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Browse files- audio_detection/__init__.py +0 -0
- audio_detection/audio_infer/__init__.py +0 -0
- audio_detection/audio_infer/__pycache__/__init__.cpython-38.pyc +0 -0
- audio_detection/audio_infer/metadata/black_list/groundtruth_weak_label_evaluation_set.csv +1350 -0
- audio_detection/audio_infer/metadata/black_list/groundtruth_weak_label_testing_set.csv +606 -0
- audio_detection/audio_infer/metadata/class_labels_indices.csv +528 -0
- audio_detection/audio_infer/pytorch/__pycache__/models.cpython-38.pyc +0 -0
- audio_detection/audio_infer/pytorch/__pycache__/pytorch_utils.cpython-38.pyc +0 -0
- audio_detection/audio_infer/pytorch/evaluate.py +42 -0
- audio_detection/audio_infer/pytorch/finetune_template.py +127 -0
- audio_detection/audio_infer/pytorch/inference.py +206 -0
- audio_detection/audio_infer/pytorch/losses.py +14 -0
- audio_detection/audio_infer/pytorch/main.py +378 -0
- audio_detection/audio_infer/pytorch/models.py +951 -0
- audio_detection/audio_infer/pytorch/pytorch_utils.py +251 -0
- audio_detection/audio_infer/results/YDlWd7Wmdi1E.png +0 -0
- audio_detection/audio_infer/useful_ckpts/audio_detection.pth +3 -0
- audio_detection/audio_infer/utils/__pycache__/config.cpython-38.pyc +0 -0
- audio_detection/audio_infer/utils/config.py +94 -0
- audio_detection/audio_infer/utils/crash.py +12 -0
- audio_detection/audio_infer/utils/create_black_list.py +64 -0
- audio_detection/audio_infer/utils/create_indexes.py +126 -0
- audio_detection/audio_infer/utils/data_generator.py +421 -0
- audio_detection/audio_infer/utils/dataset.py +224 -0
- audio_detection/audio_infer/utils/plot_for_paper.py +565 -0
- audio_detection/audio_infer/utils/plot_statistics.py +0 -0
- audio_detection/audio_infer/utils/utilities.py +172 -0
- audio_detection/target_sound_detection/src/__pycache__/models.cpython-38.pyc +0 -0
- audio_detection/target_sound_detection/src/__pycache__/utils.cpython-38.pyc +0 -0
- audio_detection/target_sound_detection/src/models.py +1288 -0
- audio_detection/target_sound_detection/src/utils.py +353 -0
- audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth +3 -0
- audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth +3 -0
- audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt +3 -0
- audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth +3 -0
audio_detection/__init__.py
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File without changes
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audio_detection/audio_infer/__init__.py
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File without changes
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audio_detection/audio_infer/__pycache__/__init__.cpython-38.pyc
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Binary file (171 Bytes). View file
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audio_detection/audio_infer/metadata/black_list/groundtruth_weak_label_evaluation_set.csv
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| 7 |
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| 9 |
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| 10 |
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| 11 |
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|
| 12 |
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| 14 |
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| 15 |
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| 16 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 50 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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|
| 70 |
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Cg-DWc9nPfQ_90.000_100.000.wav 90.000 100.000 Air horn, truck horn
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| 71 |
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D62L3husEa0_30.000_40.000.wav 30.000 40.000 Air horn, truck horn
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| 72 |
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| 73 |
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Ge_KWS-0098_30.000_40.000.wav 30.000 40.000 Air horn, truck horn
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| 74 |
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| 75 |
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| 76 |
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| 576 |
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|
| 577 |
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-uMiGr6xvRA_30.000_40.000.wav 30.000 40.000 Truck
|
| 578 |
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|
| 579 |
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|
| 580 |
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|
| 581 |
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0C3kqtF76t8_50.000_60.000.wav 50.000 60.000 Truck
|
| 582 |
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0HmiH-wKLB4_30.000_40.000.wav 30.000 40.000 Truck
|
| 583 |
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0KskqFt3DoY_15.000_25.000.wav 15.000 25.000 Truck
|
| 584 |
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0OiPtV9sd_w_30.000_40.000.wav 30.000 40.000 Truck
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0VnoYVqd-yo_30.000_40.000.wav 30.000 40.000 Truck
|
| 586 |
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3YaLkgUMhAA_110.000_120.000.wav 110.000 120.000 Truck
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| 587 |
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-nGBPqlRNg4_30.000_40.000.wav 30.000 40.000 Train
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| 588 |
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1iUXERALOOs_190.000_200.000.wav 190.000 200.000 Train
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| 603 |
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|
| 606 |
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3ntFslTK6hM_90.000_100.000.wav 90.000 100.000 Train
|
audio_detection/audio_infer/metadata/class_labels_indices.csv
ADDED
|
@@ -0,0 +1,528 @@
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|
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|
|
|
|
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|
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|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
| 1 |
+
index,mid,display_name
|
| 2 |
+
0,/m/09x0r,"Speech"
|
| 3 |
+
1,/m/05zppz,"Male speech, man speaking"
|
| 4 |
+
2,/m/02zsn,"Female speech, woman speaking"
|
| 5 |
+
3,/m/0ytgt,"Child speech, kid speaking"
|
| 6 |
+
4,/m/01h8n0,"Conversation"
|
| 7 |
+
5,/m/02qldy,"Narration, monologue"
|
| 8 |
+
6,/m/0261r1,"Babbling"
|
| 9 |
+
7,/m/0brhx,"Speech synthesizer"
|
| 10 |
+
8,/m/07p6fty,"Shout"
|
| 11 |
+
9,/m/07q4ntr,"Bellow"
|
| 12 |
+
10,/m/07rwj3x,"Whoop"
|
| 13 |
+
11,/m/07sr1lc,"Yell"
|
| 14 |
+
12,/m/04gy_2,"Battle cry"
|
| 15 |
+
13,/t/dd00135,"Children shouting"
|
| 16 |
+
14,/m/03qc9zr,"Screaming"
|
| 17 |
+
15,/m/02rtxlg,"Whispering"
|
| 18 |
+
16,/m/01j3sz,"Laughter"
|
| 19 |
+
17,/t/dd00001,"Baby laughter"
|
| 20 |
+
18,/m/07r660_,"Giggle"
|
| 21 |
+
19,/m/07s04w4,"Snicker"
|
| 22 |
+
20,/m/07sq110,"Belly laugh"
|
| 23 |
+
21,/m/07rgt08,"Chuckle, chortle"
|
| 24 |
+
22,/m/0463cq4,"Crying, sobbing"
|
| 25 |
+
23,/t/dd00002,"Baby cry, infant cry"
|
| 26 |
+
24,/m/07qz6j3,"Whimper"
|
| 27 |
+
25,/m/07qw_06,"Wail, moan"
|
| 28 |
+
26,/m/07plz5l,"Sigh"
|
| 29 |
+
27,/m/015lz1,"Singing"
|
| 30 |
+
28,/m/0l14jd,"Choir"
|
| 31 |
+
29,/m/01swy6,"Yodeling"
|
| 32 |
+
30,/m/02bk07,"Chant"
|
| 33 |
+
31,/m/01c194,"Mantra"
|
| 34 |
+
32,/t/dd00003,"Male singing"
|
| 35 |
+
33,/t/dd00004,"Female singing"
|
| 36 |
+
34,/t/dd00005,"Child singing"
|
| 37 |
+
35,/t/dd00006,"Synthetic singing"
|
| 38 |
+
36,/m/06bxc,"Rapping"
|
| 39 |
+
37,/m/02fxyj,"Humming"
|
| 40 |
+
38,/m/07s2xch,"Groan"
|
| 41 |
+
39,/m/07r4k75,"Grunt"
|
| 42 |
+
40,/m/01w250,"Whistling"
|
| 43 |
+
41,/m/0lyf6,"Breathing"
|
| 44 |
+
42,/m/07mzm6,"Wheeze"
|
| 45 |
+
43,/m/01d3sd,"Snoring"
|
| 46 |
+
44,/m/07s0dtb,"Gasp"
|
| 47 |
+
45,/m/07pyy8b,"Pant"
|
| 48 |
+
46,/m/07q0yl5,"Snort"
|
| 49 |
+
47,/m/01b_21,"Cough"
|
| 50 |
+
48,/m/0dl9sf8,"Throat clearing"
|
| 51 |
+
49,/m/01hsr_,"Sneeze"
|
| 52 |
+
50,/m/07ppn3j,"Sniff"
|
| 53 |
+
51,/m/06h7j,"Run"
|
| 54 |
+
52,/m/07qv_x_,"Shuffle"
|
| 55 |
+
53,/m/07pbtc8,"Walk, footsteps"
|
| 56 |
+
54,/m/03cczk,"Chewing, mastication"
|
| 57 |
+
55,/m/07pdhp0,"Biting"
|
| 58 |
+
56,/m/0939n_,"Gargling"
|
| 59 |
+
57,/m/01g90h,"Stomach rumble"
|
| 60 |
+
58,/m/03q5_w,"Burping, eructation"
|
| 61 |
+
59,/m/02p3nc,"Hiccup"
|
| 62 |
+
60,/m/02_nn,"Fart"
|
| 63 |
+
61,/m/0k65p,"Hands"
|
| 64 |
+
62,/m/025_jnm,"Finger snapping"
|
| 65 |
+
63,/m/0l15bq,"Clapping"
|
| 66 |
+
64,/m/01jg02,"Heart sounds, heartbeat"
|
| 67 |
+
65,/m/01jg1z,"Heart murmur"
|
| 68 |
+
66,/m/053hz1,"Cheering"
|
| 69 |
+
67,/m/028ght,"Applause"
|
| 70 |
+
68,/m/07rkbfh,"Chatter"
|
| 71 |
+
69,/m/03qtwd,"Crowd"
|
| 72 |
+
70,/m/07qfr4h,"Hubbub, speech noise, speech babble"
|
| 73 |
+
71,/t/dd00013,"Children playing"
|
| 74 |
+
72,/m/0jbk,"Animal"
|
| 75 |
+
73,/m/068hy,"Domestic animals, pets"
|
| 76 |
+
74,/m/0bt9lr,"Dog"
|
| 77 |
+
75,/m/05tny_,"Bark"
|
| 78 |
+
76,/m/07r_k2n,"Yip"
|
| 79 |
+
77,/m/07qf0zm,"Howl"
|
| 80 |
+
78,/m/07rc7d9,"Bow-wow"
|
| 81 |
+
79,/m/0ghcn6,"Growling"
|
| 82 |
+
80,/t/dd00136,"Whimper (dog)"
|
| 83 |
+
81,/m/01yrx,"Cat"
|
| 84 |
+
82,/m/02yds9,"Purr"
|
| 85 |
+
83,/m/07qrkrw,"Meow"
|
| 86 |
+
84,/m/07rjwbb,"Hiss"
|
| 87 |
+
85,/m/07r81j2,"Caterwaul"
|
| 88 |
+
86,/m/0ch8v,"Livestock, farm animals, working animals"
|
| 89 |
+
87,/m/03k3r,"Horse"
|
| 90 |
+
88,/m/07rv9rh,"Clip-clop"
|
| 91 |
+
89,/m/07q5rw0,"Neigh, whinny"
|
| 92 |
+
90,/m/01xq0k1,"Cattle, bovinae"
|
| 93 |
+
91,/m/07rpkh9,"Moo"
|
| 94 |
+
92,/m/0239kh,"Cowbell"
|
| 95 |
+
93,/m/068zj,"Pig"
|
| 96 |
+
94,/t/dd00018,"Oink"
|
| 97 |
+
95,/m/03fwl,"Goat"
|
| 98 |
+
96,/m/07q0h5t,"Bleat"
|
| 99 |
+
97,/m/07bgp,"Sheep"
|
| 100 |
+
98,/m/025rv6n,"Fowl"
|
| 101 |
+
99,/m/09b5t,"Chicken, rooster"
|
| 102 |
+
100,/m/07st89h,"Cluck"
|
| 103 |
+
101,/m/07qn5dc,"Crowing, cock-a-doodle-doo"
|
| 104 |
+
102,/m/01rd7k,"Turkey"
|
| 105 |
+
103,/m/07svc2k,"Gobble"
|
| 106 |
+
104,/m/09ddx,"Duck"
|
| 107 |
+
105,/m/07qdb04,"Quack"
|
| 108 |
+
106,/m/0dbvp,"Goose"
|
| 109 |
+
107,/m/07qwf61,"Honk"
|
| 110 |
+
108,/m/01280g,"Wild animals"
|
| 111 |
+
109,/m/0cdnk,"Roaring cats (lions, tigers)"
|
| 112 |
+
110,/m/04cvmfc,"Roar"
|
| 113 |
+
111,/m/015p6,"Bird"
|
| 114 |
+
112,/m/020bb7,"Bird vocalization, bird call, bird song"
|
| 115 |
+
113,/m/07pggtn,"Chirp, tweet"
|
| 116 |
+
114,/m/07sx8x_,"Squawk"
|
| 117 |
+
115,/m/0h0rv,"Pigeon, dove"
|
| 118 |
+
116,/m/07r_25d,"Coo"
|
| 119 |
+
117,/m/04s8yn,"Crow"
|
| 120 |
+
118,/m/07r5c2p,"Caw"
|
| 121 |
+
119,/m/09d5_,"Owl"
|
| 122 |
+
120,/m/07r_80w,"Hoot"
|
| 123 |
+
121,/m/05_wcq,"Bird flight, flapping wings"
|
| 124 |
+
122,/m/01z5f,"Canidae, dogs, wolves"
|
| 125 |
+
123,/m/06hps,"Rodents, rats, mice"
|
| 126 |
+
124,/m/04rmv,"Mouse"
|
| 127 |
+
125,/m/07r4gkf,"Patter"
|
| 128 |
+
126,/m/03vt0,"Insect"
|
| 129 |
+
127,/m/09xqv,"Cricket"
|
| 130 |
+
128,/m/09f96,"Mosquito"
|
| 131 |
+
129,/m/0h2mp,"Fly, housefly"
|
| 132 |
+
130,/m/07pjwq1,"Buzz"
|
| 133 |
+
131,/m/01h3n,"Bee, wasp, etc."
|
| 134 |
+
132,/m/09ld4,"Frog"
|
| 135 |
+
133,/m/07st88b,"Croak"
|
| 136 |
+
134,/m/078jl,"Snake"
|
| 137 |
+
135,/m/07qn4z3,"Rattle"
|
| 138 |
+
136,/m/032n05,"Whale vocalization"
|
| 139 |
+
137,/m/04rlf,"Music"
|
| 140 |
+
138,/m/04szw,"Musical instrument"
|
| 141 |
+
139,/m/0fx80y,"Plucked string instrument"
|
| 142 |
+
140,/m/0342h,"Guitar"
|
| 143 |
+
141,/m/02sgy,"Electric guitar"
|
| 144 |
+
142,/m/018vs,"Bass guitar"
|
| 145 |
+
143,/m/042v_gx,"Acoustic guitar"
|
| 146 |
+
144,/m/06w87,"Steel guitar, slide guitar"
|
| 147 |
+
145,/m/01glhc,"Tapping (guitar technique)"
|
| 148 |
+
146,/m/07s0s5r,"Strum"
|
| 149 |
+
147,/m/018j2,"Banjo"
|
| 150 |
+
148,/m/0jtg0,"Sitar"
|
| 151 |
+
149,/m/04rzd,"Mandolin"
|
| 152 |
+
150,/m/01bns_,"Zither"
|
| 153 |
+
151,/m/07xzm,"Ukulele"
|
| 154 |
+
152,/m/05148p4,"Keyboard (musical)"
|
| 155 |
+
153,/m/05r5c,"Piano"
|
| 156 |
+
154,/m/01s0ps,"Electric piano"
|
| 157 |
+
155,/m/013y1f,"Organ"
|
| 158 |
+
156,/m/03xq_f,"Electronic organ"
|
| 159 |
+
157,/m/03gvt,"Hammond organ"
|
| 160 |
+
158,/m/0l14qv,"Synthesizer"
|
| 161 |
+
159,/m/01v1d8,"Sampler"
|
| 162 |
+
160,/m/03q5t,"Harpsichord"
|
| 163 |
+
161,/m/0l14md,"Percussion"
|
| 164 |
+
162,/m/02hnl,"Drum kit"
|
| 165 |
+
163,/m/0cfdd,"Drum machine"
|
| 166 |
+
164,/m/026t6,"Drum"
|
| 167 |
+
165,/m/06rvn,"Snare drum"
|
| 168 |
+
166,/m/03t3fj,"Rimshot"
|
| 169 |
+
167,/m/02k_mr,"Drum roll"
|
| 170 |
+
168,/m/0bm02,"Bass drum"
|
| 171 |
+
169,/m/011k_j,"Timpani"
|
| 172 |
+
170,/m/01p970,"Tabla"
|
| 173 |
+
171,/m/01qbl,"Cymbal"
|
| 174 |
+
172,/m/03qtq,"Hi-hat"
|
| 175 |
+
173,/m/01sm1g,"Wood block"
|
| 176 |
+
174,/m/07brj,"Tambourine"
|
| 177 |
+
175,/m/05r5wn,"Rattle (instrument)"
|
| 178 |
+
176,/m/0xzly,"Maraca"
|
| 179 |
+
177,/m/0mbct,"Gong"
|
| 180 |
+
178,/m/016622,"Tubular bells"
|
| 181 |
+
179,/m/0j45pbj,"Mallet percussion"
|
| 182 |
+
180,/m/0dwsp,"Marimba, xylophone"
|
| 183 |
+
181,/m/0dwtp,"Glockenspiel"
|
| 184 |
+
182,/m/0dwt5,"Vibraphone"
|
| 185 |
+
183,/m/0l156b,"Steelpan"
|
| 186 |
+
184,/m/05pd6,"Orchestra"
|
| 187 |
+
185,/m/01kcd,"Brass instrument"
|
| 188 |
+
186,/m/0319l,"French horn"
|
| 189 |
+
187,/m/07gql,"Trumpet"
|
| 190 |
+
188,/m/07c6l,"Trombone"
|
| 191 |
+
189,/m/0l14_3,"Bowed string instrument"
|
| 192 |
+
190,/m/02qmj0d,"String section"
|
| 193 |
+
191,/m/07y_7,"Violin, fiddle"
|
| 194 |
+
192,/m/0d8_n,"Pizzicato"
|
| 195 |
+
193,/m/01xqw,"Cello"
|
| 196 |
+
194,/m/02fsn,"Double bass"
|
| 197 |
+
195,/m/085jw,"Wind instrument, woodwind instrument"
|
| 198 |
+
196,/m/0l14j_,"Flute"
|
| 199 |
+
197,/m/06ncr,"Saxophone"
|
| 200 |
+
198,/m/01wy6,"Clarinet"
|
| 201 |
+
199,/m/03m5k,"Harp"
|
| 202 |
+
200,/m/0395lw,"Bell"
|
| 203 |
+
201,/m/03w41f,"Church bell"
|
| 204 |
+
202,/m/027m70_,"Jingle bell"
|
| 205 |
+
203,/m/0gy1t2s,"Bicycle bell"
|
| 206 |
+
204,/m/07n_g,"Tuning fork"
|
| 207 |
+
205,/m/0f8s22,"Chime"
|
| 208 |
+
206,/m/026fgl,"Wind chime"
|
| 209 |
+
207,/m/0150b9,"Change ringing (campanology)"
|
| 210 |
+
208,/m/03qjg,"Harmonica"
|
| 211 |
+
209,/m/0mkg,"Accordion"
|
| 212 |
+
210,/m/0192l,"Bagpipes"
|
| 213 |
+
211,/m/02bxd,"Didgeridoo"
|
| 214 |
+
212,/m/0l14l2,"Shofar"
|
| 215 |
+
213,/m/07kc_,"Theremin"
|
| 216 |
+
214,/m/0l14t7,"Singing bowl"
|
| 217 |
+
215,/m/01hgjl,"Scratching (performance technique)"
|
| 218 |
+
216,/m/064t9,"Pop music"
|
| 219 |
+
217,/m/0glt670,"Hip hop music"
|
| 220 |
+
218,/m/02cz_7,"Beatboxing"
|
| 221 |
+
219,/m/06by7,"Rock music"
|
| 222 |
+
220,/m/03lty,"Heavy metal"
|
| 223 |
+
221,/m/05r6t,"Punk rock"
|
| 224 |
+
222,/m/0dls3,"Grunge"
|
| 225 |
+
223,/m/0dl5d,"Progressive rock"
|
| 226 |
+
224,/m/07sbbz2,"Rock and roll"
|
| 227 |
+
225,/m/05w3f,"Psychedelic rock"
|
| 228 |
+
226,/m/06j6l,"Rhythm and blues"
|
| 229 |
+
227,/m/0gywn,"Soul music"
|
| 230 |
+
228,/m/06cqb,"Reggae"
|
| 231 |
+
229,/m/01lyv,"Country"
|
| 232 |
+
230,/m/015y_n,"Swing music"
|
| 233 |
+
231,/m/0gg8l,"Bluegrass"
|
| 234 |
+
232,/m/02x8m,"Funk"
|
| 235 |
+
233,/m/02w4v,"Folk music"
|
| 236 |
+
234,/m/06j64v,"Middle Eastern music"
|
| 237 |
+
235,/m/03_d0,"Jazz"
|
| 238 |
+
236,/m/026z9,"Disco"
|
| 239 |
+
237,/m/0ggq0m,"Classical music"
|
| 240 |
+
238,/m/05lls,"Opera"
|
| 241 |
+
239,/m/02lkt,"Electronic music"
|
| 242 |
+
240,/m/03mb9,"House music"
|
| 243 |
+
241,/m/07gxw,"Techno"
|
| 244 |
+
242,/m/07s72n,"Dubstep"
|
| 245 |
+
243,/m/0283d,"Drum and bass"
|
| 246 |
+
244,/m/0m0jc,"Electronica"
|
| 247 |
+
245,/m/08cyft,"Electronic dance music"
|
| 248 |
+
246,/m/0fd3y,"Ambient music"
|
| 249 |
+
247,/m/07lnk,"Trance music"
|
| 250 |
+
248,/m/0g293,"Music of Latin America"
|
| 251 |
+
249,/m/0ln16,"Salsa music"
|
| 252 |
+
250,/m/0326g,"Flamenco"
|
| 253 |
+
251,/m/0155w,"Blues"
|
| 254 |
+
252,/m/05fw6t,"Music for children"
|
| 255 |
+
253,/m/02v2lh,"New-age music"
|
| 256 |
+
254,/m/0y4f8,"Vocal music"
|
| 257 |
+
255,/m/0z9c,"A capella"
|
| 258 |
+
256,/m/0164x2,"Music of Africa"
|
| 259 |
+
257,/m/0145m,"Afrobeat"
|
| 260 |
+
258,/m/02mscn,"Christian music"
|
| 261 |
+
259,/m/016cjb,"Gospel music"
|
| 262 |
+
260,/m/028sqc,"Music of Asia"
|
| 263 |
+
261,/m/015vgc,"Carnatic music"
|
| 264 |
+
262,/m/0dq0md,"Music of Bollywood"
|
| 265 |
+
263,/m/06rqw,"Ska"
|
| 266 |
+
264,/m/02p0sh1,"Traditional music"
|
| 267 |
+
265,/m/05rwpb,"Independent music"
|
| 268 |
+
266,/m/074ft,"Song"
|
| 269 |
+
267,/m/025td0t,"Background music"
|
| 270 |
+
268,/m/02cjck,"Theme music"
|
| 271 |
+
269,/m/03r5q_,"Jingle (music)"
|
| 272 |
+
270,/m/0l14gg,"Soundtrack music"
|
| 273 |
+
271,/m/07pkxdp,"Lullaby"
|
| 274 |
+
272,/m/01z7dr,"Video game music"
|
| 275 |
+
273,/m/0140xf,"Christmas music"
|
| 276 |
+
274,/m/0ggx5q,"Dance music"
|
| 277 |
+
275,/m/04wptg,"Wedding music"
|
| 278 |
+
276,/t/dd00031,"Happy music"
|
| 279 |
+
277,/t/dd00032,"Funny music"
|
| 280 |
+
278,/t/dd00033,"Sad music"
|
| 281 |
+
279,/t/dd00034,"Tender music"
|
| 282 |
+
280,/t/dd00035,"Exciting music"
|
| 283 |
+
281,/t/dd00036,"Angry music"
|
| 284 |
+
282,/t/dd00037,"Scary music"
|
| 285 |
+
283,/m/03m9d0z,"Wind"
|
| 286 |
+
284,/m/09t49,"Rustling leaves"
|
| 287 |
+
285,/t/dd00092,"Wind noise (microphone)"
|
| 288 |
+
286,/m/0jb2l,"Thunderstorm"
|
| 289 |
+
287,/m/0ngt1,"Thunder"
|
| 290 |
+
288,/m/0838f,"Water"
|
| 291 |
+
289,/m/06mb1,"Rain"
|
| 292 |
+
290,/m/07r10fb,"Raindrop"
|
| 293 |
+
291,/t/dd00038,"Rain on surface"
|
| 294 |
+
292,/m/0j6m2,"Stream"
|
| 295 |
+
293,/m/0j2kx,"Waterfall"
|
| 296 |
+
294,/m/05kq4,"Ocean"
|
| 297 |
+
295,/m/034srq,"Waves, surf"
|
| 298 |
+
296,/m/06wzb,"Steam"
|
| 299 |
+
297,/m/07swgks,"Gurgling"
|
| 300 |
+
298,/m/02_41,"Fire"
|
| 301 |
+
299,/m/07pzfmf,"Crackle"
|
| 302 |
+
300,/m/07yv9,"Vehicle"
|
| 303 |
+
301,/m/019jd,"Boat, Water vehicle"
|
| 304 |
+
302,/m/0hsrw,"Sailboat, sailing ship"
|
| 305 |
+
303,/m/056ks2,"Rowboat, canoe, kayak"
|
| 306 |
+
304,/m/02rlv9,"Motorboat, speedboat"
|
| 307 |
+
305,/m/06q74,"Ship"
|
| 308 |
+
306,/m/012f08,"Motor vehicle (road)"
|
| 309 |
+
307,/m/0k4j,"Car"
|
| 310 |
+
308,/m/0912c9,"Vehicle horn, car horn, honking"
|
| 311 |
+
309,/m/07qv_d5,"Toot"
|
| 312 |
+
310,/m/02mfyn,"Car alarm"
|
| 313 |
+
311,/m/04gxbd,"Power windows, electric windows"
|
| 314 |
+
312,/m/07rknqz,"Skidding"
|
| 315 |
+
313,/m/0h9mv,"Tire squeal"
|
| 316 |
+
314,/t/dd00134,"Car passing by"
|
| 317 |
+
315,/m/0ltv,"Race car, auto racing"
|
| 318 |
+
316,/m/07r04,"Truck"
|
| 319 |
+
317,/m/0gvgw0,"Air brake"
|
| 320 |
+
318,/m/05x_td,"Air horn, truck horn"
|
| 321 |
+
319,/m/02rhddq,"Reversing beeps"
|
| 322 |
+
320,/m/03cl9h,"Ice cream truck, ice cream van"
|
| 323 |
+
321,/m/01bjv,"Bus"
|
| 324 |
+
322,/m/03j1ly,"Emergency vehicle"
|
| 325 |
+
323,/m/04qvtq,"Police car (siren)"
|
| 326 |
+
324,/m/012n7d,"Ambulance (siren)"
|
| 327 |
+
325,/m/012ndj,"Fire engine, fire truck (siren)"
|
| 328 |
+
326,/m/04_sv,"Motorcycle"
|
| 329 |
+
327,/m/0btp2,"Traffic noise, roadway noise"
|
| 330 |
+
328,/m/06d_3,"Rail transport"
|
| 331 |
+
329,/m/07jdr,"Train"
|
| 332 |
+
330,/m/04zmvq,"Train whistle"
|
| 333 |
+
331,/m/0284vy3,"Train horn"
|
| 334 |
+
332,/m/01g50p,"Railroad car, train wagon"
|
| 335 |
+
333,/t/dd00048,"Train wheels squealing"
|
| 336 |
+
334,/m/0195fx,"Subway, metro, underground"
|
| 337 |
+
335,/m/0k5j,"Aircraft"
|
| 338 |
+
336,/m/014yck,"Aircraft engine"
|
| 339 |
+
337,/m/04229,"Jet engine"
|
| 340 |
+
338,/m/02l6bg,"Propeller, airscrew"
|
| 341 |
+
339,/m/09ct_,"Helicopter"
|
| 342 |
+
340,/m/0cmf2,"Fixed-wing aircraft, airplane"
|
| 343 |
+
341,/m/0199g,"Bicycle"
|
| 344 |
+
342,/m/06_fw,"Skateboard"
|
| 345 |
+
343,/m/02mk9,"Engine"
|
| 346 |
+
344,/t/dd00065,"Light engine (high frequency)"
|
| 347 |
+
345,/m/08j51y,"Dental drill, dentist's drill"
|
| 348 |
+
346,/m/01yg9g,"Lawn mower"
|
| 349 |
+
347,/m/01j4z9,"Chainsaw"
|
| 350 |
+
348,/t/dd00066,"Medium engine (mid frequency)"
|
| 351 |
+
349,/t/dd00067,"Heavy engine (low frequency)"
|
| 352 |
+
350,/m/01h82_,"Engine knocking"
|
| 353 |
+
351,/t/dd00130,"Engine starting"
|
| 354 |
+
352,/m/07pb8fc,"Idling"
|
| 355 |
+
353,/m/07q2z82,"Accelerating, revving, vroom"
|
| 356 |
+
354,/m/02dgv,"Door"
|
| 357 |
+
355,/m/03wwcy,"Doorbell"
|
| 358 |
+
356,/m/07r67yg,"Ding-dong"
|
| 359 |
+
357,/m/02y_763,"Sliding door"
|
| 360 |
+
358,/m/07rjzl8,"Slam"
|
| 361 |
+
359,/m/07r4wb8,"Knock"
|
| 362 |
+
360,/m/07qcpgn,"Tap"
|
| 363 |
+
361,/m/07q6cd_,"Squeak"
|
| 364 |
+
362,/m/0642b4,"Cupboard open or close"
|
| 365 |
+
363,/m/0fqfqc,"Drawer open or close"
|
| 366 |
+
364,/m/04brg2,"Dishes, pots, and pans"
|
| 367 |
+
365,/m/023pjk,"Cutlery, silverware"
|
| 368 |
+
366,/m/07pn_8q,"Chopping (food)"
|
| 369 |
+
367,/m/0dxrf,"Frying (food)"
|
| 370 |
+
368,/m/0fx9l,"Microwave oven"
|
| 371 |
+
369,/m/02pjr4,"Blender"
|
| 372 |
+
370,/m/02jz0l,"Water tap, faucet"
|
| 373 |
+
371,/m/0130jx,"Sink (filling or washing)"
|
| 374 |
+
372,/m/03dnzn,"Bathtub (filling or washing)"
|
| 375 |
+
373,/m/03wvsk,"Hair dryer"
|
| 376 |
+
374,/m/01jt3m,"Toilet flush"
|
| 377 |
+
375,/m/012xff,"Toothbrush"
|
| 378 |
+
376,/m/04fgwm,"Electric toothbrush"
|
| 379 |
+
377,/m/0d31p,"Vacuum cleaner"
|
| 380 |
+
378,/m/01s0vc,"Zipper (clothing)"
|
| 381 |
+
379,/m/03v3yw,"Keys jangling"
|
| 382 |
+
380,/m/0242l,"Coin (dropping)"
|
| 383 |
+
381,/m/01lsmm,"Scissors"
|
| 384 |
+
382,/m/02g901,"Electric shaver, electric razor"
|
| 385 |
+
383,/m/05rj2,"Shuffling cards"
|
| 386 |
+
384,/m/0316dw,"Typing"
|
| 387 |
+
385,/m/0c2wf,"Typewriter"
|
| 388 |
+
386,/m/01m2v,"Computer keyboard"
|
| 389 |
+
387,/m/081rb,"Writing"
|
| 390 |
+
388,/m/07pp_mv,"Alarm"
|
| 391 |
+
389,/m/07cx4,"Telephone"
|
| 392 |
+
390,/m/07pp8cl,"Telephone bell ringing"
|
| 393 |
+
391,/m/01hnzm,"Ringtone"
|
| 394 |
+
392,/m/02c8p,"Telephone dialing, DTMF"
|
| 395 |
+
393,/m/015jpf,"Dial tone"
|
| 396 |
+
394,/m/01z47d,"Busy signal"
|
| 397 |
+
395,/m/046dlr,"Alarm clock"
|
| 398 |
+
396,/m/03kmc9,"Siren"
|
| 399 |
+
397,/m/0dgbq,"Civil defense siren"
|
| 400 |
+
398,/m/030rvx,"Buzzer"
|
| 401 |
+
399,/m/01y3hg,"Smoke detector, smoke alarm"
|
| 402 |
+
400,/m/0c3f7m,"Fire alarm"
|
| 403 |
+
401,/m/04fq5q,"Foghorn"
|
| 404 |
+
402,/m/0l156k,"Whistle"
|
| 405 |
+
403,/m/06hck5,"Steam whistle"
|
| 406 |
+
404,/t/dd00077,"Mechanisms"
|
| 407 |
+
405,/m/02bm9n,"Ratchet, pawl"
|
| 408 |
+
406,/m/01x3z,"Clock"
|
| 409 |
+
407,/m/07qjznt,"Tick"
|
| 410 |
+
408,/m/07qjznl,"Tick-tock"
|
| 411 |
+
409,/m/0l7xg,"Gears"
|
| 412 |
+
410,/m/05zc1,"Pulleys"
|
| 413 |
+
411,/m/0llzx,"Sewing machine"
|
| 414 |
+
412,/m/02x984l,"Mechanical fan"
|
| 415 |
+
413,/m/025wky1,"Air conditioning"
|
| 416 |
+
414,/m/024dl,"Cash register"
|
| 417 |
+
415,/m/01m4t,"Printer"
|
| 418 |
+
416,/m/0dv5r,"Camera"
|
| 419 |
+
417,/m/07bjf,"Single-lens reflex camera"
|
| 420 |
+
418,/m/07k1x,"Tools"
|
| 421 |
+
419,/m/03l9g,"Hammer"
|
| 422 |
+
420,/m/03p19w,"Jackhammer"
|
| 423 |
+
421,/m/01b82r,"Sawing"
|
| 424 |
+
422,/m/02p01q,"Filing (rasp)"
|
| 425 |
+
423,/m/023vsd,"Sanding"
|
| 426 |
+
424,/m/0_ksk,"Power tool"
|
| 427 |
+
425,/m/01d380,"Drill"
|
| 428 |
+
426,/m/014zdl,"Explosion"
|
| 429 |
+
427,/m/032s66,"Gunshot, gunfire"
|
| 430 |
+
428,/m/04zjc,"Machine gun"
|
| 431 |
+
429,/m/02z32qm,"Fusillade"
|
| 432 |
+
430,/m/0_1c,"Artillery fire"
|
| 433 |
+
431,/m/073cg4,"Cap gun"
|
| 434 |
+
432,/m/0g6b5,"Fireworks"
|
| 435 |
+
433,/g/122z_qxw,"Firecracker"
|
| 436 |
+
434,/m/07qsvvw,"Burst, pop"
|
| 437 |
+
435,/m/07pxg6y,"Eruption"
|
| 438 |
+
436,/m/07qqyl4,"Boom"
|
| 439 |
+
437,/m/083vt,"Wood"
|
| 440 |
+
438,/m/07pczhz,"Chop"
|
| 441 |
+
439,/m/07pl1bw,"Splinter"
|
| 442 |
+
440,/m/07qs1cx,"Crack"
|
| 443 |
+
441,/m/039jq,"Glass"
|
| 444 |
+
442,/m/07q7njn,"Chink, clink"
|
| 445 |
+
443,/m/07rn7sz,"Shatter"
|
| 446 |
+
444,/m/04k94,"Liquid"
|
| 447 |
+
445,/m/07rrlb6,"Splash, splatter"
|
| 448 |
+
446,/m/07p6mqd,"Slosh"
|
| 449 |
+
447,/m/07qlwh6,"Squish"
|
| 450 |
+
448,/m/07r5v4s,"Drip"
|
| 451 |
+
449,/m/07prgkl,"Pour"
|
| 452 |
+
450,/m/07pqc89,"Trickle, dribble"
|
| 453 |
+
451,/t/dd00088,"Gush"
|
| 454 |
+
452,/m/07p7b8y,"Fill (with liquid)"
|
| 455 |
+
453,/m/07qlf79,"Spray"
|
| 456 |
+
454,/m/07ptzwd,"Pump (liquid)"
|
| 457 |
+
455,/m/07ptfmf,"Stir"
|
| 458 |
+
456,/m/0dv3j,"Boiling"
|
| 459 |
+
457,/m/0790c,"Sonar"
|
| 460 |
+
458,/m/0dl83,"Arrow"
|
| 461 |
+
459,/m/07rqsjt,"Whoosh, swoosh, swish"
|
| 462 |
+
460,/m/07qnq_y,"Thump, thud"
|
| 463 |
+
461,/m/07rrh0c,"Thunk"
|
| 464 |
+
462,/m/0b_fwt,"Electronic tuner"
|
| 465 |
+
463,/m/02rr_,"Effects unit"
|
| 466 |
+
464,/m/07m2kt,"Chorus effect"
|
| 467 |
+
465,/m/018w8,"Basketball bounce"
|
| 468 |
+
466,/m/07pws3f,"Bang"
|
| 469 |
+
467,/m/07ryjzk,"Slap, smack"
|
| 470 |
+
468,/m/07rdhzs,"Whack, thwack"
|
| 471 |
+
469,/m/07pjjrj,"Smash, crash"
|
| 472 |
+
470,/m/07pc8lb,"Breaking"
|
| 473 |
+
471,/m/07pqn27,"Bouncing"
|
| 474 |
+
472,/m/07rbp7_,"Whip"
|
| 475 |
+
473,/m/07pyf11,"Flap"
|
| 476 |
+
474,/m/07qb_dv,"Scratch"
|
| 477 |
+
475,/m/07qv4k0,"Scrape"
|
| 478 |
+
476,/m/07pdjhy,"Rub"
|
| 479 |
+
477,/m/07s8j8t,"Roll"
|
| 480 |
+
478,/m/07plct2,"Crushing"
|
| 481 |
+
479,/t/dd00112,"Crumpling, crinkling"
|
| 482 |
+
480,/m/07qcx4z,"Tearing"
|
| 483 |
+
481,/m/02fs_r,"Beep, bleep"
|
| 484 |
+
482,/m/07qwdck,"Ping"
|
| 485 |
+
483,/m/07phxs1,"Ding"
|
| 486 |
+
484,/m/07rv4dm,"Clang"
|
| 487 |
+
485,/m/07s02z0,"Squeal"
|
| 488 |
+
486,/m/07qh7jl,"Creak"
|
| 489 |
+
487,/m/07qwyj0,"Rustle"
|
| 490 |
+
488,/m/07s34ls,"Whir"
|
| 491 |
+
489,/m/07qmpdm,"Clatter"
|
| 492 |
+
490,/m/07p9k1k,"Sizzle"
|
| 493 |
+
491,/m/07qc9xj,"Clicking"
|
| 494 |
+
492,/m/07rwm0c,"Clickety-clack"
|
| 495 |
+
493,/m/07phhsh,"Rumble"
|
| 496 |
+
494,/m/07qyrcz,"Plop"
|
| 497 |
+
495,/m/07qfgpx,"Jingle, tinkle"
|
| 498 |
+
496,/m/07rcgpl,"Hum"
|
| 499 |
+
497,/m/07p78v5,"Zing"
|
| 500 |
+
498,/t/dd00121,"Boing"
|
| 501 |
+
499,/m/07s12q4,"Crunch"
|
| 502 |
+
500,/m/028v0c,"Silence"
|
| 503 |
+
501,/m/01v_m0,"Sine wave"
|
| 504 |
+
502,/m/0b9m1,"Harmonic"
|
| 505 |
+
503,/m/0hdsk,"Chirp tone"
|
| 506 |
+
504,/m/0c1dj,"Sound effect"
|
| 507 |
+
505,/m/07pt_g0,"Pulse"
|
| 508 |
+
506,/t/dd00125,"Inside, small room"
|
| 509 |
+
507,/t/dd00126,"Inside, large room or hall"
|
| 510 |
+
508,/t/dd00127,"Inside, public space"
|
| 511 |
+
509,/t/dd00128,"Outside, urban or manmade"
|
| 512 |
+
510,/t/dd00129,"Outside, rural or natural"
|
| 513 |
+
511,/m/01b9nn,"Reverberation"
|
| 514 |
+
512,/m/01jnbd,"Echo"
|
| 515 |
+
513,/m/096m7z,"Noise"
|
| 516 |
+
514,/m/06_y0by,"Environmental noise"
|
| 517 |
+
515,/m/07rgkc5,"Static"
|
| 518 |
+
516,/m/06xkwv,"Mains hum"
|
| 519 |
+
517,/m/0g12c5,"Distortion"
|
| 520 |
+
518,/m/08p9q4,"Sidetone"
|
| 521 |
+
519,/m/07szfh9,"Cacophony"
|
| 522 |
+
520,/m/0chx_,"White noise"
|
| 523 |
+
521,/m/0cj0r,"Pink noise"
|
| 524 |
+
522,/m/07p_0gm,"Throbbing"
|
| 525 |
+
523,/m/01jwx6,"Vibration"
|
| 526 |
+
524,/m/07c52,"Television"
|
| 527 |
+
525,/m/06bz3,"Radio"
|
| 528 |
+
526,/m/07hvw1,"Field recording"
|
audio_detection/audio_infer/pytorch/__pycache__/models.cpython-38.pyc
ADDED
|
Binary file (24.6 kB). View file
|
audio_detection/audio_infer/pytorch/__pycache__/pytorch_utils.cpython-38.pyc
ADDED
|
Binary file (7.3 kB). View file
|
audio_detection/audio_infer/pytorch/evaluate.py
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
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|
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|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from sklearn import metrics
|
| 2 |
+
|
| 3 |
+
from pytorch_utils import forward
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class Evaluator(object):
|
| 7 |
+
def __init__(self, model):
|
| 8 |
+
"""Evaluator.
