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This commit is contained in:
Cong Thanh Vu 2023-05-13 17:33:59 +00:00
parent 54e6fbc84c
commit 5f1518cfd9
23 changed files with 502787 additions and 406 deletions
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15
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# triples: 89320
# entities: 7128
# relations: 12409
# timesteps: 208
# test triples: 8255
# valid triples: 8239
# train triples: 72826
Measure method: N/A
Target Size : 0
Grow Factor: 0
Shrink Factor: 0
Epsilon Factor: 0
Search method: N/A
filter_dupes: inter
nonames: False

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0 0 2
1 3 5
2 6 7
3 8 9
4 10 12
5 13 14
6 15 16
7 17 19
8 20 21
9 22 23
10 24 26
11 27 28
12 29 30
13 31 33
14 34 35
15 36 37
16 38 40
17 41 42
18 43 44
19 45 46
20 47 48
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35 71 71
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# triples: 291818
# entities: 12554
# relations: 423
# timesteps: 70
# test triples: 19271
# valid triples: 20208
# train triples: 252339
Measure method: N/A
Target Size : 423
Grow Factor: 0
Shrink Factor: 4.0
Epsilon Factor: 0
Search method: N/A
filter_dupes: inter
nonames: False

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0 P131[0-0]
1 P131[1-1]
2 P131[2-2]
3 P131[3-3]
4 P131[4-4]
5 P131[5-5]
6 P131[6-6]
7 P131[7-7]
8 P131[8-8]
9 P131[9-9]
10 P131[10-10]
11 P131[11-11]
12 P131[12-12]
13 P131[13-13]
14 P131[14-14]
15 P131[15-15]
16 P131[16-16]
17 P131[17-17]
18 P131[18-18]
19 P131[19-19]
20 P131[20-20]
21 P131[21-21]
22 P131[22-22]
23 P131[23-23]
24 P131[24-24]
25 P131[25-25]
26 P131[26-26]
27 P131[27-27]
28 P131[28-28]
29 P131[29-29]
30 P131[30-30]
31 P131[31-31]
32 P131[32-32]
33 P131[33-33]
34 P131[34-34]
35 P131[35-35]
36 P131[36-36]
37 P131[37-37]
38 P131[38-38]
39 P131[39-39]
40 P131[40-40]
41 P131[41-41]
42 P131[42-42]
43 P131[43-43]
44 P131[44-44]
45 P131[45-45]
46 P131[46-46]
47 P131[47-47]
48 P131[48-48]
49 P131[49-49]
50 P131[50-50]
51 P131[51-51]
52 P131[52-52]
53 P131[53-53]
54 P131[54-54]
55 P131[55-55]
56 P131[56-56]
57 P131[57-57]
58 P131[58-58]
59 P131[59-59]
60 P131[60-60]
61 P131[61-61]
62 P131[62-62]
63 P131[63-63]
64 P131[64-64]
65 P131[65-65]
66 P131[66-66]
67 P131[67-67]
68 P131[68-68]
69 P131[69-69]
70 P1435[65-65]
71 P39[49-49]
72 P39[50-50]
73 P39[51-51]
74 P39[52-52]
75 P39[53-53]
76 P39[54-54]
77 P39[55-55]
78 P39[56-56]
79 P39[57-57]
80 P39[58-58]
81 P39[59-59]
82 P39[60-60]
83 P39[61-61]
84 P39[62-62]
85 P39[63-63]
86 P39[64-64]
87 P39[65-65]
88 P39[66-66]
89 P39[67-67]
90 P39[68-68]
91 P39[69-69]
92 P54[40-40]
93 P54[41-41]
94 P54[42-42]
95 P54[43-43]
96 P54[44-44]
97 P54[45-45]
98 P54[46-46]
99 P54[47-47]
100 P54[48-48]
101 P54[49-49]
102 P54[50-50]
103 P54[51-51]
104 P54[52-52]
105 P54[53-53]
106 P54[54-54]
107 P54[55-55]
108 P54[56-56]
109 P54[57-57]
110 P54[58-58]
111 P54[59-59]
112 P54[60-60]
113 P54[61-61]
114 P54[62-62]
115 P54[63-63]
116 P54[64-64]
117 P54[65-65]
118 P54[66-66]
119 P54[67-67]
120 P54[68-68]
121 P54[69-69]
122 P31[0-0]
123 P31[1-1]
124 P31[2-2]
125 P31[3-3]
126 P31[4-4]
127 P31[5-5]
