_D911-04_SC06.npy tested. Cosine sim: 0.6169879220079898
2022-09-02 09:03:06 | INFO : file Schubert_D911-09_HU33.npy tested. Cosine sim: 0.7943651570568292
2022-09-02 09:03:08 | INFO : file Schubert_D911-20_HU33.npy tested. Cosine sim: 0.8160860974914699
2022-09-02 09:03:10 | INFO : file Schubert_D911-06_SC06.npy tested. Cosine sim: 0.8912883380383751
2022-09-02 09:03:11 | INFO : file Schubert_D911-15_HU33.npy tested. Cosine sim: 0.7149869322727032
2022-09-02 09:03:13 | INFO : file Schubert_D911-24_HU33.npy tested. Cosine sim: 0.8501247171053715
2022-09-02 09:03:15 | INFO : file Schubert_D911-08_HU33.npy tested. Cosine sim: 0.6935974221098092
2022-09-02 09:03:16 | INFO : file Schubert_D911-23_SC06.npy tested. Cosine sim: 0.8483100577250318
2022-09-02 09:03:18 | INFO : file Schubert_D911-07_SC06.npy tested. Cosine sim: 0.7489263232488986
2022-09-02 09:03:20 | INFO : file Schubert_D911-07_HU33.npy tested. Cosine sim: 0.7422765971961058
2022-09-02 09:03:22 | INFO : file Schubert_D911-11_SC06.npy tested. Cosine sim: 0.7529497971145985
2022-09-02 09:03:23 | INFO : file Schubert_D911-08_SC06.npy tested. Cosine sim: 0.6890463324295377
2022-09-02 09:03:24 | INFO : file Schubert_D911-22_HU33.npy tested. Cosine sim: 0.775333219059134
2022-09-02 09:03:26 | INFO : file Schubert_D911-16_HU33.npy tested. Cosine sim: 0.7046763606729519
2022-09-02 09:03:27 | INFO : file Schubert_D911-17_SC06.npy tested. Cosine sim: 0.718479201286696
2022-09-02 09:03:29 | INFO : file Schubert_D911-17_HU33.npy tested. Cosine sim: 0.7326515852011722
2022-09-02 09:03:31 | INFO : file Schubert_D911-10_HU33.npy tested. Cosine sim: 0.8142852495848062
2022-09-02 09:03:33 | INFO : file Schubert_D911-10_SC06.npy tested. Cosine sim: 0.8202937144752789
2022-09-02 09:03:35 | INFO : file Schubert_D911-21_SC06.npy tested. Cosine sim: 0.8489093774986253
2022-09-02 09:03:37 | INFO : file Schubert_D911-06_HU33.npy tested. Cosine sim: 0.8689749325858647
2022-09-02 09:03:39 | INFO : file Schubert_D911-15_SC06.npy tested. Cosine sim: 0.6973793254205015
2022-09-02 09:03:39 | INFO : ### Testing done. Results: ########################################
2022-09-02 09:03:39 | INFO : Mean cosine_sim: 0.7699389666759058
2022-09-02 09:03:39 | INFO : Mean Precision: 0.7997145183419802
2022-09-02 09:03:39 | INFO : Mean Recall: 0.18877833279564224
2022-09-02 09:03:39 | INFO : Mean Accuracy: 0.18048106197738864
2022-09-02 09:03:39 | INFO : Mean Substitution Error: 0.043184044895582745
2022-09-02 09:03:39 | INFO : Mean Miss Error: 0.7680376223087749
2022-09-02 09:03:39 | INFO : Mean False Alarm Error: 0.004946765184077494
2022-09-02 09:03:39 | INFO : Mean Total Error: 0.8161684323884346
2022-09-02 09:03:39 | INFO : Mean Chroma Precision: 0.9042236593811896
2022-09-02 09:03:39 | INFO : Mean Chroma Recall: 0.21400654989913112
2022-09-02 09:03:39 | INFO : Mean Chroma Accuracy: 0.2093392101535442
2022-09-02 09:03:39 | INFO : Mean Chroma Substitution Error: 0.017955827792093964
2022-09-02 09:03:39 | INFO : Mean Chroma Miss Error: 0.7680376223087749
2022-09-02 09:03:39 | INFO : Mean Chroma False Alarm Error: 0.004946765184077494
2022-09-02 09:03:39 | INFO : Mean Chroma Total Error: 0.7909402152849463
2022-09-02 09:03:39 | INFO :
2022-09-02 09:03:39 | INFO : Framewise cosine_sim: 0.7825297395930215
2022-09-02 09:03:39 | INFO : Framewise Precision: 0.8137679865631983
2022-09-02 09:03:39 | INFO : Framewise Recall: 0.19242711421993175
2022-09-02 09:03:39 | INFO : Framewise Accuracy: 0.18457996808811322
2022-09-02 09:03:39 | INFO : Framewise Substitution Error: 0.039601711698501324
2022-09-02 09:03:39 | INFO : Framewise Miss Error: 0.7679711740815669
2022-09-02 09:03:39 | INFO : Framewise False Alarm Error: 0.0050500478800896195
2022-09-02 09:03:39 | INFO : Framewise Total Error: 0.8126229336601577
2022-09-02 09:03:39 | INFO : Framewise Chroma Precision: 0.9139253227187515
2022-09-02 09:03:39 | INFO : Framewise Chroma Recall: 0.21644695819075732
2022-09-02 09:03:39 | INFO : Framewise Chroma Accuracy: 0.21219402393331505
2022-09-02 09:03:39 | INFO : Framewise Chroma Substitution Error: 0.015581867727675712
2022-09-02 09:03:39 | INFO : Framewise Chroma Miss Error: 0.7679711740815669
2022-09-02 09:03:39 | INFO : Framewise Chroma False Alarm Error: 0.0050500478800896195
2022-09-02 09:03:39 | INFO : Framewise Chroma Total Error: 0.7886030896893325