|
| 9 |
+
|
| 10 |
+
Args:
|
| 11 |
+
model: object
|
| 12 |
+
"""
|
| 13 |
+
self.model = model
|
| 14 |
+
|
| 15 |
+
def evaluate(self, data_loader):
|
| 16 |
+
"""Forward evaluation data and calculate statistics.
|
| 17 |
+
|
| 18 |
+
Args:
|
| 19 |
+
data_loader: object
|
| 20 |
+
|
| 21 |
+
Returns:
|
| 22 |
+
statistics: dict,
|
| 23 |
+
{'average_precision': (classes_num,), 'auc': (classes_num,)}
|
| 24 |
+
"""
|
| 25 |
+
|
| 26 |
+
# Forward
|
| 27 |
+
output_dict = forward(
|
| 28 |
+
model=self.model,
|
| 29 |
+
generator=data_loader,
|
| 30 |
+
return_target=True)
|
| 31 |
+
|
| 32 |
+
clipwise_output = output_dict['clipwise_output'] # (audios_num, classes_num)
|
| 33 |
+
target = output_dict['target'] # (audios_num, classes_num)
|
| 34 |
+
|
| 35 |
+
average_precision = metrics.average_precision_score(
|
| 36 |
+
target, clipwise_output, average=None)
|
| 37 |
+
|
| 38 |
+
auc = metrics.roc_auc_score(target, clipwise_output, average=None)
|
| 39 |
+
|
| 40 |
+
statistics = {'average_precision': average_precision, 'auc': auc}
|
| 41 |
+
|
| 42 |
+
return statistics
|
audio_detection/audio_infer/pytorch/finetune_template.py
ADDED
|
@@ -0,0 +1,127 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import sys
|
| 3 |
+
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
|
| 4 |
+
import numpy as np
|
| 5 |
+
import argparse
|
| 6 |
+
import h5py
|
| 7 |
+
import math
|
| 8 |
+
import time
|
| 9 |
+
import logging
|
| 10 |
+
import matplotlib.pyplot as plt
|
| 11 |
+
|
| 12 |
+
import torch
|
| 13 |
+
torch.backends.cudnn.benchmark=True
|
| 14 |
+
torch.manual_seed(0)
|
| 15 |
+
import torch.nn as nn
|
| 16 |
+
import torch.nn.functional as F
|
| 17 |
+
import torch.optim as optim
|
| 18 |
+
import torch.utils.data
|
| 19 |
+
|
| 20 |
+
from utilities import get_filename
|
| 21 |
+
from models import *
|
| 22 |
+
import config
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
class Transfer_Cnn14(nn.Module):
|
| 26 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 27 |
+
fmax, classes_num, freeze_base):
|
| 28 |
+
"""Classifier for a new task using pretrained Cnn14 as a sub module.
|
| 29 |
+
"""
|
| 30 |
+
super(Transfer_Cnn14, self).__init__()
|
| 31 |
+
audioset_classes_num = 527
|
| 32 |
+
|
| 33 |
+
self.base = Cnn14(sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 34 |
+
fmax, audioset_classes_num)
|
| 35 |
+
|
| 36 |
+
# Transfer to another task layer
|
| 37 |
+
self.fc_transfer = nn.Linear(2048, classes_num, bias=True)
|
| 38 |
+
|
| 39 |
+
if freeze_base:
|
| 40 |
+
# Freeze AudioSet pretrained layers
|
| 41 |
+
for param in self.base.parameters():
|
| 42 |
+
param.requires_grad = False
|
| 43 |
+
|
| 44 |
+
self.init_weights()
|
| 45 |
+
|
| 46 |
+
def init_weights(self):
|
| 47 |
+
init_layer(self.fc_transfer)
|
| 48 |
+
|
| 49 |
+
def load_from_pretrain(self, pretrained_checkpoint_path):
|
| 50 |
+
checkpoint = torch.load(pretrained_checkpoint_path)
|
| 51 |
+
self.base.load_state_dict(checkpoint['model'])
|
| 52 |
+
|
| 53 |
+
def forward(self, input, mixup_lambda=None):
|
| 54 |
+
"""Input: (batch_size, data_length)
|
| 55 |
+
"""
|
| 56 |
+
output_dict = self.base(input, mixup_lambda)
|
| 57 |
+
embedding = output_dict['embedding']
|
| 58 |
+
|
| 59 |
+
clipwise_output = torch.log_softmax(self.fc_transfer(embedding), dim=-1)
|
| 60 |
+
output_dict['clipwise_output'] = clipwise_output
|
| 61 |
+
|
| 62 |
+
return output_dict
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def train(args):
|
| 66 |
+
|
| 67 |
+
# Arugments & parameters
|
| 68 |
+
sample_rate = args.sample_rate
|
| 69 |
+
window_size = args.window_size
|
| 70 |
+
hop_size = args.hop_size
|
| 71 |
+
mel_bins = args.mel_bins
|
| 72 |
+
fmin = args.fmin
|
| 73 |
+
fmax = args.fmax
|
| 74 |
+
model_type = args.model_type
|
| 75 |
+
pretrained_checkpoint_path = args.pretrained_checkpoint_path
|
| 76 |
+
freeze_base = args.freeze_base
|
| 77 |
+
device = 'cuda' if (args.cuda and torch.cuda.is_available()) else 'cpu'
|
| 78 |
+
|
| 79 |
+
classes_num = config.classes_num
|
| 80 |
+
pretrain = True if pretrained_checkpoint_path else False
|
| 81 |
+
|
| 82 |
+
# Model
|
| 83 |
+
Model = eval(model_type)
|
| 84 |
+
model = Model(sample_rate, window_size, hop_size, mel_bins, fmin, fmax,
|
| 85 |
+
classes_num, freeze_base)
|
| 86 |
+
|
| 87 |
+
# Load pretrained model
|
| 88 |
+
if pretrain:
|
| 89 |
+
logging.info('Load pretrained model from {}'.format(pretrained_checkpoint_path))
|
| 90 |
+
model.load_from_pretrain(pretrained_checkpoint_path)
|
| 91 |
+
|
| 92 |
+
# Parallel
|
| 93 |
+
print('GPU number: {}'.format(torch.cuda.device_count()))
|
| 94 |
+
model = torch.nn.DataParallel(model)
|
| 95 |
+
|
| 96 |
+
if 'cuda' in device:
|
| 97 |
+
model.to(device)
|
| 98 |
+
|
| 99 |
+
print('Load pretrained model successfully!')
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
if __name__ == '__main__':
|
| 103 |
+
parser = argparse.ArgumentParser(description='Example of parser. ')
|
| 104 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 105 |
+
|
| 106 |
+
# Train
|
| 107 |
+
parser_train = subparsers.add_parser('train')
|
| 108 |
+
parser_train.add_argument('--sample_rate', type=int, required=True)
|
| 109 |
+
parser_train.add_argument('--window_size', type=int, required=True)
|
| 110 |
+
parser_train.add_argument('--hop_size', type=int, required=True)
|
| 111 |
+
parser_train.add_argument('--mel_bins', type=int, required=True)
|
| 112 |
+
parser_train.add_argument('--fmin', type=int, required=True)
|
| 113 |
+
parser_train.add_argument('--fmax', type=int, required=True)
|
| 114 |
+
parser_train.add_argument('--model_type', type=str, required=True)
|
| 115 |
+
parser_train.add_argument('--pretrained_checkpoint_path', type=str)
|
| 116 |
+
parser_train.add_argument('--freeze_base', action='store_true', default=False)
|
| 117 |
+
parser_train.add_argument('--cuda', action='store_true', default=False)
|
| 118 |
+
|
| 119 |
+
# Parse arguments
|
| 120 |
+
args = parser.parse_args()
|
| 121 |
+
args.filename = get_filename(__file__)
|
| 122 |
+
|
| 123 |
+
if args.mode == 'train':
|
| 124 |
+
train(args)
|
| 125 |
+
|
| 126 |
+
else:
|
| 127 |
+
raise Exception('Error argument!')
|
audio_detection/audio_infer/pytorch/inference.py
ADDED
|
@@ -0,0 +1,206 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import sys
|
| 3 |
+
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
|
| 4 |
+
import numpy as np
|
| 5 |
+
import argparse
|
| 6 |
+
import librosa
|
| 7 |
+
import matplotlib.pyplot as plt
|
| 8 |
+
import torch
|
| 9 |
+
|
| 10 |
+
from utilities import create_folder, get_filename
|
| 11 |
+
from models import *
|
| 12 |
+
from pytorch_utils import move_data_to_device
|
| 13 |
+
import config
|
| 14 |
+
|
| 15 |
+
def audio_tagging(args):
|
| 16 |
+
"""Inference audio tagging result of an audio clip.
|
| 17 |
+
"""
|
| 18 |
+
|
| 19 |
+
# Arugments & parameters
|
| 20 |
+
sample_rate = args.sample_rate
|
| 21 |
+
window_size = args.window_size
|
| 22 |
+
hop_size = args.hop_size
|
| 23 |
+
mel_bins = args.mel_bins
|
| 24 |
+
fmin = args.fmin
|
| 25 |
+
fmax = args.fmax
|
| 26 |
+
model_type = args.model_type
|
| 27 |
+
checkpoint_path = args.checkpoint_path
|
| 28 |
+
audio_path = args.audio_path
|
| 29 |
+
device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
|
| 30 |
+
|
| 31 |
+
classes_num = config.classes_num
|
| 32 |
+
labels = config.labels
|
| 33 |
+
|
| 34 |
+
# Model
|
| 35 |
+
Model = eval(model_type)
|
| 36 |
+
model = Model(sample_rate=sample_rate, window_size=window_size,
|
| 37 |
+
hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax,
|
| 38 |
+
classes_num=classes_num)
|
| 39 |
+
|
| 40 |
+
checkpoint = torch.load(checkpoint_path, map_location=device)
|
| 41 |
+
model.load_state_dict(checkpoint['model'])
|
| 42 |
+
|
| 43 |
+
# Parallel
|
| 44 |
+
if 'cuda' in str(device):
|
| 45 |
+
model.to(device)
|
| 46 |
+
print('GPU number: {}'.format(torch.cuda.device_count()))
|
| 47 |
+
model = torch.nn.DataParallel(model)
|
| 48 |
+
else:
|
| 49 |
+
print('Using CPU.')
|
| 50 |
+
|
| 51 |
+
# Load audio
|
| 52 |
+
(waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
|
| 53 |
+
|
| 54 |
+
waveform = waveform[None, :] # (1, audio_length)
|
| 55 |
+
waveform = move_data_to_device(waveform, device)
|
| 56 |
+
|
| 57 |
+
# Forward
|
| 58 |
+
with torch.no_grad():
|
| 59 |
+
model.eval()
|
| 60 |
+
batch_output_dict = model(waveform, None)
|
| 61 |
+
|
| 62 |
+
clipwise_output = batch_output_dict['clipwise_output'].data.cpu().numpy()[0]
|
| 63 |
+
"""(classes_num,)"""
|
| 64 |
+
|
| 65 |
+
sorted_indexes = np.argsort(clipwise_output)[::-1]
|
| 66 |
+
|
| 67 |
+
# Print audio tagging top probabilities
|
| 68 |
+
for k in range(10):
|
| 69 |
+
print('{}: {:.3f}'.format(np.array(labels)[sorted_indexes[k]],
|
| 70 |
+
clipwise_output[sorted_indexes[k]]))
|
| 71 |
+
|
| 72 |
+
# Print embedding
|
| 73 |
+
if 'embedding' in batch_output_dict.keys():
|
| 74 |
+
embedding = batch_output_dict['embedding'].data.cpu().numpy()[0]
|
| 75 |
+
print('embedding: {}'.format(embedding.shape))
|
| 76 |
+
|
| 77 |
+
return clipwise_output, labels
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
def sound_event_detection(args):
|
| 81 |
+
"""Inference sound event detection result of an audio clip.
|
| 82 |
+
"""
|
| 83 |
+
|
| 84 |
+
# Arugments & parameters
|
| 85 |
+
sample_rate = args.sample_rate
|
| 86 |
+
window_size = args.window_size
|
| 87 |
+
hop_size = args.hop_size
|
| 88 |
+
mel_bins = args.mel_bins
|
| 89 |
+
fmin = args.fmin
|
| 90 |
+
fmax = args.fmax
|
| 91 |
+
model_type = args.model_type
|
| 92 |
+
checkpoint_path = args.checkpoint_path
|
| 93 |
+
audio_path = args.audio_path
|
| 94 |
+
device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
|
| 95 |
+
|
| 96 |
+
classes_num = config.classes_num
|
| 97 |
+
labels = config.labels
|
| 98 |
+
frames_per_second = sample_rate // hop_size
|
| 99 |
+
|
| 100 |
+
# Paths
|
| 101 |
+
fig_path = os.path.join('results', '{}.png'.format(get_filename(audio_path)))
|
| 102 |
+
create_folder(os.path.dirname(fig_path))
|
| 103 |
+
|
| 104 |
+
# Model
|
| 105 |
+
Model = eval(model_type)
|
| 106 |
+
model = Model(sample_rate=sample_rate, window_size=window_size,
|
| 107 |
+
hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax,
|
| 108 |
+
classes_num=classes_num)
|
| 109 |
+
|
| 110 |
+
checkpoint = torch.load(checkpoint_path, map_location=device)
|
| 111 |
+
model.load_state_dict(checkpoint['model'])
|
| 112 |
+
|
| 113 |
+
# Parallel
|
| 114 |
+
print('GPU number: {}'.format(torch.cuda.device_count()))
|
| 115 |
+
model = torch.nn.DataParallel(model)
|
| 116 |
+
|
| 117 |
+
if 'cuda' in str(device):
|
| 118 |
+
model.to(device)
|
| 119 |
+
|
| 120 |
+
# Load audio
|
| 121 |
+
(waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
|
| 122 |
+
|
| 123 |
+
waveform = waveform[None, :] # (1, audio_length)
|
| 124 |
+
waveform = move_data_to_device(waveform, device)
|
| 125 |
+
|
| 126 |
+
# Forward
|
| 127 |
+
with torch.no_grad():
|
| 128 |
+
model.eval()
|
| 129 |
+
batch_output_dict = model(waveform, None)
|
| 130 |
+
|
| 131 |
+
framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0]
|
| 132 |
+
"""(time_steps, classes_num)"""
|
| 133 |
+
|
| 134 |
+
print('Sound event detection result (time_steps x classes_num): {}'.format(
|
| 135 |
+
framewise_output.shape))
|
| 136 |
+
|
| 137 |
+
sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1]
|
| 138 |
+
|
| 139 |
+
top_k = 10 # Show top results
|
| 140 |
+
top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]]
|
| 141 |
+
"""(time_steps, top_k)"""
|
| 142 |
+
|
| 143 |
+
# Plot result
|
| 144 |
+
stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=window_size,
|
| 145 |
+
hop_length=hop_size, window='hann', center=True)
|
| 146 |
+
frames_num = stft.shape[-1]
|
| 147 |
+
|
| 148 |
+
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4))
|
| 149 |
+
axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet')
|
| 150 |
+
axs[0].set_ylabel('Frequency bins')
|
| 151 |
+
axs[0].set_title('Log spectrogram')
|
| 152 |
+
axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1)
|
| 153 |
+
axs[1].xaxis.set_ticks(np.arange(0, frames_num, frames_per_second))
|
| 154 |
+
axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / frames_per_second))
|
| 155 |
+
axs[1].yaxis.set_ticks(np.arange(0, top_k))
|
| 156 |
+
axs[1].yaxis.set_ticklabels(np.array(labels)[sorted_indexes[0 : top_k]])
|
| 157 |
+
axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3)
|
| 158 |
+
axs[1].set_xlabel('Seconds')
|
| 159 |
+
axs[1].xaxis.set_ticks_position('bottom')
|
| 160 |
+
|
| 161 |
+
plt.tight_layout()
|
| 162 |
+
plt.savefig(fig_path)
|
| 163 |
+
print('Save sound event detection visualization to {}'.format(fig_path))
|
| 164 |
+
|
| 165 |
+
return framewise_output, labels
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
if __name__ == '__main__':
|
| 169 |
+
|
| 170 |
+
parser = argparse.ArgumentParser(description='Example of parser. ')
|
| 171 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 172 |
+
|
| 173 |
+
parser_at = subparsers.add_parser('audio_tagging')
|
| 174 |
+
parser_at.add_argument('--sample_rate', type=int, default=32000)
|
| 175 |
+
parser_at.add_argument('--window_size', type=int, default=1024)
|
| 176 |
+
parser_at.add_argument('--hop_size', type=int, default=320)
|
| 177 |
+
parser_at.add_argument('--mel_bins', type=int, default=64)
|
| 178 |
+
parser_at.add_argument('--fmin', type=int, default=50)
|
| 179 |
+
parser_at.add_argument('--fmax', type=int, default=14000)
|
| 180 |
+
parser_at.add_argument('--model_type', type=str, required=True)
|
| 181 |
+
parser_at.add_argument('--checkpoint_path', type=str, required=True)
|
| 182 |
+
parser_at.add_argument('--audio_path', type=str, required=True)
|
| 183 |
+
parser_at.add_argument('--cuda', action='store_true', default=False)
|
| 184 |
+
|
| 185 |
+
parser_sed = subparsers.add_parser('sound_event_detection')
|
| 186 |
+
parser_sed.add_argument('--sample_rate', type=int, default=32000)
|
| 187 |
+
parser_sed.add_argument('--window_size', type=int, default=1024)
|
| 188 |
+
parser_sed.add_argument('--hop_size', type=int, default=320)
|
| 189 |
+
parser_sed.add_argument('--mel_bins', type=int, default=64)
|
| 190 |
+
parser_sed.add_argument('--fmin', type=int, default=50)
|
| 191 |
+
parser_sed.add_argument('--fmax', type=int, default=14000)
|
| 192 |
+
parser_sed.add_argument('--model_type', type=str, required=True)
|
| 193 |
+
parser_sed.add_argument('--checkpoint_path', type=str, required=True)
|
| 194 |
+
parser_sed.add_argument('--audio_path', type=str, required=True)
|
| 195 |
+
parser_sed.add_argument('--cuda', action='store_true', default=False)
|
| 196 |
+
|
| 197 |
+
args = parser.parse_args()
|
| 198 |
+
|
| 199 |
+
if args.mode == 'audio_tagging':
|
| 200 |
+
audio_tagging(args)
|
| 201 |
+
|
| 202 |
+
elif args.mode == 'sound_event_detection':
|
| 203 |
+
sound_event_detection(args)
|
| 204 |
+
|
| 205 |
+
else:
|
| 206 |
+
raise Exception('Error argument!')
|
audio_detection/audio_infer/pytorch/losses.py
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
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|
|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn.functional as F
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
def clip_bce(output_dict, target_dict):
|
| 6 |
+
"""Binary crossentropy loss.
|
| 7 |
+
"""
|
| 8 |
+
return F.binary_cross_entropy(
|
| 9 |
+
output_dict['clipwise_output'], target_dict['target'])
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def get_loss_func(loss_type):
|
| 13 |
+
if loss_type == 'clip_bce':
|
| 14 |
+
return clip_bce
|
audio_detection/audio_infer/pytorch/main.py
ADDED
|
@@ -0,0 +1,378 @@
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|
|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
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|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import sys
|
| 3 |
+
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
|
| 4 |
+
import numpy as np
|
| 5 |
+
import argparse
|
| 6 |
+
import time
|
| 7 |
+
import logging
|
| 8 |
+
|
| 9 |
+
import torch
|
| 10 |
+
import torch.nn as nn
|
| 11 |
+
import torch.nn.functional as F
|
| 12 |
+
import torch.optim as optim
|
| 13 |
+
import torch.utils.data
|
| 14 |
+
|
| 15 |
+
from utilities import (create_folder, get_filename, create_logging, Mixup,
|
| 16 |
+
StatisticsContainer)
|
| 17 |
+
from models import (PVT, PVT2, PVT_lr, PVT_nopretrain, PVT_2layer, Cnn14, Cnn14_no_specaug, Cnn14_no_dropout,
|
| 18 |
+
Cnn6, Cnn10, ResNet22, ResNet38, ResNet54, Cnn14_emb512, Cnn14_emb128,
|
| 19 |
+
Cnn14_emb32, MobileNetV1, MobileNetV2, LeeNet11, LeeNet24, DaiNet19,
|
| 20 |
+
Res1dNet31, Res1dNet51, Wavegram_Cnn14, Wavegram_Logmel_Cnn14,
|
| 21 |
+
Wavegram_Logmel128_Cnn14, Cnn14_16k, Cnn14_8k, Cnn14_mel32, Cnn14_mel128,
|
| 22 |
+
Cnn14_mixup_time_domain, Cnn14_DecisionLevelMax, Cnn14_DecisionLevelAtt, Cnn6_Transformer, GLAM, GLAM2, GLAM3, Cnn4, EAT)
|
| 23 |
+
#from models_test import (PVT_test)
|
| 24 |
+
#from models1 import (PVT1)
|
| 25 |
+
#from models_vig import (VIG, VIG2)
|
| 26 |
+
#from models_vvt import (VVT)
|
| 27 |
+
#from models2 import (MPVIT, MPVIT2)
|
| 28 |
+
#from models_reshape import (PVT_reshape, PVT_tscam)
|
| 29 |
+
#from models_swin import (Swin, Swin_nopretrain)
|
| 30 |
+
#from models_swin2 import (Swin2)
|
| 31 |
+
#from models_van import (Van, Van_tiny)
|
| 32 |
+
#from models_focal import (Focal)
|
| 33 |
+
#from models_cross import (Cross)
|
| 34 |
+
#from models_cov import (Cov)
|
| 35 |
+
#from models_cnn import (Cnn_light)
|
| 36 |
+
#from models_twins import (Twins)
|
| 37 |
+
#from models_cmt import (Cmt, Cmt1)
|
| 38 |
+
#from models_shunted import (Shunted)
|
| 39 |
+
#from models_quadtree import (Quadtree, Quadtree2, Quadtree_nopretrain)
|
| 40 |
+
#from models_davit import (Davit_tscam, Davit, Davit_nopretrain)
|
| 41 |
+
from pytorch_utils import (move_data_to_device, count_parameters, count_flops,
|
| 42 |
+
do_mixup)
|
| 43 |
+
from data_generator import (AudioSetDataset, TrainSampler, BalancedTrainSampler,
|
| 44 |
+
AlternateTrainSampler, EvaluateSampler, collate_fn)
|
| 45 |
+
from evaluate import Evaluator
|
| 46 |
+
import config
|
| 47 |
+
from losses import get_loss_func
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def train(args):
|
| 51 |
+
"""Train AudioSet tagging model.
|
| 52 |
+
|
| 53 |
+
Args:
|
| 54 |
+
dataset_dir: str
|
| 55 |
+
workspace: str
|
| 56 |
+
data_type: 'balanced_train' | 'full_train'
|
| 57 |
+
window_size: int
|
| 58 |
+
hop_size: int
|
| 59 |
+
mel_bins: int
|
| 60 |
+
model_type: str
|
| 61 |
+
loss_type: 'clip_bce'
|
| 62 |
+
balanced: 'none' | 'balanced' | 'alternate'
|
| 63 |
+
augmentation: 'none' | 'mixup'
|
| 64 |
+
batch_size: int
|
| 65 |
+
learning_rate: float
|
| 66 |
+
resume_iteration: int
|
| 67 |
+
early_stop: int
|
| 68 |
+
accumulation_steps: int
|
| 69 |
+
cuda: bool
|
| 70 |
+
"""
|
| 71 |
+
|
| 72 |
+
# Arugments & parameters
|
| 73 |
+
workspace = args.workspace
|
| 74 |
+
data_type = args.data_type
|
| 75 |
+
sample_rate = args.sample_rate
|
| 76 |
+
window_size = args.window_size
|
| 77 |
+
hop_size = args.hop_size
|
| 78 |
+
mel_bins = args.mel_bins
|
| 79 |
+
fmin = args.fmin
|
| 80 |
+
fmax = args.fmax
|
| 81 |
+
model_type = args.model_type
|
| 82 |
+
loss_type = args.loss_type
|
| 83 |
+
balanced = args.balanced
|
| 84 |
+
augmentation = args.augmentation
|
| 85 |
+
batch_size = args.batch_size
|
| 86 |
+
learning_rate = args.learning_rate
|
| 87 |
+
resume_iteration = args.resume_iteration
|
| 88 |
+
early_stop = args.early_stop
|
| 89 |
+
device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
|
| 90 |
+
filename = args.filename
|
| 91 |
+
|
| 92 |
+
num_workers = 8
|
| 93 |
+
clip_samples = config.clip_samples
|
| 94 |
+
classes_num = config.classes_num
|
| 95 |
+
loss_func = get_loss_func(loss_type)
|
| 96 |
+
|
| 97 |
+
# Paths
|
| 98 |
+
black_list_csv = None
|
| 99 |
+
|
| 100 |
+
train_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes',
|
| 101 |
+
'{}.h5'.format(data_type))
|
| 102 |
+
|
| 103 |
+
eval_bal_indexes_hdf5_path = os.path.join(workspace,
|
| 104 |
+
'hdf5s', 'indexes', 'balanced_train.h5')
|
| 105 |
+
|
| 106 |
+
eval_test_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes',
|
| 107 |
+
'eval.h5')
|
| 108 |
+
|
| 109 |
+
checkpoints_dir = os.path.join(workspace, 'checkpoints', filename,
|
| 110 |
+
'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
|
| 111 |
+
sample_rate, window_size, hop_size, mel_bins, fmin, fmax),
|
| 112 |
+
'data_type={}'.format(data_type), model_type,
|
| 113 |
+
'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced),
|
| 114 |
+
'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size))
|
| 115 |
+
create_folder(checkpoints_dir)
|
| 116 |
+
|
| 117 |
+
statistics_path = os.path.join(workspace, 'statistics', filename,
|
| 118 |
+
'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
|
| 119 |
+
sample_rate, window_size, hop_size, mel_bins, fmin, fmax),
|
| 120 |
+
'data_type={}'.format(data_type), model_type,
|
| 121 |
+
'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced),
|
| 122 |
+
'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size),
|
| 123 |
+
'statistics.pkl')
|
| 124 |
+
create_folder(os.path.dirname(statistics_path))
|
| 125 |
+
|
| 126 |
+
logs_dir = os.path.join(workspace, 'logs', filename,
|
| 127 |
+
'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
|
| 128 |
+
sample_rate, window_size, hop_size, mel_bins, fmin, fmax),
|
| 129 |
+
'data_type={}'.format(data_type), model_type,
|
| 130 |
+
'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced),
|
| 131 |
+
'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size))
|
| 132 |
+
|
| 133 |
+
create_logging(logs_dir, filemode='w')
|
| 134 |
+
logging.info(args)
|
| 135 |
+
|
| 136 |
+
if 'cuda' in str(device):
|
| 137 |
+
logging.info('Using GPU.')
|
| 138 |
+
device = 'cuda'
|
| 139 |
+
else:
|
| 140 |
+
logging.info('Using CPU. Set --cuda flag to use GPU.')
|
| 141 |
+
device = 'cpu'
|
| 142 |
+
|
| 143 |
+
# Model
|
| 144 |
+
Model = eval(model_type)
|
| 145 |
+
model = Model(sample_rate=sample_rate, window_size=window_size,
|
| 146 |
+
hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax,
|
| 147 |
+
classes_num=classes_num)
|
| 148 |
+
total = sum(p.numel() for p in model.parameters())
|
| 149 |
+
print("Total params: %.2fM" % (total/1e6))
|
| 150 |
+
logging.info("Total params: %.2fM" % (total/1e6))
|
| 151 |
+
#params_num = count_parameters(model)
|
| 152 |
+
# flops_num = count_flops(model, clip_samples)
|
| 153 |
+
#logging.info('Parameters num: {}'.format(params_num))
|
| 154 |
+
# logging.info('Flops num: {:.3f} G'.format(flops_num / 1e9))
|
| 155 |
+
|
| 156 |
+
# Dataset will be used by DataLoader later. Dataset takes a meta as input
|
| 157 |
+
# and return a waveform and a target.
|
| 158 |
+
dataset = AudioSetDataset(sample_rate=sample_rate)
|
| 159 |
+
|
| 160 |
+
# Train sampler
|
| 161 |
+
if balanced == 'none':
|
| 162 |
+
Sampler = TrainSampler
|
| 163 |
+
elif balanced == 'balanced':
|
| 164 |
+
Sampler = BalancedTrainSampler
|
| 165 |
+
elif balanced == 'alternate':
|
| 166 |
+
Sampler = AlternateTrainSampler
|
| 167 |
+
|
| 168 |
+
train_sampler = Sampler(
|
| 169 |
+
indexes_hdf5_path=train_indexes_hdf5_path,
|
| 170 |
+
batch_size=batch_size * 2 if 'mixup' in augmentation else batch_size,
|
| 171 |
+
black_list_csv=black_list_csv)
|
| 172 |
+
|
| 173 |
+
# Evaluate sampler
|
| 174 |
+
eval_bal_sampler = EvaluateSampler(
|
| 175 |
+
indexes_hdf5_path=eval_bal_indexes_hdf5_path, batch_size=batch_size)
|
| 176 |
+
|
| 177 |
+
eval_test_sampler = EvaluateSampler(
|
| 178 |
+
indexes_hdf5_path=eval_test_indexes_hdf5_path, batch_size=batch_size)
|
| 179 |
+
|
| 180 |
+
# Data loader
|
| 181 |
+
train_loader = torch.utils.data.DataLoader(dataset=dataset,
|
| 182 |
+
batch_sampler=train_sampler, collate_fn=collate_fn,
|
| 183 |
+
num_workers=num_workers, pin_memory=True)
|
| 184 |
+
|
| 185 |
+
eval_bal_loader = torch.utils.data.DataLoader(dataset=dataset,
|
| 186 |
+
batch_sampler=eval_bal_sampler, collate_fn=collate_fn,
|
| 187 |
+
num_workers=num_workers, pin_memory=True)
|
| 188 |
+
|
| 189 |
+
eval_test_loader = torch.utils.data.DataLoader(dataset=dataset,
|
| 190 |
+
batch_sampler=eval_test_sampler, collate_fn=collate_fn,
|
| 191 |
+
num_workers=num_workers, pin_memory=True)
|
| 192 |
+
mix=0.5
|
| 193 |
+
if 'mixup' in augmentation:
|
| 194 |
+
mixup_augmenter = Mixup(mixup_alpha=mix)
|
| 195 |
+
print(mix)
|
| 196 |
+
logging.info(mix)
|
| 197 |
+
|
| 198 |
+
# Evaluator
|
| 199 |
+
evaluator = Evaluator(model=model)
|
| 200 |
+
|
| 201 |
+
# Statistics
|
| 202 |
+
statistics_container = StatisticsContainer(statistics_path)
|
| 203 |
+
|
| 204 |
+
# Optimizer
|
| 205 |
+
optimizer = optim.AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.05, amsgrad=True)
|
| 206 |
+
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=4, min_lr=1e-06, verbose=True)
|
| 207 |
+
train_bgn_time = time.time()
|
| 208 |
+
|
| 209 |
+
# Resume training
|
| 210 |
+
if resume_iteration > 0:
|
| 211 |
+
resume_checkpoint_path = os.path.join(workspace, 'checkpoints', filename,
|
| 212 |
+
'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
|
| 213 |
+
sample_rate, window_size, hop_size, mel_bins, fmin, fmax),
|
| 214 |
+
'data_type={}'.format(data_type), model_type,
|
| 215 |
+
'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced),
|
| 216 |
+
'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size),
|
| 217 |
+
'{}_iterations.pth'.format(resume_iteration))
|
| 218 |
+
|
| 219 |
+
logging.info('Loading checkpoint {}'.format(resume_checkpoint_path))
|
| 220 |
+
checkpoint = torch.load(resume_checkpoint_path)
|
| 221 |
+
model.load_state_dict(checkpoint['model'])
|
| 222 |
+
train_sampler.load_state_dict(checkpoint['sampler'])
|
| 223 |
+
statistics_container.load_state_dict(resume_iteration)
|
| 224 |
+
iteration = checkpoint['iteration']
|
| 225 |
+
|
| 226 |
+
else:
|
| 227 |
+
iteration = 0
|
| 228 |
+
|
| 229 |
+
# Parallel
|
| 230 |
+
print('GPU number: {}'.format(torch.cuda.device_count()))
|
| 231 |
+
model = torch.nn.DataParallel(model)
|
| 232 |
+
|
| 233 |
+
if 'cuda' in str(device):
|
| 234 |
+
model.to(device)
|
| 235 |
+
|
| 236 |
+
if resume_iteration:
|
| 237 |
+
optimizer.load_state_dict(checkpoint['optimizer'])
|
| 238 |
+
scheduler.load_state_dict(checkpoint['scheduler'])
|
| 239 |
+
print(optimizer.state_dict()['param_groups'][0]['lr'])
|
| 240 |
+
|
| 241 |
+
time1 = time.time()
|
| 242 |
+
|
| 243 |
+
for batch_data_dict in train_loader:
|
| 244 |
+
"""batch_data_dict: {
|
| 245 |
+
'audio_name': (batch_size [*2 if mixup],),
|
| 246 |
+
'waveform': (batch_size [*2 if mixup], clip_samples),
|
| 247 |
+
'target': (batch_size [*2 if mixup], classes_num),
|
| 248 |
+
(ifexist) 'mixup_lambda': (batch_size * 2,)}
|
| 249 |
+
"""
|
| 250 |
+
|
| 251 |
+
# Evaluate
|
| 252 |
+
if (iteration % 2000 == 0 and iteration >= resume_iteration) or (iteration == 0):
|
| 253 |
+
train_fin_time = time.time()
|
| 254 |
+
|
| 255 |
+
bal_statistics = evaluator.evaluate(eval_bal_loader)
|
| 256 |
+
test_statistics = evaluator.evaluate(eval_test_loader)
|
| 257 |
+
|
| 258 |
+
logging.info('Validate bal mAP: {:.3f}'.format(
|
| 259 |
+
np.mean(bal_statistics['average_precision'])))
|
| 260 |
+
|
| 261 |
+
logging.info('Validate test mAP: {:.3f}'.format(
|
| 262 |
+
np.mean(test_statistics['average_precision'])))
|
| 263 |
+
|
| 264 |
+
statistics_container.append(iteration, bal_statistics, data_type='bal')
|
| 265 |
+
statistics_container.append(iteration, test_statistics, data_type='test')
|
| 266 |
+
statistics_container.dump()
|
| 267 |
+
|
| 268 |
+
train_time = train_fin_time - train_bgn_time
|
| 269 |
+
validate_time = time.time() - train_fin_time
|
| 270 |
+
|
| 271 |
+
logging.info(
|
| 272 |
+
'iteration: {}, train time: {:.3f} s, validate time: {:.3f} s'
|
| 273 |
+
''.format(iteration, train_time, validate_time))
|
| 274 |
+
|
| 275 |
+
logging.info('------------------------------------')
|
| 276 |
+
|
| 277 |
+
train_bgn_time = time.time()
|
| 278 |
+
|
| 279 |
+
# Save model
|
| 280 |
+
if iteration % 2000 == 0:
|
| 281 |
+
checkpoint = {
|
| 282 |
+
'iteration': iteration,
|
| 283 |
+
'model': model.module.state_dict(),
|
| 284 |
+
'sampler': train_sampler.state_dict(),
|
| 285 |
+
'optimizer': optimizer.state_dict(),
|
| 286 |
+
'scheduler': scheduler.state_dict()}
|
| 287 |
+
|
| 288 |
+
checkpoint_path = os.path.join(
|
| 289 |
+
checkpoints_dir, '{}_iterations.pth'.format(iteration))
|
| 290 |
+
|
| 291 |
+
torch.save(checkpoint, checkpoint_path)
|
| 292 |
+
logging.info('Model saved to {}'.format(checkpoint_path))
|
| 293 |
+
|
| 294 |
+
# Mixup lambda
|
| 295 |
+
if 'mixup' in augmentation:
|
| 296 |
+
batch_data_dict['mixup_lambda'] = mixup_augmenter.get_lambda(
|
| 297 |
+
batch_size=len(batch_data_dict['waveform']))
|
| 298 |
+
|
| 299 |
+
# Move data to device
|
| 300 |
+
for key in batch_data_dict.keys():
|
| 301 |
+
batch_data_dict[key] = move_data_to_device(batch_data_dict[key], device)
|
| 302 |
+
|
| 303 |
+
# Forward
|
| 304 |
+
model.train()
|
| 305 |
+
|
| 306 |
+
if 'mixup' in augmentation:
|
| 307 |
+
batch_output_dict = model(batch_data_dict['waveform'],
|
| 308 |
+
batch_data_dict['mixup_lambda'])
|
| 309 |
+
"""{'clipwise_output': (batch_size, classes_num), ...}"""
|
| 310 |
+
|
| 311 |
+
batch_target_dict = {'target': do_mixup(batch_data_dict['target'],
|
| 312 |
+
batch_data_dict['mixup_lambda'])}
|
| 313 |
+
"""{'target': (batch_size, classes_num)}"""
|
| 314 |
+
else:
|
| 315 |
+
batch_output_dict = model(batch_data_dict['waveform'], None)
|
| 316 |
+
"""{'clipwise_output': (batch_size, classes_num), ...}"""
|
| 317 |
+
|
| 318 |
+
batch_target_dict = {'target': batch_data_dict['target']}
|
| 319 |
+
"""{'target': (batch_size, classes_num)}"""
|
| 320 |
+
|
| 321 |
+
# Loss
|
| 322 |
+
loss = loss_func(batch_output_dict, batch_target_dict)
|
| 323 |
+
# Backward
|
| 324 |
+
loss.backward()
|
| 325 |
+
|
| 326 |
+
optimizer.step()
|
| 327 |
+
optimizer.zero_grad()
|
| 328 |
+
|
| 329 |
+
if iteration % 10 == 0:
|
| 330 |
+
print(iteration, loss)
|
| 331 |
+
#print('--- Iteration: {}, train time: {:.3f} s / 10 iterations ---'\
|
| 332 |
+
# .format(iteration, time.time() - time1))
|
| 333 |
+
#time1 = time.time()
|
| 334 |
+
|
| 335 |
+
if iteration % 2000 == 0:
|
| 336 |
+
scheduler.step(np.mean(test_statistics['average_precision']))
|
| 337 |
+
print(optimizer.state_dict()['param_groups'][0]['lr'])
|
| 338 |
+
logging.info(optimizer.state_dict()['param_groups'][0]['lr'])
|
| 339 |
+
|
| 340 |
+
# Stop learning
|
| 341 |
+
if iteration == early_stop:
|
| 342 |
+
break
|
| 343 |
+
|
| 344 |
+
iteration += 1
|
| 345 |
+
|
| 346 |
+
|
| 347 |
+
if __name__ == '__main__':
|
| 348 |
+
|
| 349 |
+
parser = argparse.ArgumentParser(description='Example of parser. ')
|
| 350 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 351 |
+
|
| 352 |
+
parser_train = subparsers.add_parser('train')
|
| 353 |
+
parser_train.add_argument('--workspace', type=str, required=True)
|
| 354 |
+
parser_train.add_argument('--data_type', type=str, default='full_train', choices=['balanced_train', 'full_train'])
|
| 355 |
+
parser_train.add_argument('--sample_rate', type=int, default=32000)
|
| 356 |
+
parser_train.add_argument('--window_size', type=int, default=1024)
|
| 357 |
+
parser_train.add_argument('--hop_size', type=int, default=320)
|
| 358 |
+
parser_train.add_argument('--mel_bins', type=int, default=64)
|
| 359 |
+
parser_train.add_argument('--fmin', type=int, default=50)
|
| 360 |
+
parser_train.add_argument('--fmax', type=int, default=14000)
|
| 361 |
+
parser_train.add_argument('--model_type', type=str, required=True)
|
| 362 |
+
parser_train.add_argument('--loss_type', type=str, default='clip_bce', choices=['clip_bce'])
|
| 363 |
+
parser_train.add_argument('--balanced', type=str, default='balanced', choices=['none', 'balanced', 'alternate'])
|
| 364 |
+
parser_train.add_argument('--augmentation', type=str, default='mixup', choices=['none', 'mixup'])
|
| 365 |
+
parser_train.add_argument('--batch_size', type=int, default=32)
|
| 366 |
+
parser_train.add_argument('--learning_rate', type=float, default=1e-3)
|
| 367 |
+
parser_train.add_argument('--resume_iteration', type=int, default=0)
|
| 368 |
+
parser_train.add_argument('--early_stop', type=int, default=1000000)
|
| 369 |
+
parser_train.add_argument('--cuda', action='store_true', default=False)
|
| 370 |
+
|
| 371 |
+
args = parser.parse_args()
|
| 372 |
+
args.filename = get_filename(__file__)
|
| 373 |
+
|
| 374 |
+
if args.mode == 'train':
|
| 375 |
+
train(args)
|
| 376 |
+
|
| 377 |
+
else:
|
| 378 |
+
raise Exception('Error argument!')