128 P31[6-6]
129 P31[7-7]
130 P31[8-8]
131 P31[9-9]
132 P31[10-10]
133 P31[11-11]
134 P31[12-12]
135 P31[13-13]
136 P31[14-14]
137 P31[15-15]
138 P31[16-16]
139 P31[17-17]
140 P31[18-18]
141 P31[19-19]
142 P31[20-20]
143 P31[21-21]
144 P31[22-22]
145 P31[23-23]
146 P31[24-24]
147 P31[25-25]
148 P31[26-26]
149 P31[27-27]
150 P31[28-28]
151 P31[29-29]
152 P31[30-30]
153 P31[31-31]
154 P31[32-32]
155 P31[33-33]
156 P31[34-34]
157 P31[35-35]
158 P31[36-36]
159 P31[37-37]
160 P31[38-38]
161 P31[39-39]
162 P31[40-40]
163 P31[41-41]
164 P31[42-42]
165 P31[43-43]
166 P31[44-44]
167 P31[45-45]
168 P31[46-46]
169 P31[47-47]
170 P31[48-48]
171 P31[49-49]
172 P31[50-50]
173 P31[51-51]
174 P31[52-52]
175 P31[53-53]
176 P31[54-54]
177 P31[55-55]
178 P31[56-56]
179 P31[57-57]
180 P31[58-58]
181 P31[59-59]
182 P31[60-60]
183 P31[61-61]
184 P31[62-62]
185 P31[63-63]
186 P31[64-64]
187 P31[65-65]
188 P31[66-66]
189 P31[67-67]
190 P31[68-68]
191 P31[69-69]
192 P463[26-26]
193 P463[27-27]
194 P463[28-28]
195 P463[29-29]
196 P463[30-30]
197 P463[31-31]
198 P463[32-32]
199 P463[33-33]
200 P463[34-34]
201 P463[35-35]
202 P463[36-36]
203 P463[37-37]
204 P463[38-38]
205 P463[39-39]
206 P463[40-40]
207 P463[41-41]
208 P463[42-42]
209 P463[43-43]
210 P463[44-44]
211 P463[45-45]
212 P463[46-46]
213 P463[47-47]
214 P463[48-48]
215 P463[49-49]
216 P463[50-50]
217 P463[51-51]
218 P463[52-52]
219 P463[53-53]
220 P463[54-54]
221 P463[55-55]
222 P463[56-56]
223 P463[57-57]
224 P463[58-58]
225 P463[59-59]
226 P463[60-60]
227 P463[61-61]
228 P463[62-62]
229 P463[63-63]
230 P463[64-64]
231 P463[65-65]
232 P463[66-66]
233 P463[67-67]
234 P463[68-68]
235 P463[69-69]
236 P512[4-69]
237 P190[0-29]
238 P150[0-3]
239 P1376[39-47]
240 P463[0-7]
241 P166[0-7]
242 P2962[18-30]
243 P108[29-36]
244 P39[0-3]
245 P17[47-48]
246 P166[21-23]
247 P793[46-69]
248 P69[32-41]
249 P17[57-58]
250 P190[42-45]
251 P2962[39-42]
252 P54[0-18]
253 P26[56-61]
254 P150[14-17]
255 P463[16-17]
256 P26[39-46]
257 P579[36-43]
258 P579[16-23]
259 P2962[59-60]
260 P1411[59-61]
261 P26[20-27]
262 P6[4-69]
263 P1435[33-34]
264 P166[52-53]
265 P108[49-57]
266 P150[10-13]
267 P1346[47-68]
268 P150[18-21]
269 P1346[13-46]
270 P69[20-23]
271 P39[31-32]
272 P1411[32-37]
273 P166[62-63]
274 P150[44-47]
275 P2962[61-62]
276 P150[48-51]
277 P150[52-55]
278 P1411[62-67]
279 P1435[35-36]
280 P1411[48-51]
281 P150[22-25]
282 P2962[63-64]
283 P2962[65-66]
284 P166[58-59]
285 P190[46-49]
286 P54[34-35]
287 P1435[4-16]
288 P463[18-19]
289 P150[31-34]
290 P150[35-38]
291 P39[35-36]
292 P26[62-69]
293 P1411[56-58]
294 P1435[37-38]
295 P166[60-61]
296 P39[33-34]
297 P102[24-31]
298 P2962[43-46]
299 P108[37-48]
300 P190[50-53]
301 P39[4-6]
302 P1435[39-40]
303 P793[0-45]
304 P150[64-69]
305 P39[19-22]
306 P27[30-38]
307 P2962[31-38]
308 P1411[24-31]
309 P102[40-45]
310 P39[37-38]
311 P463[8-11]
312 P1435[41-42]
313 P27[52-59]
314 P69[16-19]
315 P17[16-18]
316 P190[54-57]
317 P1435[43-44]
318 P166[8-15]
319 P166[45-47]
320 P2962[47-50]
321 P39[39-40]
322 P1411[52-55]
323 P108[58-69]
324 P463[20-21]
325 P39[41-42]
326 P150[26-30]
327 P150[39-43]
328 P1435[45-46]
329 P26[28-38]
330 P54[27-30]
331 P190[58-61]
332 P17[59-61]
333 P54[36-37]
334 P166[16-20]
335 P166[37-40]
336 P1435[47-48]
337 P17[0-3]
338 P26[47-55]
339 P1435[49-50]
340 P1435[25-28]
341 P150[4-9]
342 P102[63-69]
343 P26[0-19]
344 P1435[17-24]
345 P39[23-26]
346 P1435[51-52]
347 P39[7-11]
348 P69[12-15]
349 P69[24-31]
350 P102[0-23]
351 P39[43-44]
352 P579[24-35]
353 P190[62-65]
354 P1435[53-54]
355 P1376[0-18]
356 P27[0-14]
357 P463[12-15]
358 P166[33-36]
359 P102[32-39]
360 P17[4-7]
361 P190[30-41]
362 P166[24-28]
363 P190[66-69]
364 P69[42-69]
365 P1435[55-56]
366 P54[31-33]
367 P39[45-46]
368 P17[12-15]
369 P1435[57-58]
370 P54[19-26]
371 P2962[51-54]
372 P2962[67-69]
373 P1435[59-60]
374 P579[44-56]
375 P1435[61-62]
376 P166[41-44]
377 P17[19-22]
378 P1376[19-38]
379 P17[23-26]
380 P1376[48-69]
381 P463[22-23]
382 P17[27-30]
383 P1435[63-64]
384 P69[0-3]
385 P1435[66-67]
386 P17[35-38]
387 P69[8-11]
388 P1435[68-69]
389 P17[31-34]
390 P102[46-53]
391 P27[60-69]
392 P579[57-69]
393 P69[4-7]
394 P1411[7-14]
395 P551[0-35]
396 P108[0-28]
397 P17[8-11]
398 P1411[38-47]
399 P17[43-46]
400 P17[49-52]
401 P166[64-69]
402 P1435[29-32]
403 P54[38-39]
404 P39[27-30]
405 P2962[55-58]
406 P463[24-25]
407 P17[39-42]
408 P17[53-56]
409 P17[66-69]
410 P17[62-65]
411 P1411[15-23]
412 P166[48-51]
413 P27[15-29]
414 P150[56-63]
415 P27[39-51]
416 P39[47-48]
417 P166[29-32]
418 P39[12-18]
419 P166[54-57]
420 P551[36-69]
421 P579[0-15]
422 P102[54-62]

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0 19 19
1 20 1643
2 1644 1790
3 1791 1816
4 1817 1855
5 1856 1871
6 1872 1893
7 1894 1905
8 1906 1913
9 1914 1918
10 1919 1920
11 1921 1924
12 1925 1929
13 1930 1933
14 1934 1937
15 1938 1941
16 1942 1945
17 1946 1948
18 1949 1950
19 1951 1953
20 1954 1956
21 1957 1959
22 1960 1961
23 1962 1963
24 1964 1965
25 1966 1967
26 1968 1968
27 1969 1970
28 1971 1972
29 1973 1974
30 1975 1976
31 1977 1978
32 1979 1980
33 1981 1982
34 1983 1983
35 1984 1984
36 1985 1985
37 1986 1986
38 1987 1987
39 1988 1988
40 1989 1989
41 1990 1990
42 1991 1991
43 1992 1992
44 1993 1993
45 1994 1994
46 1995 1995
47 1996 1996
48 1997 1997
49 1998 1998
50 1999 1999
51 2000 2000
52 2001 2001
53 2002 2002
54 2003 2003
55 2004 2004
56 2005 2005
57 2006 2006
58 2007 2007
59 2008 2008
60 2009 2009
61 2010 2010
62 2011 2011
63 2012 2012
64 2013 2013
65 2014 2014
66 2015 2015
67 2016 2016
68 2017 2017
69 2018 2020
70 2021 2021

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data/yago11k/about.txt Normal file
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# triples: 78032
# entities: 10526
# relations: 177
# timesteps: 46
# test triples: 6909
# valid triples: 7198
# train triples: 63925
Measure method: N/A
Target Size : 0
Grow Factor: 0
Shrink Factor: 0
Epsilon Factor: 5.0
Search method: N/A
filter_dupes: inter
nonames: False

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data/yago11k/relations.dict Normal file
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0 <wasBornIn>[0-2]
1 <wasBornIn>[2-5]
2 <wasBornIn>[5-7]
3 <wasBornIn>[7-10]
4 <wasBornIn>[10-12]
5 <wasBornIn>[12-15]
6 <wasBornIn>[15-17]
7 <wasBornIn>[17-20]