|
audio_detection/audio_infer/pytorch/models.py
ADDED
|
@@ -0,0 +1,951 @@
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|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn as nn
|
| 3 |
+
import torch.nn.functional as F
|
| 4 |
+
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
| 5 |
+
from torchlibrosa.augmentation import SpecAugmentation
|
| 6 |
+
|
| 7 |
+
from audio_infer.pytorch.pytorch_utils import do_mixup, interpolate, pad_framewise_output
|
| 8 |
+
import os
|
| 9 |
+
import sys
|
| 10 |
+
import math
|
| 11 |
+
import numpy as np
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn as nn
|
| 15 |
+
import torch.nn.functional as F
|
| 16 |
+
from torch.nn.parameter import Parameter
|
| 17 |
+
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
| 18 |
+
from torchlibrosa.augmentation import SpecAugmentation
|
| 19 |
+
from audio_infer.pytorch.pytorch_utils import do_mixup
|
| 20 |
+
import torch.utils.checkpoint as checkpoint
|
| 21 |
+
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
| 22 |
+
import warnings
|
| 23 |
+
from functools import partial
|
| 24 |
+
#from mmdet.models.builder import BACKBONES
|
| 25 |
+
from mmdet.utils import get_root_logger
|
| 26 |
+
from mmcv.runner import load_checkpoint
|
| 27 |
+
os.environ['TORCH_HOME'] = '../pretrained_models'
|
| 28 |
+
from copy import deepcopy
|
| 29 |
+
from timm.models.helpers import load_pretrained
|
| 30 |
+
from torch.cuda.amp import autocast
|
| 31 |
+
from collections import OrderedDict
|
| 32 |
+
import io
|
| 33 |
+
import re
|
| 34 |
+
from mmcv.runner import _load_checkpoint, load_state_dict
|
| 35 |
+
import mmcv.runner
|
| 36 |
+
import copy
|
| 37 |
+
import random
|
| 38 |
+
from einops import rearrange
|
| 39 |
+
from einops.layers.torch import Rearrange, Reduce
|
| 40 |
+
from torch import nn, einsum
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
def load_checkpoint(model,
|
| 44 |
+
filename,
|
| 45 |
+
map_location=None,
|
| 46 |
+
strict=False,
|
| 47 |
+
logger=None,
|
| 48 |
+
revise_keys=[(r'^module\.', '')]):
|
| 49 |
+
"""Load checkpoint from a file or URI.
|
| 50 |
+
|
| 51 |
+
Args:
|
| 52 |
+
model (Module): Module to load checkpoint.
|
| 53 |
+
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
|
| 54 |
+
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
|
| 55 |
+
details.
|
| 56 |
+
map_location (str): Same as :func:`torch.load`.
|
| 57 |
+
strict (bool): Whether to allow different params for the model and
|
| 58 |
+
checkpoint.
|
| 59 |
+
logger (:mod:`logging.Logger` or None): The logger for error message.
|
| 60 |
+
revise_keys (list): A list of customized keywords to modify the
|
| 61 |
+
state_dict in checkpoint. Each item is a (pattern, replacement)
|
| 62 |
+
pair of the regular expression operations. Default: strip
|
| 63 |
+
the prefix 'module.' by [(r'^module\\.', '')].
|
| 64 |
+
|
| 65 |
+
Returns:
|
| 66 |
+
dict or OrderedDict: The loaded checkpoint.
|
| 67 |
+
"""
|
| 68 |
+
|
| 69 |
+
checkpoint = _load_checkpoint(filename, map_location, logger)
|
| 70 |
+
new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
|
| 71 |
+
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
|
| 72 |
+
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
|
| 73 |
+
# OrderedDict is a subclass of dict
|
| 74 |
+
if not isinstance(checkpoint, dict):
|
| 75 |
+
raise RuntimeError(
|
| 76 |
+
f'No state_dict found in checkpoint file {filename}')
|
| 77 |
+
# get state_dict from checkpoint
|
| 78 |
+
if 'state_dict' in checkpoint:
|
| 79 |
+
state_dict = checkpoint['state_dict']
|
| 80 |
+
else:
|
| 81 |
+
state_dict = checkpoint
|
| 82 |
+
|
| 83 |
+
# strip prefix of state_dict
|
| 84 |
+
metadata = getattr(state_dict, '_metadata', OrderedDict())
|
| 85 |
+
for p, r in revise_keys:
|
| 86 |
+
state_dict = OrderedDict(
|
| 87 |
+
{re.sub(p, r, k): v
|
| 88 |
+
for k, v in state_dict.items()})
|
| 89 |
+
state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()})
|
| 90 |
+
# Keep metadata in state_dict
|
| 91 |
+
state_dict._metadata = metadata
|
| 92 |
+
|
| 93 |
+
# load state_dict
|
| 94 |
+
load_state_dict(model, state_dict, strict, logger)
|
| 95 |
+
return checkpoint
|
| 96 |
+
|
| 97 |
+
def init_layer(layer):
|
| 98 |
+
"""Initialize a Linear or Convolutional layer. """
|
| 99 |
+
nn.init.xavier_uniform_(layer.weight)
|
| 100 |
+
|
| 101 |
+
if hasattr(layer, 'bias'):
|
| 102 |
+
if layer.bias is not None:
|
| 103 |
+
layer.bias.data.fill_(0.)
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
def init_bn(bn):
|
| 107 |
+
"""Initialize a Batchnorm layer. """
|
| 108 |
+
bn.bias.data.fill_(0.)
|
| 109 |
+
bn.weight.data.fill_(1.)
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
class TimeShift(nn.Module):
|
| 115 |
+
def __init__(self, mean, std):
|
| 116 |
+
super().__init__()
|
| 117 |
+
self.mean = mean
|
| 118 |
+
self.std = std
|
| 119 |
+
|
| 120 |
+
def forward(self, x):
|
| 121 |
+
if self.training:
|
| 122 |
+
shift = torch.empty(1).normal_(self.mean, self.std).int().item()
|
| 123 |
+
x = torch.roll(x, shift, dims=2)
|
| 124 |
+
return x
|
| 125 |
+
|
| 126 |
+
class LinearSoftPool(nn.Module):
|
| 127 |
+
"""LinearSoftPool
|
| 128 |
+
Linear softmax, takes logits and returns a probability, near to the actual maximum value.
|
| 129 |
+
Taken from the paper:
|
| 130 |
+
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
|
| 131 |
+
https://arxiv.org/abs/1810.09050
|
| 132 |
+
"""
|
| 133 |
+
def __init__(self, pooldim=1):
|
| 134 |
+
super().__init__()
|
| 135 |
+
self.pooldim = pooldim
|
| 136 |
+
|
| 137 |
+
def forward(self, logits, time_decision):
|
| 138 |
+
return (time_decision**2).sum(self.pooldim) / time_decision.sum(
|
| 139 |
+
self.pooldim)
|
| 140 |
+
|
| 141 |
+
class PVT(nn.Module):
|
| 142 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 143 |
+
fmax, classes_num):
|
| 144 |
+
|
| 145 |
+
super(PVT, self).__init__()
|
| 146 |
+
|
| 147 |
+
window = 'hann'
|
| 148 |
+
center = True
|
| 149 |
+
pad_mode = 'reflect'
|
| 150 |
+
ref = 1.0
|
| 151 |
+
amin = 1e-10
|
| 152 |
+
top_db = None
|
| 153 |
+
|
| 154 |
+
# Spectrogram extractor
|
| 155 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 156 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 157 |
+
freeze_parameters=True)
|
| 158 |
+
|
| 159 |
+
# Logmel feature extractor
|
| 160 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 161 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 162 |
+
freeze_parameters=True)
|
| 163 |
+
|
| 164 |
+
self.time_shift = TimeShift(0, 10)
|
| 165 |
+
# Spec augmenter
|
| 166 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 167 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 168 |
+
|
| 169 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 170 |
+
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
|
| 171 |
+
fdim=64,
|
| 172 |
+
patch_size=7,
|
| 173 |
+
stride=4,
|
| 174 |
+
in_chans=1,
|
| 175 |
+
num_classes=classes_num,
|
| 176 |
+
embed_dims=[64, 128, 320, 512],
|
| 177 |
+
depths=[3, 4, 6, 3],
|
| 178 |
+
num_heads=[1, 2, 5, 8],
|
| 179 |
+
mlp_ratios=[8, 8, 4, 4],
|
| 180 |
+
qkv_bias=True,
|
| 181 |
+
qk_scale=None,
|
| 182 |
+
drop_rate=0.0,
|
| 183 |
+
drop_path_rate=0.1,
|
| 184 |
+
sr_ratios=[8, 4, 2, 1],
|
| 185 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
| 186 |
+
num_stages=4,
|
| 187 |
+
#pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
|
| 188 |
+
)
|
| 189 |
+
#self.temp_pool = LinearSoftPool()
|
| 190 |
+
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
| 191 |
+
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
|
| 192 |
+
|
| 193 |
+
self.init_weights()
|
| 194 |
+
|
| 195 |
+
def init_weights(self):
|
| 196 |
+
init_bn(self.bn0)
|
| 197 |
+
init_layer(self.fc_audioset)
|
| 198 |
+
|
| 199 |
+
def forward(self, input, mixup_lambda=None):
|
| 200 |
+
"""Input: (batch_size, times_steps, freq_bins)"""
|
| 201 |
+
|
| 202 |
+
interpolate_ratio = 32
|
| 203 |
+
|
| 204 |
+
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 205 |
+
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 206 |
+
frames_num = x.shape[2]
|
| 207 |
+
x = x.transpose(1, 3)
|
| 208 |
+
x = self.bn0(x)
|
| 209 |
+
x = x.transpose(1, 3)
|
| 210 |
+
|
| 211 |
+
if self.training:
|
| 212 |
+
x = self.time_shift(x)
|
| 213 |
+
x = self.spec_augmenter(x)
|
| 214 |
+
|
| 215 |
+
# Mixup on spectrogram
|
| 216 |
+
if self.training and mixup_lambda is not None:
|
| 217 |
+
x = do_mixup(x, mixup_lambda)
|
| 218 |
+
#print(x.shape) #torch.Size([10, 1, 1001, 64])
|
| 219 |
+
x = self.pvt_transformer(x)
|
| 220 |
+
#print(x.shape) #torch.Size([10, 800, 128])
|
| 221 |
+
x = torch.mean(x, dim=3)
|
| 222 |
+
|
| 223 |
+
x = x.transpose(1, 2).contiguous()
|
| 224 |
+
framewise_output = torch.sigmoid(self.fc_audioset(x))
|
| 225 |
+
#clipwise_output = torch.mean(framewise_output, dim=1)
|
| 226 |
+
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
|
| 227 |
+
x = framewise_output.transpose(1, 2).contiguous()
|
| 228 |
+
x = self.avgpool(x)
|
| 229 |
+
clipwise_output = torch.flatten(x, 1)
|
| 230 |
+
#print(framewise_output.shape) #torch.Size([10, 100, 17])
|
| 231 |
+
framewise_output = interpolate(framewise_output, interpolate_ratio)
|
| 232 |
+
#framewise_output = framewise_output[:,:1000,:]
|
| 233 |
+
#framewise_output = pad_framewise_output(framewise_output, frames_num)
|
| 234 |
+
output_dict = {'framewise_output': framewise_output,
|
| 235 |
+
'clipwise_output': clipwise_output}
|
| 236 |
+
|
| 237 |
+
return output_dict
|
| 238 |
+
|
| 239 |
+
class PVT2(nn.Module):
|
| 240 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 241 |
+
fmax, classes_num):
|
| 242 |
+
|
| 243 |
+
super(PVT2, self).__init__()
|
| 244 |
+
|
| 245 |
+
window = 'hann'
|
| 246 |
+
center = True
|
| 247 |
+
pad_mode = 'reflect'
|
| 248 |
+
ref = 1.0
|
| 249 |
+
amin = 1e-10
|
| 250 |
+
top_db = None
|
| 251 |
+
|
| 252 |
+
# Spectrogram extractor
|
| 253 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 254 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 255 |
+
freeze_parameters=True)
|
| 256 |
+
|
| 257 |
+
# Logmel feature extractor
|
| 258 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 259 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 260 |
+
freeze_parameters=True)
|
| 261 |
+
|
| 262 |
+
self.time_shift = TimeShift(0, 10)
|
| 263 |
+
# Spec augmenter
|
| 264 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 265 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 266 |
+
|
| 267 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 268 |
+
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
|
| 269 |
+
fdim=64,
|
| 270 |
+
patch_size=7,
|
| 271 |
+
stride=4,
|
| 272 |
+
in_chans=1,
|
| 273 |
+
num_classes=classes_num,
|
| 274 |
+
embed_dims=[64, 128, 320, 512],
|
| 275 |
+
depths=[3, 4, 6, 3],
|
| 276 |
+
num_heads=[1, 2, 5, 8],
|
| 277 |
+
mlp_ratios=[8, 8, 4, 4],
|
| 278 |
+
qkv_bias=True,
|
| 279 |
+
qk_scale=None,
|
| 280 |
+
drop_rate=0.0,
|
| 281 |
+
drop_path_rate=0.1,
|
| 282 |
+
sr_ratios=[8, 4, 2, 1],
|
| 283 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
| 284 |
+
num_stages=4,
|
| 285 |
+
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
|
| 286 |
+
)
|
| 287 |
+
#self.temp_pool = LinearSoftPool()
|
| 288 |
+
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
|
| 289 |
+
|
| 290 |
+
self.init_weights()
|
| 291 |
+
|
| 292 |
+
def init_weights(self):
|
| 293 |
+
init_bn(self.bn0)
|
| 294 |
+
init_layer(self.fc_audioset)
|
| 295 |
+
|
| 296 |
+
def forward(self, input, mixup_lambda=None):
|
| 297 |
+
"""Input: (batch_size, times_steps, freq_bins)"""
|
| 298 |
+
|
| 299 |
+
interpolate_ratio = 32
|
| 300 |
+
|
| 301 |
+
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 302 |
+
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 303 |
+
frames_num = x.shape[2]
|
| 304 |
+
x = x.transpose(1, 3)
|
| 305 |
+
x = self.bn0(x)
|
| 306 |
+
x = x.transpose(1, 3)
|
| 307 |
+
|
| 308 |
+
if self.training:
|
| 309 |
+
#x = self.time_shift(x)
|
| 310 |
+
x = self.spec_augmenter(x)
|
| 311 |
+
|
| 312 |
+
# Mixup on spectrogram
|
| 313 |
+
if self.training and mixup_lambda is not None:
|
| 314 |
+
x = do_mixup(x, mixup_lambda)
|
| 315 |
+
#print(x.shape) #torch.Size([10, 1, 1001, 64])
|
| 316 |
+
x = self.pvt_transformer(x)
|
| 317 |
+
#print(x.shape) #torch.Size([10, 800, 128])
|
| 318 |
+
x = torch.mean(x, dim=3)
|
| 319 |
+
|
| 320 |
+
x = x.transpose(1, 2).contiguous()
|
| 321 |
+
framewise_output = torch.sigmoid(self.fc_audioset(x))
|
| 322 |
+
clipwise_output = torch.mean(framewise_output, dim=1)
|
| 323 |
+
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
|
| 324 |
+
#print(framewise_output.shape) #torch.Size([10, 100, 17])
|
| 325 |
+
framewise_output = interpolate(framewise_output, interpolate_ratio)
|
| 326 |
+
#framewise_output = framewise_output[:,:1000,:]
|
| 327 |
+
#framewise_output = pad_framewise_output(framewise_output, frames_num)
|
| 328 |
+
output_dict = {'framewise_output': framewise_output,
|
| 329 |
+
'clipwise_output': clipwise_output}
|
| 330 |
+
|
| 331 |
+
return output_dict
|
| 332 |
+
|
| 333 |
+
class PVT_2layer(nn.Module):
|
| 334 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 335 |
+
fmax, classes_num):
|
| 336 |
+
|
| 337 |
+
super(PVT_2layer, self).__init__()
|
| 338 |
+
|
| 339 |
+
window = 'hann'
|
| 340 |
+
center = True
|
| 341 |
+
pad_mode = 'reflect'
|
| 342 |
+
ref = 1.0
|
| 343 |
+
amin = 1e-10
|
| 344 |
+
top_db = None
|
| 345 |
+
|
| 346 |
+
# Spectrogram extractor
|
| 347 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 348 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 349 |
+
freeze_parameters=True)
|
| 350 |
+
|
| 351 |
+
# Logmel feature extractor
|
| 352 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 353 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 354 |
+
freeze_parameters=True)
|
| 355 |
+
|
| 356 |
+
self.time_shift = TimeShift(0, 10)
|
| 357 |
+
# Spec augmenter
|
| 358 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 359 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 360 |
+
|
| 361 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 362 |
+
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
|
| 363 |
+
fdim=64,
|
| 364 |
+
patch_size=7,
|
| 365 |
+
stride=4,
|
| 366 |
+
in_chans=1,
|
| 367 |
+
num_classes=classes_num,
|
| 368 |
+
embed_dims=[64, 128],
|
| 369 |
+
depths=[3, 4],
|
| 370 |
+
num_heads=[1, 2],
|
| 371 |
+
mlp_ratios=[8, 8],
|
| 372 |
+
qkv_bias=True,
|
| 373 |
+
qk_scale=None,
|
| 374 |
+
drop_rate=0.0,
|
| 375 |
+
drop_path_rate=0.1,
|
| 376 |
+
sr_ratios=[8, 4],
|
| 377 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
| 378 |
+
num_stages=2,
|
| 379 |
+
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
|
| 380 |
+
)
|
| 381 |
+
#self.temp_pool = LinearSoftPool()
|
| 382 |
+
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
| 383 |
+
self.fc_audioset = nn.Linear(128, classes_num, bias=True)
|
| 384 |
+
|
| 385 |
+
self.init_weights()
|
| 386 |
+
|
| 387 |
+
def init_weights(self):
|
| 388 |
+
init_bn(self.bn0)
|
| 389 |
+
init_layer(self.fc_audioset)
|
| 390 |
+
|
| 391 |
+
def forward(self, input, mixup_lambda=None):
|
| 392 |
+
"""Input: (batch_size, times_steps, freq_bins)"""
|
| 393 |
+
|
| 394 |
+
interpolate_ratio = 8
|
| 395 |
+
|
| 396 |
+
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 397 |
+
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 398 |
+
frames_num = x.shape[2]
|
| 399 |
+
x = x.transpose(1, 3)
|
| 400 |
+
x = self.bn0(x)
|
| 401 |
+
x = x.transpose(1, 3)
|
| 402 |
+
|
| 403 |
+
if self.training:
|
| 404 |
+
x = self.time_shift(x)
|
| 405 |
+
x = self.spec_augmenter(x)
|
| 406 |
+
|
| 407 |
+
# Mixup on spectrogram
|
| 408 |
+
if self.training and mixup_lambda is not None:
|
| 409 |
+
x = do_mixup(x, mixup_lambda)
|
| 410 |
+
#print(x.shape) #torch.Size([10, 1, 1001, 64])
|
| 411 |
+
x = self.pvt_transformer(x)
|
| 412 |
+
#print(x.shape) #torch.Size([10, 800, 128])
|
| 413 |
+
x = torch.mean(x, dim=3)
|
| 414 |
+
|
| 415 |
+
x = x.transpose(1, 2).contiguous()
|
| 416 |
+
framewise_output = torch.sigmoid(self.fc_audioset(x))
|
| 417 |
+
#clipwise_output = torch.mean(framewise_output, dim=1)
|
| 418 |
+
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
|
| 419 |
+
x = framewise_output.transpose(1, 2).contiguous()
|
| 420 |
+
x = self.avgpool(x)
|
| 421 |
+
clipwise_output = torch.flatten(x, 1)
|
| 422 |
+
#print(framewise_output.shape) #torch.Size([10, 100, 17])
|
| 423 |
+
framewise_output = interpolate(framewise_output, interpolate_ratio)
|
| 424 |
+
#framewise_output = framewise_output[:,:1000,:]
|
| 425 |
+
#framewise_output = pad_framewise_output(framewise_output, frames_num)
|
| 426 |
+
output_dict = {'framewise_output': framewise_output,
|
| 427 |
+
'clipwise_output': clipwise_output}
|
| 428 |
+
|
| 429 |
+
return output_dict
|
| 430 |
+
|
| 431 |
+
class PVT_lr(nn.Module):
|
| 432 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 433 |
+
fmax, classes_num):
|
| 434 |
+
|
| 435 |
+
super(PVT_lr, self).__init__()
|
| 436 |
+
|
| 437 |
+
window = 'hann'
|
| 438 |
+
center = True
|
| 439 |
+
pad_mode = 'reflect'
|
| 440 |
+
ref = 1.0
|
| 441 |
+
amin = 1e-10
|
| 442 |
+
top_db = None
|
| 443 |
+
|
| 444 |
+
# Spectrogram extractor
|
| 445 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 446 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 447 |
+
freeze_parameters=True)
|
| 448 |
+
|
| 449 |
+
# Logmel feature extractor
|
| 450 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 451 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 452 |
+
freeze_parameters=True)
|
| 453 |
+
|
| 454 |
+
self.time_shift = TimeShift(0, 10)
|
| 455 |
+
# Spec augmenter
|
| 456 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 457 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 458 |
+
|
| 459 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 460 |
+
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
|
| 461 |
+
fdim=64,
|
| 462 |
+
patch_size=7,
|
| 463 |
+
stride=4,
|
| 464 |
+
in_chans=1,
|
| 465 |
+
num_classes=classes_num,
|
| 466 |
+
embed_dims=[64, 128, 320, 512],
|
| 467 |
+
depths=[3, 4, 6, 3],
|
| 468 |
+
num_heads=[1, 2, 5, 8],
|
| 469 |
+
mlp_ratios=[8, 8, 4, 4],
|
| 470 |
+
qkv_bias=True,
|
| 471 |
+
qk_scale=None,
|
| 472 |
+
drop_rate=0.0,
|
| 473 |
+
drop_path_rate=0.1,
|
| 474 |
+
sr_ratios=[8, 4, 2, 1],
|
| 475 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
| 476 |
+
num_stages=4,
|
| 477 |
+
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
|
| 478 |
+
)
|
| 479 |
+
self.temp_pool = LinearSoftPool()
|
| 480 |
+
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
|
| 481 |
+
|
| 482 |
+
self.init_weights()
|
| 483 |
+
|
| 484 |
+
def init_weights(self):
|
| 485 |
+
init_bn(self.bn0)
|
| 486 |
+
init_layer(self.fc_audioset)
|
| 487 |
+
|
| 488 |
+
def forward(self, input, mixup_lambda=None):
|
| 489 |
+
"""Input: (batch_size, times_steps, freq_bins)"""
|
| 490 |
+
|
| 491 |
+
interpolate_ratio = 32
|
| 492 |
+
|
| 493 |
+
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 494 |
+
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 495 |
+
frames_num = x.shape[2]
|
| 496 |
+
x = x.transpose(1, 3)
|
| 497 |
+
x = self.bn0(x)
|
| 498 |
+
x = x.transpose(1, 3)
|
| 499 |
+
|
| 500 |
+
if self.training:
|
| 501 |
+
x = self.time_shift(x)
|
| 502 |
+
x = self.spec_augmenter(x)
|
| 503 |
+
|
| 504 |
+
# Mixup on spectrogram
|
| 505 |
+
if self.training and mixup_lambda is not None:
|
| 506 |
+
x = do_mixup(x, mixup_lambda)
|
| 507 |
+
#print(x.shape) #torch.Size([10, 1, 1001, 64])
|
| 508 |
+
x = self.pvt_transformer(x)
|
| 509 |
+
#print(x.shape) #torch.Size([10, 800, 128])
|
| 510 |
+
x = torch.mean(x, dim=3)
|
| 511 |
+
|
| 512 |
+
x = x.transpose(1, 2).contiguous()
|
| 513 |
+
framewise_output = torch.sigmoid(self.fc_audioset(x))
|
| 514 |
+
clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
|
| 515 |
+
#print(framewise_output.shape) #torch.Size([10, 100, 17])
|
| 516 |
+
framewise_output = interpolate(framewise_output, interpolate_ratio)
|
| 517 |
+
#framewise_output = framewise_output[:,:1000,:]
|
| 518 |
+
#framewise_output = pad_framewise_output(framewise_output, frames_num)
|
| 519 |
+
output_dict = {'framewise_output': framewise_output,
|
| 520 |
+
'clipwise_output': clipwise_output}
|
| 521 |
+
|
| 522 |
+
return output_dict
|
| 523 |
+
|
| 524 |
+
|
| 525 |
+
class PVT_nopretrain(nn.Module):
|
| 526 |
+
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 527 |
+
fmax, classes_num):
|
| 528 |
+
|
| 529 |
+
super(PVT_nopretrain, self).__init__()
|
| 530 |
+
|
| 531 |
+
window = 'hann'
|
| 532 |
+
center = True
|
| 533 |
+
pad_mode = 'reflect'
|
| 534 |
+
ref = 1.0
|
| 535 |
+
amin = 1e-10
|
| 536 |
+
top_db = None
|
| 537 |
+
|
| 538 |
+
# Spectrogram extractor
|
| 539 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 540 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 541 |
+
freeze_parameters=True)
|
| 542 |
+
|
| 543 |
+
# Logmel feature extractor
|
| 544 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 545 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 546 |
+
freeze_parameters=True)
|
| 547 |
+
|
| 548 |
+
self.time_shift = TimeShift(0, 10)
|
| 549 |
+
# Spec augmenter
|
| 550 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 551 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 552 |
+
|
| 553 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 554 |
+
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
|
| 555 |
+
fdim=64,
|
| 556 |
+
patch_size=7,
|
| 557 |
+
stride=4,
|
| 558 |
+
in_chans=1,
|
| 559 |
+
num_classes=classes_num,
|
| 560 |
+
embed_dims=[64, 128, 320, 512],
|
| 561 |
+
depths=[3, 4, 6, 3],
|
| 562 |
+
num_heads=[1, 2, 5, 8],
|
| 563 |
+
mlp_ratios=[8, 8, 4, 4],
|
| 564 |
+
qkv_bias=True,
|
| 565 |
+
qk_scale=None,
|
| 566 |
+
drop_rate=0.0,
|
| 567 |
+
drop_path_rate=0.1,
|
| 568 |
+
sr_ratios=[8, 4, 2, 1],
|
| 569 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
| 570 |
+
num_stages=4,
|
| 571 |
+
#pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
|
| 572 |
+
)
|
| 573 |
+
self.temp_pool = LinearSoftPool()
|
| 574 |
+
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
|
| 575 |
+
|
| 576 |
+
self.init_weights()
|
| 577 |
+
|
| 578 |
+
def init_weights(self):
|
| 579 |
+
init_bn(self.bn0)
|
| 580 |
+
init_layer(self.fc_audioset)
|
| 581 |
+
|
| 582 |
+
def forward(self, input, mixup_lambda=None):
|
| 583 |
+
"""Input: (batch_size, times_steps, freq_bins)"""
|
| 584 |
+
|
| 585 |
+
interpolate_ratio = 32
|
| 586 |
+
|
| 587 |
+
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 588 |
+
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 589 |
+
frames_num = x.shape[2]
|
| 590 |
+
x = x.transpose(1, 3)
|
| 591 |
+
x = self.bn0(x)
|
| 592 |
+
x = x.transpose(1, 3)
|
| 593 |
+
|
| 594 |
+
if self.training:
|
| 595 |
+
x = self.time_shift(x)
|
| 596 |
+
x = self.spec_augmenter(x)
|
| 597 |
+
|
| 598 |
+
# Mixup on spectrogram
|
| 599 |
+
if self.training and mixup_lambda is not None:
|
| 600 |
+
x = do_mixup(x, mixup_lambda)
|
| 601 |
+
#print(x.shape) #torch.Size([10, 1, 1001, 64])
|
| 602 |
+
x = self.pvt_transformer(x)
|
| 603 |
+
#print(x.shape) #torch.Size([10, 800, 128])
|
| 604 |
+
x = torch.mean(x, dim=3)
|
| 605 |
+
|
| 606 |
+
x = x.transpose(1, 2).contiguous()
|
| 607 |
+
framewise_output = torch.sigmoid(self.fc_audioset(x))
|
| 608 |
+
clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
|
| 609 |
+
#print(framewise_output.shape) #torch.Size([10, 100, 17])
|
| 610 |
+
framewise_output = interpolate(framewise_output, interpolate_ratio)
|
| 611 |
+
framewise_output = framewise_output[:,:1000,:]
|
| 612 |
+
#framewise_output = pad_framewise_output(framewise_output, frames_num)
|
| 613 |
+
output_dict = {'framewise_output': framewise_output,
|
| 614 |
+
'clipwise_output': clipwise_output}
|
| 615 |
+
|
| 616 |
+
return output_dict
|
| 617 |
+
|
| 618 |
+
|
| 619 |
+
class Mlp(nn.Module):
|
| 620 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
|
| 621 |
+
super().__init__()
|
| 622 |
+
out_features = out_features or in_features
|
| 623 |
+
hidden_features = hidden_features or in_features
|
| 624 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
| 625 |
+
self.dwconv = DWConv(hidden_features)
|
| 626 |
+
self.act = act_layer()
|
| 627 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
| 628 |
+
self.drop = nn.Dropout(drop)
|
| 629 |
+
self.linear = linear
|
| 630 |
+
if self.linear:
|
| 631 |
+
self.relu = nn.ReLU()
|
| 632 |
+
self.apply(self._init_weights)
|
| 633 |
+
|
| 634 |
+
def _init_weights(self, m):
|
| 635 |
+
if isinstance(m, nn.Linear):
|
| 636 |
+
trunc_normal_(m.weight, std=.02)
|
| 637 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 638 |
+
nn.init.constant_(m.bias, 0)
|
| 639 |
+
elif isinstance(m, nn.LayerNorm):
|
| 640 |
+
nn.init.constant_(m.bias, 0)
|
| 641 |
+
nn.init.constant_(m.weight, 1.0)
|
| 642 |
+
elif isinstance(m, nn.Conv2d):
|
| 643 |
+
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
| 644 |
+
fan_out //= m.groups
|
| 645 |
+
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
| 646 |
+
if m.bias is not None:
|
| 647 |
+
m.bias.data.zero_()
|
| 648 |
+
|
| 649 |
+
def forward(self, x, H, W):
|
| 650 |
+
x = self.fc1(x)
|
| 651 |
+
if self.linear:
|
| 652 |
+
x = self.relu(x)
|
| 653 |
+
x = self.dwconv(x, H, W)
|
| 654 |
+
x = self.act(x)
|
| 655 |
+
x = self.drop(x)
|
| 656 |
+
x = self.fc2(x)
|
| 657 |
+
x = self.drop(x)
|
| 658 |
+
return x
|
| 659 |
+
|
| 660 |
+
|
| 661 |
+
class Attention(nn.Module):
|
| 662 |
+
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False):
|
| 663 |
+
super().__init__()
|
| 664 |
+
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
| 665 |
+
|
| 666 |
+
self.dim = dim
|
| 667 |
+
self.num_heads = num_heads
|
| 668 |
+
head_dim = dim // num_heads
|
| 669 |
+
self.scale = qk_scale or head_dim ** -0.5
|
| 670 |
+
|
| 671 |
+
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
| 672 |
+
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
| 673 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
| 674 |
+
self.proj = nn.Linear(dim, dim)
|
| 675 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
| 676 |
+
|
| 677 |
+
self.linear = linear
|
| 678 |
+
self.sr_ratio = sr_ratio
|
| 679 |
+
if not linear:
|
| 680 |
+
if sr_ratio > 1:
|
| 681 |
+
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
| 682 |
+
self.norm = nn.LayerNorm(dim)
|
| 683 |
+
else:
|
| 684 |
+
self.pool = nn.AdaptiveAvgPool2d(7)
|
| 685 |
+
self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
|
| 686 |
+
self.norm = nn.LayerNorm(dim)
|
| 687 |
+
self.act = nn.GELU()
|
| 688 |
+
self.apply(self._init_weights)
|
| 689 |
+
|
| 690 |
+
def _init_weights(self, m):
|
| 691 |
+
if isinstance(m, nn.Linear):
|
| 692 |
+
trunc_normal_(m.weight, std=.02)
|
| 693 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 694 |
+
nn.init.constant_(m.bias, 0)
|
| 695 |
+
elif isinstance(m, nn.LayerNorm):
|
| 696 |
+
nn.init.constant_(m.bias, 0)
|
| 697 |
+
nn.init.constant_(m.weight, 1.0)
|
| 698 |
+
elif isinstance(m, nn.Conv2d):
|
| 699 |
+
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
| 700 |
+
fan_out //= m.groups
|
| 701 |
+
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
| 702 |
+
if m.bias is not None:
|
| 703 |
+
m.bias.data.zero_()
|
| 704 |
+
|
| 705 |
+
def forward(self, x, H, W):
|
| 706 |
+
B, N, C = x.shape
|
| 707 |
+
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
| 708 |
+
|
| 709 |
+
if not self.linear:
|
| 710 |
+
if self.sr_ratio > 1:
|
| 711 |
+
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
| 712 |
+
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
| 713 |
+
x_ = self.norm(x_)
|
| 714 |
+
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
| 715 |
+
else:
|
| 716 |
+
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
| 717 |
+
else:
|
| 718 |
+
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
| 719 |
+
x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1)
|
| 720 |
+
x_ = self.norm(x_)
|
| 721 |
+
x_ = self.act(x_)
|
| 722 |
+
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
| 723 |
+
k, v = kv[0], kv[1]
|
| 724 |
+
|
| 725 |
+
attn = (q @ k.transpose(-2, -1)) * self.scale
|
| 726 |
+
attn = attn.softmax(dim=-1)
|
| 727 |
+
attn = self.attn_drop(attn)
|
| 728 |
+
|
| 729 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
| 730 |
+
x = self.proj(x)
|
| 731 |
+
x = self.proj_drop(x)
|
| 732 |
+
|
| 733 |
+
return x
|
| 734 |
+
|
| 735 |
+
|
| 736 |
+
class Pooling(nn.Module):
|
| 737 |
+
"""
|
| 738 |
+
Implementation of pooling for PoolFormer
|
| 739 |
+
--pool_size: pooling size
|
| 740 |
+
"""
|
| 741 |
+
def __init__(self, pool_size=3):
|
| 742 |
+
super().__init__()
|
| 743 |
+
self.pool = nn.AvgPool2d(
|
| 744 |
+
pool_size, stride=1, padding=pool_size//2, count_include_pad=False)
|
| 745 |
+
|
| 746 |
+
def forward(self, x):
|
| 747 |
+
return self.pool(x) - x
|
| 748 |
+
|
| 749 |
+
class Block(nn.Module):
|
| 750 |
+
|
| 751 |
+
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
| 752 |
+
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, linear=False):
|
| 753 |
+
super().__init__()
|
| 754 |
+
self.norm1 = norm_layer(dim)
|
| 755 |
+
self.attn = Attention(
|
| 756 |
+
dim,
|
| 757 |
+
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
| 758 |
+
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, linear=linear)
|
| 759 |
+
#self.norm3 = norm_layer(dim)
|
| 760 |
+
#self.token_mixer = Pooling(pool_size=3)
|
| 761 |
+
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
| 762 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
| 763 |
+
self.norm2 = norm_layer(dim)
|
| 764 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
| 765 |
+
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
|
| 766 |
+
self.apply(self._init_weights)
|
| 767 |
+
|
| 768 |
+
def _init_weights(self, m):
|
| 769 |
+
if isinstance(m, nn.Linear):
|
| 770 |
+
trunc_normal_(m.weight, std=.02)
|
| 771 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 772 |
+
nn.init.constant_(m.bias, 0)
|
| 773 |
+
elif isinstance(m, nn.LayerNorm):
|
| 774 |
+
nn.init.constant_(m.bias, 0)
|
| 775 |
+
nn.init.constant_(m.weight, 1.0)
|
| 776 |
+
elif isinstance(m, nn.Conv2d):
|
| 777 |
+
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
| 778 |
+
fan_out //= m.groups
|
| 779 |
+
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
| 780 |
+
if m.bias is not None:
|
| 781 |
+
m.bias.data.zero_()
|
| 782 |
+
|
| 783 |
+
def forward(self, x, H, W):
|
| 784 |
+
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
| 785 |
+
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
| 786 |
+
return x
|
| 787 |
+
|
| 788 |
+
|
| 789 |
+
class OverlapPatchEmbed(nn.Module):
|
| 790 |
+
""" Image to Patch Embedding
|
| 791 |
+
"""
|
| 792 |
+
|
| 793 |
+
def __init__(self, tdim, fdim, patch_size=7, stride=4, in_chans=3, embed_dim=768):
|
| 794 |
+
super().__init__()
|
| 795 |
+
img_size = (tdim, fdim)
|
| 796 |
+
patch_size = to_2tuple(patch_size)
|
| 797 |
+
|
| 798 |
+
self.img_size = img_size
|
| 799 |
+
self.patch_size = patch_size
|
| 800 |
+
self.H, self.W = img_size[0] // stride, img_size[1] // stride
|
| 801 |
+
self.num_patches = self.H * self.W
|
| 802 |
+
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
|
| 803 |
+
padding=(patch_size[0] // 3, patch_size[1] // 3))
|
| 804 |
+
self.norm = nn.LayerNorm(embed_dim)
|
| 805 |
+
|
| 806 |
+
self.apply(self._init_weights)
|
| 807 |
+
|
| 808 |
+
def _init_weights(self, m):
|
| 809 |
+
if isinstance(m, nn.Linear):
|
| 810 |
+
trunc_normal_(m.weight, std=.02)
|
| 811 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 812 |
+
nn.init.constant_(m.bias, 0)
|
| 813 |
+
elif isinstance(m, nn.LayerNorm):
|
| 814 |
+
nn.init.constant_(m.bias, 0)
|
| 815 |
+
nn.init.constant_(m.weight, 1.0)
|
| 816 |
+
elif isinstance(m, nn.Conv2d):
|
| 817 |
+
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
| 818 |
+
fan_out //= m.groups
|
| 819 |
+
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
| 820 |
+
if m.bias is not None:
|
| 821 |
+
m.bias.data.zero_()
|
| 822 |
+
|
| 823 |
+
def forward(self, x):
|
| 824 |
+
x = self.proj(x)
|
| 825 |
+
_, _, H, W = x.shape
|
| 826 |
+
x = x.flatten(2).transpose(1, 2)
|
| 827 |
+
x = self.norm(x)
|
| 828 |
+
|
| 829 |
+
return x, H, W
|
| 830 |
+
|
| 831 |
+
|
| 832 |
+
class PyramidVisionTransformerV2(nn.Module):
|
| 833 |
+
def __init__(self, tdim=1001, fdim=64, patch_size=16, stride=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
|
| 834 |
+
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
|
| 835 |
+
attn_drop_rate=0., drop_path_rate=0.1, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3],
|
| 836 |
+
sr_ratios=[8, 4, 2, 1], num_stages=2, linear=False, pretrained=None):
|
| 837 |
+
super().__init__()
|
| 838 |
+
# self.num_classes = num_classes
|
| 839 |
+
self.depths = depths
|
| 840 |
+
self.num_stages = num_stages
|
| 841 |
+
self.linear = linear
|
| 842 |
+
|
| 843 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
| 844 |
+
cur = 0
|
| 845 |
+
|
| 846 |
+
for i in range(num_stages):
|
| 847 |
+
patch_embed = OverlapPatchEmbed(tdim=tdim if i == 0 else tdim // (2 ** (i + 1)),
|
| 848 |
+
fdim=fdim if i == 0 else tdim // (2 ** (i + 1)),
|
| 849 |
+
patch_size=7 if i == 0 else 3,
|
| 850 |
+
stride=stride if i == 0 else 2,
|
| 851 |
+
in_chans=in_chans if i == 0 else embed_dims[i - 1],
|
| 852 |
+
embed_dim=embed_dims[i])
|
| 853 |
+
block = nn.ModuleList([Block(
|
| 854 |
+
dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias,
|
| 855 |
+
qk_scale=qk_scale,
|
| 856 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer,
|
| 857 |
+
sr_ratio=sr_ratios[i], linear=linear)
|
| 858 |
+
for j in range(depths[i])])
|
| 859 |
+
norm = norm_layer(embed_dims[i])
|
| 860 |
+
cur += depths[i]
|
| 861 |
+
|
| 862 |
+
setattr(self, f"patch_embed{i + 1}", patch_embed)
|
| 863 |
+
setattr(self, f"block{i + 1}", block)
|
| 864 |
+
setattr(self, f"norm{i + 1}", norm)
|
| 865 |
+
#self.n = nn.Linear(125, 250, bias=True)
|
| 866 |
+
# classification head
|
| 867 |
+
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
|
| 868 |
+
self.apply(self._init_weights)
|
| 869 |
+
self.init_weights(pretrained)
|
| 870 |
+
|
| 871 |
+
def _init_weights(self, m):
|
| 872 |
+
if isinstance(m, nn.Linear):
|
| 873 |
+
trunc_normal_(m.weight, std=.02)
|
| 874 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
| 875 |
+
nn.init.constant_(m.bias, 0)
|
| 876 |
+
elif isinstance(m, nn.LayerNorm):
|
| 877 |
+
nn.init.constant_(m.bias, 0)
|
| 878 |
+
nn.init.constant_(m.weight, 1.0)
|
| 879 |
+
elif isinstance(m, nn.Conv2d):
|
| 880 |
+
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
| 881 |
+
fan_out //= m.groups
|
| 882 |
+
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
| 883 |
+
if m.bias is not None:
|
| 884 |
+
m.bias.data.zero_()
|
| 885 |
+
|
| 886 |
+
def init_weights(self, pretrained=None):
|
| 887 |
+
if isinstance(pretrained, str):
|
| 888 |
+
logger = get_root_logger()
|
| 889 |
+
load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
|
| 890 |
+
|
| 891 |
+
def freeze_patch_emb(self):
|
| 892 |
+
self.patch_embed1.requires_grad = False
|
| 893 |
+
|
| 894 |
+
@torch.jit.ignore
|
| 895 |
+
def no_weight_decay(self):
|
| 896 |
+
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
|
| 897 |
+
|
| 898 |
+
def get_classifier(self):
|
| 899 |
+
return self.head
|
| 900 |
+
|
| 901 |
+
def reset_classifier(self, num_classes, global_pool=''):
|
| 902 |
+
self.num_classes = num_classes
|
| 903 |
+
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
| 904 |
+
|
| 905 |
+
def forward_features(self, x):
|
| 906 |
+
B = x.shape[0]
|
| 907 |
+
|
| 908 |
+
for i in range(self.num_stages):
|
| 909 |
+
patch_embed = getattr(self, f"patch_embed{i + 1}")
|
| 910 |
+
block = getattr(self, f"block{i + 1}")
|
| 911 |
+
norm = getattr(self, f"norm{i + 1}")
|
| 912 |
+
x, H, W = patch_embed(x)
|
| 913 |
+
#print(x.shape)
|
| 914 |
+
for blk in block:
|
| 915 |
+
x = blk(x, H, W)
|
| 916 |
+
#print(x.shape)
|
| 917 |
+
x = norm(x)
|
| 918 |
+
#if i != self.num_stages - 1:
|
| 919 |
+
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
| 920 |
+
#print(x.shape)
|
| 921 |
+
return x
|
| 922 |
+
|
| 923 |
+
def forward(self, x):
|
| 924 |
+
x = self.forward_features(x)
|
| 925 |
+
# x = self.head(x)
|
| 926 |
+
|
| 927 |
+
return x
|
| 928 |
+
|
| 929 |
+
class DWConv(nn.Module):
|
| 930 |
+
def __init__(self, dim=768):
|
| 931 |
+
super(DWConv, self).__init__()
|
| 932 |
+
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
|
| 933 |
+
|
| 934 |
+
def forward(self, x, H, W):
|
| 935 |
+
B, N, C = x.shape
|
| 936 |
+
x = x.transpose(1, 2).view(B, C, H, W)
|
| 937 |
+
x = self.dwconv(x)
|
| 938 |
+
x = x.flatten(2).transpose(1, 2)
|
| 939 |
+
|
| 940 |
+
return x
|
| 941 |
+
|
| 942 |
+
|
| 943 |
+
def _conv_filter(state_dict, patch_size=16):
|
| 944 |
+
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
| 945 |
+
out_dict = {}
|
| 946 |
+
for k, v in state_dict.items():
|
| 947 |
+
if 'patch_embed.proj.weight' in k:
|
| 948 |
+
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
|
| 949 |
+
out_dict[k] = v
|
| 950 |
+
|
| 951 |
+
return out_dict
|
audio_detection/audio_infer/pytorch/pytorch_utils.py
ADDED
|
@@ -0,0 +1,251 @@
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import time
|
| 3 |
+
import torch
|
| 4 |
+
import torch.nn as nn
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def move_data_to_device(x, device):
|
| 8 |
+
if 'float' in str(x.dtype):
|
| 9 |
+
x = torch.Tensor(x)
|
| 10 |
+
elif 'int' in str(x.dtype):
|
| 11 |
+
x = torch.LongTensor(x)
|
| 12 |
+
else:
|
| 13 |
+
return x
|
| 14 |
+
|
| 15 |
+
return x.to(device)
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def do_mixup(x, mixup_lambda):
|
| 19 |
+
"""Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes
|
| 20 |
+
(1, 3, 5, ...).