8 <wasBornIn>[20-22]
9 <wasBornIn>[22-25]
10 <wasBornIn>[25-27]
11 <wasBornIn>[27-30]
12 <wasBornIn>[30-32]
13 <wasBornIn>[32-35]
14 <wasBornIn>[35-45]
15 <wasBornIn>[52-52]
16 <diedIn>[0-3]
17 <diedIn>[3-5]
18 <diedIn>[5-7]
19 <diedIn>[7-10]
20 <diedIn>[10-12]
21 <diedIn>[12-14]
22 <diedIn>[14-17]
23 <diedIn>[17-19]
24 <diedIn>[19-21]
25 <diedIn>[21-23]
26 <diedIn>[23-25]
27 <diedIn>[25-27]
28 <diedIn>[27-29]
29 <diedIn>[29-32]
30 <diedIn>[32-34]
31 <diedIn>[34-36]
32 <diedIn>[36-38]
33 <diedIn>[38-40]
34 <diedIn>[40-42]
35 <diedIn>[42-44]
36 <diedIn>[44-47]
37 <diedIn>[47-49]
38 <diedIn>[49-51]
39 <diedIn>[51-53]
40 <diedIn>[53-55]
41 <diedIn>[55-57]
42 <diedIn>[59-59]
43 <worksAt>[0-3]
44 <worksAt>[3-5]
45 <worksAt>[5-7]
46 <worksAt>[7-10]
47 <worksAt>[10-12]
48 <worksAt>[12-14]
49 <worksAt>[14-17]
50 <worksAt>[17-19]
51 <worksAt>[19-21]
52 <worksAt>[21-23]
53 <worksAt>[23-25]
54 <worksAt>[25-27]
55 <worksAt>[27-29]
56 <worksAt>[29-32]
57 <worksAt>[32-34]
58 <worksAt>[34-36]
59 <worksAt>[36-40]
60 <worksAt>[40-42]
61 <worksAt>[42-47]
62 <worksAt>[47-53]
63 <worksAt>[59-59]
64 <playsFor>[0-3]
65 <playsFor>[3-5]
66 <playsFor>[5-23]
67 <playsFor>[23-25]
68 <playsFor>[25-27]
69 <playsFor>[27-29]
70 <playsFor>[29-32]
71 <playsFor>[32-34]
72 <playsFor>[34-36]
73 <playsFor>[36-38]
74 <playsFor>[38-40]
75 <playsFor>[40-42]
76 <playsFor>[42-44]
77 <playsFor>[44-47]
78 <playsFor>[47-51]
79 <playsFor>[59-59]
80 <hasWonPrize>[1-4]
81 <hasWonPrize>[4-6]
82 <hasWonPrize>[6-8]
83 <hasWonPrize>[8-11]
84 <hasWonPrize>[11-15]
85 <hasWonPrize>[15-18]
86 <hasWonPrize>[18-22]
87 <hasWonPrize>[22-26]
88 <hasWonPrize>[26-30]
89 <hasWonPrize>[30-33]
90 <hasWonPrize>[33-37]
91 <hasWonPrize>[37-47]
92 <hasWonPrize>[47-53]
93 <hasWonPrize>[59-59]
94 <isMarriedTo>[0-3]
95 <isMarriedTo>[3-5]
96 <isMarriedTo>[5-7]
97 <isMarriedTo>[7-10]
98 <isMarriedTo>[10-12]
99 <isMarriedTo>[12-14]
100 <isMarriedTo>[14-17]
101 <isMarriedTo>[17-19]
102 <isMarriedTo>[19-21]
103 <isMarriedTo>[21-23]
104 <isMarriedTo>[23-25]
105 <isMarriedTo>[25-27]
106 <isMarriedTo>[27-29]
107 <isMarriedTo>[29-32]
108 <isMarriedTo>[32-34]
109 <isMarriedTo>[34-38]
110 <isMarriedTo>[38-42]
111 <isMarriedTo>[42-47]
112 <isMarriedTo>[47-51]
113 <isMarriedTo>[51-55]
114 <isMarriedTo>[59-59]
115 <owns>[0-10]
116 <owns>[10-17]
117 <owns>[17-19]
118 <owns>[19-23]
119 <owns>[23-36]
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data/yago11k/time_map.dict Normal file
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@ -0,0 +1,60 @@
0 -431 1782
1 1783 1848
2 1849 1870
3 1871 1888
4 1889 1899
5 1900 1906
6 1907 1912
7 1913 1917
8 1918 1922
9 1923 1926
10 1927 1930
11 1931 1934
12 1935 1938
13 1939 1941
14 1942 1944
15 1945 1947
16 1948 1950
17 1951 1953
18 1954 1956
19 1957 1959
20 1960 1962
21 1963 1965
22 1966 1967
23 1968 1969
24 1970 1971
25 1972 1973
26 1974 1975
27 1976 1977
28 1978 1979
29 1980 1981
30 1982 1983
31 1984 1985
32 1986 1987
33 1988 1989
34 1990 1991
35 1992 1993
36 1994 1994
37 1995 1996
38 1997 1997
39 1998 1998
40 1999 1999
41 2000 2000
42 2001 2001
43 2002 2002
44 2003 2003
45 2004 2004
46 2005 2005
47 2006 2006
48 2007 2007
49 2008 2008
50 2009 2009
51 2010 2010
52 2011 2011
53 2012 2012
54 2013 2013
55 2014 2014
56 2015 2015
57 2016 2016
58 2017 2017
59 2018 2018

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main.py
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@ -81,8 +81,17 @@ class Main(object):
rel_set.add(rel) rel_set.add(rel)
ent_set.add(obj) ent_set.add(obj)
self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)} self.ent2id = {}
self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)} for line in open('./data/{}/{}'.format(self.p.dataset, "entities.dict")):
id, ent = map(str.lower, line.strip().split('\t'))
self.ent2id[ent] = int(id)
self.rel2id = {}
for line in open('./data/{}/{}'.format(self.p.dataset, "relations.dict")):
id, rel = map(str.lower, line.strip().split('\t'))
self.rel2id[rel] = int(id)
# self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)}
# self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)}
self.rel2id.update({rel+'_reverse': idx+len(self.rel2id) self.rel2id.update({rel+'_reverse': idx+len(self.rel2id)
for idx, rel in enumerate(rel_set)}) for idx, rel in enumerate(rel_set)})
@ -569,9 +578,9 @@ if __name__ == "__main__":
help='Dropout for Feature. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5') help='Dropout for Feature. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5')
parser.add_argument('--inp_drop', dest="inp_drop", default=0.2, type=float, parser.add_argument('--inp_drop', dest="inp_drop", default=0.2, type=float,
help='Dropout for Input layer. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5') help='Dropout for Input layer. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5')
parser.add_argument('--drop_path', dest="drop_path", default=0.1, type=float, parser.add_argument('--drop_path', dest="drop_path", default=0.0, type=float,
help='Path dropout. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5') help='Path dropout. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5')
parser.add_argument('--drop', dest="drop", default=0.2, type=float, parser.add_argument('--drop', dest="drop", default=0.0, type=float,
help='Inner drop. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5') help='Inner drop. Default: 0.5. Test: 0.2, 0.3, 0.4, 0.5')
# Configuration for in/output channels for ConvE, HypER, HypE # Configuration for in/output channels for ConvE, HypER, HypE
@ -616,6 +625,7 @@ if __name__ == "__main__":
default='./config/', help='Config directory') default='./config/', help='Config directory')
parser.add_argument('--test_only', action='store_true', default=False) parser.add_argument('--test_only', action='store_true', default=False)
parser.add_argument('--filtered', action='store_true', default=False)
args = parser.parse_args() args = parser.parse_args()

401
pvt.py
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@ -1,401 +0,0 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
import math
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.linear = linear
if self.linear:
self.relu = nn.ReLU(inplace=True)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
if self.linear:
x = self.relu(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.linear = linear
self.sr_ratio = sr_ratio
if not linear:
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
else:
self.pool = nn.AdaptiveAvgPool2d(7)
self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
self.norm = nn.LayerNorm(dim)
self.act = nn.GELU()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if not self.linear:
if self.sr_ratio > 1:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
x_ = self.act(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, linear=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, linear=linear)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
assert max(patch_size) > stride, "Set larger patch_size than stride"
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // stride, img_size[1] // stride
self.num_patches = self.H * self.W
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class PyramidVisionTransformerV2(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], num_stages=4, linear=False):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.num_stages = num_stages
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
for i in range(num_stages):
patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
patch_size=7 if i == 0 else 3,
stride=4 if i == 0 else 2,
in_chans=in_chans if i == 0 else embed_dims[i - 1],
embed_dim=embed_dims[i])
block = nn.ModuleList([Block(
dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer,
sr_ratio=sr_ratios[i], linear=linear)
for j in range(depths[i])])
norm = norm_layer(embed_dims[i])
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
# classification head
self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def freeze_patch_emb(self):
self.patch_embed1.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
for blk in block:
x = blk(x, H, W)
x = norm(x)
if i != self.num_stages - 1:
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
return x.mean(dim=1)
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
def _conv_filter(state_dict, patch_size=16):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
out_dict = {}
for k, v in state_dict.items():
if 'patch_embed.proj.weight' in k:
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
out_dict[k] = v
return out_dict
@register_model
def pvt_v2_b0(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
**kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b1(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
**kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b2(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b3(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
**kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b4(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
**kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b5(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
**kwargs)
model.default_cfg = _cfg()
return model
@register_model
def pvt_v2_b2_li(pretrained=False, **kwargs):
model = PyramidVisionTransformerV2(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], linear=True, **kwargs)
model.default_cfg = _cfg()
return model