|
| 21 |
+
|
| 22 |
+
Args:
|
| 23 |
+
x: (batch_size * 2, ...)
|
| 24 |
+
mixup_lambda: (batch_size * 2,)
|
| 25 |
+
|
| 26 |
+
Returns:
|
| 27 |
+
out: (batch_size, ...)
|
| 28 |
+
"""
|
| 29 |
+
out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
|
| 30 |
+
x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
|
| 31 |
+
return out
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
def append_to_dict(dict, key, value):
|
| 35 |
+
if key in dict.keys():
|
| 36 |
+
dict[key].append(value)
|
| 37 |
+
else:
|
| 38 |
+
dict[key] = [value]
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def forward(model, generator, return_input=False,
|
| 42 |
+
return_target=False):
|
| 43 |
+
"""Forward data to a model.
|
| 44 |
+
|
| 45 |
+
Args:
|
| 46 |
+
model: object
|
| 47 |
+
generator: object
|
| 48 |
+
return_input: bool
|
| 49 |
+
return_target: bool
|
| 50 |
+
|
| 51 |
+
Returns:
|
| 52 |
+
audio_name: (audios_num,)
|
| 53 |
+
clipwise_output: (audios_num, classes_num)
|
| 54 |
+
(ifexist) segmentwise_output: (audios_num, segments_num, classes_num)
|
| 55 |
+
(ifexist) framewise_output: (audios_num, frames_num, classes_num)
|
| 56 |
+
(optional) return_input: (audios_num, segment_samples)
|
| 57 |
+
(optional) return_target: (audios_num, classes_num)
|
| 58 |
+
"""
|
| 59 |
+
output_dict = {}
|
| 60 |
+
device = next(model.parameters()).device
|
| 61 |
+
time1 = time.time()
|
| 62 |
+
|
| 63 |
+
# Forward data to a model in mini-batches
|
| 64 |
+
for n, batch_data_dict in enumerate(generator):
|
| 65 |
+
print(n)
|
| 66 |
+
batch_waveform = move_data_to_device(batch_data_dict['waveform'], device)
|
| 67 |
+
|
| 68 |
+
with torch.no_grad():
|
| 69 |
+
model.eval()
|
| 70 |
+
batch_output = model(batch_waveform)
|
| 71 |
+
|
| 72 |
+
append_to_dict(output_dict, 'audio_name', batch_data_dict['audio_name'])
|
| 73 |
+
|
| 74 |
+
append_to_dict(output_dict, 'clipwise_output',
|
| 75 |
+
batch_output['clipwise_output'].data.cpu().numpy())
|
| 76 |
+
|
| 77 |
+
if 'segmentwise_output' in batch_output.keys():
|
| 78 |
+
append_to_dict(output_dict, 'segmentwise_output',
|
| 79 |
+
batch_output['segmentwise_output'].data.cpu().numpy())
|
| 80 |
+
|
| 81 |
+
if 'framewise_output' in batch_output.keys():
|
| 82 |
+
append_to_dict(output_dict, 'framewise_output',
|
| 83 |
+
batch_output['framewise_output'].data.cpu().numpy())
|
| 84 |
+
|
| 85 |
+
if return_input:
|
| 86 |
+
append_to_dict(output_dict, 'waveform', batch_data_dict['waveform'])
|
| 87 |
+
|
| 88 |
+
if return_target:
|
| 89 |
+
if 'target' in batch_data_dict.keys():
|
| 90 |
+
append_to_dict(output_dict, 'target', batch_data_dict['target'])
|
| 91 |
+
|
| 92 |
+
if n % 10 == 0:
|
| 93 |
+
print(' --- Inference time: {:.3f} s / 10 iterations ---'.format(
|
| 94 |
+
time.time() - time1))
|
| 95 |
+
time1 = time.time()
|
| 96 |
+
|
| 97 |
+
for key in output_dict.keys():
|
| 98 |
+
output_dict[key] = np.concatenate(output_dict[key], axis=0)
|
| 99 |
+
|
| 100 |
+
return output_dict
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
def interpolate(x, ratio):
|
| 104 |
+
"""Interpolate data in time domain. This is used to compensate the
|
| 105 |
+
resolution reduction in downsampling of a CNN.
|
| 106 |
+
|
| 107 |
+
Args:
|
| 108 |
+
x: (batch_size, time_steps, classes_num)
|
| 109 |
+
ratio: int, ratio to interpolate
|
| 110 |
+
|
| 111 |
+
Returns:
|
| 112 |
+
upsampled: (batch_size, time_steps * ratio, classes_num)
|
| 113 |
+
"""
|
| 114 |
+
(batch_size, time_steps, classes_num) = x.shape
|
| 115 |
+
upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
|
| 116 |
+
upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
|
| 117 |
+
return upsampled
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def pad_framewise_output(framewise_output, frames_num):
|
| 121 |
+
"""Pad framewise_output to the same length as input frames. The pad value
|
| 122 |
+
is the same as the value of the last frame.
|
| 123 |
+
|
| 124 |
+
Args:
|
| 125 |
+
framewise_output: (batch_size, frames_num, classes_num)
|
| 126 |
+
frames_num: int, number of frames to pad
|
| 127 |
+
|
| 128 |
+
Outputs:
|
| 129 |
+
output: (batch_size, frames_num, classes_num)
|
| 130 |
+
"""
|
| 131 |
+
pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1)
|
| 132 |
+
"""tensor for padding"""
|
| 133 |
+
|
| 134 |
+
output = torch.cat((framewise_output, pad), dim=1)
|
| 135 |
+
"""(batch_size, frames_num, classes_num)"""
|
| 136 |
+
|
| 137 |
+
return output
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
def count_parameters(model):
|
| 141 |
+
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
def count_flops(model, audio_length):
|
| 145 |
+
"""Count flops. Code modified from others' implementation.
|
| 146 |
+
"""
|
| 147 |
+
multiply_adds = True
|
| 148 |
+
list_conv2d=[]
|
| 149 |
+
def conv2d_hook(self, input, output):
|
| 150 |
+
batch_size, input_channels, input_height, input_width = input[0].size()
|
| 151 |
+
output_channels, output_height, output_width = output[0].size()
|
| 152 |
+
|
| 153 |
+
kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
|
| 154 |
+
bias_ops = 1 if self.bias is not None else 0
|
| 155 |
+
|
| 156 |
+
params = output_channels * (kernel_ops + bias_ops)
|
| 157 |
+
flops = batch_size * params * output_height * output_width
|
| 158 |
+
|
| 159 |
+
list_conv2d.append(flops)
|
| 160 |
+
|
| 161 |
+
list_conv1d=[]
|
| 162 |
+
def conv1d_hook(self, input, output):
|
| 163 |
+
batch_size, input_channels, input_length = input[0].size()
|
| 164 |
+
output_channels, output_length = output[0].size()
|
| 165 |
+
|
| 166 |
+
kernel_ops = self.kernel_size[0] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
|
| 167 |
+
bias_ops = 1 if self.bias is not None else 0
|
| 168 |
+
|
| 169 |
+
params = output_channels * (kernel_ops + bias_ops)
|
| 170 |
+
flops = batch_size * params * output_length
|
| 171 |
+
|
| 172 |
+
list_conv1d.append(flops)
|
| 173 |
+
|
| 174 |
+
list_linear=[]
|
| 175 |
+
def linear_hook(self, input, output):
|
| 176 |
+
batch_size = input[0].size(0) if input[0].dim() == 2 else 1
|
| 177 |
+
|
| 178 |
+
weight_ops = self.weight.nelement() * (2 if multiply_adds else 1)
|
| 179 |
+
bias_ops = self.bias.nelement()
|
| 180 |
+
|
| 181 |
+
flops = batch_size * (weight_ops + bias_ops)
|
| 182 |
+
list_linear.append(flops)
|
| 183 |
+
|
| 184 |
+
list_bn=[]
|
| 185 |
+
def bn_hook(self, input, output):
|
| 186 |
+
list_bn.append(input[0].nelement() * 2)
|
| 187 |
+
|
| 188 |
+
list_relu=[]
|
| 189 |
+
def relu_hook(self, input, output):
|
| 190 |
+
list_relu.append(input[0].nelement() * 2)
|
| 191 |
+
|
| 192 |
+
list_pooling2d=[]
|
| 193 |
+
def pooling2d_hook(self, input, output):
|
| 194 |
+
batch_size, input_channels, input_height, input_width = input[0].size()
|
| 195 |
+
output_channels, output_height, output_width = output[0].size()
|
| 196 |
+
|
| 197 |
+
kernel_ops = self.kernel_size * self.kernel_size
|
| 198 |
+
bias_ops = 0
|
| 199 |
+
params = output_channels * (kernel_ops + bias_ops)
|
| 200 |
+
flops = batch_size * params * output_height * output_width
|
| 201 |
+
|
| 202 |
+
list_pooling2d.append(flops)
|
| 203 |
+
|
| 204 |
+
list_pooling1d=[]
|
| 205 |
+
def pooling1d_hook(self, input, output):
|
| 206 |
+
batch_size, input_channels, input_length = input[0].size()
|
| 207 |
+
output_channels, output_length = output[0].size()
|
| 208 |
+
|
| 209 |
+
kernel_ops = self.kernel_size[0]
|
| 210 |
+
bias_ops = 0
|
| 211 |
+
|
| 212 |
+
params = output_channels * (kernel_ops + bias_ops)
|
| 213 |
+
flops = batch_size * params * output_length
|
| 214 |
+
|
| 215 |
+
list_pooling2d.append(flops)
|
| 216 |
+
|
| 217 |
+
def foo(net):
|
| 218 |
+
childrens = list(net.children())
|
| 219 |
+
if not childrens:
|
| 220 |
+
if isinstance(net, nn.Conv2d):
|
| 221 |
+
net.register_forward_hook(conv2d_hook)
|
| 222 |
+
elif isinstance(net, nn.Conv1d):
|
| 223 |
+
net.register_forward_hook(conv1d_hook)
|
| 224 |
+
elif isinstance(net, nn.Linear):
|
| 225 |
+
net.register_forward_hook(linear_hook)
|
| 226 |
+
elif isinstance(net, nn.BatchNorm2d) or isinstance(net, nn.BatchNorm1d):
|
| 227 |
+
net.register_forward_hook(bn_hook)
|
| 228 |
+
elif isinstance(net, nn.ReLU):
|
| 229 |
+
net.register_forward_hook(relu_hook)
|
| 230 |
+
elif isinstance(net, nn.AvgPool2d) or isinstance(net, nn.MaxPool2d):
|
| 231 |
+
net.register_forward_hook(pooling2d_hook)
|
| 232 |
+
elif isinstance(net, nn.AvgPool1d) or isinstance(net, nn.MaxPool1d):
|
| 233 |
+
net.register_forward_hook(pooling1d_hook)
|
| 234 |
+
else:
|
| 235 |
+
print('Warning: flop of module {} is not counted!'.format(net))
|
| 236 |
+
return
|
| 237 |
+
for c in childrens:
|
| 238 |
+
foo(c)
|
| 239 |
+
|
| 240 |
+
# Register hook
|
| 241 |
+
foo(model)
|
| 242 |
+
|
| 243 |
+
device = device = next(model.parameters()).device
|
| 244 |
+
input = torch.rand(1, audio_length).to(device)
|
| 245 |
+
|
| 246 |
+
out = model(input)
|
| 247 |
+
|
| 248 |
+
total_flops = sum(list_conv2d) + sum(list_conv1d) + sum(list_linear) + \
|
| 249 |
+
sum(list_bn) + sum(list_relu) + sum(list_pooling2d) + sum(list_pooling1d)
|
| 250 |
+
|
| 251 |
+
return total_flops
|
audio_detection/audio_infer/results/YDlWd7Wmdi1E.png
ADDED
|
audio_detection/audio_infer/useful_ckpts/audio_detection.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9f909808f17d424dc29063a21953ff2be103489518a4f60a6c649d2e3e7d3e81
|
| 3 |
+
size 441042195
|
audio_detection/audio_infer/utils/__pycache__/config.cpython-38.pyc
ADDED
|
Binary file (6.33 kB). View file
|
audio_detection/audio_infer/utils/config.py
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import csv
|
| 3 |
+
|
| 4 |
+
sample_rate = 32000
|
| 5 |
+
clip_samples = sample_rate * 10 # Audio clips are 10-second
|
| 6 |
+
|
| 7 |
+
# Load label
|
| 8 |
+
with open('./audio_detection/audio_infer/metadata/class_labels_indices.csv', 'r') as f:
|
| 9 |
+
reader = csv.reader(f, delimiter=',')
|
| 10 |
+
lines = list(reader)
|
| 11 |
+
|
| 12 |
+
labels = []
|
| 13 |
+
ids = [] # Each label has a unique id such as "/m/068hy"
|
| 14 |
+
for i1 in range(1, len(lines)):
|
| 15 |
+
id = lines[i1][1]
|
| 16 |
+
label = lines[i1][2]
|
| 17 |
+
ids.append(id)
|
| 18 |
+
labels.append(label)
|
| 19 |
+
|
| 20 |
+
classes_num = len(labels)
|
| 21 |
+
|
| 22 |
+
lb_to_ix = {label : i for i, label in enumerate(labels)}
|
| 23 |
+
ix_to_lb = {i : label for i, label in enumerate(labels)}
|
| 24 |
+
|
| 25 |
+
id_to_ix = {id : i for i, id in enumerate(ids)}
|
| 26 |
+
ix_to_id = {i : id for i, id in enumerate(ids)}
|
| 27 |
+
|
| 28 |
+
full_samples_per_class = np.array([
|
| 29 |
+
937432, 16344, 7822, 10271, 2043, 14420, 733, 1511,
|
| 30 |
+
1258, 424, 1751, 704, 369, 590, 1063, 1375,
|
| 31 |
+
5026, 743, 853, 1648, 714, 1497, 1251, 2139,
|
| 32 |
+
1093, 133, 224, 39469, 6423, 407, 1559, 4546,
|
| 33 |
+
6826, 7464, 2468, 549, 4063, 334, 587, 238,
|
| 34 |
+
1766, 691, 114, 2153, 236, 209, 421, 740,
|
| 35 |
+
269, 959, 137, 4192, 485, 1515, 655, 274,
|
| 36 |
+
69, 157, 1128, 807, 1022, 346, 98, 680,
|
| 37 |
+
890, 352, 4169, 2061, 1753, 9883, 1339, 708,
|
| 38 |
+
37857, 18504, 12864, 2475, 2182, 757, 3624, 677,
|
| 39 |
+
1683, 3583, 444, 1780, 2364, 409, 4060, 3097,
|
| 40 |
+
3143, 502, 723, 600, 230, 852, 1498, 1865,
|
| 41 |
+
1879, 2429, 5498, 5430, 2139, 1761, 1051, 831,
|
| 42 |
+
2401, 2258, 1672, 1711, 987, 646, 794, 25061,
|
| 43 |
+
5792, 4256, 96, 8126, 2740, 752, 513, 554,
|
| 44 |
+
106, 254, 1592, 556, 331, 615, 2841, 737,
|
| 45 |
+
265, 1349, 358, 1731, 1115, 295, 1070, 972,
|
| 46 |
+
174, 937780, 112337, 42509, 49200, 11415, 6092, 13851,
|
| 47 |
+
2665, 1678, 13344, 2329, 1415, 2244, 1099, 5024,
|
| 48 |
+
9872, 10948, 4409, 2732, 1211, 1289, 4807, 5136,
|
| 49 |
+
1867, 16134, 14519, 3086, 19261, 6499, 4273, 2790,
|
| 50 |
+
8820, 1228, 1575, 4420, 3685, 2019, 664, 324,
|
| 51 |
+
513, 411, 436, 2997, 5162, 3806, 1389, 899,
|
| 52 |
+
8088, 7004, 1105, 3633, 2621, 9753, 1082, 26854,
|
| 53 |
+
3415, 4991, 2129, 5546, 4489, 2850, 1977, 1908,
|
| 54 |
+
1719, 1106, 1049, 152, 136, 802, 488, 592,
|
| 55 |
+
2081, 2712, 1665, 1128, 250, 544, 789, 2715,
|
| 56 |
+
8063, 7056, 2267, 8034, 6092, 3815, 1833, 3277,
|
| 57 |
+
8813, 2111, 4662, 2678, 2954, 5227, 1472, 2591,
|
| 58 |
+
3714, 1974, 1795, 4680, 3751, 6585, 2109, 36617,
|
| 59 |
+
6083, 16264, 17351, 3449, 5034, 3931, 2599, 4134,
|
| 60 |
+
3892, 2334, 2211, 4516, 2766, 2862, 3422, 1788,
|
| 61 |
+
2544, 2403, 2892, 4042, 3460, 1516, 1972, 1563,
|
| 62 |
+
1579, 2776, 1647, 4535, 3921, 1261, 6074, 2922,
|
| 63 |
+
3068, 1948, 4407, 712, 1294, 1019, 1572, 3764,
|
| 64 |
+
5218, 975, 1539, 6376, 1606, 6091, 1138, 1169,
|
| 65 |
+
7925, 3136, 1108, 2677, 2680, 1383, 3144, 2653,
|
| 66 |
+
1986, 1800, 1308, 1344, 122231, 12977, 2552, 2678,
|
| 67 |
+
7824, 768, 8587, 39503, 3474, 661, 430, 193,
|
| 68 |
+
1405, 1442, 3588, 6280, 10515, 785, 710, 305,
|
| 69 |
+
206, 4990, 5329, 3398, 1771, 3022, 6907, 1523,
|
| 70 |
+
8588, 12203, 666, 2113, 7916, 434, 1636, 5185,
|
| 71 |
+
1062, 664, 952, 3490, 2811, 2749, 2848, 15555,
|
| 72 |
+
363, 117, 1494, 1647, 5886, 4021, 633, 1013,
|
| 73 |
+
5951, 11343, 2324, 243, 372, 943, 734, 242,
|
| 74 |
+
3161, 122, 127, 201, 1654, 768, 134, 1467,
|
| 75 |
+
642, 1148, 2156, 1368, 1176, 302, 1909, 61,
|
| 76 |
+
223, 1812, 287, 422, 311, 228, 748, 230,
|
| 77 |
+
1876, 539, 1814, 737, 689, 1140, 591, 943,
|
| 78 |
+
353, 289, 198, 490, 7938, 1841, 850, 457,
|
| 79 |
+
814, 146, 551, 728, 1627, 620, 648, 1621,
|
| 80 |
+
2731, 535, 88, 1736, 736, 328, 293, 3170,
|
| 81 |
+
344, 384, 7640, 433, 215, 715, 626, 128,
|
| 82 |
+
3059, 1833, 2069, 3732, 1640, 1508, 836, 567,
|
| 83 |
+
2837, 1151, 2068, 695, 1494, 3173, 364, 88,
|
| 84 |
+
188, 740, 677, 273, 1533, 821, 1091, 293,
|
| 85 |
+
647, 318, 1202, 328, 532, 2847, 526, 721,
|
| 86 |
+
370, 258, 956, 1269, 1641, 339, 1322, 4485,
|
| 87 |
+
286, 1874, 277, 757, 1393, 1330, 380, 146,
|
| 88 |
+
377, 394, 318, 339, 1477, 1886, 101, 1435,
|
| 89 |
+
284, 1425, 686, 621, 221, 117, 87, 1340,
|
| 90 |
+
201, 1243, 1222, 651, 1899, 421, 712, 1016,
|
| 91 |
+
1279, 124, 351, 258, 7043, 368, 666, 162,
|
| 92 |
+
7664, 137, 70159, 26179, 6321, 32236, 33320, 771,
|
| 93 |
+
1169, 269, 1103, 444, 364, 2710, 121, 751,
|
| 94 |
+
1609, 855, 1141, 2287, 1940, 3943, 289])
|
audio_detection/audio_infer/utils/crash.py
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
|
| 3 |
+
class ExceptionHook:
|
| 4 |
+
instance = None
|
| 5 |
+
def __call__(self, *args, **kwargs):
|
| 6 |
+
if self.instance is None:
|
| 7 |
+
from IPython.core import ultratb
|
| 8 |
+
self.instance = ultratb.FormattedTB(mode='Plain',
|
| 9 |
+
color_scheme='Linux', call_pdb=1)
|
| 10 |
+
return self.instance(*args, **kwargs)
|
| 11 |
+
|
| 12 |
+
sys.excepthook = ExceptionHook()
|
audio_detection/audio_infer/utils/create_black_list.py
ADDED
|
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import argparse
|
| 2 |
+
import csv
|
| 3 |
+
import os
|
| 4 |
+
|
| 5 |
+
from utilities import create_folder
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
def dcase2017task4(args):
|
| 9 |
+
"""Create black list. Black list is a list of audio ids that will be
|
| 10 |
+
skipped in training.
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
# Augments & parameters
|
| 14 |
+
workspace = args.workspace
|
| 15 |
+
|
| 16 |
+
# Black list from DCASE 2017 Task 4
|
| 17 |
+
test_weak_csv = 'metadata/black_list/groundtruth_weak_label_testing_set.csv'
|
| 18 |
+
evaluation_weak_csv = 'metadata/black_list/groundtruth_weak_label_evaluation_set.csv'
|
| 19 |
+
|
| 20 |
+
black_list_csv = os.path.join(workspace, 'black_list', 'dcase2017task4.csv')
|
| 21 |
+
create_folder(os.path.dirname(black_list_csv))
|
| 22 |
+
|
| 23 |
+
def get_id_sets(csv_path):
|
| 24 |
+
with open(csv_path, 'r') as fr:
|
| 25 |
+
reader = csv.reader(fr, delimiter='\t')
|
| 26 |
+
lines = list(reader)
|
| 27 |
+
|
| 28 |
+
ids_set = []
|
| 29 |
+
|
| 30 |
+
for line in lines:
|
| 31 |
+
"""line: ['-5QrBL6MzLg_60.000_70.000.wav', '60.000', '70.000', 'Train horn']"""
|
| 32 |
+
ids_set.append(line[0][0 : 11])
|
| 33 |
+
|
| 34 |
+
ids_set = list(set(ids_set))
|
| 35 |
+
return ids_set
|
| 36 |
+
|
| 37 |
+
test_ids_set = get_id_sets(test_weak_csv)
|
| 38 |
+
evaluation_ids_set = get_id_sets(evaluation_weak_csv)
|
| 39 |
+
|
| 40 |
+
full_ids_set = test_ids_set + evaluation_ids_set
|
| 41 |
+
|
| 42 |
+
# Write black list
|
| 43 |
+
fw = open(black_list_csv, 'w')
|
| 44 |
+
|
| 45 |
+
for id in full_ids_set:
|
| 46 |
+
fw.write('{}\n'.format(id))
|
| 47 |
+
|
| 48 |
+
print('Write black list to {}'.format(black_list_csv))
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
if __name__ == '__main__':
|
| 52 |
+
parser = argparse.ArgumentParser(description='')
|
| 53 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 54 |
+
|
| 55 |
+
parser_dcase2017task4 = subparsers.add_parser('dcase2017task4')
|
| 56 |
+
parser_dcase2017task4.add_argument('--workspace', type=str, required=True)
|
| 57 |
+
|
| 58 |
+
args = parser.parse_args()
|
| 59 |
+
|
| 60 |
+
if args.mode == 'dcase2017task4':
|
| 61 |
+
dcase2017task4(args)
|
| 62 |
+
|
| 63 |
+
else:
|
| 64 |
+
raise Exception('Error argument!')
|
audio_detection/audio_infer/utils/create_indexes.py
ADDED
|
@@ -0,0 +1,126 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import argparse
|
| 3 |
+
import csv
|
| 4 |
+
import os
|
| 5 |
+
import glob
|
| 6 |
+
import datetime
|
| 7 |
+
import time
|
| 8 |
+
import logging
|
| 9 |
+
import h5py
|
| 10 |
+
import librosa
|
| 11 |
+
|
| 12 |
+
from utilities import create_folder, get_sub_filepaths
|
| 13 |
+
import config
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
def create_indexes(args):
|
| 17 |
+
"""Create indexes a for dataloader to read for training. When users have
|
| 18 |
+
a new task and their own data, they need to create similar indexes. The
|
| 19 |
+
indexes contain meta information of "where to find the data for training".
|
| 20 |
+
"""
|
| 21 |
+
|
| 22 |
+
# Arguments & parameters
|
| 23 |
+
waveforms_hdf5_path = args.waveforms_hdf5_path
|
| 24 |
+
indexes_hdf5_path = args.indexes_hdf5_path
|
| 25 |
+
|
| 26 |
+
# Paths
|
| 27 |
+
create_folder(os.path.dirname(indexes_hdf5_path))
|
| 28 |
+
|
| 29 |
+
with h5py.File(waveforms_hdf5_path, 'r') as hr:
|
| 30 |
+
with h5py.File(indexes_hdf5_path, 'w') as hw:
|
| 31 |
+
audios_num = len(hr['audio_name'])
|
| 32 |
+
hw.create_dataset('audio_name', data=hr['audio_name'][:], dtype='S20')
|
| 33 |
+
hw.create_dataset('target', data=hr['target'][:], dtype=np.bool)
|
| 34 |
+
hw.create_dataset('hdf5_path', data=[waveforms_hdf5_path.encode()] * audios_num, dtype='S200')
|
| 35 |
+
hw.create_dataset('index_in_hdf5', data=np.arange(audios_num), dtype=np.int32)
|
| 36 |
+
|
| 37 |
+
print('Write to {}'.format(indexes_hdf5_path))
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def combine_full_indexes(args):
|
| 41 |
+
"""Combine all balanced and unbalanced indexes hdf5s to a single hdf5. This
|
| 42 |
+
combined indexes hdf5 is used for training with full data (~20k balanced
|
| 43 |
+
audio clips + ~1.9m unbalanced audio clips).
|
| 44 |
+
"""
|
| 45 |
+
|
| 46 |
+
# Arguments & parameters
|
| 47 |
+
indexes_hdf5s_dir = args.indexes_hdf5s_dir
|
| 48 |
+
full_indexes_hdf5_path = args.full_indexes_hdf5_path
|
| 49 |
+
|
| 50 |
+
classes_num = config.classes_num
|
| 51 |
+
|
| 52 |
+
# Paths
|
| 53 |
+
paths = get_sub_filepaths(indexes_hdf5s_dir)
|
| 54 |
+
paths = [path for path in paths if (
|
| 55 |
+
'train' in path and 'full_train' not in path and 'mini' not in path)]
|
| 56 |
+
|
| 57 |
+
print('Total {} hdf5 to combine.'.format(len(paths)))
|
| 58 |
+
|
| 59 |
+
with h5py.File(full_indexes_hdf5_path, 'w') as full_hf:
|
| 60 |
+
full_hf.create_dataset(
|
| 61 |
+
name='audio_name',
|
| 62 |
+
shape=(0,),
|
| 63 |
+
maxshape=(None,),
|
| 64 |
+
dtype='S20')
|
| 65 |
+
|
| 66 |
+
full_hf.create_dataset(
|
| 67 |
+
name='target',
|
| 68 |
+
shape=(0, classes_num),
|
| 69 |
+
maxshape=(None, classes_num),
|
| 70 |
+
dtype=np.bool)
|
| 71 |
+
|
| 72 |
+
full_hf.create_dataset(
|
| 73 |
+
name='hdf5_path',
|
| 74 |
+
shape=(0,),
|
| 75 |
+
maxshape=(None,),
|
| 76 |
+
dtype='S200')
|
| 77 |
+
|
| 78 |
+
full_hf.create_dataset(
|
| 79 |
+
name='index_in_hdf5',
|
| 80 |
+
shape=(0,),
|
| 81 |
+
maxshape=(None,),
|
| 82 |
+
dtype=np.int32)
|
| 83 |
+
|
| 84 |
+
for path in paths:
|
| 85 |
+
with h5py.File(path, 'r') as part_hf:
|
| 86 |
+
print(path)
|
| 87 |
+
n = len(full_hf['audio_name'][:])
|
| 88 |
+
new_n = n + len(part_hf['audio_name'][:])
|
| 89 |
+
|
| 90 |
+
full_hf['audio_name'].resize((new_n,))
|
| 91 |
+
full_hf['audio_name'][n : new_n] = part_hf['audio_name'][:]
|
| 92 |
+
|
| 93 |
+
full_hf['target'].resize((new_n, classes_num))
|
| 94 |
+
full_hf['target'][n : new_n] = part_hf['target'][:]
|
| 95 |
+
|
| 96 |
+
full_hf['hdf5_path'].resize((new_n,))
|
| 97 |
+
full_hf['hdf5_path'][n : new_n] = part_hf['hdf5_path'][:]
|
| 98 |
+
|
| 99 |
+
full_hf['index_in_hdf5'].resize((new_n,))
|
| 100 |
+
full_hf['index_in_hdf5'][n : new_n] = part_hf['index_in_hdf5'][:]
|
| 101 |
+
|
| 102 |
+
print('Write combined full hdf5 to {}'.format(full_indexes_hdf5_path))
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
if __name__ == '__main__':
|
| 106 |
+
parser = argparse.ArgumentParser()
|
| 107 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 108 |
+
|
| 109 |
+
parser_create_indexes = subparsers.add_parser('create_indexes')
|
| 110 |
+
parser_create_indexes.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path of packed waveforms hdf5.')
|
| 111 |
+
parser_create_indexes.add_argument('--indexes_hdf5_path', type=str, required=True, help='Path to write out indexes hdf5.')
|
| 112 |
+
|
| 113 |
+
parser_combine_full_indexes = subparsers.add_parser('combine_full_indexes')
|
| 114 |
+
parser_combine_full_indexes.add_argument('--indexes_hdf5s_dir', type=str, required=True, help='Directory containing indexes hdf5s to be combined.')
|
| 115 |
+
parser_combine_full_indexes.add_argument('--full_indexes_hdf5_path', type=str, required=True, help='Path to write out full indexes hdf5 file.')
|
| 116 |
+
|
| 117 |
+
args = parser.parse_args()
|
| 118 |
+
|
| 119 |
+
if args.mode == 'create_indexes':
|
| 120 |
+
create_indexes(args)
|
| 121 |
+
|
| 122 |
+
elif args.mode == 'combine_full_indexes':
|
| 123 |
+
combine_full_indexes(args)
|
| 124 |
+
|
| 125 |
+
else:
|
| 126 |
+
raise Exception('Incorrect arguments!')
|
audio_detection/audio_infer/utils/data_generator.py
ADDED
|
@@ -0,0 +1,421 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
| 1 |
+
import numpy as np
|
| 2 |
+
import h5py
|
| 3 |
+
import csv
|
| 4 |
+
import time
|
| 5 |
+
import logging
|
| 6 |
+
|
| 7 |
+
from utilities import int16_to_float32
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
def read_black_list(black_list_csv):
|
| 11 |
+
"""Read audio names from black list.
|
| 12 |
+
"""
|
| 13 |
+
with open(black_list_csv, 'r') as fr:
|
| 14 |
+
reader = csv.reader(fr)
|
| 15 |
+
lines = list(reader)
|
| 16 |
+
|
| 17 |
+
black_list_names = ['Y{}.wav'.format(line[0]) for line in lines]
|
| 18 |
+
return black_list_names
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
class AudioSetDataset(object):
|
| 22 |
+
def __init__(self, sample_rate=32000):
|
| 23 |
+
"""This class takes the meta of an audio clip as input, and return
|
| 24 |
+
the waveform and target of the audio clip. This class is used by DataLoader.
|
| 25 |
+
"""
|
| 26 |
+
self.sample_rate = sample_rate
|
| 27 |
+
|
| 28 |
+
def __getitem__(self, meta):
|
| 29 |
+
"""Load waveform and target of an audio clip.
|
| 30 |
+
|
| 31 |
+
Args:
|
| 32 |
+
meta: {
|
| 33 |
+
'hdf5_path': str,
|
| 34 |
+
'index_in_hdf5': int}
|
| 35 |
+
|
| 36 |
+
Returns:
|
| 37 |
+
data_dict: {
|
| 38 |
+
'audio_name': str,
|
| 39 |
+
'waveform': (clip_samples,),
|
| 40 |
+
'target': (classes_num,)}
|
| 41 |
+
"""
|
| 42 |
+
hdf5_path = meta['hdf5_path']
|
| 43 |
+
index_in_hdf5 = meta['index_in_hdf5']
|
| 44 |
+
with h5py.File(hdf5_path, 'r') as hf:
|
| 45 |
+
audio_name = hf['audio_name'][index_in_hdf5].decode()
|
| 46 |
+
waveform = int16_to_float32(hf['waveform'][index_in_hdf5])
|
| 47 |
+
waveform = self.resample(waveform)
|
| 48 |
+
target = hf['target'][index_in_hdf5].astype(np.float32)
|
| 49 |
+
|
| 50 |
+
data_dict = {
|
| 51 |
+
'audio_name': audio_name, 'waveform': waveform, 'target': target}
|
| 52 |
+
|
| 53 |
+
return data_dict
|
| 54 |
+
|
| 55 |
+
def resample(self, waveform):
|
| 56 |
+
"""Resample.
|
| 57 |
+
|
| 58 |
+
Args:
|
| 59 |
+
waveform: (clip_samples,)
|
| 60 |
+
|
| 61 |
+
Returns:
|
| 62 |
+
(resampled_clip_samples,)
|
| 63 |
+
"""
|
| 64 |
+
if self.sample_rate == 32000:
|
| 65 |
+
return waveform
|
| 66 |
+
elif self.sample_rate == 16000:
|
| 67 |
+
return waveform[0 :: 2]
|
| 68 |
+
elif self.sample_rate == 8000:
|
| 69 |
+
return waveform[0 :: 4]
|
| 70 |
+
else:
|
| 71 |
+
raise Exception('Incorrect sample rate!')
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
class Base(object):
|
| 75 |
+
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv, random_seed):
|
| 76 |
+
"""Base class of train sampler.
|
| 77 |
+
|
| 78 |
+
Args:
|
| 79 |
+
indexes_hdf5_path: string
|
| 80 |
+
batch_size: int
|
| 81 |
+
black_list_csv: string
|
| 82 |
+
random_seed: int
|
| 83 |
+
"""
|
| 84 |
+
self.batch_size = batch_size
|
| 85 |
+
self.random_state = np.random.RandomState(random_seed)
|
| 86 |
+
|
| 87 |
+
# Black list
|
| 88 |
+
if black_list_csv:
|
| 89 |
+
self.black_list_names = read_black_list(black_list_csv)
|
| 90 |
+
else:
|
| 91 |
+
self.black_list_names = []
|
| 92 |
+
|
| 93 |
+
logging.info('Black list samples: {}'.format(len(self.black_list_names)))
|
| 94 |
+
|
| 95 |
+
# Load target
|
| 96 |
+
load_time = time.time()
|
| 97 |
+
|
| 98 |
+
with h5py.File(indexes_hdf5_path, 'r') as hf:
|
| 99 |
+
self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
|
| 100 |
+
self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
|
| 101 |
+
self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
|
| 102 |
+
self.targets = hf['target'][:].astype(np.float32)
|
| 103 |
+
|
| 104 |
+
(self.audios_num, self.classes_num) = self.targets.shape
|
| 105 |
+
logging.info('Training number: {}'.format(self.audios_num))
|
| 106 |
+
logging.info('Load target time: {:.3f} s'.format(time.time() - load_time))
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
class TrainSampler(Base):
|
| 110 |
+
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
|
| 111 |
+
random_seed=1234):
|
| 112 |
+
"""Balanced sampler. Generate batch meta for training.
|
| 113 |
+
|
| 114 |
+
Args:
|
| 115 |
+
indexes_hdf5_path: string
|
| 116 |
+
batch_size: int
|
| 117 |
+
black_list_csv: string
|
| 118 |
+
random_seed: int
|
| 119 |
+
"""
|
| 120 |
+
super(TrainSampler, self).__init__(indexes_hdf5_path, batch_size,
|
| 121 |
+
black_list_csv, random_seed)
|
| 122 |
+
|
| 123 |
+
self.indexes = np.arange(self.audios_num)
|
| 124 |
+
|
| 125 |
+
# Shuffle indexes
|
| 126 |
+
self.random_state.shuffle(self.indexes)
|
| 127 |
+
|
| 128 |
+
self.pointer = 0
|
| 129 |
+
|
| 130 |
+
def __iter__(self):
|
| 131 |
+
"""Generate batch meta for training.
|
| 132 |
+
|
| 133 |
+
Returns:
|
| 134 |
+
batch_meta: e.g.: [
|
| 135 |
+
{'hdf5_path': string, 'index_in_hdf5': int},
|
| 136 |
+
...]
|
| 137 |
+
"""
|
| 138 |
+
batch_size = self.batch_size
|
| 139 |
+
|
| 140 |
+
while True:
|
| 141 |
+
batch_meta = []
|
| 142 |
+
i = 0
|
| 143 |
+
while i < batch_size:
|
| 144 |
+
index = self.indexes[self.pointer]
|
| 145 |
+
self.pointer += 1
|
| 146 |
+
|
| 147 |
+
# Shuffle indexes and reset pointer
|
| 148 |
+
if self.pointer >= self.audios_num:
|
| 149 |
+
self.pointer = 0
|
| 150 |
+
self.random_state.shuffle(self.indexes)
|
| 151 |
+
|
| 152 |
+
# If audio in black list then continue
|
| 153 |
+
if self.audio_names[index] in self.black_list_names:
|
| 154 |
+
continue
|
| 155 |
+
else:
|
| 156 |
+
batch_meta.append({
|
| 157 |
+
'hdf5_path': self.hdf5_paths[index],
|
| 158 |
+
'index_in_hdf5': self.indexes_in_hdf5[index]})
|
| 159 |
+
i += 1
|
| 160 |
+
|
| 161 |
+
yield batch_meta
|
| 162 |
+
|
| 163 |
+
def state_dict(self):
|
| 164 |
+
state = {
|
| 165 |
+
'indexes': self.indexes,
|
| 166 |
+
'pointer': self.pointer}
|
| 167 |
+
return state
|
| 168 |
+
|
| 169 |
+
def load_state_dict(self, state):
|
| 170 |
+
self.indexes = state['indexes']
|
| 171 |
+
self.pointer = state['pointer']
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
class BalancedTrainSampler(Base):
|
| 175 |
+
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
|
| 176 |
+
random_seed=1234):
|
| 177 |
+
"""Balanced sampler. Generate batch meta for training. Data are equally
|
| 178 |
+
sampled from different sound classes.
|
| 179 |
+
|
| 180 |
+
Args:
|
| 181 |
+
indexes_hdf5_path: string
|
| 182 |
+
batch_size: int
|
| 183 |
+
black_list_csv: string
|
| 184 |
+
random_seed: int
|
| 185 |
+
"""
|
| 186 |
+
super(BalancedTrainSampler, self).__init__(indexes_hdf5_path,
|
| 187 |
+
batch_size, black_list_csv, random_seed)
|
| 188 |
+
|
| 189 |
+
self.samples_num_per_class = np.sum(self.targets, axis=0)
|
| 190 |
+
logging.info('samples_num_per_class: {}'.format(
|
| 191 |
+
self.samples_num_per_class.astype(np.int32)))
|
| 192 |
+
|
| 193 |
+
# Training indexes of all sound classes. E.g.:
|
| 194 |
+
# [[0, 11, 12, ...], [3, 4, 15, 16, ...], [7, 8, ...], ...]
|
| 195 |
+
self.indexes_per_class = []
|
| 196 |
+
|
| 197 |
+
for k in range(self.classes_num):
|
| 198 |
+
self.indexes_per_class.append(
|
| 199 |
+
np.where(self.targets[:, k] == 1)[0])
|
| 200 |
+
|
| 201 |
+
# Shuffle indexes
|
| 202 |
+
for k in range(self.classes_num):
|
| 203 |
+
self.random_state.shuffle(self.indexes_per_class[k])
|
| 204 |
+
|
| 205 |
+
self.queue = []
|
| 206 |
+
self.pointers_of_classes = [0] * self.classes_num
|
| 207 |
+
|
| 208 |
+
def expand_queue(self, queue):
|
| 209 |
+
classes_set = np.arange(self.classes_num).tolist()
|
| 210 |
+
self.random_state.shuffle(classes_set)
|
| 211 |
+
queue += classes_set
|
| 212 |
+
return queue
|
| 213 |
+
|
| 214 |
+
def __iter__(self):
|
| 215 |
+
"""Generate batch meta for training.
|
| 216 |
+
|
| 217 |
+
Returns:
|
| 218 |
+
batch_meta: e.g.: [
|
| 219 |
+
{'hdf5_path': string, 'index_in_hdf5': int},
|
| 220 |
+
...]
|
| 221 |
+
"""
|
| 222 |
+
batch_size = self.batch_size
|
| 223 |
+
|
| 224 |
+
while True:
|
| 225 |
+
batch_meta = []
|
| 226 |
+
i = 0
|
| 227 |
+
while i < batch_size:
|
| 228 |
+
if len(self.queue) == 0:
|
| 229 |
+
self.queue = self.expand_queue(self.queue)
|
| 230 |
+
|
| 231 |
+
class_id = self.queue.pop(0)
|
| 232 |
+
pointer = self.pointers_of_classes[class_id]
|
| 233 |
+
self.pointers_of_classes[class_id] += 1
|
| 234 |
+
index = self.indexes_per_class[class_id][pointer]
|
| 235 |
+
|
| 236 |
+
# When finish one epoch of a sound class, then shuffle its indexes and reset pointer
|
| 237 |
+
if self.pointers_of_classes[class_id] >= self.samples_num_per_class[class_id]:
|
| 238 |
+
self.pointers_of_classes[class_id] = 0
|
| 239 |
+
self.random_state.shuffle(self.indexes_per_class[class_id])
|
| 240 |
+
|
| 241 |
+
# If audio in black list then continue
|
| 242 |
+
if self.audio_names[index] in self.black_list_names:
|
| 243 |
+
continue
|
| 244 |
+
else:
|
| 245 |
+
batch_meta.append({
|
| 246 |
+
'hdf5_path': self.hdf5_paths[index],
|
| 247 |
+
'index_in_hdf5': self.indexes_in_hdf5[index]})
|
| 248 |
+
i += 1
|
| 249 |
+
|
| 250 |
+
yield batch_meta
|
| 251 |
+
|
| 252 |
+
def state_dict(self):
|
| 253 |
+
state = {
|
| 254 |
+
'indexes_per_class': self.indexes_per_class,
|
| 255 |
+
'queue': self.queue,
|
| 256 |
+
'pointers_of_classes': self.pointers_of_classes}
|
| 257 |
+
return state
|
| 258 |
+
|
| 259 |
+
def load_state_dict(self, state):
|
| 260 |
+
self.indexes_per_class = state['indexes_per_class']
|
| 261 |
+
self.queue = state['queue']
|
| 262 |
+
self.pointers_of_classes = state['pointers_of_classes']
|
| 263 |
+
|
| 264 |
+
|
| 265 |
+
class AlternateTrainSampler(Base):
|
| 266 |
+
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
|
| 267 |
+
random_seed=1234):
|
| 268 |
+
"""AlternateSampler is a combination of Sampler and Balanced Sampler.
|
| 269 |
+
AlternateSampler alternately sample data from Sampler and Blanced Sampler.
|
| 270 |
+
|
| 271 |
+
Args:
|
| 272 |
+
indexes_hdf5_path: string
|
| 273 |
+
batch_size: int
|
| 274 |
+
black_list_csv: string
|
| 275 |
+
random_seed: int
|
| 276 |
+
"""
|
| 277 |
+
self.sampler1 = TrainSampler(indexes_hdf5_path, batch_size,
|
| 278 |
+
black_list_csv, random_seed)
|
| 279 |
+
|
| 280 |
+
self.sampler2 = BalancedTrainSampler(indexes_hdf5_path, batch_size,
|
| 281 |
+
black_list_csv, random_seed)
|
| 282 |
+
|
| 283 |
+
self.batch_size = batch_size
|
| 284 |
+
self.count = 0
|
| 285 |
+
|
| 286 |
+
def __iter__(self):
|
| 287 |
+
"""Generate batch meta for training.
|
| 288 |
+
|
| 289 |
+
Returns:
|
| 290 |
+
batch_meta: e.g.: [
|
| 291 |
+
{'hdf5_path': string, 'index_in_hdf5': int},
|
| 292 |
+
...]
|
| 293 |
+
"""
|
| 294 |
+
batch_size = self.batch_size
|
| 295 |
+
|
| 296 |
+
while True:
|
| 297 |
+
self.count += 1
|
| 298 |
+
|
| 299 |
+
if self.count % 2 == 0:
|
| 300 |
+
batch_meta = []
|
| 301 |
+
i = 0
|
| 302 |
+
while i < batch_size:
|
| 303 |
+
index = self.sampler1.indexes[self.sampler1.pointer]
|
| 304 |
+
self.sampler1.pointer += 1
|
| 305 |
+
|
| 306 |
+
# Shuffle indexes and reset pointer
|
| 307 |
+
if self.sampler1.pointer >= self.sampler1.audios_num:
|
| 308 |
+
self.sampler1.pointer = 0
|
| 309 |
+
self.sampler1.random_state.shuffle(self.sampler1.indexes)
|
| 310 |
+
|
| 311 |
+
# If audio in black list then continue
|
| 312 |
+
if self.sampler1.audio_names[index] in self.sampler1.black_list_names:
|
| 313 |
+
continue
|
| 314 |
+
else:
|
| 315 |
+
batch_meta.append({
|
| 316 |
+
'hdf5_path': self.sampler1.hdf5_paths[index],
|
| 317 |
+
'index_in_hdf5': self.sampler1.indexes_in_hdf5[index]})
|
| 318 |
+
i += 1
|
| 319 |
+
|
| 320 |
+
elif self.count % 2 == 1:
|
| 321 |
+
batch_meta = []
|
| 322 |
+
i = 0
|
| 323 |
+
while i < batch_size:
|
| 324 |
+
if len(self.sampler2.queue) == 0:
|
| 325 |
+
self.sampler2.queue = self.sampler2.expand_queue(self.sampler2.queue)
|
| 326 |
+
|
| 327 |
+
class_id = self.sampler2.queue.pop(0)
|
| 328 |
+
pointer = self.sampler2.pointers_of_classes[class_id]
|
| 329 |
+
self.sampler2.pointers_of_classes[class_id] += 1
|
| 330 |
+
index = self.sampler2.indexes_per_class[class_id][pointer]
|
| 331 |
+
|
| 332 |
+
# When finish one epoch of a sound class, then shuffle its indexes and reset pointer
|
| 333 |
+
if self.sampler2.pointers_of_classes[class_id] >= self.sampler2.samples_num_per_class[class_id]:
|
| 334 |
+
self.sampler2.pointers_of_classes[class_id] = 0
|
| 335 |
+
self.sampler2.random_state.shuffle(self.sampler2.indexes_per_class[class_id])
|
| 336 |
+
|
| 337 |
+
# If audio in black list then continue
|
| 338 |
+
if self.sampler2.audio_names[index] in self.sampler2.black_list_names:
|
| 339 |
+
continue
|
| 340 |
+
else:
|
| 341 |
+
batch_meta.append({
|
| 342 |
+
'hdf5_path': self.sampler2.hdf5_paths[index],
|
| 343 |
+
'index_in_hdf5': self.sampler2.indexes_in_hdf5[index]})
|
| 344 |
+
i += 1
|
| 345 |
+
|
| 346 |
+
yield batch_meta
|
| 347 |
+
|
| 348 |
+
def state_dict(self):
|
| 349 |
+
state = {
|
| 350 |
+
'sampler1': self.sampler1.state_dict(),
|
| 351 |
+
'sampler2': self.sampler2.state_dict()}
|
| 352 |
+
return state
|
| 353 |
+
|
| 354 |
+
def load_state_dict(self, state):
|
| 355 |
+
self.sampler1.load_state_dict(state['sampler1'])
|
| 356 |
+
self.sampler2.load_state_dict(state['sampler2'])
|
| 357 |
+
|
| 358 |
+
|
| 359 |
+
class EvaluateSampler(object):
|
| 360 |
+
def __init__(self, indexes_hdf5_path, batch_size):
|
| 361 |
+
"""Evaluate sampler. Generate batch meta for evaluation.
|
| 362 |
+
|
| 363 |
+
Args:
|
| 364 |
+
indexes_hdf5_path: string
|
| 365 |
+
batch_size: int
|
| 366 |
+
"""
|
| 367 |
+
self.batch_size = batch_size
|
| 368 |
+
|
| 369 |
+
with h5py.File(indexes_hdf5_path, 'r') as hf:
|
| 370 |
+
self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
|
| 371 |
+
self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
|
| 372 |
+
self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
|
| 373 |
+
self.targets = hf['target'][:].astype(np.float32)
|
| 374 |
+
|
| 375 |
+
self.audios_num = len(self.audio_names)
|
| 376 |
+
|
| 377 |
+
def __iter__(self):
|
| 378 |
+
"""Generate batch meta for training.
|
| 379 |
+
|
| 380 |
+
Returns:
|
| 381 |
+
batch_meta: e.g.: [
|
| 382 |
+
{'hdf5_path': string,
|
| 383 |
+
'index_in_hdf5': int}
|
| 384 |
+
...]
|
| 385 |
+
"""
|
| 386 |
+
batch_size = self.batch_size
|
| 387 |
+
pointer = 0
|
| 388 |
+
|
| 389 |
+
while pointer < self.audios_num:
|
| 390 |
+
batch_indexes = np.arange(pointer,
|
| 391 |
+
min(pointer + batch_size, self.audios_num))
|
| 392 |
+
|
| 393 |
+
batch_meta = []
|
| 394 |
+
|
| 395 |
+
for index in batch_indexes:
|
| 396 |
+
batch_meta.append({
|
| 397 |
+
'audio_name': self.audio_names[index],
|
| 398 |
+
'hdf5_path': self.hdf5_paths[index],
|
| 399 |
+
'index_in_hdf5': self.indexes_in_hdf5[index],
|
| 400 |
+
'target': self.targets[index]})
|
| 401 |
+
|
| 402 |
+
pointer += batch_size
|
| 403 |
+
yield batch_meta
|
| 404 |
+
|
| 405 |
+
|
| 406 |
+
def collate_fn(list_data_dict):
|
| 407 |
+
"""Collate data.
|
| 408 |
+
Args:
|
| 409 |
+
list_data_dict, e.g., [{'audio_name': str, 'waveform': (clip_samples,), ...},
|
| 410 |
+
{'audio_name': str, 'waveform': (clip_samples,), ...},
|
| 411 |
+
...]
|
| 412 |
+
Returns:
|
| 413 |
+
np_data_dict, dict, e.g.,
|
| 414 |
+
{'audio_name': (batch_size,), 'waveform': (batch_size, clip_samples), ...}
|
| 415 |
+
"""
|
| 416 |
+
np_data_dict = {}
|
| 417 |
+
|
| 418 |
+
for key in list_data_dict[0].keys():
|
| 419 |
+
np_data_dict[key] = np.array([data_dict[key] for data_dict in list_data_dict])
|
| 420 |
+
|
| 421 |
+
return np_data_dict
|
audio_detection/audio_infer/utils/dataset.py
ADDED
|
@@ -0,0 +1,224 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import argparse
|
| 3 |
+
import csv
|
| 4 |
+
import os
|
| 5 |
+
import glob
|
| 6 |
+
import datetime
|
| 7 |
+
import time
|
| 8 |
+
import logging
|
| 9 |
+
import h5py
|
| 10 |
+
import librosa
|
| 11 |
+
|
| 12 |
+
from utilities import (create_folder, get_filename, create_logging,
|
| 13 |
+
float32_to_int16, pad_or_truncate, read_metadata)
|
| 14 |
+
import config
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def split_unbalanced_csv_to_partial_csvs(args):
|
| 18 |
+
"""Split unbalanced csv to part csvs. Each part csv contains up to 50000 ids.
|
| 19 |
+
"""
|
| 20 |
+
|
| 21 |
+
unbalanced_csv_path = args.unbalanced_csv
|
| 22 |
+
unbalanced_partial_csvs_dir = args.unbalanced_partial_csvs_dir
|
| 23 |
+
|
| 24 |
+
create_folder(unbalanced_partial_csvs_dir)
|
| 25 |
+
|
| 26 |
+
with open(unbalanced_csv_path, 'r') as f:
|
| 27 |
+
lines = f.readlines()
|
| 28 |
+
|
| 29 |
+
lines = lines[3:] # Remove head info
|
| 30 |
+
audios_num_per_file = 50000
|
| 31 |
+
|
| 32 |
+
files_num = int(np.ceil(len(lines) / float(audios_num_per_file)))
|
| 33 |
+
|
| 34 |
+
for r in range(files_num):
|
| 35 |
+
lines_per_file = lines[r * audios_num_per_file :
|
| 36 |
+
(r + 1) * audios_num_per_file]
|
| 37 |
+
|
| 38 |
+
out_csv_path = os.path.join(unbalanced_partial_csvs_dir,
|
| 39 |
+
'unbalanced_train_segments_part{:02d}.csv'.format(r))
|
| 40 |
+
|
| 41 |
+
with open(out_csv_path, 'w') as f:
|
| 42 |
+
f.write('empty\n')
|
| 43 |
+
f.write('empty\n')
|
| 44 |
+
f.write('empty\n')
|
| 45 |
+
for line in lines_per_file:
|
| 46 |
+
f.write(line)
|
| 47 |
+
|
| 48 |
+
print('Write out csv to {}'.format(out_csv_path))
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
def download_wavs(args):
|
| 52 |
+
"""Download videos and extract audio in wav format.
|
| 53 |
+
"""
|
| 54 |
+
|
| 55 |
+
# Paths
|
| 56 |
+
csv_path = args.csv_path
|
| 57 |
+
audios_dir = args.audios_dir
|
| 58 |
+
mini_data = args.mini_data
|
| 59 |
+
|
| 60 |
+
if mini_data:
|
| 61 |
+
logs_dir = '_logs/download_dataset/{}'.format(get_filename(csv_path))
|
| 62 |
+
else:
|
| 63 |
+
logs_dir = '_logs/download_dataset_minidata/{}'.format(get_filename(csv_path))
|
| 64 |
+
|
| 65 |
+
create_folder(audios_dir)
|
| 66 |
+
create_folder(logs_dir)
|
| 67 |
+
create_logging(logs_dir, filemode='w')
|
| 68 |
+
logging.info('Download log is saved to {}'.format(logs_dir))
|
| 69 |
+
|
| 70 |
+
# Read csv
|
| 71 |
+
with open(csv_path, 'r') as f:
|
| 72 |
+
lines = f.readlines()
|
| 73 |
+
|
| 74 |
+
lines = lines[3:] # Remove csv head info
|
| 75 |
+
|
| 76 |
+
if mini_data:
|
| 77 |
+
lines = lines[0 : 10] # Download partial data for debug
|
| 78 |
+
|
| 79 |
+
download_time = time.time()
|
| 80 |
+
|
| 81 |
+
# Download
|
| 82 |
+
for (n, line) in enumerate(lines):
|
| 83 |
+
|
| 84 |
+
items = line.split(', ')
|
| 85 |
+
audio_id = items[0]
|
| 86 |
+
start_time = float(items[1])
|
| 87 |
+
end_time = float(items[2])
|
| 88 |
+
duration = end_time - start_time
|
| 89 |
+
|
| 90 |
+
logging.info('{} {} start_time: {:.1f}, end_time: {:.1f}'.format(
|
| 91 |
+
n, audio_id, start_time, end_time))
|
| 92 |
+
|
| 93 |
+
# Download full video of whatever format
|
| 94 |
+
video_name = os.path.join(audios_dir, '_Y{}.%(ext)s'.format(audio_id))
|
| 95 |
+
os.system("youtube-dl --quiet -o '{}' -x https://www.youtube.com/watch?v={}"\
|
| 96 |
+
.format(video_name, audio_id))
|
| 97 |
+
|
| 98 |
+
video_paths = glob.glob(os.path.join(audios_dir, '_Y' + audio_id + '.*'))
|
| 99 |
+
|
| 100 |
+
# If download successful
|
| 101 |
+
if len(video_paths) > 0:
|
| 102 |
+
video_path = video_paths[0] # Choose one video
|
| 103 |
+
|
| 104 |
+
# Add 'Y' to the head because some video ids are started with '-'
|
| 105 |
+
# which will cause problem
|
| 106 |
+
audio_path = os.path.join(audios_dir, 'Y' + audio_id + '.wav')
|
| 107 |
+
|
| 108 |
+
# Extract audio in wav format
|
| 109 |
+
os.system("ffmpeg -loglevel panic -i {} -ac 1 -ar 32000 -ss {} -t 00:00:{} {} "\
|
| 110 |
+
.format(video_path,
|
| 111 |
+
str(datetime.timedelta(seconds=start_time)), duration,
|
| 112 |
+
audio_path))
|
| 113 |
+
|
| 114 |
+
# Remove downloaded video
|
| 115 |
+
os.system("rm {}".format(video_path))
|
| 116 |
+
|
| 117 |
+
logging.info("Download and convert to {}".format(audio_path))
|
| 118 |
+
|
| 119 |
+
logging.info('Download finished! Time spent: {:.3f} s'.format(
|
| 120 |
+
time.time() - download_time))
|
| 121 |
+
|
| 122 |
+
logging.info('Logs can be viewed in {}'.format(logs_dir))
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
def pack_waveforms_to_hdf5(args):
|
| 126 |
+
"""Pack waveform and target of several audio clips to a single hdf5 file.
|
| 127 |
+
This can speed up loading and training.
|
| 128 |
+
"""
|
| 129 |
+
|
| 130 |
+
# Arguments & parameters
|
| 131 |
+
audios_dir = args.audios_dir
|
| 132 |
+
csv_path = args.csv_path
|
| 133 |
+
waveforms_hdf5_path = args.waveforms_hdf5_path
|
| 134 |
+
mini_data = args.mini_data
|
| 135 |
+
|
| 136 |
+
clip_samples = config.clip_samples
|
| 137 |
+
classes_num = config.classes_num
|
| 138 |
+
sample_rate = config.sample_rate
|
| 139 |
+
id_to_ix = config.id_to_ix
|
| 140 |
+
|
| 141 |
+
# Paths
|
| 142 |
+
if mini_data:
|
| 143 |
+
prefix = 'mini_'
|
| 144 |
+
waveforms_hdf5_path += '.mini'
|
| 145 |
+
else:
|
| 146 |
+
prefix = ''
|
| 147 |
+
|
| 148 |
+
create_folder(os.path.dirname(waveforms_hdf5_path))
|
| 149 |
+
|
| 150 |
+
logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))
|
| 151 |
+
create_folder(logs_dir)
|
| 152 |
+
create_logging(logs_dir, filemode='w')
|
| 153 |
+
logging.info('Write logs to {}'.format(logs_dir))
|
| 154 |
+
|
| 155 |
+
# Read csv file
|
| 156 |
+
meta_dict = read_metadata(csv_path, classes_num, id_to_ix)
|
| 157 |
+
|
| 158 |
+
if mini_data:
|
| 159 |
+
mini_num = 10
|
| 160 |
+
for key in meta_dict.keys():
|
| 161 |
+
meta_dict[key] = meta_dict[key][0 : mini_num]
|
| 162 |
+
|
| 163 |
+
audios_num = len(meta_dict['audio_name'])
|
| 164 |
+
|
| 165 |
+
# Pack waveform to hdf5
|
| 166 |
+
total_time = time.time()
|
| 167 |
+
|
| 168 |
+
with h5py.File(waveforms_hdf5_path, 'w') as hf:
|
| 169 |
+
hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')
|
| 170 |
+
hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)
|
| 171 |
+
hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)
|
| 172 |
+
hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)
|
| 173 |
+
|
| 174 |
+
# Pack waveform & target of several audio clips to a single hdf5 file
|
| 175 |
+
for n in range(audios_num):
|
| 176 |
+
audio_path = os.path.join(audios_dir, meta_dict['audio_name'][n])
|
| 177 |
+
|
| 178 |
+
if os.path.isfile(audio_path):
|
| 179 |
+
logging.info('{} {}'.format(n, audio_path))
|
| 180 |
+
(audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
|
| 181 |
+
audio = pad_or_truncate(audio, clip_samples)
|
| 182 |
+
|
| 183 |
+
hf['audio_name'][n] = meta_dict['audio_name'][n].encode()
|
| 184 |
+
hf['waveform'][n] = float32_to_int16(audio)
|
| 185 |
+
hf['target'][n] = meta_dict['target'][n]
|
| 186 |
+
else:
|
| 187 |
+
logging.info('{} File does not exist! {}'.format(n, audio_path))
|
| 188 |
+
|
| 189 |
+
logging.info('Write to {}'.format(waveforms_hdf5_path))
|
| 190 |
+
logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))
|
| 191 |
+
|
| 192 |
+
|
| 193 |
+
if __name__ == '__main__':
|
| 194 |
+
parser = argparse.ArgumentParser()
|
| 195 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 196 |
+
|
| 197 |
+
parser_split = subparsers.add_parser('split_unbalanced_csv_to_partial_csvs')
|
| 198 |
+
parser_split.add_argument('--unbalanced_csv', type=str, required=True, help='Path of unbalanced_csv file to read.')
|
| 199 |
+
parser_split.add_argument('--unbalanced_partial_csvs_dir', type=str, required=True, help='Directory to save out split unbalanced partial csv.')
|
| 200 |
+
|
| 201 |
+
parser_download_wavs = subparsers.add_parser('download_wavs')
|
| 202 |
+
parser_download_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
|
| 203 |
+
parser_download_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
|
| 204 |
+
parser_download_wavs.add_argument('--mini_data', action='store_true', default=True, help='Set true to only download 10 audios for debugging.')
|
| 205 |
+
|
| 206 |
+
parser_pack_wavs = subparsers.add_parser('pack_waveforms_to_hdf5')
|
| 207 |
+
parser_pack_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
|
| 208 |
+
parser_pack_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
|
| 209 |
+
parser_pack_wavs.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path to save out packed hdf5.')
|
| 210 |
+
parser_pack_wavs.add_argument('--mini_data', action='store_true', default=False, help='Set true to only download 10 audios for debugging.')
|
| 211 |
+
|
| 212 |
+
args = parser.parse_args()
|
| 213 |
+
|
| 214 |
+
if args.mode == 'split_unbalanced_csv_to_partial_csvs':
|
| 215 |
+
split_unbalanced_csv_to_partial_csvs(args)
|
| 216 |
+
|
| 217 |
+
elif args.mode == 'download_wavs':
|
| 218 |
+
download_wavs(args)
|
| 219 |
+
|
| 220 |
+
elif args.mode == 'pack_waveforms_to_hdf5':
|
| 221 |
+
pack_waveforms_to_hdf5(args)
|
| 222 |
+
|
| 223 |
+
else:
|
| 224 |
+
raise Exception('Incorrect arguments!')
|
audio_detection/audio_infer/utils/plot_for_paper.py
ADDED
|
@@ -0,0 +1,565 @@
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|
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|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import argparse
|
| 5 |
+
import h5py
|
| 6 |
+
import time
|
| 7 |
+
import pickle
|
| 8 |
+
import matplotlib.pyplot as plt
|
| 9 |
+
import csv
|
| 10 |
+
from sklearn import metrics
|
| 11 |
+
|
| 12 |
+
from utilities import (create_folder, get_filename, d_prime)
|
| 13 |
+
import config
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
def load_statistics(statistics_path):
|
| 17 |
+
statistics_dict = pickle.load(open(statistics_path, 'rb'))
|
| 18 |
+
|
| 19 |
+
bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num)
|
| 20 |
+
bal_map = np.mean(bal_map, axis=-1)
|
| 21 |
+
test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num)
|
| 22 |
+
test_map = np.mean(test_map, axis=-1)
|
| 23 |
+
|
| 24 |
+
return bal_map, test_map
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
def crop_label(label):
|
| 28 |
+
max_len = 16
|
| 29 |
+
if len(label) <= max_len:
|
| 30 |
+
return label
|
| 31 |
+
else:
|
| 32 |
+
words = label.split(' ')
|
| 33 |
+
cropped_label = ''
|
| 34 |
+
for w in words:
|
| 35 |
+
if len(cropped_label + ' ' + w) > max_len:
|
| 36 |
+
break
|
| 37 |
+
else:
|
| 38 |
+
cropped_label += ' {}'.format(w)
|
| 39 |
+
return cropped_label
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def add_comma(integer):
|
| 43 |
+
"""E.g., 1234567 -> 1,234,567
|
| 44 |
+
"""
|
| 45 |
+
integer = int(integer)
|
| 46 |
+
if integer >= 1000:
|
| 47 |
+
return str(integer // 1000) + ',' + str(integer % 1000)
|
| 48 |
+
else:
|
| 49 |
+
return str(integer)
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def plot_classwise_iteration_map(args):
|
| 53 |
+
|
| 54 |
+
# Paths
|
| 55 |
+
save_out_path = 'results/classwise_iteration_map.pdf'
|
| 56 |
+
create_folder(os.path.dirname(save_out_path))
|
| 57 |
+
|
| 58 |
+
# Load statistics
|
| 59 |
+
statistics_dict = pickle.load(open('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl', 'rb'))
|
| 60 |
+
|
| 61 |
+
mAP_mat = np.array([e['average_precision'] for e in statistics_dict['test']])
|
| 62 |
+
mAP_mat = mAP_mat[0 : 300, :] # 300 * 2000 = 600k iterations
|
| 63 |
+
sorted_indexes = np.argsort(config.full_samples_per_class)[::-1]
|
| 64 |
+
|
| 65 |
+
fig, axs = plt.subplots(1, 3, figsize=(20, 5))
|
| 66 |
+
ranges = [np.arange(0, 10), np.arange(250, 260), np.arange(517, 527)]
|
| 67 |
+
axs[0].set_ylabel('AP')
|
| 68 |
+
|
| 69 |
+
for col in range(0, 3):
|
| 70 |
+
axs[col].set_ylim(0, 1.)
|
| 71 |
+
axs[col].set_xlim(0, 301)
|
| 72 |
+
axs[col].set_xlabel('Iterations')
|
| 73 |
+
axs[col].set_ylabel('AP')
|
| 74 |
+
axs[col].xaxis.set_ticks(np.arange(0, 301, 100))
|
| 75 |
+
axs[col].xaxis.set_ticklabels(['0', '200k', '400k', '600k'])
|
| 76 |
+
lines = []
|
| 77 |
+
for _ix in ranges[col]:
|
| 78 |
+
_label = crop_label(config.labels[sorted_indexes[_ix]]) + \
|
| 79 |
+
' ({})'.format(add_comma(config.full_samples_per_class[sorted_indexes[_ix]]))
|
| 80 |
+
line, = axs[col].plot(mAP_mat[:, sorted_indexes[_ix]], label=_label)
|
| 81 |
+
lines.append(line)
|
| 82 |
+
box = axs[col].get_position()
|
| 83 |
+
axs[col].set_position([box.x0, box.y0, box.width * 1., box.height])
|
| 84 |
+
axs[col].legend(handles=lines, bbox_to_anchor=(1., 1.))
|
| 85 |
+
axs[col].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
|
| 86 |
+
|
| 87 |
+
plt.tight_layout(pad=4, w_pad=1, h_pad=1)
|
| 88 |
+
plt.savefig(save_out_path)
|
| 89 |
+
print(save_out_path)
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
def plot_six_figures(args):
|
| 93 |
+
|
| 94 |
+
# Arguments & parameters
|
| 95 |
+
classes_num = config.classes_num
|
| 96 |
+
labels = config.labels
|
| 97 |
+
max_plot_iteration = 540000
|
| 98 |
+
iterations = np.arange(0, max_plot_iteration, 2000)
|
| 99 |
+
|
| 100 |
+
# Paths
|
| 101 |
+
class_labels_indices_path = os.path.join('metadata', 'class_labels_indices.csv')
|
| 102 |
+
save_out_path = 'results/six_figures.pdf'
|
| 103 |
+
create_folder(os.path.dirname(save_out_path))
|
| 104 |
+
|
| 105 |
+
# Plot
|
| 106 |
+
fig, ax = plt.subplots(2, 3, figsize=(14, 7))
|
| 107 |
+
bal_alpha = 0.3
|
| 108 |
+
test_alpha = 1.0
|
| 109 |
+
linewidth = 1.
|
| 110 |
+
|
| 111 |
+
# (a) Comparison of architectures
|
| 112 |
+
if True:
|
| 113 |
+
lines = []
|
| 114 |
+
|
| 115 |
+
# Wavegram-Logmel-CNN
|
| 116 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl')
|
| 117 |
+
line, = ax[0, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
|
| 118 |
+
line, = ax[0, 0].plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 119 |
+
lines.append(line)
|
| 120 |
+
|
| 121 |
+
# Cnn14
|
| 122 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 123 |
+
line, = ax[0, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 124 |
+
line, = ax[0, 0].plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 125 |
+
lines.append(line)
|
| 126 |
+
|
| 127 |
+
# MobileNetV1
|
| 128 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_MobileNetV1_balanced_mixup_bs32.pkl')
|
| 129 |
+
line, = ax[0, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
|
| 130 |
+
line, = ax[0, 0].plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 131 |
+
lines.append(line)
|
| 132 |
+
|
| 133 |
+
ax[0, 0].legend(handles=lines, loc=2)
|
| 134 |
+
ax[0, 0].set_title('(a) Comparison of architectures')
|
| 135 |
+
|
| 136 |
+
# (b) Comparison of training data and augmentation'
|
| 137 |
+
if True:
|
| 138 |
+
lines = []
|
| 139 |
+
|
| 140 |
+
# Full data + balanced sampler + mixup
|
| 141 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 142 |
+
line, = ax[0, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 143 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 144 |
+
lines.append(line)
|
| 145 |
+
|
| 146 |
+
# Full data + balanced sampler + mixup in time domain
|
| 147 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_timedomain_bs32.pkl')
|
| 148 |
+
line, = ax[0, 1].plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth)
|
| 149 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth)
|
| 150 |
+
lines.append(line)
|
| 151 |
+
|
| 152 |
+
# Full data + balanced sampler + no mixup
|
| 153 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_nomixup_bs32.pkl')
|
| 154 |
+
line, = ax[0, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
|
| 155 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 156 |
+
lines.append(line)
|
| 157 |
+
|
| 158 |
+
# Full data + uniform sampler + no mixup
|
| 159 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_nobalanced_nomixup_bs32.pkl')
|
| 160 |
+
line, = ax[0, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
|
| 161 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 162 |
+
lines.append(line)
|
| 163 |
+
|
| 164 |
+
# Balanced data + balanced sampler + mixup
|
| 165 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 166 |
+
line, = ax[0, 1].plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth)
|
| 167 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth)
|
| 168 |
+
lines.append(line)
|
| 169 |
+
|
| 170 |
+
# Balanced data + balanced sampler + no mixup
|
| 171 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_nomixup_bs32.pkl')
|
| 172 |
+
line, = ax[0, 1].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth)
|
| 173 |
+
line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth)
|
| 174 |
+
lines.append(line)
|
| 175 |
+
|
| 176 |
+
ax[0, 1].legend(handles=lines, loc=2, fontsize=8)
|
| 177 |
+
ax[0, 1].set_title('(b) Comparison of training data and augmentation')
|
| 178 |
+
|
| 179 |
+
# (c) Comparison of embedding size
|
| 180 |
+
if True:
|
| 181 |
+
lines = []
|
| 182 |
+
|
| 183 |
+
# Embedding size 2048
|
| 184 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 185 |
+
line, = ax[0, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 186 |
+
line, = ax[0, 2].plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 187 |
+
lines.append(line)
|
| 188 |
+
|
| 189 |
+
# Embedding size 128
|
| 190 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb128_balanced_mixup_bs32.pkl')
|
| 191 |
+
line, = ax[0, 2].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
|
| 192 |
+
line, = ax[0, 2].plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 193 |
+
lines.append(line)
|
| 194 |
+
|
| 195 |
+
# Embedding size 32
|
| 196 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb32_balanced_mixup_bs32.pkl')
|
| 197 |
+
line, = ax[0, 2].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
|
| 198 |
+
line, = ax[0, 2].plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 199 |
+
lines.append(line)
|
| 200 |
+
|
| 201 |
+
ax[0, 2].legend(handles=lines, loc=2)
|
| 202 |
+
ax[0, 2].set_title('(c) Comparison of embedding size')
|
| 203 |
+
|
| 204 |
+
# (d) Comparison of amount of training data
|
| 205 |
+
if True:
|
| 206 |
+
lines = []
|
| 207 |
+
|
| 208 |
+
# 100% of full training data
|
| 209 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 210 |
+
line, = ax[1, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 211 |
+
line, = ax[1, 0].plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 212 |
+
lines.append(line)
|
| 213 |
+
|
| 214 |
+
# 80% of full training data
|
| 215 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.8full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 216 |
+
line, = ax[1, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
|
| 217 |
+
line, = ax[1, 0].plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 218 |
+
lines.append(line)
|
| 219 |
+
|
| 220 |
+
# 50% of full training data
|
| 221 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.5full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 222 |
+
line, = ax[1, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
|
| 223 |
+
line, = ax[1, 0].plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 224 |
+
lines.append(line)
|
| 225 |
+
|
| 226 |
+
ax[1, 0].legend(handles=lines, loc=2)
|
| 227 |
+
ax[1, 0].set_title('(d) Comparison of amount of training data')
|
| 228 |
+
|
| 229 |
+
# (e) Comparison of sampling rate
|
| 230 |
+
if True:
|
| 231 |
+
lines = []
|
| 232 |
+
|
| 233 |
+
# Cnn14 + 32 kHz
|
| 234 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 235 |
+
line, = ax[1, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 236 |
+
line, = ax[1, 1].plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 237 |
+
lines.append(line)
|
| 238 |
+
|
| 239 |
+
# Cnn14 + 16 kHz
|
| 240 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_16k_balanced_mixup_bs32.pkl')
|
| 241 |
+
line, = ax[1, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
|
| 242 |
+
line, = ax[1, 1].plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 243 |
+
lines.append(line)
|
| 244 |
+
|
| 245 |
+
# Cnn14 + 8 kHz
|
| 246 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_8k_balanced_mixup_bs32.pkl')
|
| 247 |
+
line, = ax[1, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
|
| 248 |
+
line, = ax[1, 1].plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 249 |
+
lines.append(line)
|
| 250 |
+
|
| 251 |
+
ax[1, 1].legend(handles=lines, loc=2)
|
| 252 |
+
ax[1, 1].set_title('(e) Comparison of sampling rate')
|
| 253 |
+
|
| 254 |
+
# (f) Comparison of mel bins number
|
| 255 |
+
if True:
|
| 256 |
+
lines = []
|
| 257 |
+
|
| 258 |
+
# Cnn14 + 128 mel bins
|
| 259 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel128_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 260 |
+
line, = ax[1, 2].plot(bal_map, color='g', alpha=bal_alpha)
|
| 261 |
+
line, = ax[1, 2].plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth)
|
| 262 |
+
lines.append(line)
|
| 263 |
+
|
| 264 |
+
# Cnn14 + 64 mel bins
|
| 265 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 266 |
+
line, = ax[1, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
|
| 267 |
+
line, = ax[1, 2].plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth)
|
| 268 |
+
lines.append(line)
|
| 269 |
+
|
| 270 |
+
# Cnn14 + 32 mel bins
|
| 271 |
+
(bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel32_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
|
| 272 |
+
line, = ax[1, 2].plot(bal_map, color='b', alpha=bal_alpha)
|
| 273 |
+
line, = ax[1, 2].plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth)
|
| 274 |
+
lines.append(line)
|
| 275 |
+
|
| 276 |
+
ax[1, 2].legend(handles=lines, loc=2)
|
| 277 |
+
ax[1, 2].set_title('(f) Comparison of mel bins number')
|
| 278 |
+
|
| 279 |
+
for i in range(2):
|
| 280 |
+
for j in range(3):
|
| 281 |
+
ax[i, j].set_ylim(0, 0.8)
|
| 282 |
+
ax[i, j].set_xlim(0, len(iterations))
|
| 283 |
+
ax[i, j].set_xlabel('Iterations')
|
| 284 |
+
ax[i, j].set_ylabel('mAP')
|
| 285 |
+
ax[i, j].xaxis.set_ticks(np.arange(0, len(iterations), 50))
|
| 286 |
+
ax[i, j].xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k'])
|
| 287 |
+
ax[i, j].yaxis.set_ticks(np.arange(0, 0.81, 0.05))
|
| 288 |
+
ax[i, j].yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3',
|
| 289 |
+
'', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8'])
|
| 290 |
+
ax[i, j].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
|
| 291 |
+
ax[i, j].xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
|
| 292 |
+
|
| 293 |
+
plt.tight_layout(0, 1, 0)
|
| 294 |
+
plt.savefig(save_out_path)
|
| 295 |
+
print('Save figure to {}'.format(save_out_path))
|
| 296 |
+
|
| 297 |
+
|
| 298 |
+
def plot_complexity_map(args):
|
| 299 |
+
|
| 300 |
+
# Paths
|
| 301 |
+
save_out_path = 'results/complexity_mAP.pdf'
|
| 302 |
+
create_folder(os.path.dirname(save_out_path))
|
| 303 |
+
|
| 304 |
+
plt.figure(figsize=(5, 5))
|
| 305 |
+
fig, ax = plt.subplots(1, 1)
|
| 306 |
+
|
| 307 |
+
model_types = np.array(['Cnn6', 'Cnn10', 'Cnn14', 'ResNet22', 'ResNet38', 'ResNet54',
|
| 308 |
+
'MobileNetV1', 'MobileNetV2', 'DaiNet', 'LeeNet', 'LeeNet18',
|
| 309 |
+
'Res1dNet30', 'Res1dNet44', 'Wavegram-CNN', 'Wavegram-\nLogmel-CNN'])
|
| 310 |
+
flops = np.array([21.986, 28.166, 42.220, 30.081, 48.962, 54.563, 3.614, 2.810,
|
| 311 |
+
30.395, 4.741, 26.369, 32.688, 61.833, 44.234, 53.510])
|
| 312 |
+
mAPs = np.array([0.343, 0.380, 0.431, 0.430, 0.434, 0.429, 0.389, 0.383, 0.295,
|
| 313 |
+
0.266, 0.336, 0.365, 0.355, 0.389, 0.439])
|
| 314 |
+
|
| 315 |
+
sorted_indexes = np.sort(flops)
|
| 316 |
+
ax.scatter(flops, mAPs)
|
| 317 |
+
|
| 318 |
+
shift = [[-5.5, -0.004], [1, -0.004], [-1, -0.014], [-2, 0.006], [-7, 0.006],
|
| 319 |
+
[1, -0.01], [0.5, 0.004], [-1, -0.014], [1, -0.007], [0.8, -0.008],
|
| 320 |
+
[1, -0.007], [1, 0.002], [-6, -0.015], [1, -0.008], [0.8, 0]]
|
| 321 |
+
|
| 322 |
+
for i, model_type in enumerate(model_types):
|
| 323 |
+
ax.annotate(model_type, (flops[i] + shift[i][0], mAPs[i] + shift[i][1]))
|
| 324 |
+
|
| 325 |
+
ax.plot(flops[[0, 1, 2]], mAPs[[0, 1, 2]])
|
| 326 |
+
ax.plot(flops[[3, 4, 5]], mAPs[[3, 4, 5]])
|
| 327 |
+
ax.plot(flops[[6, 7]], mAPs[[6, 7]])
|
| 328 |
+
ax.plot(flops[[9, 10]], mAPs[[9, 10]])
|
| 329 |
+
ax.plot(flops[[11, 12]], mAPs[[11, 12]])
|
| 330 |
+
ax.plot(flops[[13, 14]], mAPs[[13, 14]])
|
| 331 |
+
|
| 332 |
+
ax.set_xlim(0, 70)
|
| 333 |
+
ax.set_ylim(0.2, 0.5)
|
| 334 |
+
ax.set_xlabel('Multi-load_statisticss (million)', fontsize=15)
|
| 335 |
+
ax.set_ylabel('mAP', fontsize=15)
|
| 336 |
+
ax.tick_params(axis='x', labelsize=12)
|
| 337 |
+
ax.tick_params(axis='y', labelsize=12)
|
| 338 |
+
|
| 339 |
+
plt.tight_layout(0, 0, 0)
|
| 340 |
+
|
| 341 |
+
plt.savefig(save_out_path)
|
| 342 |
+
print('Write out figure to {}'.format(save_out_path))
|
| 343 |
+
|
| 344 |
+
|
| 345 |
+
def plot_long_fig(args):
|
| 346 |
+
|
| 347 |
+
# Paths
|
| 348 |
+
stats = pickle.load(open('paper_statistics/stats_for_long_fig.pkl', 'rb'))
|
| 349 |
+
|
| 350 |
+
save_out_path = 'results/long_fig.pdf'
|
| 351 |
+
create_folder(os.path.dirname(save_out_path))
|
| 352 |
+
|
| 353 |
+
# Load meta
|
| 354 |
+
N = len(config.labels)
|
| 355 |
+
sorted_indexes = stats['sorted_indexes_for_plot']
|
| 356 |
+
sorted_labels = np.array(config.labels)[sorted_indexes]
|
| 357 |
+
audio_clips_per_class = stats['official_balanced_training_samples'] + stats['official_unbalanced_training_samples']
|
| 358 |
+
audio_clips_per_class = audio_clips_per_class[sorted_indexes]
|
| 359 |
+
|
| 360 |
+
# Prepare axes for plot
|
| 361 |
+
(ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b) = prepare_plot_long_4_rows(sorted_labels)
|
| 362 |
+
|
| 363 |
+
# plot the number of training samples
|
| 364 |
+
ax1a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
|
| 365 |
+
ax2a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
|
| 366 |
+
ax3a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
|
| 367 |
+
ax4a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
|
| 368 |
+
|
| 369 |
+
# Load mAP of different systems
|
| 370 |
+
"""Average instance system of [1] with an mAP of 0.317.
|
| 371 |
+
[1] Kong, Qiuqiang, Changsong Yu, Yong Xu, Turab Iqbal, Wenwu Wang, and
|
| 372 |
+
Mark D. Plumbley. "Weakly labelled audioset tagging with attention neural
|
| 373 |
+
networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing
|
| 374 |
+
27, no. 11 (2019): 1791-1802."""
|
| 375 |
+
maps_avg_instances = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision']
|
| 376 |
+
maps_avg_instances = maps_avg_instances[sorted_indexes]
|
| 377 |
+
|
| 378 |
+
# PANNs Cnn14
|
| 379 |
+
maps_panns_cnn14 = stats['panns_cnn14']['eval']['average_precision']
|
| 380 |
+
maps_panns_cnn14 = maps_panns_cnn14[sorted_indexes]
|
| 381 |
+
|
| 382 |
+
# PANNs MobileNetV1
|
| 383 |
+
maps_panns_mobilenetv1 = stats['panns_mobilenetv1']['eval']['average_precision']
|
| 384 |
+
maps_panns_mobilenetv1 = maps_panns_mobilenetv1[sorted_indexes]
|
| 385 |
+
|
| 386 |
+
# PANNs Wavegram-Logmel-Cnn14
|
| 387 |
+
maps_panns_wavegram_logmel_cnn14 = stats['panns_wavegram_logmel_cnn14']['eval']['average_precision']
|
| 388 |
+
maps_panns_wavegram_logmel_cnn14 = maps_panns_wavegram_logmel_cnn14[sorted_indexes]
|
| 389 |
+
|
| 390 |
+
# Plot mAPs
|
| 391 |
+
_scatter_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='g')
|
| 392 |
+
_scatter_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='r')
|
| 393 |
+
_scatter_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, s=5, c='b')
|
| 394 |
+
_scatter_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, s=5, c='k')
|
| 395 |
+
|
| 396 |
+
linewidth = 0.7
|
| 397 |
+
line0te = _plot_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b,
|
| 398 |
+
c='g', linewidth=linewidth, label='AP with Wavegram-Logmel-CNN')
|
| 399 |
+
line1te = _plot_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, c='r',
|
| 400 |
+
linewidth=linewidth, label='AP with CNN14')
|
| 401 |
+
line2te = _plot_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, c='b',
|
| 402 |
+
linewidth=linewidth, label='AP with MobileNetV1')
|
| 403 |
+
line3te = _plot_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, c='k',
|
| 404 |
+
linewidth=linewidth, label='AP with averaging instances (baseline)')
|
| 405 |
+
|
| 406 |
+
# Plot label quality
|
| 407 |
+
label_quality = stats['label_quality']
|
| 408 |
+
sorted_label_quality = np.array(label_quality)[sorted_indexes]
|
| 409 |
+
for k in range(len(sorted_label_quality)):
|
| 410 |
+
if sorted_label_quality[k] and sorted_label_quality[k] == 1:
|
| 411 |
+
sorted_label_quality[k] = 0.99
|
| 412 |
+
|
| 413 |
+
ax1b.scatter(np.arange(N)[sorted_label_quality != None],
|
| 414 |
+
sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
|
| 415 |
+
ax2b.scatter(np.arange(N)[sorted_label_quality != None],
|
| 416 |
+
sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
|
| 417 |
+
ax3b.scatter(np.arange(N)[sorted_label_quality != None],
|
| 418 |
+
sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
|
| 419 |
+
line_label_quality = ax4b.scatter(np.arange(N)[sorted_label_quality != None],
|
| 420 |
+
sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+', label='Label quality')
|
| 421 |
+
ax1b.scatter(np.arange(N)[sorted_label_quality == None],
|
| 422 |
+
0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
|
| 423 |
+
ax2b.scatter(np.arange(N)[sorted_label_quality == None],
|
| 424 |
+
0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
|
| 425 |
+
ax3b.scatter(np.arange(N)[sorted_label_quality == None],
|
| 426 |
+
0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
|
| 427 |
+
ax4b.scatter(np.arange(N)[sorted_label_quality == None],
|
| 428 |
+
0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
|
| 429 |
+
|
| 430 |
+
plt.legend(handles=[line0te, line1te, line2te, line3te, line_label_quality], fontsize=6, loc=1)
|
| 431 |
+
plt.tight_layout(0, 0, 0)
|
| 432 |
+
plt.savefig(save_out_path)
|
| 433 |
+
print('Save fig to {}'.format(save_out_path))
|
| 434 |
+
|
| 435 |
+
|
| 436 |
+
def prepare_plot_long_4_rows(sorted_lbs):
|
| 437 |
+
N = len(sorted_lbs)
|
| 438 |
+
|
| 439 |
+
f,(ax1a, ax2a, ax3a, ax4a) = plt.subplots(4, 1, sharey=False, facecolor='w', figsize=(10, 10.5))
|
| 440 |
+
|
| 441 |
+
fontsize = 5
|
| 442 |
+
|
| 443 |
+
K = 132
|
| 444 |
+
ax1a.set_xlim(0, K)
|
| 445 |
+
ax2a.set_xlim(K, 2 * K)
|
| 446 |
+
ax3a.set_xlim(2 * K, 3 * K)
|
| 447 |
+
ax4a.set_xlim(3 * K, N)
|
| 448 |
+
|
| 449 |
+
truncated_sorted_lbs = []
|
| 450 |
+
for lb in sorted_lbs:
|
| 451 |
+
lb = lb[0 : 25]
|
| 452 |
+
words = lb.split(' ')
|
| 453 |
+
if len(words[-1]) < 3:
|
| 454 |
+
lb = ' '.join(words[0:-1])
|
| 455 |
+
truncated_sorted_lbs.append(lb)
|
| 456 |
+
|
| 457 |
+
ax1a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
|
| 458 |
+
ax2a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
|
| 459 |
+
ax3a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
|
| 460 |
+
ax4a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
|
| 461 |
+
|
| 462 |
+
ax1a.set_yscale('log')
|
| 463 |
+
ax2a.set_yscale('log')
|
| 464 |
+
ax3a.set_yscale('log')
|
| 465 |
+
ax4a.set_yscale('log')
|
| 466 |
+
|
| 467 |
+
ax1b = ax1a.twinx()
|
| 468 |
+
ax2b = ax2a.twinx()
|
| 469 |
+
ax3b = ax3a.twinx()
|
| 470 |
+
ax4b = ax4a.twinx()
|
| 471 |
+
ax1b.set_ylim(0., 1.)
|
| 472 |
+
ax2b.set_ylim(0., 1.)
|
| 473 |
+
ax3b.set_ylim(0., 1.)
|
| 474 |
+
ax4b.set_ylim(0., 1.)
|
| 475 |
+
ax1b.set_ylabel('Average precision')
|
| 476 |
+
ax2b.set_ylabel('Average precision')
|
| 477 |
+
ax3b.set_ylabel('Average precision')
|
| 478 |
+
ax4b.set_ylabel('Average precision')
|
| 479 |
+
|
| 480 |
+
ax1b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
|
| 481 |
+
ax2b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
|
| 482 |
+
ax3b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
|
| 483 |
+
ax4b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
|
| 484 |
+
|
| 485 |
+
ax1a.xaxis.set_ticks(np.arange(K))
|
| 486 |
+
ax1a.xaxis.set_ticklabels(truncated_sorted_lbs[0:K], rotation=90, fontsize=fontsize)
|
| 487 |
+
ax1a.xaxis.tick_bottom()
|
| 488 |
+
ax1a.set_ylabel("Number of audio clips")
|
| 489 |
+
|
| 490 |
+
ax2a.xaxis.set_ticks(np.arange(K, 2*K))
|
| 491 |
+
ax2a.xaxis.set_ticklabels(truncated_sorted_lbs[K:2*K], rotation=90, fontsize=fontsize)
|
| 492 |
+
ax2a.xaxis.tick_bottom()
|
| 493 |
+
ax2a.set_ylabel("Number of audio clips")
|
| 494 |
+
|
| 495 |
+
ax3a.xaxis.set_ticks(np.arange(2*K, 3*K))
|
| 496 |
+
ax3a.xaxis.set_ticklabels(truncated_sorted_lbs[2*K:3*K], rotation=90, fontsize=fontsize)
|
| 497 |
+
ax3a.xaxis.tick_bottom()
|
| 498 |
+
ax3a.set_ylabel("Number of audio clips")
|
| 499 |
+
|
| 500 |
+
ax4a.xaxis.set_ticks(np.arange(3*K, N))
|
| 501 |
+
ax4a.xaxis.set_ticklabels(truncated_sorted_lbs[3*K:], rotation=90, fontsize=fontsize)
|
| 502 |
+
ax4a.xaxis.tick_bottom()
|
| 503 |
+
ax4a.set_ylabel("Number of audio clips")
|
| 504 |
+
|
| 505 |
+
ax1a.spines['right'].set_visible(False)
|
| 506 |
+
ax1b.spines['right'].set_visible(False)
|
| 507 |
+
ax2a.spines['left'].set_visible(False)
|
| 508 |
+
ax2b.spines['left'].set_visible(False)
|
| 509 |
+
ax2a.spines['right'].set_visible(False)
|
| 510 |
+
ax2b.spines['right'].set_visible(False)
|
| 511 |
+
ax3a.spines['left'].set_visible(False)
|
| 512 |
+
ax3b.spines['left'].set_visible(False)
|
| 513 |
+
ax3a.spines['right'].set_visible(False)
|
| 514 |
+
ax3b.spines['right'].set_visible(False)
|
| 515 |
+
ax4a.spines['left'].set_visible(False)
|
| 516 |
+
ax4b.spines['left'].set_visible(False)
|
| 517 |
+
|
| 518 |
+
plt.subplots_adjust(hspace = 0.8)
|
| 519 |
+
|
| 520 |
+
return ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b
|
| 521 |
+
|
| 522 |
+
|
| 523 |
+
def _scatter_4_rows(x, ax, ax2, ax3, ax4, s, c, marker='.', alpha=1.):
|
| 524 |
+
N = len(x)
|
| 525 |
+
ax.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
|
| 526 |
+
ax2.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
|
| 527 |
+
ax3.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
|
| 528 |
+
ax4.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
|
| 529 |
+
|
| 530 |
+
|
| 531 |
+
def _plot_4_rows(x, ax, ax2, ax3, ax4, c, linewidth=1.0, alpha=1.0, label=""):
|
| 532 |
+
N = len(x)
|
| 533 |
+
ax.plot(x, c=c, linewidth=linewidth, alpha=alpha)
|
| 534 |
+
ax2.plot(x, c=c, linewidth=linewidth, alpha=alpha)
|
| 535 |
+
ax3.plot(x, c=c, linewidth=linewidth, alpha=alpha)
|
| 536 |
+
line, = ax4.plot(x, c=c, linewidth=linewidth, alpha=alpha, label=label)
|
| 537 |
+
return line
|
| 538 |
+
|
| 539 |
+
|
| 540 |
+
if __name__ == '__main__':
|
| 541 |
+
|
| 542 |
+
parser = argparse.ArgumentParser(description='')
|
| 543 |
+
subparsers = parser.add_subparsers(dest='mode')
|
| 544 |
+
|
| 545 |
+
parser_classwise_iteration_map = subparsers.add_parser('plot_classwise_iteration_map')
|
| 546 |
+
parser_six_figures = subparsers.add_parser('plot_six_figures')
|
| 547 |
+
parser_complexity_map = subparsers.add_parser('plot_complexity_map')
|
| 548 |
+
parser_long_fig = subparsers.add_parser('plot_long_fig')
|
| 549 |
+
|
| 550 |
+
args = parser.parse_args()
|
| 551 |
+
|
| 552 |
+
if args.mode == 'plot_classwise_iteration_map':
|
| 553 |
+
plot_classwise_iteration_map(args)
|
| 554 |
+
|
| 555 |
+
elif args.mode == 'plot_six_figures':
|
| 556 |
+
plot_six_figures(args)
|
| 557 |
+
|
| 558 |
+
elif args.mode == 'plot_complexity_map':
|
| 559 |
+
plot_complexity_map(args)
|
| 560 |
+
|
| 561 |
+
elif args.mode == 'plot_long_fig':
|
| 562 |
+
plot_long_fig(args)
|
| 563 |
+
|
| 564 |
+
else:
|
| 565 |
+
raise Exception('Incorrect argument!')
|
audio_detection/audio_infer/utils/plot_statistics.py
ADDED
|
The diff for this file is too large to render.
See raw diff
|
audio_detection/audio_infer/utils/utilities.py
ADDED
|
@@ -0,0 +1,172 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import logging
|
| 3 |
+
import h5py
|
| 4 |
+
import soundfile
|
| 5 |
+
import librosa
|
| 6 |
+
import numpy as np
|
| 7 |
+
import pandas as pd
|
| 8 |
+
from scipy import stats
|
| 9 |
+
import datetime
|
| 10 |
+
import pickle
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
def create_folder(fd):
|
| 14 |
+
if not os.path.exists(fd):
|
| 15 |
+
os.makedirs(fd)
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def get_filename(path):
|
| 19 |
+
path = os.path.realpath(path)
|
| 20 |
+
na_ext = path.split('/')[-1]
|
| 21 |
+
na = os.path.splitext(na_ext)[0]
|
| 22 |
+
return na
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def get_sub_filepaths(folder):
|
| 26 |
+
paths = []
|
| 27 |
+
for root, dirs, files in os.walk(folder):
|
| 28 |
+
for name in files:
|
| 29 |
+
path = os.path.join(root, name)
|
| 30 |
+
paths.append(path)
|
| 31 |
+
return paths
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
def create_logging(log_dir, filemode):
|
| 35 |
+
create_folder(log_dir)
|
| 36 |
+
i1 = 0
|
| 37 |
+
|
| 38 |
+
while os.path.isfile(os.path.join(log_dir, '{:04d}.log'.format(i1))):
|
| 39 |
+
i1 += 1
|
| 40 |
+
|
| 41 |
+
log_path = os.path.join(log_dir, '{:04d}.log'.format(i1))
|
| 42 |
+
logging.basicConfig(
|
| 43 |
+
level=logging.DEBUG,
|
| 44 |
+
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
|
| 45 |
+
datefmt='%a, %d %b %Y %H:%M:%S',
|
| 46 |
+
filename=log_path,
|
| 47 |
+
filemode=filemode)
|
| 48 |
+
|
| 49 |
+
# Print to console
|
| 50 |
+
console = logging.StreamHandler()
|
| 51 |
+
console.setLevel(logging.INFO)
|
| 52 |
+
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
|
| 53 |
+
console.setFormatter(formatter)
|
| 54 |
+
logging.getLogger('').addHandler(console)
|
| 55 |
+
|
| 56 |
+
return logging
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
def read_metadata(csv_path, classes_num, id_to_ix):
|
| 60 |
+
"""Read metadata of AudioSet from a csv file.
|
| 61 |
+
|
| 62 |
+
Args:
|
| 63 |
+
csv_path: str
|
| 64 |
+
|
| 65 |
+
Returns:
|
| 66 |
+
meta_dict: {'audio_name': (audios_num,), 'target': (audios_num, classes_num)}
|
| 67 |
+
"""
|
| 68 |
+
|
| 69 |
+
with open(csv_path, 'r') as fr:
|
| 70 |
+
lines = fr.readlines()
|
| 71 |
+
lines = lines[3:] # Remove heads
|
| 72 |
+
|
| 73 |
+
audios_num = len(lines)
|
| 74 |
+
targets = np.zeros((audios_num, classes_num), dtype=np.bool)
|
| 75 |
+
audio_names = []
|
| 76 |
+
|
| 77 |
+
for n, line in enumerate(lines):
|
| 78 |
+
items = line.split(', ')
|
| 79 |
+
"""items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']"""
|
| 80 |
+
|
| 81 |
+
audio_name = 'Y{}.wav'.format(items[0]) # Audios are started with an extra 'Y' when downloading
|
| 82 |
+
label_ids = items[3].split('"')[1].split(',')
|
| 83 |
+
|
| 84 |
+
audio_names.append(audio_name)
|
| 85 |
+
|
| 86 |
+
# Target
|
| 87 |
+
for id in label_ids:
|
| 88 |
+
ix = id_to_ix[id]
|
| 89 |
+
targets[n, ix] = 1
|
| 90 |
+
|
| 91 |
+
meta_dict = {'audio_name': np.array(audio_names), 'target': targets}
|
| 92 |
+
return meta_dict
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def float32_to_int16(x):
|
| 96 |
+
assert np.max(np.abs(x)) <= 1.2
|
| 97 |
+
x = np.clip(x, -1, 1)
|
| 98 |
+
return (x * 32767.).astype(np.int16)
|
| 99 |
+
|
| 100 |
+
def int16_to_float32(x):
|
| 101 |
+
return (x / 32767.).astype(np.float32)
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
def pad_or_truncate(x, audio_length):
|
| 105 |
+
"""Pad all audio to specific length."""
|
| 106 |
+
if len(x) <= audio_length:
|
| 107 |
+
return np.concatenate((x, np.zeros(audio_length - len(x))), axis=0)
|
| 108 |
+
else:
|
| 109 |
+
return x[0 : audio_length]
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def d_prime(auc):
|
| 113 |
+
d_prime = stats.norm().ppf(auc) * np.sqrt(2.0)
|
| 114 |
+
return d_prime
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
class Mixup(object):
|
| 118 |
+
def __init__(self, mixup_alpha, random_seed=1234):
|
| 119 |
+
"""Mixup coefficient generator.
|
| 120 |
+
"""
|
| 121 |
+
self.mixup_alpha = mixup_alpha
|
| 122 |
+
self.random_state = np.random.RandomState(random_seed)
|
| 123 |
+
|
| 124 |
+
def get_lambda(self, batch_size):
|
| 125 |
+
"""Get mixup random coefficients.
|
| 126 |
+
Args:
|
| 127 |
+
batch_size: int
|
| 128 |
+
Returns:
|
| 129 |
+
mixup_lambdas: (batch_size,)
|
| 130 |
+
"""
|
| 131 |
+
mixup_lambdas = []
|
| 132 |
+
for n in range(0, batch_size, 2):
|
| 133 |
+
lam = self.random_state.beta(self.mixup_alpha, self.mixup_alpha, 1)[0]
|
| 134 |
+
mixup_lambdas.append(lam)
|
| 135 |
+
mixup_lambdas.append(1. - lam)
|
| 136 |
+
|
| 137 |
+
return np.array(mixup_lambdas)
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
class StatisticsContainer(object):
|
| 141 |
+
def __init__(self, statistics_path):
|
| 142 |
+
"""Contain statistics of different training iterations.
|
| 143 |
+
"""
|
| 144 |
+
self.statistics_path = statistics_path
|
| 145 |
+
|
| 146 |
+
self.backup_statistics_path = '{}_{}.pkl'.format(
|
| 147 |
+
os.path.splitext(self.statistics_path)[0],
|
| 148 |
+
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
|
| 149 |
+
|
| 150 |
+
self.statistics_dict = {'bal': [], 'test': []}
|
| 151 |
+
|
| 152 |
+
def append(self, iteration, statistics, data_type):
|
| 153 |
+
statistics['iteration'] = iteration
|
| 154 |
+
self.statistics_dict[data_type].append(statistics)
|
| 155 |
+
|
| 156 |
+
def dump(self):
|
| 157 |
+
pickle.dump(self.statistics_dict, open(self.statistics_path, 'wb'))
|
| 158 |
+
pickle.dump(self.statistics_dict, open(self.backup_statistics_path, 'wb'))
|
| 159 |
+
logging.info(' Dump statistics to {}'.format(self.statistics_path))
|
| 160 |
+
logging.info(' Dump statistics to {}'.format(self.backup_statistics_path))
|
| 161 |
+
|
| 162 |
+
def load_state_dict(self, resume_iteration):
|
| 163 |
+
self.statistics_dict = pickle.load(open(self.statistics_path, 'rb'))
|
| 164 |
+
|
| 165 |
+
resume_statistics_dict = {'bal': [], 'test': []}
|
| 166 |
+
|
| 167 |
+
for key in self.statistics_dict.keys():
|
| 168 |
+
for statistics in self.statistics_dict[key]:
|
| 169 |
+
if statistics['iteration'] <= resume_iteration:
|
| 170 |
+
resume_statistics_dict[key].append(statistics)
|
| 171 |
+
|
| 172 |
+
self.statistics_dict = resume_statistics_dict
|
audio_detection/target_sound_detection/src/__pycache__/models.cpython-38.pyc
ADDED
|
Binary file (37.9 kB). View file
|
audio_detection/target_sound_detection/src/__pycache__/utils.cpython-38.pyc
ADDED
|
Binary file (11.1 kB). View file
|
audio_detection/target_sound_detection/src/models.py
ADDED
|
@@ -0,0 +1,1288 @@
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|
| 1 |
+
# !/usr/bin/env python
|
| 2 |
+
# -*- coding: utf-8 -*-
|
| 3 |
+
# @Time : 2021/3/9 16:33
|
| 4 |
+
# @Author : dongchao yang
|
| 5 |
+
# @File : train.py
|
| 6 |
+
from itertools import zip_longest
|
| 7 |
+
import numpy as np
|
| 8 |
+
from scipy import ndimage
|
| 9 |
+
import torch
|
| 10 |
+
import torch.nn as nn
|
| 11 |
+
import torch.nn.functional as F
|
| 12 |
+
import time
|
| 13 |
+
from torchlibrosa.augmentation import SpecAugmentation
|
| 14 |
+
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
| 15 |
+
import math
|
| 16 |
+
from sklearn.cluster import KMeans
|
| 17 |
+
import os
|
| 18 |
+
import time
|
| 19 |
+
from functools import partial
|
| 20 |
+
# import timm
|
| 21 |
+
# from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
| 22 |
+
import warnings
|
| 23 |
+
from functools import partial
|
| 24 |
+
# from timm.models.registry import register_model
|
| 25 |
+
# from timm.models.vision_transformer import _cfg
|
| 26 |
+
# from mmdet.utils import get_root_logger
|
| 27 |
+
# from mmcv.runner import load_checkpoint
|
| 28 |
+
# from mmcv.runner import _load_checkpoint, load_state_dict
|
| 29 |
+
# import mmcv.runner
|
| 30 |
+
import copy
|
| 31 |
+
from collections import OrderedDict
|
| 32 |
+
import io
|
| 33 |
+
import re
|
| 34 |
+
DEBUG=0
|
| 35 |
+
event_labels = ['Alarm', 'Alarm_clock', 'Animal', 'Applause', 'Arrow', 'Artillery_fire',
|
| 36 |
+
'Babbling', 'Baby_laughter', 'Bark', 'Basketball_bounce', 'Battle_cry',
|
| 37 |
+
'Bell', 'Bird', 'Bleat', 'Bouncing', 'Breathing', 'Buzz', 'Camera',
|
| 38 |
+
'Cap_gun', 'Car', 'Car_alarm', 'Cat', 'Caw', 'Cheering', 'Child_singing',
|
| 39 |
+
'Choir', 'Chop', 'Chopping_(food)', 'Clapping', 'Clickety-clack', 'Clicking',
|
| 40 |
+
'Clip-clop', 'Cluck', 'Coin_(dropping)', 'Computer_keyboard', 'Conversation',
|
| 41 |
+
'Coo', 'Cough', 'Cowbell', 'Creak', 'Cricket', 'Croak', 'Crow', 'Crowd', 'DTMF',
|
| 42 |
+
'Dog', 'Door', 'Drill', 'Drip', 'Engine', 'Engine_starting', 'Explosion', 'Fart',
|
| 43 |
+
'Female_singing', 'Filing_(rasp)', 'Finger_snapping', 'Fire', 'Fire_alarm', 'Firecracker',
|
| 44 |
+
'Fireworks', 'Frog', 'Gasp', 'Gears', 'Giggle', 'Glass', 'Glass_shatter', 'Gobble', 'Groan',
|
| 45 |
+
'Growling', 'Hammer', 'Hands', 'Hiccup', 'Honk', 'Hoot', 'Howl', 'Human_sounds', 'Human_voice',
|
| 46 |
+
'Insect', 'Laughter', 'Liquid', 'Machine_gun', 'Male_singing', 'Mechanisms', 'Meow', 'Moo',
|
| 47 |
+
'Motorcycle', 'Mouse', 'Music', 'Oink', 'Owl', 'Pant', 'Pant_(dog)', 'Patter', 'Pig', 'Plop',
|
| 48 |
+
'Pour', 'Power_tool', 'Purr', 'Quack', 'Radio', 'Rain_on_surface', 'Rapping', 'Rattle',
|
| 49 |
+
'Reversing_beeps', 'Ringtone', 'Roar', 'Run', 'Rustle', 'Scissors', 'Scrape', 'Scratch',
|
| 50 |
+
'Screaming', 'Sewing_machine', 'Shout', 'Shuffle', 'Shuffling_cards', 'Singing',
|
| 51 |
+
'Single-lens_reflex_camera', 'Siren', 'Skateboard', 'Sniff', 'Snoring', 'Speech',
|
| 52 |
+
'Speech_synthesizer', 'Spray', 'Squeak', 'Squeal', 'Steam', 'Stir', 'Surface_contact',
|
| 53 |
+
'Tap', 'Tap_dance', 'Telephone_bell_ringing', 'Television', 'Tick', 'Tick-tock', 'Tools',
|
| 54 |
+
'Train', 'Train_horn', 'Train_wheels_squealing', 'Truck', 'Turkey', 'Typewriter', 'Typing',
|
| 55 |
+
'Vehicle', 'Video_game_sound', 'Water', 'Whimper_(dog)', 'Whip', 'Whispering', 'Whistle',
|
| 56 |
+
'Whistling', 'Whoop', 'Wind', 'Writing', 'Yip', 'and_pans', 'bird_song', 'bleep', 'clink',
|
| 57 |
+
'cock-a-doodle-doo', 'crinkling', 'dove', 'dribble', 'eructation', 'faucet', 'flapping_wings',
|
| 58 |
+
'footsteps', 'gunfire', 'heartbeat', 'infant_cry', 'kid_speaking', 'man_speaking', 'mastication',
|
| 59 |
+
'mice', 'river', 'rooster', 'silverware', 'skidding', 'smack', 'sobbing', 'speedboat', 'splatter',
|
| 60 |
+
'surf', 'thud', 'thwack', 'toot', 'truck_horn', 'tweet', 'vroom', 'waterfowl', 'woman_speaking']
|
| 61 |
+
def load_checkpoint(model,
|
| 62 |
+
filename,
|
| 63 |
+
map_location=None,
|
| 64 |
+
strict=False,
|
| 65 |
+
logger=None,
|
| 66 |
+
revise_keys=[(r'^module\.', '')]):
|
| 67 |
+
"""Load checkpoint from a file or URI.
|
| 68 |
+
Args:
|
| 69 |
+
model (Module): Module to load checkpoint.
|
| 70 |
+
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
|
| 71 |
+
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
|
| 72 |
+
details.
|
| 73 |
+
map_location (str): Same as :func:`torch.load`.
|
| 74 |
+
strict (bool): Whether to allow different params for the model and
|
| 75 |
+
checkpoint.
|
| 76 |
+
logger (:mod:`logging.Logger` or None): The logger for error message.
|
| 77 |
+
revise_keys (list): A list of customized keywords to modify the
|
| 78 |
+
state_dict in checkpoint. Each item is a (pattern, replacement)
|
| 79 |
+
pair of the regular expression operations. Default: strip
|
| 80 |
+
the prefix 'module.' by [(r'^module\\.', '')].
|
| 81 |
+
Returns:
|
| 82 |
+
dict or OrderedDict: The loaded checkpoint.
|
| 83 |
+
"""
|
| 84 |
+
|
| 85 |
+
checkpoint = _load_checkpoint(filename, map_location, logger)
|
| 86 |
+
'''
|
| 87 |
+
new_proj = torch.nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
|
| 88 |
+
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
|
| 89 |
+
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
|
| 90 |
+
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=2).unsqueeze(2).repeat(1,1,3,1))
|
| 91 |
+
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
|
| 92 |
+
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=3).unsqueeze(3).repeat(1,1,1,3))
|
| 93 |
+
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
|
| 94 |
+
'''
|
| 95 |
+
new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
|
| 96 |
+
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
|
| 97 |
+
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
|
| 98 |
+
# OrderedDict is a subclass of dict
|
| 99 |
+
if not isinstance(checkpoint, dict):
|
| 100 |
+
raise RuntimeError(
|
| 101 |
+
f'No state_dict found in checkpoint file {filename}')
|
| 102 |
+
# get state_dict from checkpoint
|
| 103 |
+
if 'state_dict' in checkpoint:
|
| 104 |
+
state_dict = checkpoint['state_dict']
|
| 105 |
+
else:
|
| 106 |
+
state_dict = checkpoint
|
| 107 |
+
|
| 108 |
+
# strip prefix of state_dict
|
| 109 |
+
metadata = getattr(state_dict, '_metadata', OrderedDict())
|
| 110 |
+
for p, r in revise_keys:
|
| 111 |
+
state_dict = OrderedDict(
|
| 112 |
+
{re.sub(p, r, k): v
|
| 113 |
+
for k, v in state_dict.items()})
|
| 114 |
+
state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()})
|
| 115 |
+
# Keep metadata in state_dict
|
| 116 |
+
state_dict._metadata = metadata
|
| 117 |
+
|
| 118 |
+
# load state_dict
|
| 119 |
+
load_state_dict(model, state_dict, strict, logger)
|
| 120 |
+
return checkpoint
|
| 121 |
+
|
| 122 |
+
def init_weights(m):
|
| 123 |
+
if isinstance(m, (nn.Conv2d, nn.Conv1d)):
|
| 124 |
+
nn.init.kaiming_normal_(m.weight)
|
| 125 |
+
if m.bias is not None:
|
| 126 |
+
nn.init.constant_(m.bias, 0)
|
| 127 |
+
elif isinstance(m, nn.BatchNorm2d):
|
| 128 |
+
nn.init.constant_(m.weight, 1)
|
| 129 |
+
if m.bias is not None:
|
| 130 |
+
nn.init.constant_(m.bias, 0)
|
| 131 |
+
if isinstance(m, nn.Linear):
|
| 132 |
+
nn.init.kaiming_uniform_(m.weight)
|
| 133 |
+
if m.bias is not None:
|
| 134 |
+
nn.init.constant_(m.bias, 0)
|
| 135 |
+
def init_layer(layer):
|
| 136 |
+
"""Initialize a Linear or Convolutional layer. """
|
| 137 |
+
nn.init.xavier_uniform_(layer.weight)
|
| 138 |
+
if hasattr(layer, 'bias'):
|
| 139 |
+
if layer.bias is not None:
|
| 140 |
+
layer.bias.data.fill_(0.)
|
| 141 |
+
|
| 142 |
+
|
| 143 |
+
def init_bn(bn):
|
| 144 |
+
"""Initialize a Batchnorm layer. """
|
| 145 |
+
bn.bias.data.fill_(0.)
|
| 146 |
+
bn.weight.data.fill_(1.)
|
| 147 |
+
|
| 148 |
+
class MaxPool(nn.Module):
|
| 149 |
+
def __init__(self, pooldim=1):
|
| 150 |
+
super().__init__()
|
| 151 |
+
self.pooldim = pooldim
|
| 152 |
+
|
| 153 |
+
def forward(self, logits, decision):
|
| 154 |
+
return torch.max(decision, dim=self.pooldim)[0]
|
| 155 |
+
|
| 156 |
+
|
| 157 |
+
class LinearSoftPool(nn.Module):
|
| 158 |
+
"""LinearSoftPool
|
| 159 |
+
Linear softmax, takes logits and returns a probability, near to the actual maximum value.
|
| 160 |
+
Taken from the paper:
|
| 161 |
+
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
|
| 162 |
+
https://arxiv.org/abs/1810.09050
|
| 163 |
+
"""
|
| 164 |
+
def __init__(self, pooldim=1):
|
| 165 |
+
super().__init__()
|
| 166 |
+
self.pooldim = pooldim
|
| 167 |
+
|
| 168 |
+
def forward(self, logits, time_decision):
|
| 169 |
+
return (time_decision**2).sum(self.pooldim) / (time_decision.sum(
|
| 170 |
+
self.pooldim)+1e-7)
|
| 171 |
+
|
| 172 |
+
class ConvBlock(nn.Module):
|
| 173 |
+
def __init__(self, in_channels, out_channels):
|
| 174 |
+
|
| 175 |
+
super(ConvBlock, self).__init__()
|
| 176 |
+
|
| 177 |
+
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 178 |
+
out_channels=out_channels,
|
| 179 |
+
kernel_size=(3, 3), stride=(1, 1),
|
| 180 |
+
padding=(1, 1), bias=False)
|
| 181 |
+
|
| 182 |
+
self.conv2 = nn.Conv2d(in_channels=out_channels,
|
| 183 |
+
out_channels=out_channels,
|
| 184 |
+
kernel_size=(3, 3), stride=(1, 1),
|
| 185 |
+
padding=(1, 1), bias=False)
|
| 186 |
+
|
| 187 |
+
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 188 |
+
self.bn2 = nn.BatchNorm2d(out_channels)
|
| 189 |
+
|
| 190 |
+
self.init_weight()
|
| 191 |
+
|
| 192 |
+
def init_weight(self):
|
| 193 |
+
init_layer(self.conv1)
|
| 194 |
+
init_layer(self.conv2)
|
| 195 |
+
init_bn(self.bn1)
|
| 196 |
+
init_bn(self.bn2)
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 200 |
+
|
| 201 |
+
x = input
|
| 202 |
+
x = F.relu_(self.bn1(self.conv1(x)))
|
| 203 |
+
x = F.relu_(self.bn2(self.conv2(x)))
|
| 204 |
+
if pool_type == 'max':
|
| 205 |
+
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 206 |
+
elif pool_type == 'avg':
|
| 207 |
+
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 208 |
+
elif pool_type == 'avg+max':
|
| 209 |
+
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 210 |
+
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 211 |
+
x = x1 + x2
|
| 212 |
+
else:
|
| 213 |
+
raise Exception('Incorrect argument!')
|
| 214 |
+
|
| 215 |
+
return x
|
| 216 |
+
|
| 217 |
+
class ConvBlock_GLU(nn.Module):
|
| 218 |
+
def __init__(self, in_channels, out_channels,kernel_size=(3,3)):
|
| 219 |
+
super(ConvBlock_GLU, self).__init__()
|
| 220 |
+
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 221 |
+
out_channels=out_channels,
|
| 222 |
+
kernel_size=kernel_size, stride=(1, 1),
|
| 223 |
+
padding=(1, 1), bias=False)
|
| 224 |
+
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 225 |
+
self.sigmoid = nn.Sigmoid()
|
| 226 |
+
self.init_weight()
|
| 227 |
+
|
| 228 |
+
def init_weight(self):
|
| 229 |
+
init_layer(self.conv1)
|
| 230 |
+
init_bn(self.bn1)
|
| 231 |
+
|
| 232 |
+
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 233 |
+
x = input
|
| 234 |
+
x = self.bn1(self.conv1(x))
|
| 235 |
+
cnn1 = self.sigmoid(x[:, :x.shape[1]//2, :, :])
|
| 236 |
+
cnn2 = x[:,x.shape[1]//2:,:,:]
|
| 237 |
+
x = cnn1*cnn2
|
| 238 |
+
if pool_type == 'max':
|
| 239 |
+
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 240 |
+
elif pool_type == 'avg':
|
| 241 |
+
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 242 |
+
elif pool_type == 'avg+max':
|
| 243 |
+
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 244 |
+
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 245 |
+
x = x1 + x2
|
| 246 |
+
elif pool_type == 'None':
|
| 247 |
+
pass
|
| 248 |
+
elif pool_type == 'LP':
|
| 249 |
+
pass
|
| 250 |
+
#nn.LPPool2d(4, pool_size)
|
| 251 |
+
else:
|
| 252 |
+
raise Exception('Incorrect argument!')
|
| 253 |
+
return x
|
| 254 |
+
|
| 255 |
+
class Mul_scale_GLU(nn.Module):
|
| 256 |
+
def __init__(self):
|
| 257 |
+
super(Mul_scale_GLU,self).__init__()
|
| 258 |
+
self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1)) # 1*1
|
| 259 |
+
self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3)) # 3*3
|
| 260 |
+
self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5)) # 5*5
|
| 261 |
+
self.conv_block2 = ConvBlock_GLU(in_channels=96, out_channels=128*2)
|
| 262 |
+
# self.conv_block3 = ConvBlock(in_channels=64, out_channels=128)
|
| 263 |
+
self.conv_block3 = ConvBlock_GLU(in_channels=128, out_channels=128*2)
|
| 264 |
+
self.conv_block4 = ConvBlock_GLU(in_channels=128, out_channels=256*2)
|
| 265 |
+
self.conv_block5 = ConvBlock_GLU(in_channels=256, out_channels=256*2)
|
| 266 |
+
self.conv_block6 = ConvBlock_GLU(in_channels=256, out_channels=512*2)
|
| 267 |
+
self.conv_block7 = ConvBlock_GLU(in_channels=512, out_channels=512*2)
|
| 268 |
+
self.padding = nn.ReplicationPad2d((0,1,0,1))
|
| 269 |
+
|
| 270 |
+
def forward(self, input, fi=None):
|
| 271 |
+
"""
|
| 272 |
+
Input: (batch_size, data_length)"""
|
| 273 |
+
x1 = self.conv_block1_1(input, pool_size=(2, 2), pool_type='avg')
|
| 274 |
+
x1 = x1[:,:,:500,:32]
|
| 275 |
+
#print('x1 ',x1.shape)
|
| 276 |
+
x2 = self.conv_block1_2(input,pool_size=(2,2),pool_type='avg')
|
| 277 |
+
#print('x2 ',x2.shape)
|
| 278 |
+
x3 = self.conv_block1_3(input,pool_size=(2,2),pool_type='avg')
|
| 279 |
+
x3 = self.padding(x3)
|
| 280 |
+
#print('x3 ',x3.shape)
|
| 281 |
+
# assert 1==2
|
| 282 |
+
x = torch.cat([x1,x2],dim=1)
|
| 283 |
+
x = torch.cat([x,x3],dim=1)
|
| 284 |
+
#print('x ',x.shape)
|
| 285 |
+
x = self.conv_block2(x, pool_size=(2, 2), pool_type='None')
|
| 286 |
+
x = self.conv_block3(x,pool_size=(2,2),pool_type='avg')
|
| 287 |
+
x = F.dropout(x, p=0.2, training=self.training) #
|
| 288 |
+
#print('x2,3 ',x.shape)
|
| 289 |
+
x = self.conv_block4(x, pool_size=(2, 4), pool_type='None')
|
| 290 |
+
x = self.conv_block5(x,pool_size=(2,4),pool_type='avg')
|
| 291 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 292 |
+
#print('x4,5 ',x.shape)
|
| 293 |
+
|
| 294 |
+
x = self.conv_block6(x, pool_size=(1, 4), pool_type='None')
|
| 295 |
+
x = self.conv_block7(x, pool_size=(1, 4), pool_type='avg')
|
| 296 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 297 |
+
# print('x6,7 ',x.shape)
|
| 298 |
+
# assert 1==2
|
| 299 |
+
return x
|
| 300 |
+
|
| 301 |
+
class Cnn14(nn.Module):
|
| 302 |
+
def __init__(self, sample_rate=32000, window_size=1024, hop_size=320, mel_bins=64, fmin=50,
|
| 303 |
+
fmax=14000, classes_num=527):
|
| 304 |
+
|
| 305 |
+
super(Cnn14, self).__init__()
|
| 306 |
+
|
| 307 |
+
window = 'hann'
|
| 308 |
+
center = True
|
| 309 |
+
pad_mode = 'reflect'
|
| 310 |
+
ref = 1.0
|
| 311 |
+
amin = 1e-10
|
| 312 |
+
top_db = None
|
| 313 |
+
|
| 314 |
+
# Spectrogram extractor
|
| 315 |
+
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 316 |
+
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 317 |
+
freeze_parameters=True)
|
| 318 |
+
|
| 319 |
+
# Logmel feature extractor
|
| 320 |
+
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 321 |
+
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 322 |
+
freeze_parameters=True)
|
| 323 |
+
|
| 324 |
+
# Spec augmenter
|
| 325 |
+
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
| 326 |
+
freq_drop_width=8, freq_stripes_num=2)
|
| 327 |
+
|
| 328 |
+
self.bn0 = nn.BatchNorm2d(64)
|
| 329 |
+
|
| 330 |
+
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 331 |
+
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 332 |
+
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 333 |
+
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 334 |
+
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
|
| 335 |
+
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
|
| 336 |
+
|
| 337 |
+
self.fc1 = nn.Linear(2048, 128, bias=True)
|
| 338 |
+
self.fc_audioset = nn.Linear(128, classes_num, bias=True)
|
| 339 |
+
|
| 340 |
+
self.init_weight()
|
| 341 |
+
|
| 342 |
+
def init_weight(self):
|
| 343 |
+
init_layer(self.fc1)
|
| 344 |
+
init_layer(self.fc_audioset)
|
| 345 |
+
|
| 346 |
+
def forward(self, input_, mixup_lambda=None):
|
| 347 |
+
"""
|
| 348 |
+
Input: (batch_size, data_length)"""
|
| 349 |
+
input_ = input_.unsqueeze(1)
|
| 350 |
+
x = self.conv_block1(input_, pool_size=(2, 2), pool_type='avg')
|
| 351 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 352 |
+
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
| 353 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 354 |
+
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
|
| 355 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 356 |
+
x = self.conv_block4(x, pool_size=(1, 2), pool_type='avg')
|
| 357 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 358 |
+
x = self.conv_block5(x, pool_size=(1, 2), pool_type='avg')
|
| 359 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 360 |
+
x = self.conv_block6(x, pool_size=(1, 2), pool_type='avg')
|
| 361 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 362 |
+
# print(x.shape)
|
| 363 |
+
# x = torch.mean(x, dim=3)
|
| 364 |
+
x = x.transpose(1, 2).contiguous().flatten(-2)
|
| 365 |
+
x = self.fc1(x)
|
| 366 |
+
# print(x.shape)
|
| 367 |
+
# assert 1==2
|
| 368 |
+
# (x1,_) = torch.max(x, dim=2)
|
| 369 |
+
# x2 = torch.mean(x, dim=2)
|
| 370 |
+
# x = x1 + x2
|
| 371 |
+
# x = F.dropout(x, p=0.5, training=self.training)
|
| 372 |
+
# x = F.relu_(self.fc1(x))
|
| 373 |
+
# embedding = F.dropout(x, p=0.5, training=self.training)
|
| 374 |
+
return x
|
| 375 |
+
|
| 376 |
+
class Cnn10_fi(nn.Module):
|
| 377 |
+
def __init__(self):
|
| 378 |
+
super(Cnn10_fi, self).__init__()
|
| 379 |
+
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 380 |
+
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 381 |
+
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 382 |
+
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 383 |
+
|
| 384 |
+
# self.fc1 = nn.Linear(512, 512, bias=True)
|
| 385 |
+
# self.fc_audioset = nn.Linear(512, classes_num, bias=True)
|
| 386 |
+
|
| 387 |
+
# self.init_weight()
|
| 388 |
+
|
| 389 |
+
def forward(self, input, fi=None):
|
| 390 |
+
"""
|
| 391 |
+
Input: (batch_size, data_length)"""
|
| 392 |
+
|
| 393 |
+
x = self.conv_block1(input, pool_size=(2, 2), pool_type='avg')
|
| 394 |
+
if fi != None:
|
| 395 |
+
gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 396 |
+
beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 397 |
+
x = (gamma)*x + beta
|
| 398 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 399 |
+
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
| 400 |
+
if fi != None:
|
| 401 |
+
gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 402 |
+
beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 403 |
+
x = (gamma)*x + beta
|
| 404 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 405 |
+
x = self.conv_block3(x, pool_size=(2, 4), pool_type='avg')
|
| 406 |
+
if fi != None:
|
| 407 |
+
gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 408 |
+
beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 409 |
+
x = (gamma)*x + beta
|
| 410 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 411 |
+
x = self.conv_block4(x, pool_size=(1, 4), pool_type='avg')
|
| 412 |
+
if fi != None:
|
| 413 |
+
gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 414 |
+
beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
|
| 415 |
+
x = (gamma)*x + beta
|
| 416 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 417 |
+
return x
|
| 418 |
+
|
| 419 |
+
class Cnn10_mul_scale(nn.Module):
|
| 420 |
+
def __init__(self,scale=8):
|
| 421 |
+
super(Cnn10_mul_scale, self).__init__()
|
| 422 |
+
self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1))
|
| 423 |
+
self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3))
|
| 424 |
+
self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5))
|
| 425 |
+
self.conv_block2 = ConvBlock(in_channels=96, out_channels=128)
|
| 426 |
+
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 427 |
+
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 428 |
+
self.scale = scale
|
| 429 |
+
self.padding = nn.ReplicationPad2d((0,1,0,1))
|
| 430 |
+
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 431 |
+
"""
|
| 432 |
+
Input: (batch_size, data_length)"""
|
| 433 |
+
if self.scale == 8:
|
| 434 |
+
pool_size1 = (2,2)
|
| 435 |
+
pool_size2 = (2,2)
|
| 436 |
+
pool_size3 = (2,4)
|
| 437 |
+
pool_size4 = (1,4)
|
| 438 |
+
elif self.scale == 4:
|
| 439 |
+
pool_size1 = (2,2)
|
| 440 |
+
pool_size2 = (2,2)
|
| 441 |
+
pool_size3 = (1,4)
|
| 442 |
+
pool_size4 = (1,4)
|
| 443 |
+
elif self.scale == 2:
|
| 444 |
+
pool_size1 = (2,2)
|
| 445 |
+
pool_size2 = (1,2)
|
| 446 |
+
pool_size3 = (1,4)
|
| 447 |
+
pool_size4 = (1,4)
|
| 448 |
+
else:
|
| 449 |
+
pool_size1 = (1,2)
|
| 450 |
+
pool_size2 = (1,2)
|
| 451 |
+
pool_size3 = (1,4)
|
| 452 |
+
pool_size4 = (1,4)
|
| 453 |
+
# print('input ',input.shape)
|
| 454 |
+
x1 = self.conv_block1_1(input, pool_size=pool_size1, pool_type='avg')
|
| 455 |
+
x1 = x1[:,:,:500,:32]
|
| 456 |
+
#print('x1 ',x1.shape)
|
| 457 |
+
x2 = self.conv_block1_2(input, pool_size=pool_size1, pool_type='avg')
|
| 458 |
+
#print('x2 ',x2.shape)
|
| 459 |
+
x3 = self.conv_block1_3(input, pool_size=pool_size1, pool_type='avg')
|
| 460 |
+
x3 = self.padding(x3)
|
| 461 |
+
#print('x3 ',x3.shape)
|
| 462 |
+
# assert 1==2
|
| 463 |
+
m_i = min(x3.shape[2],min(x1.shape[2],x2.shape[2]))
|
| 464 |
+
#print('m_i ', m_i)
|
| 465 |
+
x = torch.cat([x1[:,:,:m_i,:],x2[:,:, :m_i,:],x3[:,:, :m_i,:]],dim=1)
|
| 466 |
+
# x = torch.cat([x,x3],dim=1)
|
| 467 |
+
|
| 468 |
+
# x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg')
|
| 469 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 470 |
+
x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg')
|
| 471 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 472 |
+
x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg')
|
| 473 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 474 |
+
x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg')
|
| 475 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 476 |
+
return x
|
| 477 |
+
|
| 478 |
+
|
| 479 |
+
class Cnn10(nn.Module):
|
| 480 |
+
def __init__(self,scale=8):
|
| 481 |
+
super(Cnn10, self).__init__()
|
| 482 |
+
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 483 |
+
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 484 |
+
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 485 |
+
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 486 |
+
self.scale = scale
|
| 487 |
+
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 488 |
+
"""
|
| 489 |
+
Input: (batch_size, data_length)"""
|
| 490 |
+
if self.scale == 8:
|
| 491 |
+
pool_size1 = (2,2)
|
| 492 |
+
pool_size2 = (2,2)
|
| 493 |
+
pool_size3 = (2,4)
|
| 494 |
+
pool_size4 = (1,4)
|
| 495 |
+
elif self.scale == 4:
|
| 496 |
+
pool_size1 = (2,2)
|
| 497 |
+
pool_size2 = (2,2)
|
| 498 |
+
pool_size3 = (1,4)
|
| 499 |
+
pool_size4 = (1,4)
|
| 500 |
+
elif self.scale == 2:
|
| 501 |
+
pool_size1 = (2,2)
|
| 502 |
+
pool_size2 = (1,2)
|
| 503 |
+
pool_size3 = (1,4)
|
| 504 |
+
pool_size4 = (1,4)
|
| 505 |
+
else:
|
| 506 |
+
pool_size1 = (1,2)
|
| 507 |
+
pool_size2 = (1,2)
|
| 508 |
+
pool_size3 = (1,4)
|
| 509 |
+
pool_size4 = (1,4)
|
| 510 |
+
x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg')
|
| 511 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 512 |
+
x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg')
|
| 513 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 514 |
+
x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg')
|
| 515 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 516 |
+
x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg')
|
| 517 |
+
x = F.dropout(x, p=0.2, training=self.training)
|
| 518 |
+
return x
|
| 519 |
+
|
| 520 |
+
class MeanPool(nn.Module):
|
| 521 |
+
def __init__(self, pooldim=1):
|
| 522 |
+
super().__init__()
|
| 523 |
+
self.pooldim = pooldim
|
| 524 |
+
|
| 525 |
+
def forward(self, logits, decision):
|
| 526 |
+
return torch.mean(decision, dim=self.pooldim)
|
| 527 |
+
|
| 528 |
+
class ResPool(nn.Module):
|
| 529 |
+
def __init__(self, pooldim=1):
|
| 530 |
+
super().__init__()
|
| 531 |
+
self.pooldim = pooldim
|
| 532 |
+
self.linPool = LinearSoftPool(pooldim=1)
|
| 533 |
+
|
| 534 |
+
class AutoExpPool(nn.Module):
|
| 535 |
+
def __init__(self, outputdim=10, pooldim=1):
|
| 536 |
+
super().__init__()
|
| 537 |
+
self.outputdim = outputdim
|
| 538 |
+
self.alpha = nn.Parameter(torch.full((outputdim, ), 1))
|
| 539 |
+
self.pooldim = pooldim
|
| 540 |
+
|
| 541 |
+
def forward(self, logits, decision):
|
| 542 |
+
scaled = self.alpha * decision # \alpha * P(Y|x) in the paper
|
| 543 |
+
return (logits * torch.exp(scaled)).sum(
|
| 544 |
+
self.pooldim) / torch.exp(scaled).sum(self.pooldim)
|
| 545 |
+
|
| 546 |
+
|
| 547 |
+
class SoftPool(nn.Module):
|
| 548 |
+
def __init__(self, T=1, pooldim=1):
|
| 549 |
+
super().__init__()
|
| 550 |
+
self.pooldim = pooldim
|
| 551 |
+
self.T = T
|
| 552 |
+
|
| 553 |
+
def forward(self, logits, decision):
|
| 554 |
+
w = torch.softmax(decision / self.T, dim=self.pooldim)
|
| 555 |
+
return torch.sum(decision * w, dim=self.pooldim)
|
| 556 |
+
|
| 557 |
+
|
| 558 |
+
class AutoPool(nn.Module):
|
| 559 |
+
"""docstring for AutoPool"""
|
| 560 |
+
def __init__(self, outputdim=10, pooldim=1):
|
| 561 |
+
super().__init__()
|
| 562 |
+
self.outputdim = outputdim
|
| 563 |
+
self.alpha = nn.Parameter(torch.ones(outputdim))
|
| 564 |
+
self.dim = pooldim
|
| 565 |
+
|
| 566 |
+
def forward(self, logits, decision):
|
| 567 |
+
scaled = self.alpha * decision # \alpha * P(Y|x) in the paper
|
| 568 |
+
weight = torch.softmax(scaled, dim=self.dim)
|
| 569 |
+
return torch.sum(decision * weight, dim=self.dim) # B x C
|
| 570 |
+
|
| 571 |
+
|
| 572 |
+
class ExtAttentionPool(nn.Module):
|
| 573 |
+
def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
|
| 574 |
+
super().__init__()
|
| 575 |
+
self.inputdim = inputdim
|
| 576 |
+
self.outputdim = outputdim
|
| 577 |
+
self.pooldim = pooldim
|
| 578 |
+
self.attention = nn.Linear(inputdim, outputdim)
|
| 579 |
+
nn.init.zeros_(self.attention.weight)
|
| 580 |
+
nn.init.zeros_(self.attention.bias)
|
| 581 |
+
self.activ = nn.Softmax(dim=self.pooldim)
|
| 582 |
+
|
| 583 |
+
def forward(self, logits, decision):
|
| 584 |
+
# Logits of shape (B, T, D), decision of shape (B, T, C)
|
| 585 |
+
w_x = self.activ(self.attention(logits) / self.outputdim)
|
| 586 |
+
h = (logits.permute(0, 2, 1).contiguous().unsqueeze(-2) *
|
| 587 |
+
w_x.unsqueeze(-1)).flatten(-2).contiguous()
|
| 588 |
+
return torch.sum(h, self.pooldim)
|
| 589 |
+
|
| 590 |
+
|
| 591 |
+
class AttentionPool(nn.Module):
|
| 592 |
+
"""docstring for AttentionPool"""
|
| 593 |
+
def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
|
| 594 |
+
super().__init__()
|
| 595 |
+
self.inputdim = inputdim
|
| 596 |
+
self.outputdim = outputdim
|
| 597 |
+
self.pooldim = pooldim
|
| 598 |
+
self.transform = nn.Linear(inputdim, outputdim)
|
| 599 |
+
self.activ = nn.Softmax(dim=self.pooldim)
|
| 600 |
+
self.eps = 1e-7
|
| 601 |
+
|
| 602 |
+
def forward(self, logits, decision):
|
| 603 |
+
# Input is (B, T, D)
|
| 604 |
+
# B, T , D
|
| 605 |
+
w = self.activ(torch.clamp(self.transform(logits), -15, 15))
|
| 606 |
+
detect = (decision * w).sum(
|
| 607 |
+
self.pooldim) / (w.sum(self.pooldim) + self.eps)
|
| 608 |
+
# B, T, D
|
| 609 |
+
return detect
|
| 610 |
+
|
| 611 |
+
class Block2D(nn.Module):
|
| 612 |
+
def __init__(self, cin, cout, kernel_size=3, padding=1):
|
| 613 |
+
super().__init__()
|
| 614 |
+
self.block = nn.Sequential(
|
| 615 |
+
nn.BatchNorm2d(cin),
|
| 616 |
+
nn.Conv2d(cin,
|
| 617 |
+
cout,
|
| 618 |
+
kernel_size=kernel_size,
|
| 619 |
+
padding=padding,
|
| 620 |
+
bias=False),
|
| 621 |
+
nn.LeakyReLU(inplace=True, negative_slope=0.1))
|
| 622 |
+
|
| 623 |
+
def forward(self, x):
|
| 624 |
+
return self.block(x)
|
| 625 |
+
|
| 626 |
+
class AudioCNN(nn.Module):
|
| 627 |
+
def __init__(self, classes_num):
|
| 628 |
+
super(AudioCNN, self).__init__()
|
| 629 |
+
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 630 |
+
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 631 |
+
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 632 |
+
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 633 |
+
self.fc1 = nn.Linear(512,128,bias=True)
|
| 634 |
+
self.fc = nn.Linear(128, classes_num, bias=True)
|
| 635 |
+
self.init_weights()
|
| 636 |
+
|
| 637 |
+
def init_weights(self):
|
| 638 |
+
init_layer(self.fc)
|
| 639 |
+
|
| 640 |
+
def forward(self, input):
|
| 641 |
+
'''
|
| 642 |
+
Input: (batch_size, times_steps, freq_bins)'''
|
| 643 |
+
# [128, 801, 168] --> [128,1,801,168]
|
| 644 |
+
x = input[:, None, :, :]
|
| 645 |
+
'''(batch_size, 1, times_steps, freq_bins)'''
|
| 646 |
+
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') # 128,64,400,84
|
| 647 |
+
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') # 128,128,200,42
|
| 648 |
+
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') # 128,256,100,21
|
| 649 |
+
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') # 128,512,50,10
|
| 650 |
+
'''(batch_size, feature_maps, time_steps, freq_bins)'''
|
| 651 |
+
x = torch.mean(x, dim=3) # (batch_size, feature_maps, time_stpes) # 128,512,50
|
| 652 |
+
(x, _) = torch.max(x, dim=2) # (batch_size, feature_maps) 128,512
|
| 653 |
+
x = self.fc1(x) # 128,128
|
| 654 |
+
output = self.fc(x) # 128,10
|
| 655 |
+
return x,output
|
| 656 |
+
|
| 657 |
+
def extract(self,input):
|
| 658 |
+
'''Input: (batch_size, times_steps, freq_bins)'''
|
| 659 |
+
x = input[:, None, :, :]
|
| 660 |
+
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
|
| 661 |
+
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
| 662 |
+
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
|
| 663 |
+
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
|
| 664 |
+
'''(batch_size, feature_maps, time_steps, freq_bins)'''
|
| 665 |
+
x = torch.mean(x, dim=3) # (batch_size, feature_maps, time_stpes)
|
| 666 |
+
(x, _) = torch.max(x, dim=2) # (batch_size, feature_maps)
|
| 667 |
+
x = self.fc1(x) # 128,128
|
| 668 |
+
return x
|
| 669 |
+
|
| 670 |
+
def parse_poolingfunction(poolingfunction_name='mean', **kwargs):
|
| 671 |
+
"""parse_poolingfunction
|
| 672 |
+
A heler function to parse any temporal pooling
|
| 673 |
+
Pooling is done on dimension 1
|
| 674 |
+
:param poolingfunction_name:
|
| 675 |
+
:param **kwargs:
|
| 676 |
+
"""
|
| 677 |
+
poolingfunction_name = poolingfunction_name.lower()
|
| 678 |
+
if poolingfunction_name == 'mean':
|
| 679 |
+
return MeanPool(pooldim=1)
|
| 680 |
+
elif poolingfunction_name == 'max':
|
| 681 |
+
return MaxPool(pooldim=1)
|
| 682 |
+
elif poolingfunction_name == 'linear':
|
| 683 |
+
return LinearSoftPool(pooldim=1)
|
| 684 |
+
elif poolingfunction_name == 'expalpha':
|
| 685 |
+
return AutoExpPool(outputdim=kwargs['outputdim'], pooldim=1)
|
| 686 |
+
|
| 687 |
+
elif poolingfunction_name == 'soft':
|
| 688 |
+
return SoftPool(pooldim=1)
|
| 689 |
+
elif poolingfunction_name == 'auto':
|
| 690 |
+
return AutoPool(outputdim=kwargs['outputdim'])
|
| 691 |
+
elif poolingfunction_name == 'attention':
|
| 692 |
+
return AttentionPool(inputdim=kwargs['inputdim'],
|
| 693 |
+
outputdim=kwargs['outputdim'])
|
| 694 |
+
class conv1d(nn.Module):
|
| 695 |
+
def __init__(self, nin, nout, kernel_size=3, stride=1, padding='VALID', dilation=1):
|
| 696 |
+
super(conv1d, self).__init__()
|
| 697 |
+
if padding == 'VALID':
|
| 698 |
+
dconv_pad = 0
|
| 699 |
+
elif padding == 'SAME':
|
| 700 |
+
dconv_pad = dilation * ((kernel_size - 1) // 2)
|
| 701 |
+
else:
|
| 702 |
+
raise ValueError("Padding Mode Error!")
|
| 703 |
+
self.conv = nn.Conv1d(nin, nout, kernel_size=kernel_size, stride=stride, padding=dconv_pad)
|
| 704 |
+
self.act = nn.ReLU()
|
| 705 |
+
self.init_layer(self.conv)
|
| 706 |
+
|
| 707 |
+
def init_layer(self, layer, nonlinearity='relu'):
|
| 708 |
+
"""Initialize a Linear or Convolutional layer. """
|
| 709 |
+
nn.init.kaiming_normal_(layer.weight, nonlinearity=nonlinearity)
|
| 710 |
+
nn.init.constant_(layer.bias, 0.1)
|
| 711 |
+
|
| 712 |
+
def forward(self, x):
|
| 713 |
+
out = self.act(self.conv(x))
|
| 714 |
+
return out
|
| 715 |
+
|
| 716 |
+
class Atten_1(nn.Module):
|
| 717 |
+
def __init__(self, input_dim, context=2, dropout_rate=0.2):
|
| 718 |
+
super(Atten_1, self).__init__()
|
| 719 |
+
self._matrix_k = nn.Linear(input_dim, input_dim // 4)
|
| 720 |
+
self._matrix_q = nn.Linear(input_dim, input_dim // 4)
|
| 721 |
+
self.relu = nn.ReLU()
|
| 722 |
+
self.context = context
|
| 723 |
+
self._dropout_layer = nn.Dropout(dropout_rate)
|
| 724 |
+
self.init_layer(self._matrix_k)
|
| 725 |
+
self.init_layer(self._matrix_q)
|
| 726 |
+
|
| 727 |
+
def init_layer(self, layer, nonlinearity='leaky_relu'):
|
| 728 |
+
"""Initialize a Linear or Convolutional layer. """
|
| 729 |
+
nn.init.kaiming_uniform_(layer.weight, nonlinearity=nonlinearity)
|
| 730 |
+
if hasattr(layer, 'bias'):
|
| 731 |
+
if layer.bias is not None:
|
| 732 |
+
layer.bias.data.fill_(0.)
|
| 733 |
+
|
| 734 |
+
def forward(self, input_x):
|
| 735 |
+
k_x = input_x
|
| 736 |
+
k_x = self.relu(self._matrix_k(k_x))
|
| 737 |
+
k_x = self._dropout_layer(k_x)
|
| 738 |
+
# print('k_x ',k_x.shape)
|
| 739 |
+
q_x = input_x[:, self.context, :]
|
| 740 |
+
# print('q_x ',q_x.shape)
|
| 741 |
+
q_x = q_x[:, None, :]
|
| 742 |
+
# print('q_x1 ',q_x.shape)
|
| 743 |
+
q_x = self.relu(self._matrix_q(q_x))
|
| 744 |
+
q_x = self._dropout_layer(q_x)
|
| 745 |
+
# print('q_x2 ',q_x.shape)
|
| 746 |
+
x_ = torch.matmul(k_x, q_x.transpose(-2, -1) / math.sqrt(k_x.size(-1)))
|
| 747 |
+
# print('x_ ',x_.shape)
|
| 748 |
+
x_ = x_.squeeze(2)
|
| 749 |
+
alpha = F.softmax(x_, dim=-1)
|
| 750 |
+
att_ = alpha
|
| 751 |
+
# print('alpha ',alpha)
|
| 752 |
+
alpha = alpha.unsqueeze(2).repeat(1,1,input_x.shape[2])
|
| 753 |
+
# print('alpha ',alpha)
|
| 754 |
+
# alpha = alpha.view(alpha.size(0), alpha.size(1), alpha.size(2), 1)
|
| 755 |
+
out = alpha * input_x
|
| 756 |
+
# print('out ', out.shape)
|
| 757 |
+
# out = out.mean(2)
|
| 758 |
+
out = out.mean(1)
|
| 759 |
+
# print('out ',out.shape)
|
| 760 |
+
# assert 1==2
|
| 761 |
+
#y = alpha * input_x
|
| 762 |
+
#return y, att_
|
| 763 |
+
out = input_x[:, self.context, :] + out
|
| 764 |
+
return out
|
| 765 |
+
|
| 766 |
+
class Fusion(nn.Module):
|
| 767 |
+
def __init__(self, inputdim, inputdim2, n_fac):
|
| 768 |
+
super().__init__()
|
| 769 |
+
self.fuse_layer1 = conv1d(inputdim, inputdim2*n_fac,1)
|
| 770 |
+
self.fuse_layer2 = conv1d(inputdim2, inputdim2*n_fac,1)
|
| 771 |
+
self.avg_pool = nn.AvgPool1d(n_fac, stride=n_fac) # 沿着最后一个维度进行pooling
|
| 772 |
+
|
| 773 |
+
def forward(self,embedding,mix_embed):
|
| 774 |
+
embedding = embedding.permute(0,2,1)
|
| 775 |
+
fuse1_out = self.fuse_layer1(embedding) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度
|
| 776 |
+
fuse1_out = fuse1_out.permute(0,2,1)
|
| 777 |
+
|
| 778 |
+
mix_embed = mix_embed.permute(0,2,1)
|
| 779 |
+
fuse2_out = self.fuse_layer2(mix_embed) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度
|
| 780 |
+
fuse2_out = fuse2_out.permute(0,2,1)
|
| 781 |
+
as_embs = torch.mul(fuse1_out, fuse2_out) # 相乘 [2, 501, 2560]
|
| 782 |
+
# (10, 501, 512)
|
| 783 |
+
as_embs = self.avg_pool(as_embs) # [2, 501, 512] 相当于 2560//5
|
| 784 |
+
return as_embs
|
| 785 |
+
|
| 786 |
+
class CDur_fusion(nn.Module):
|
| 787 |
+
def __init__(self, inputdim, outputdim, **kwargs):
|
| 788 |
+
super().__init__()
|
| 789 |
+
self.features = nn.Sequential(
|
| 790 |
+
Block2D(1, 32),
|
| 791 |
+
nn.LPPool2d(4, (2, 4)),
|
| 792 |
+
Block2D(32, 128),
|
| 793 |
+
Block2D(128, 128),
|
| 794 |
+
nn.LPPool2d(4, (2, 4)),
|
| 795 |
+
Block2D(128, 128),
|
| 796 |
+
Block2D(128, 128),
|
| 797 |
+
nn.LPPool2d(4, (1, 4)),
|
| 798 |
+
nn.Dropout(0.3),
|
| 799 |
+
)
|
| 800 |
+
with torch.no_grad():
|
| 801 |
+
rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 802 |
+
rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 803 |
+
|
| 804 |
+
self.gru = nn.GRU(128, 128, bidirectional=True, batch_first=True)
|
| 805 |
+
self.fusion = Fusion(128,2)
|
| 806 |
+
self.fc = nn.Linear(256,256)
|
| 807 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 808 |
+
self.features.apply(init_weights)
|
| 809 |
+
self.outputlayer.apply(init_weights)
|
| 810 |
+
|
| 811 |
+
def forward(self, x, embedding): #
|
| 812 |
+
batch, time, dim = x.shape
|
| 813 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 814 |
+
x = self.features(x) #
|
| 815 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
|
| 816 |
+
embedding = embedding.unsqueeze(1)
|
| 817 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 818 |
+
x = self.fusion(embedding,x)
|
| 819 |
+
#x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 820 |
+
if not hasattr(self, '_flattened'):
|
| 821 |
+
self.gru.flatten_parameters()
|
| 822 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 823 |
+
x = self.fc(x)
|
| 824 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 825 |
+
decision_up = torch.nn.functional.interpolate(
|
| 826 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 827 |
+
time, # 501
|
| 828 |
+
mode='linear',
|
| 829 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 830 |
+
return decision_time[:,:,0],decision_up
|
| 831 |
+
|
| 832 |
+
class CDur(nn.Module):
|
| 833 |
+
def __init__(self, inputdim, outputdim,time_resolution, **kwargs):
|
| 834 |
+
super().__init__()
|
| 835 |
+
self.features = nn.Sequential(
|
| 836 |
+
Block2D(1, 32),
|
| 837 |
+
nn.LPPool2d(4, (2, 4)),
|
| 838 |
+
Block2D(32, 128),
|
| 839 |
+
Block2D(128, 128),
|
| 840 |
+
nn.LPPool2d(4, (2, 4)),
|
| 841 |
+
Block2D(128, 128),
|
| 842 |
+
Block2D(128, 128),
|
| 843 |
+
nn.LPPool2d(4, (2, 4)),
|
| 844 |
+
nn.Dropout(0.3),
|
| 845 |
+
)
|
| 846 |
+
with torch.no_grad():
|
| 847 |
+
rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 848 |
+
rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 849 |
+
|
| 850 |
+
self.gru = nn.GRU(256, 256, bidirectional=True, batch_first=True)
|
| 851 |
+
self.fc = nn.Linear(512,256)
|
| 852 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 853 |
+
self.features.apply(init_weights)
|
| 854 |
+
self.outputlayer.apply(init_weights)
|
| 855 |
+
|
| 856 |
+
def forward(self, x, embedding,one_hot=None): #
|
| 857 |
+
batch, time, dim = x.shape
|
| 858 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 859 |
+
x = self.features(x) #
|
| 860 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
|
| 861 |
+
embedding = embedding.unsqueeze(1)
|
| 862 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 863 |
+
x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 864 |
+
if not hasattr(self, '_flattened'):
|
| 865 |
+
self.gru.flatten_parameters()
|
| 866 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 867 |
+
x = self.fc(x)
|
| 868 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 869 |
+
decision_up = torch.nn.functional.interpolate(
|
| 870 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 871 |
+
time, # 501
|
| 872 |
+
mode='linear',
|
| 873 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 874 |
+
return decision_time[:,:,0],decision_up
|
| 875 |
+
|
| 876 |
+
class CDur_big(nn.Module):
|
| 877 |
+
def __init__(self, inputdim, outputdim, **kwargs):
|
| 878 |
+
super().__init__()
|
| 879 |
+
self.features = nn.Sequential(
|
| 880 |
+
Block2D(1, 64),
|
| 881 |
+
Block2D(64, 64),
|
| 882 |
+
nn.LPPool2d(4, (2, 2)),
|
| 883 |
+
Block2D(64, 128),
|
| 884 |
+
Block2D(128, 128),
|
| 885 |
+
nn.LPPool2d(4, (2, 2)),
|
| 886 |
+
Block2D(128, 256),
|
| 887 |
+
Block2D(256, 256),
|
| 888 |
+
nn.LPPool2d(4, (2, 4)),
|
| 889 |
+
Block2D(256, 512),
|
| 890 |
+
Block2D(512, 512),
|
| 891 |
+
nn.LPPool2d(4, (1, 4)),
|
| 892 |
+
nn.Dropout(0.3),)
|
| 893 |
+
with torch.no_grad():
|
| 894 |
+
rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 895 |
+
rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 896 |
+
self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
|
| 897 |
+
self.fc = nn.Linear(1024,256)
|
| 898 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 899 |
+
self.features.apply(init_weights)
|
| 900 |
+
self.outputlayer.apply(init_weights)
|
| 901 |
+
|
| 902 |
+
def forward(self, x, embedding): #
|
| 903 |
+
batch, time, dim = x.shape
|
| 904 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 905 |
+
x = self.features(x) #
|
| 906 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
|
| 907 |
+
embedding = embedding.unsqueeze(1)
|
| 908 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 909 |
+
x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 910 |
+
if not hasattr(self, '_flattened'):
|
| 911 |
+
self.gru.flatten_parameters()
|
| 912 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 913 |
+
x = self.fc(x)
|
| 914 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 915 |
+
decision_up = torch.nn.functional.interpolate(
|
| 916 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 917 |
+
time, # 501
|
| 918 |
+
mode='linear',
|
| 919 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 920 |
+
return decision_time[:,:,0],decision_up
|
| 921 |
+
|
| 922 |
+
class CDur_GLU(nn.Module):
|
| 923 |
+
def __init__(self, inputdim, outputdim, **kwargs):
|
| 924 |
+
super().__init__()
|
| 925 |
+
self.features = Mul_scale_GLU()
|
| 926 |
+
# with torch.no_grad():
|
| 927 |
+
# rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 928 |
+
# rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 929 |
+
self.gru = nn.GRU(640, 512,1, bidirectional=True, batch_first=True) # previous is 640
|
| 930 |
+
# self.gru = LSTMModel(640, 512,1)
|
| 931 |
+
self.fc = nn.Linear(1024,256)
|
| 932 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 933 |
+
# self.features.apply(init_weights)
|
| 934 |
+
self.outputlayer.apply(init_weights)
|
| 935 |
+
|
| 936 |
+
def forward(self, x, embedding,one_hot=None): #
|
| 937 |
+
batch, time, dim = x.shape
|
| 938 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 939 |
+
x = self.features(x) #
|
| 940 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
|
| 941 |
+
# print('x ',x.shape)
|
| 942 |
+
# assert 1==2
|
| 943 |
+
embedding = embedding.unsqueeze(1)
|
| 944 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 945 |
+
|
| 946 |
+
x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 947 |
+
if not hasattr(self, '_flattened'):
|
| 948 |
+
self.gru.flatten_parameters()
|
| 949 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 950 |
+
# x = self.gru(x) # x torch.Size([16, 125, 256])
|
| 951 |
+
x = self.fc(x)
|
| 952 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 953 |
+
decision_up = torch.nn.functional.interpolate(
|
| 954 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 955 |
+
time, # 501
|
| 956 |
+
mode='linear',
|
| 957 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 958 |
+
return decision_time[:,:,0],decision_up
|
| 959 |
+
|
| 960 |
+
class CDur_CNN14(nn.Module):
|
| 961 |
+
def __init__(self, inputdim, outputdim,time_resolution,**kwargs):
|
| 962 |
+
super().__init__()
|
| 963 |
+
if time_resolution==125:
|
| 964 |
+
self.features = Cnn10(8)
|
| 965 |
+
elif time_resolution == 250:
|
| 966 |
+
#print('time_resolution ',time_resolution)
|
| 967 |
+
self.features = Cnn10(4)
|
| 968 |
+
elif time_resolution == 500:
|
| 969 |
+
self.features = Cnn10(2)
|
| 970 |
+
else:
|
| 971 |
+
self.features = Cnn10(0)
|
| 972 |
+
with torch.no_grad():
|
| 973 |
+
rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 974 |
+
rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 975 |
+
# self.features = Cnn10()
|
| 976 |
+
self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
|
| 977 |
+
# self.gru = LSTMModel(640, 512,1)
|
| 978 |
+
self.fc = nn.Linear(1024,256)
|
| 979 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 980 |
+
# self.features.apply(init_weights)
|
| 981 |
+
self.outputlayer.apply(init_weights)
|
| 982 |
+
|
| 983 |
+
def forward(self, x, embedding,one_hot=None):
|
| 984 |
+
batch, time, dim = x.shape
|
| 985 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 986 |
+
x = self.features(x) #
|
| 987 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
|
| 988 |
+
# print('x ',x.shape)
|
| 989 |
+
# assert 1==2
|
| 990 |
+
embedding = embedding.unsqueeze(1)
|
| 991 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 992 |
+
x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 993 |
+
if not hasattr(self, '_flattened'):
|
| 994 |
+
self.gru.flatten_parameters()
|
| 995 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 996 |
+
# x = self.gru(x) # x torch.Size([16, 125, 256])
|
| 997 |
+
x = self.fc(x)
|
| 998 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 999 |
+
decision_up = torch.nn.functional.interpolate(
|
| 1000 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 1001 |
+
time, # 501
|
| 1002 |
+
mode='linear',
|
| 1003 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 1004 |
+
return decision_time[:,:,0],decision_up
|
| 1005 |
+
|
| 1006 |
+
class CDur_CNN_mul_scale(nn.Module):
|
| 1007 |
+
def __init__(self, inputdim, outputdim,time_resolution,**kwargs):
|
| 1008 |
+
super().__init__()
|
| 1009 |
+
if time_resolution==125:
|
| 1010 |
+
self.features = Cnn10_mul_scale(8)
|
| 1011 |
+
elif time_resolution == 250:
|
| 1012 |
+
#print('time_resolution ',time_resolution)
|
| 1013 |
+
self.features = Cnn10_mul_scale(4)
|
| 1014 |
+
elif time_resolution == 500:
|
| 1015 |
+
self.features = Cnn10_mul_scale(2)
|
| 1016 |
+
else:
|
| 1017 |
+
self.features = Cnn10_mul_scale(0)
|
| 1018 |
+
# with torch.no_grad():
|
| 1019 |
+
# rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 1020 |
+
# rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 1021 |
+
# self.features = Cnn10()
|
| 1022 |
+
self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
|
| 1023 |
+
# self.gru = LSTMModel(640, 512,1)
|
| 1024 |
+
self.fc = nn.Linear(1024,256)
|
| 1025 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 1026 |
+
# self.features.apply(init_weights)
|
| 1027 |
+
self.outputlayer.apply(init_weights)
|
| 1028 |
+
|
| 1029 |
+
def forward(self, x, embedding,one_hot=None):
|
| 1030 |
+
# print('x ',x.shape)
|
| 1031 |
+
# assert 1==2
|
| 1032 |
+
batch, time, dim = x.shape
|
| 1033 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 1034 |
+
x = self.features(x) #
|
| 1035 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
|
| 1036 |
+
# print('x ',x.shape)
|
| 1037 |
+
# assert 1==2
|
| 1038 |
+
embedding = embedding.unsqueeze(1)
|
| 1039 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 1040 |
+
x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 1041 |
+
if not hasattr(self, '_flattened'):
|
| 1042 |
+
self.gru.flatten_parameters()
|
| 1043 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 1044 |
+
# x = self.gru(x) # x torch.Size([16, 125, 256])
|
| 1045 |
+
x = self.fc(x)
|
| 1046 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 1047 |
+
decision_up = torch.nn.functional.interpolate(
|
| 1048 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 1049 |
+
time, # 501
|
| 1050 |
+
mode='linear',
|
| 1051 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 1052 |
+
return decision_time[:,:,0],decision_up
|
| 1053 |
+
|
| 1054 |
+
class CDur_CNN_mul_scale_fusion(nn.Module):
|
| 1055 |
+
def __init__(self, inputdim, outputdim, time_resolution,**kwargs):
|
| 1056 |
+
super().__init__()
|
| 1057 |
+
if time_resolution==125:
|
| 1058 |
+
self.features = Cnn10_mul_scale(8)
|
| 1059 |
+
elif time_resolution == 250:
|
| 1060 |
+
#print('time_resolution ',time_resolution)
|
| 1061 |
+
self.features = Cnn10_mul_scale(4)
|
| 1062 |
+
elif time_resolution == 500:
|
| 1063 |
+
self.features = Cnn10_mul_scale(2)
|
| 1064 |
+
else:
|
| 1065 |
+
self.features = Cnn10_mul_scale(0)
|
| 1066 |
+
# with torch.no_grad():
|
| 1067 |
+
# rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
|
| 1068 |
+
# rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
|
| 1069 |
+
# self.features = Cnn10()
|
| 1070 |
+
self.gru = nn.GRU(512, 512, bidirectional=True, batch_first=True)
|
| 1071 |
+
# self.gru = LSTMModel(640, 512,1)
|
| 1072 |
+
self.fc = nn.Linear(1024,256)
|
| 1073 |
+
self.fusion = Fusion(128,512,2)
|
| 1074 |
+
self.outputlayer = nn.Linear(256, outputdim)
|
| 1075 |
+
# self.features.apply(init_weights)
|
| 1076 |
+
self.outputlayer.apply(init_weights)
|
| 1077 |
+
|
| 1078 |
+
def forward(self, x, embedding,one_hot=None):
|
| 1079 |
+
# print('x ',x.shape)
|
| 1080 |
+
# assert 1==2
|
| 1081 |
+
batch, time, dim = x.shape
|
| 1082 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 1083 |
+
x = self.features(x) #
|
| 1084 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
|
| 1085 |
+
# print('x ',x.shape)
|
| 1086 |
+
# assert 1==2
|
| 1087 |
+
embedding = embedding.unsqueeze(1)
|
| 1088 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 1089 |
+
x = self.fusion(embedding, x)
|
| 1090 |
+
#x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 1091 |
+
if not hasattr(self, '_flattened'):
|
| 1092 |
+
self.gru.flatten_parameters()
|
| 1093 |
+
x, _ = self.gru(x) # x torch.Size([16, 125, 256])
|
| 1094 |
+
# x = self.gru(x) # x torch.Size([16, 125, 256])
|
| 1095 |
+
x = self.fc(x)
|
| 1096 |
+
decision_time = torch.softmax(self.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 1097 |
+
decision_up = torch.nn.functional.interpolate(
|
| 1098 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 1099 |
+
time, # 501
|
| 1100 |
+
mode='linear',
|
| 1101 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 1102 |
+
return decision_time[:,:,0],decision_up
|
| 1103 |
+
|
| 1104 |
+
|
| 1105 |
+
class RaDur_fusion(nn.Module):
|
| 1106 |
+
def __init__(self, model_config, inputdim, outputdim, time_resolution, **kwargs):
|
| 1107 |
+
super().__init__()
|
| 1108 |
+
self.encoder = Cnn14()
|
| 1109 |
+
self.detection = CDur_CNN_mul_scale_fusion(inputdim, outputdim, time_resolution)
|
| 1110 |
+
self.softmax = nn.Softmax(dim=2)
|
| 1111 |
+
#self.temperature = 5
|
| 1112 |
+
# if model_config['pre_train']:
|
| 1113 |
+
# self.encoder.load_state_dict(torch.load(model_config['encoder_path'])['model'])
|
| 1114 |
+
# self.detection.load_state_dict(torch.load(model_config['CDur_path']))
|
| 1115 |
+
|
| 1116 |
+
self.q = nn.Linear(128,128)
|
| 1117 |
+
self.k = nn.Linear(128,128)
|
| 1118 |
+
self.q_ee = nn.Linear(128, 128)
|
| 1119 |
+
self.k_ee = nn.Linear(128, 128)
|
| 1120 |
+
self.temperature = 11.3 # sqrt(128)
|
| 1121 |
+
self.att_pool = model_config['att_pool']
|
| 1122 |
+
self.enhancement = model_config['enhancement']
|
| 1123 |
+
self.tao = model_config['tao']
|
| 1124 |
+
self.top = model_config['top']
|
| 1125 |
+
self.bn = nn.BatchNorm1d(128)
|
| 1126 |
+
self.EE_fusion = Fusion(128, 128, 4)
|
| 1127 |
+
|
| 1128 |
+
def get_w(self,q,k):
|
| 1129 |
+
q = self.q(q)
|
| 1130 |
+
k = self.k(k)
|
| 1131 |
+
q = q.unsqueeze(1)
|
| 1132 |
+
attn = torch.bmm(q, k.transpose(1, 2))
|
| 1133 |
+
attn = attn/self.temperature
|
| 1134 |
+
attn = self.softmax(attn)
|
| 1135 |
+
return attn
|
| 1136 |
+
|
| 1137 |
+
def get_w_ee(self,q,k):
|
| 1138 |
+
q = self.q_ee(q)
|
| 1139 |
+
k = self.k_ee(k)
|
| 1140 |
+
q = q.unsqueeze(1)
|
| 1141 |
+
attn = torch.bmm(q, k.transpose(1, 2))
|
| 1142 |
+
attn = attn/self.temperature
|
| 1143 |
+
attn = self.softmax(attn)
|
| 1144 |
+
return attn
|
| 1145 |
+
|
| 1146 |
+
def attention_pooling(self, embeddings, mean_embedding):
|
| 1147 |
+
att_pool_w = self.get_w(mean_embedding,embeddings)
|
| 1148 |
+
embedding = torch.bmm(att_pool_w, embeddings).squeeze(1)
|
| 1149 |
+
# print(embedding.shape)
|
| 1150 |
+
# print(att_pool_w.shape)
|
| 1151 |
+
# print(att_pool_w[0])
|
| 1152 |
+
# assert 1==2
|
| 1153 |
+
return embedding
|
| 1154 |
+
|
| 1155 |
+
def select_topk_embeddings(self, scores, embeddings, k):
|
| 1156 |
+
_, idx_DESC = scores.sort(descending=True, dim=1) # 根据分数进行排序
|
| 1157 |
+
top_k = _[:,:k]
|
| 1158 |
+
# print('top_k ', top_k)
|
| 1159 |
+
# top_k = top_k.mean(1)
|
| 1160 |
+
idx_topk = idx_DESC[:, :k] # 取top_k个
|
| 1161 |
+
# print('index ', idx_topk)
|
| 1162 |
+
idx_topk = idx_topk.unsqueeze(2).expand([-1, -1, embeddings.shape[2]])
|
| 1163 |
+
selected_embeddings = torch.gather(embeddings, 1, idx_topk)
|
| 1164 |
+
return selected_embeddings,top_k
|
| 1165 |
+
|
| 1166 |
+
def sum_with_attention(self, embedding, top_k, selected_embeddings):
|
| 1167 |
+
# print('embedding ',embedding)
|
| 1168 |
+
# print('selected_embeddings ',selected_embeddings.shape)
|
| 1169 |
+
att_1 = self.get_w_ee(embedding, selected_embeddings)
|
| 1170 |
+
att_1 = att_1.squeeze(1)
|
| 1171 |
+
#print('att_1 ',att_1.shape)
|
| 1172 |
+
larger = top_k > self.tao
|
| 1173 |
+
# print('larger ',larger)
|
| 1174 |
+
top_k = top_k*larger
|
| 1175 |
+
# print('top_k ',top_k.shape)
|
| 1176 |
+
# print('top_k ',top_k)
|
| 1177 |
+
att_1 = att_1*top_k
|
| 1178 |
+
#print('att_1 ',att_1.shape)
|
| 1179 |
+
# assert 1==2
|
| 1180 |
+
att_2 = att_1.unsqueeze(2).repeat(1,1,128)
|
| 1181 |
+
Es = selected_embeddings*att_2
|
| 1182 |
+
return Es
|
| 1183 |
+
|
| 1184 |
+
def orcal_EE(self, x, embedding, label):
|
| 1185 |
+
batch, time, dim = x.shape
|
| 1186 |
+
|
| 1187 |
+
mixture_embedding = self.encoder(x) # 8, 125, 128
|
| 1188 |
+
mixture_embedding = mixture_embedding.transpose(1,2)
|
| 1189 |
+
mixture_embedding = self.bn(mixture_embedding)
|
| 1190 |
+
mixture_embedding = mixture_embedding.transpose(1,2)
|
| 1191 |
+
|
| 1192 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 1193 |
+
x = self.detection.features(x) #
|
| 1194 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
|
| 1195 |
+
embedding_pre = embedding.unsqueeze(1)
|
| 1196 |
+
embedding_pre = embedding_pre.repeat(1, x.shape[1], 1)
|
| 1197 |
+
f = self.detection.fusion(embedding_pre, x) # the first stage results
|
| 1198 |
+
#f = torch.cat((x, embedding_pre), dim=2) # [B, T, 128 + emb_dim]
|
| 1199 |
+
if not hasattr(self, '_flattened'):
|
| 1200 |
+
self.detection.gru.flatten_parameters()
|
| 1201 |
+
f, _ = self.detection.gru(f) # x torch.Size([16, 125, 256])
|
| 1202 |
+
f = self.detection.fc(f)
|
| 1203 |
+
decision_time = torch.softmax(self.detection.outputlayer(f),dim=2) # x torch.Size([16, 125, 2])
|
| 1204 |
+
|
| 1205 |
+
selected_embeddings, top_k = self.select_topk_embeddings(decision_time[:,:,0], mixture_embedding, self.top)
|
| 1206 |
+
|
| 1207 |
+
selected_embeddings = self.sum_with_attention(embedding, top_k, selected_embeddings) # add the weight
|
| 1208 |
+
|
| 1209 |
+
mix_embedding = selected_embeddings.mean(1).unsqueeze(1) #
|
| 1210 |
+
mix_embedding = mix_embedding.repeat(1, x.shape[1], 1)
|
| 1211 |
+
embedding = embedding.unsqueeze(1)
|
| 1212 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 1213 |
+
mix_embedding = self.EE_fusion(mix_embedding, embedding) # 使用神经网络进行融合
|
| 1214 |
+
# mix_embedding2 = selected_embeddings2.mean(1)
|
| 1215 |
+
#mix_embedding = embedding + mix_embedding # 直接相加
|
| 1216 |
+
# new detection results
|
| 1217 |
+
# embedding_now = mix_embedding.unsqueeze(1)
|
| 1218 |
+
# embedding_now = embedding_now.repeat(1, x.shape[1], 1)
|
| 1219 |
+
f_now = self.detection.fusion(mix_embedding, x)
|
| 1220 |
+
#f_now = torch.cat((x, embedding_now), dim=2) #
|
| 1221 |
+
f_now, _ = self.detection.gru(f_now) # x torch.Size([16, 125, 256])
|
| 1222 |
+
f_now = self.detection.fc(f_now)
|
| 1223 |
+
decision_time_now = torch.softmax(self.detection.outputlayer(f_now), dim=2) # x torch.Size([16, 125, 2])
|
| 1224 |
+
|
| 1225 |
+
top_k = top_k.mean(1) # get avg score,higher score will have more weight
|
| 1226 |
+
larger = top_k > self.tao
|
| 1227 |
+
top_k = top_k * larger
|
| 1228 |
+
top_k = top_k/2.0
|
| 1229 |
+
# print('top_k ',top_k)
|
| 1230 |
+
# assert 1==2
|
| 1231 |
+
# print('tok_k[ ',top_k.shape)
|
| 1232 |
+
# print('decision_time ',decision_time.shape)
|
| 1233 |
+
# print('decision_time_now ',decision_time_now.shape)
|
| 1234 |
+
neg_w = top_k.unsqueeze(1).unsqueeze(2)
|
| 1235 |
+
neg_w = neg_w.repeat(1, decision_time_now.shape[1], decision_time_now.shape[2])
|
| 1236 |
+
# print('neg_w ',neg_w.shape)
|
| 1237 |
+
#print('neg_w ',neg_w[:,0:10,0])
|
| 1238 |
+
pos_w = 1-neg_w
|
| 1239 |
+
#print('pos_w ',pos_w[:,0:10,0])
|
| 1240 |
+
decision_time_final = decision_time*pos_w + neg_w*decision_time_now
|
| 1241 |
+
#print('decision_time_final ',decision_time_final[0,0:10,0])
|
| 1242 |
+
# print(decision_time_final[0,:,:])
|
| 1243 |
+
#assert 1==2
|
| 1244 |
+
return decision_time_final
|
| 1245 |
+
|
| 1246 |
+
def forward(self, x, ref, label=None):
|
| 1247 |
+
batch, time, dim = x.shape
|
| 1248 |
+
logit = torch.zeros(1).cuda()
|
| 1249 |
+
embeddings = self.encoder(ref)
|
| 1250 |
+
mean_embedding = embeddings.mean(1)
|
| 1251 |
+
if self.att_pool == True:
|
| 1252 |
+
mean_embedding = self.bn(mean_embedding)
|
| 1253 |
+
embeddings = embeddings.transpose(1,2)
|
| 1254 |
+
embeddings = self.bn(embeddings)
|
| 1255 |
+
embeddings = embeddings.transpose(1,2)
|
| 1256 |
+
embedding = self.attention_pooling(embeddings, mean_embedding)
|
| 1257 |
+
else:
|
| 1258 |
+
embedding = mean_embedding
|
| 1259 |
+
if self.enhancement == True:
|
| 1260 |
+
decision_time = self.orcal_EE(x, embedding, label)
|
| 1261 |
+
decision_up = torch.nn.functional.interpolate(
|
| 1262 |
+
decision_time.transpose(1, 2), # [16, 2, 125]
|
| 1263 |
+
time, # 501
|
| 1264 |
+
mode='linear',
|
| 1265 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 1266 |
+
return decision_time[:,:,0], decision_up, logit
|
| 1267 |
+
|
| 1268 |
+
x = x.unsqueeze(1) # (b,1,t,d)
|
| 1269 |
+
x = self.detection.features(x) #
|
| 1270 |
+
x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
|
| 1271 |
+
embedding = embedding.unsqueeze(1)
|
| 1272 |
+
embedding = embedding.repeat(1, x.shape[1], 1)
|
| 1273 |
+
# x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 1274 |
+
x = self.detection.fusion(embedding, x)
|
| 1275 |
+
# embedding = embedding.unsqueeze(1)
|
| 1276 |
+
# embedding = embedding.repeat(1, x.shape[1], 1)
|
| 1277 |
+
# x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
|
| 1278 |
+
if not hasattr(self, '_flattened'):
|
| 1279 |
+
self.detection.gru.flatten_parameters()
|
| 1280 |
+
x, _ = self.detection.gru(x) # x torch.Size([16, 125, 256])
|
| 1281 |
+
x = self.detection.fc(x)
|
| 1282 |
+
decision_time = torch.softmax(self.detection.outputlayer(x),dim=2) # x torch.Size([16, 125, 2])
|
| 1283 |
+
decision_up = torch.nn.functional.interpolate(
|
| 1284 |
+
decision_time.transpose(1, 2),
|
| 1285 |
+
time, # 501
|
| 1286 |
+
mode='linear',
|
| 1287 |
+
align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
|
| 1288 |
+
return decision_time[:,:,0], decision_up, logit
|
audio_detection/target_sound_detection/src/utils.py
ADDED
|
@@ -0,0 +1,353 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
# !/usr/bin/env python
|
| 2 |
+
# -*- coding: utf-8 -*-
|
| 3 |
+
# @Time : 2021/3/9 16:33
|
| 4 |
+
# @Author : dongchao yang
|
| 5 |
+
# @File : train.py
|
| 6 |
+
|
| 7 |
+
import collections
|
| 8 |
+
import sys
|
| 9 |
+
from loguru import logger
|
| 10 |
+
from pprint import pformat
|
| 11 |
+
|
| 12 |
+
import numpy as np
|
| 13 |
+
import pandas as pd
|
| 14 |
+
import scipy
|
| 15 |
+
import six
|
| 16 |
+
import sklearn.preprocessing as pre
|
| 17 |
+
import torch
|
| 18 |
+
import tqdm
|
| 19 |
+
import yaml
|
| 20 |
+
|
| 21 |
+
from scipy.interpolate import interp1d
|
| 22 |
+
|
| 23 |
+
def parse_config_or_kwargs(config_file, **kwargs):
|
| 24 |
+
"""parse_config_or_kwargs
|
| 25 |
+
:param config_file: Config file that has parameters, yaml format
|
| 26 |
+
:param **kwargs: Other alternative parameters or overwrites for config
|
| 27 |
+
"""
|
| 28 |
+
with open(config_file) as con_read:
|
| 29 |
+
yaml_config = yaml.load(con_read, Loader=yaml.FullLoader)
|
| 30 |
+
arguments = dict(yaml_config, **kwargs)
|
| 31 |
+
return arguments
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
def find_contiguous_regions(activity_array): # in this part, if you cannot understand the binary operation, I think you can write a O(n) complexity method
|
| 35 |
+
"""Find contiguous regions from bool valued numpy.array.
|
| 36 |
+
Copy of https://dcase-repo.github.io/dcase_util/_modules/dcase_util/data/decisions.html#DecisionEncoder
|
| 37 |
+
Reason is:
|
| 38 |
+
1. This does not belong to a class necessarily
|
| 39 |
+
2. Import DecisionEncoder requires sndfile over some other imports..which causes some problems on clusters
|
| 40 |
+
"""
|
| 41 |
+
change_indices = np.logical_xor(activity_array[1:], activity_array[:-1]).nonzero()[0]
|
| 42 |
+
change_indices += 1
|
| 43 |
+
if activity_array[0]:
|
| 44 |
+
# If the first element of activity_array is True add 0 at the beginning
|
| 45 |
+
change_indices = np.r_[0, change_indices]
|
| 46 |
+
|
| 47 |
+
if activity_array[-1]:
|
| 48 |
+
# If the last element of activity_array is True, add the length of the array
|
| 49 |
+
change_indices = np.r_[change_indices, activity_array.size]
|
| 50 |
+
# print(change_indices.reshape((-1, 2)))
|
| 51 |
+
# Reshape the result into two columns
|
| 52 |
+
return change_indices.reshape((-1, 2))
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
def split_train_cv(
|
| 56 |
+
data_frame: pd.DataFrame,
|
| 57 |
+
frac: float = 0.9,
|
| 58 |
+
y=None, # Only for stratified, computes necessary split
|
| 59 |
+
**kwargs):
|
| 60 |
+
"""split_train_cv
|
| 61 |
+
|
| 62 |
+
:param data_frame:
|
| 63 |
+
:type data_frame: pd.DataFrame
|
| 64 |
+
:param frac:
|
| 65 |
+
:type frac: float
|
| 66 |
+
"""
|
| 67 |
+
if kwargs.get('mode',
|
| 68 |
+
None) == 'urbansed': # Filenames are DATA_-1 DATA_-2 etc
|
| 69 |
+
data_frame.loc[:, 'id'] = data_frame.groupby(
|
| 70 |
+
data_frame['filename'].str.split('_').apply(
|
| 71 |
+
lambda x: '_'.join(x[:-1]))).ngroup()
|
| 72 |
+
sampler = np.random.permutation(data_frame['id'].nunique())
|
| 73 |
+
num_train = int(frac * len(sampler))
|
| 74 |
+
train_indexes = sampler[:num_train]
|
| 75 |
+
cv_indexes = sampler[num_train:]
|
| 76 |
+
train_data = data_frame[data_frame['id'].isin(train_indexes)]
|
| 77 |
+
cv_data = data_frame[data_frame['id'].isin(cv_indexes)]
|
| 78 |
+
del train_data['id']
|
| 79 |
+
del cv_data['id']
|
| 80 |
+
elif kwargs.get('mode', None) == 'stratified': # stratified --> 分层的 ?
|
| 81 |
+
# Use statified sampling
|
| 82 |
+
from skmultilearn.model_selection import iterative_train_test_split
|
| 83 |
+
index_train, _, index_cv, _ = iterative_train_test_split(
|
| 84 |
+
data_frame.index.values.reshape(-1, 1), y, test_size=1. - frac)
|
| 85 |
+
train_data = data_frame[data_frame.index.isin(index_train.squeeze())]
|
| 86 |
+
cv_data = data_frame[data_frame.index.isin(index_cv.squeeze())] # cv --> cross validation
|
| 87 |
+
else:
|
| 88 |
+
# Simply split train_test
|
| 89 |
+
train_data = data_frame.sample(frac=frac, random_state=10)
|
| 90 |
+
cv_data = data_frame[~data_frame.index.isin(train_data.index)]
|
| 91 |
+
return train_data, cv_data
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def pprint_dict(in_dict, outputfun=sys.stdout.write, formatter='yaml'): # print yaml file
|
| 96 |
+
"""pprint_dict
|
| 97 |
+
:param outputfun: function to use, defaults to sys.stdout
|
| 98 |
+
:param in_dict: dict to print
|
| 99 |
+
"""
|
| 100 |
+
if formatter == 'yaml':
|
| 101 |
+
format_fun = yaml.dump
|
| 102 |
+
elif formatter == 'pretty':
|
| 103 |
+
format_fun = pformat
|
| 104 |
+
for line in format_fun(in_dict).split('\n'):
|
| 105 |
+
outputfun(line)
|
| 106 |
+
|
| 107 |
+
|
| 108 |
+
def getfile_outlogger(outputfile):
|
| 109 |
+
log_format = "[<green>{time:YYYY-MM-DD HH:mm:ss}</green>] {message}"
|
| 110 |
+
logger.configure(handlers=[{"sink": sys.stderr, "format": log_format}])
|
| 111 |
+
if outputfile:
|
| 112 |
+
logger.add(outputfile, enqueue=True, format=log_format)
|
| 113 |
+
return logger
|
| 114 |
+
|
| 115 |
+
# according label, get encoder
|
| 116 |
+
def train_labelencoder(labels: pd.Series, sparse=True):
|
| 117 |
+
"""encode_labels
|
| 118 |
+
|
| 119 |
+
Encodes labels
|
| 120 |
+
|
| 121 |
+
:param labels: pd.Series representing the raw labels e.g., Speech, Water
|
| 122 |
+
:param encoder (optional): Encoder already fitted
|
| 123 |
+
returns encoded labels (many hot) and the encoder
|
| 124 |
+
"""
|
| 125 |
+
assert isinstance(labels, pd.Series), "Labels need to be series"
|
| 126 |
+
if isinstance(labels[0], six.string_types):
|
| 127 |
+
# In case of using non processed strings, e.g., Vaccum, Speech
|
| 128 |
+
label_array = labels.str.split(',').values.tolist() # split label according to ','
|
| 129 |
+
elif isinstance(labels[0], np.ndarray):
|
| 130 |
+
# Encoder does not like to see numpy array
|
| 131 |
+
label_array = [lab.tolist() for lab in labels]
|
| 132 |
+
elif isinstance(labels[0], collections.Iterable):
|
| 133 |
+
label_array = labels
|
| 134 |
+
encoder = pre.MultiLabelBinarizer(sparse_output=sparse)
|
| 135 |
+
encoder.fit(label_array)
|
| 136 |
+
return encoder
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
def encode_labels(labels: pd.Series, encoder=None, sparse=True):
|
| 140 |
+
"""encode_labels
|
| 141 |
+
|
| 142 |
+
Encodes labels
|
| 143 |
+
|
| 144 |
+
:param labels: pd.Series representing the raw labels e.g., Speech, Water
|
| 145 |
+
:param encoder (optional): Encoder already fitted
|
| 146 |
+
returns encoded labels (many hot) and the encoder
|
| 147 |
+
"""
|
| 148 |
+
assert isinstance(labels, pd.Series), "Labels need to be series"
|
| 149 |
+
instance = labels.iloc[0]
|
| 150 |
+
if isinstance(instance, six.string_types):
|
| 151 |
+
# In case of using non processed strings, e.g., Vaccum, Speech
|
| 152 |
+
label_array = labels.str.split(',').values.tolist()
|
| 153 |
+
elif isinstance(instance, np.ndarray):
|
| 154 |
+
# Encoder does not like to see numpy array
|
| 155 |
+
label_array = [lab.tolist() for lab in labels]
|
| 156 |
+
elif isinstance(instance, collections.Iterable):
|
| 157 |
+
label_array = labels
|
| 158 |
+
# get label_array, it is a list ,contain a lot of label, this label are string type
|
| 159 |
+
if not encoder:
|
| 160 |
+
encoder = pre.MultiLabelBinarizer(sparse_output=sparse) # if we encoder is None, we should init a encoder firstly.
|
| 161 |
+
encoder.fit(label_array)
|
| 162 |
+
labels_encoded = encoder.transform(label_array) # transform string to digit
|
| 163 |
+
return labels_encoded, encoder
|
| 164 |
+
|
| 165 |
+
# return pd.arrays.SparseArray(
|
| 166 |
+
# [row.toarray().ravel() for row in labels_encoded]), encoder
|
| 167 |
+
|
| 168 |
+
|
| 169 |
+
def decode_with_timestamps(events,labels: np.array):
|
| 170 |
+
"""decode_with_timestamps
|
| 171 |
+
Decodes the predicted label array (2d) into a list of
|
| 172 |
+
[(Labelname, onset, offset), ...]
|
| 173 |
+
|
| 174 |
+
:param encoder: Encoder during training
|
| 175 |
+
:type encoder: pre.MultiLabelBinarizer
|
| 176 |
+
:param labels: n-dim array
|
| 177 |
+
:type labels: np.array
|
| 178 |
+
"""
|
| 179 |
+
# print('events ',events)
|
| 180 |
+
# print('labels ',labels.shape)
|
| 181 |
+
#assert 1==2
|
| 182 |
+
if labels.ndim == 2:
|
| 183 |
+
#print('...')
|
| 184 |
+
return [_decode_with_timestamps(events[i],labels[i]) for i in range(labels.shape[0])]
|
| 185 |
+
else:
|
| 186 |
+
return _decode_with_timestamps(events,labels)
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
def median_filter(x, window_size, threshold=0.5):
|
| 190 |
+
"""median_filter
|
| 191 |
+
:param x: input prediction array of shape (B, T, C) or (B, T).
|
| 192 |
+
Input is a sequence of probabilities 0 <= x <= 1
|
| 193 |
+
:param window_size: An integer to use
|
| 194 |
+
:param threshold: Binary thresholding threshold
|
| 195 |
+
"""
|
| 196 |
+
x = binarize(x, threshold=threshold) # transfer to 0 or 1
|
| 197 |
+
if x.ndim == 3:
|
| 198 |
+
size = (1, window_size, 1)
|
| 199 |
+
elif x.ndim == 2 and x.shape[0] == 1:
|
| 200 |
+
# Assume input is class-specific median filtering
|
| 201 |
+
# E.g, Batch x Time [1, 501]
|
| 202 |
+
size = (1, window_size)
|
| 203 |
+
elif x.ndim == 2 and x.shape[0] > 1:
|
| 204 |
+
# Assume input is standard median pooling, class-independent
|
| 205 |
+
# E.g., Time x Class [501, 10]
|
| 206 |
+
size = (window_size, 1)
|
| 207 |
+
return scipy.ndimage.median_filter(x, size=size)
|
| 208 |
+
|
| 209 |
+
|
| 210 |
+
def _decode_with_timestamps(events,labels):
|
| 211 |
+
result_labels = []
|
| 212 |
+
# print('.......')
|
| 213 |
+
# print('labels ',labels.shape)
|
| 214 |
+
# print(labels)
|
| 215 |
+
change_indices = find_contiguous_regions(labels)
|
| 216 |
+
# print(change_indices)
|
| 217 |
+
# assert 1==2
|
| 218 |
+
for row in change_indices:
|
| 219 |
+
result_labels.append((events,row[0], row[1]))
|
| 220 |
+
return result_labels
|
| 221 |
+
|
| 222 |
+
def inverse_transform_labels(encoder, pred):
|
| 223 |
+
if pred.ndim == 3:
|
| 224 |
+
return [encoder.inverse_transform(x) for x in pred]
|
| 225 |
+
else:
|
| 226 |
+
return encoder.inverse_transform(pred)
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
def binarize(pred, threshold=0.5):
|
| 230 |
+
# Batch_wise
|
| 231 |
+
if pred.ndim == 3:
|
| 232 |
+
return np.array(
|
| 233 |
+
[pre.binarize(sub, threshold=threshold) for sub in pred])
|
| 234 |
+
else:
|
| 235 |
+
return pre.binarize(pred, threshold=threshold)
|
| 236 |
+
|
| 237 |
+
|
| 238 |
+
def double_threshold(x, high_thres, low_thres, n_connect=1):
|
| 239 |
+
"""double_threshold
|
| 240 |
+
Helper function to calculate double threshold for n-dim arrays
|
| 241 |
+
|
| 242 |
+
:param x: input array
|
| 243 |
+
:param high_thres: high threshold value
|
| 244 |
+
:param low_thres: Low threshold value
|
| 245 |
+
:param n_connect: Distance of <= n clusters will be merged
|
| 246 |
+
"""
|
| 247 |
+
assert x.ndim <= 3, "Whoops something went wrong with the input ({}), check if its <= 3 dims".format(
|
| 248 |
+
x.shape)
|
| 249 |
+
if x.ndim == 3:
|
| 250 |
+
apply_dim = 1
|
| 251 |
+
elif x.ndim < 3:
|
| 252 |
+
apply_dim = 0
|
| 253 |
+
# x is assumed to be 3d: (batch, time, dim)
|
| 254 |
+
# Assumed to be 2d : (time, dim)
|
| 255 |
+
# Assumed to be 1d : (time)
|
| 256 |
+
# time axis is therefore at 1 for 3d and 0 for 2d (
|
| 257 |
+
return np.apply_along_axis(lambda x: _double_threshold(
|
| 258 |
+
x, high_thres, low_thres, n_connect=n_connect),
|
| 259 |
+
axis=apply_dim,
|
| 260 |
+
arr=x)
|
| 261 |
+
|
| 262 |
+
|
| 263 |
+
def _double_threshold(x, high_thres, low_thres, n_connect=1, return_arr=True): # in nature, double_threshold considers boundary question
|
| 264 |
+
"""_double_threshold
|
| 265 |
+
Computes a double threshold over the input array
|
| 266 |
+
|
| 267 |
+
:param x: input array, needs to be 1d
|
| 268 |
+
:param high_thres: High threshold over the array
|
| 269 |
+
:param low_thres: Low threshold over the array
|
| 270 |
+
:param n_connect: Postprocessing, maximal distance between clusters to connect
|
| 271 |
+
:param return_arr: By default this function returns the filtered indiced, but if return_arr = True it returns an array of tsame size as x filled with ones and zeros.
|
| 272 |
+
"""
|
| 273 |
+
assert x.ndim == 1, "Input needs to be 1d"
|
| 274 |
+
high_locations = np.where(x > high_thres)[0] # return the index, where value is greater than high_thres
|
| 275 |
+
locations = x > low_thres # return true of false
|
| 276 |
+
encoded_pairs = find_contiguous_regions(locations)
|
| 277 |
+
# print('encoded_pairs ',encoded_pairs)
|
| 278 |
+
filtered_list = list(
|
| 279 |
+
filter(
|
| 280 |
+
lambda pair:
|
| 281 |
+
((pair[0] <= high_locations) & (high_locations <= pair[1])).any(),
|
| 282 |
+
encoded_pairs)) # find encoded_pair where inclide a high_lacations
|
| 283 |
+
#print('filtered_list ',filtered_list)
|
| 284 |
+
filtered_list = connect_(filtered_list, n_connect) # if the distance of two pair is less than n_connect, we can merge them
|
| 285 |
+
if return_arr:
|
| 286 |
+
zero_one_arr = np.zeros_like(x, dtype=int)
|
| 287 |
+
for sl in filtered_list:
|
| 288 |
+
zero_one_arr[sl[0]:sl[1]] = 1
|
| 289 |
+
return zero_one_arr
|
| 290 |
+
return filtered_list
|
| 291 |
+
|
| 292 |
+
|
| 293 |
+
def connect_clusters(x, n=1):
|
| 294 |
+
if x.ndim == 1:
|
| 295 |
+
return connect_clusters_(x, n)
|
| 296 |
+
if x.ndim >= 2:
|
| 297 |
+
return np.apply_along_axis(lambda a: connect_clusters_(a, n=n), -2, x)
|
| 298 |
+
|
| 299 |
+
|
| 300 |
+
def connect_clusters_(x, n=1):
|
| 301 |
+
"""connect_clusters_
|
| 302 |
+
Connects clustered predictions (0,1) in x with range n
|
| 303 |
+
|
| 304 |
+
:param x: Input array. zero-one format
|
| 305 |
+
:param n: Number of frames to skip until connection can be made
|
| 306 |
+
"""
|
| 307 |
+
assert x.ndim == 1, "input needs to be 1d"
|
| 308 |
+
reg = find_contiguous_regions(x)
|
| 309 |
+
start_end = connect_(reg, n=n)
|
| 310 |
+
zero_one_arr = np.zeros_like(x, dtype=int)
|
| 311 |
+
for sl in start_end:
|
| 312 |
+
zero_one_arr[sl[0]:sl[1]] = 1
|
| 313 |
+
return zero_one_arr
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
def connect_(pairs, n=1):
|
| 317 |
+
"""connect_
|
| 318 |
+
Connects two adjacent clusters if their distance is <= n
|
| 319 |
+
|
| 320 |
+
:param pairs: Clusters of iterateables e.g., [(1,5),(7,10)]
|
| 321 |
+
:param n: distance between two clusters
|
| 322 |
+
"""
|
| 323 |
+
if len(pairs) == 0:
|
| 324 |
+
return []
|
| 325 |
+
start_, end_ = pairs[0]
|
| 326 |
+
new_pairs = []
|
| 327 |
+
for i, (next_item, cur_item) in enumerate(zip(pairs[1:], pairs[0:])):
|
| 328 |
+
end_ = next_item[1]
|
| 329 |
+
if next_item[0] - cur_item[1] <= n:
|
| 330 |
+
pass
|
| 331 |
+
else:
|
| 332 |
+
new_pairs.append((start_, cur_item[1]))
|
| 333 |
+
start_ = next_item[0]
|
| 334 |
+
new_pairs.append((start_, end_))
|
| 335 |
+
return new_pairs
|
| 336 |
+
|
| 337 |
+
|
| 338 |
+
def predictions_to_time(df, ratio):
|
| 339 |
+
df.onset = df.onset * ratio
|
| 340 |
+
df.offset = df.offset * ratio
|
| 341 |
+
return df
|
| 342 |
+
|
| 343 |
+
def upgrade_resolution(arr, scale):
|
| 344 |
+
print('arr ',arr.shape)
|
| 345 |
+
x = np.arange(0, arr.shape[0])
|
| 346 |
+
f = interp1d(x, arr, kind='linear', axis=0, fill_value='extrapolate')
|
| 347 |
+
scale_x = np.arange(0, arr.shape[0], 1 / scale)
|
| 348 |
+
up_scale = f(scale_x)
|
| 349 |
+
return up_scale
|
| 350 |
+
# a = [0.1,0.2,0.3,0.8,0.4,0.1,0.3,0.9,0.4]
|
| 351 |
+
# a = np.array(a)
|
| 352 |
+
# b = a>0.2
|
| 353 |
+
# _double_threshold(a,0.7,0.2)
|
audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5e4525ad12621117c3a0fcfe974fd55e51583cd219106bf510438f4bec4edc18
|
| 3 |
+
size 140604911
|
audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1331dab1e4c3ac2bc5850156f2000a95fe333bdf06d08ce9b490550726548ab0
|
| 3 |
+
size 2479
|
audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c9b44e30c4800462c177806bbd7009953d70d531c873e3791ca9aa85375d524d
|
| 3 |
+
size 343538489
|
audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:de482358747778181e4dc530ec61ae94f53ae0b202ac92e99491fe4ceb3cbb1c
|
| 3 |
+
size 255398
|