update viz util

data/wikidata12k/about.txt

@@ -1,15 +1,15 @@
-# triples: 291818 
+# triples: 291818 
-# entities: 12554 
+# entities: 12554 
-# relations: 423 
+# relations: 423 
-# timesteps: 70 
+# timesteps: 70 
-# test triples: 19271 
+# test triples: 19271 
-# valid triples: 20208 
+# valid triples: 20208 
-# train triples: 252339 
+# train triples: 252339 
-Measure method:  N/A  
+Measure method:  N/A  
-Target Size :  423  
+Target Size :  423  
-Grow Factor:  0  
+Grow Factor:  0  
-Shrink Factor:  4.0  
+Shrink Factor:  4.0  
-Epsilon Factor: 0  
+Epsilon Factor: 0  
-Search method: N/A  
+Search method: N/A  
-filter_dupes: inter
+filter_dupes: inter
-nonames: False
+nonames: False

data/wikidata12k/relations.dict

@@ -1,423 +1,423 @@
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-339	P1435[49-50]
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-341	P150[4-9]
+341	P150[4-9]
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-417	P166[29-32]
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+420	P551[36-69]
-421	P579[0-15]
+421	P579[0-15]
-422	P102[54-62]
+422	P102[54-62]

data/wikidata12k/time_map.dict

@@ -1,71 +1,71 @@
-0	19	19
+0	19	19
-1	20	1643
+1	20	1643
-2	1644	1790
+2	1644	1790
-3	1791	1816
+3	1791	1816
-4	1817	1855
+4	1817	1855
-5	1856	1871
+5	1856	1871
-6	1872	1893
+6	1872	1893
-7	1894	1905
+7	1894	1905
-8	1906	1913
+8	1906	1913
-9	1914	1918
+9	1914	1918
-10	1919	1920
+10	1919	1920
-11	1921	1924
+11	1921	1924
-12	1925	1929
+12	1925	1929
-13	1930	1933
+13	1930	1933
-14	1934	1937
+14	1934	1937
-15	1938	1941
+15	1938	1941
-16	1942	1945
+16	1942	1945
-17	1946	1948
+17	1946	1948
-18	1949	1950
+18	1949	1950
-19	1951	1953
+19	1951	1953
-20	1954	1956
+20	1954	1956
-21	1957	1959
+21	1957	1959
-22	1960	1961
+22	1960	1961
-23	1962	1963
+23	1962	1963
-24	1964	1965
+24	1964	1965
-25	1966	1967
+25	1966	1967
-26	1968	1968
+26	1968	1968
-27	1969	1970
+27	1969	1970
-28	1971	1972
+28	1971	1972
-29	1973	1974
+29	1973	1974
-30	1975	1976
+30	1975	1976
-31	1977	1978
+31	1977	1978
-32	1979	1980
+32	1979	1980
-33	1981	1982
+33	1981	1982
-34	1983	1983
+34	1983	1983
-35	1984	1984
+35	1984	1984
-36	1985	1985
+36	1985	1985
-37	1986	1986
+37	1986	1986
-38	1987	1987
+38	1987	1987
-39	1988	1988
+39	1988	1988
-40	1989	1989
+40	1989	1989
-41	1990	1990
+41	1990	1990
-42	1991	1991
+42	1991	1991
-43	1992	1992
+43	1992	1992
-44	1993	1993
+44	1993	1993
-45	1994	1994
+45	1994	1994
-46	1995	1995
+46	1995	1995
-47	1996	1996
+47	1996	1996
-48	1997	1997
+48	1997	1997
-49	1998	1998
+49	1998	1998
-50	1999	1999
+50	1999	1999
-51	2000	2000
+51	2000	2000
-52	2001	2001
+52	2001	2001
-53	2002	2002
+53	2002	2002
-54	2003	2003
+54	2003	2003
-55	2004	2004
+55	2004	2004
-56	2005	2005
+56	2005	2005
-57	2006	2006
+57	2006	2006
-58	2007	2007
+58	2007	2007
-59	2008	2008
+59	2008	2008
-60	2009	2009
+60	2009	2009
-61	2010	2010
+61	2010	2010
-62	2011	2011
+62	2011	2011
-63	2012	2012
+63	2012	2012
-64	2013	2013
+64	2013	2013
-65	2014	2014
+65	2014	2014
-66	2015	2015
+66	2015	2015
-67	2016	2016
+67	2016	2016
-68	2017	2017
+68	2017	2017
-69	2018	2020
+69	2018	2020
-70	2021	2021
+70	2021	2021

data/wikidata12k_old/about.txt

@@ -0,0 +1,15 @@
+# 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

data/wikidata12k_old/raw_rel2id.txt

@@ -0,0 +1,24 @@
+P1376	0
+P512	4
+P579	3
+P150	18
+P190	5
+P551	19
+P131	1
+P793	21
+P1435	13
+P39	14
+P17	6
+P54	22
+P31	15
+P6	7
+P1411	20
+P2962	2
+P463	9
+P1346	16
+P108	10
+P69	23
+P166	17
+P102	11
+P27	12
+P26	8

data/wikidata12k_old/relations.dict

@@ -0,0 +1,423 @@
+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]
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+21	P131[21-21]
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+23	P131[23-23]
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+421	P579[0-15]
+422	P102[54-62]

data/wikidata12k_old/time_map.dict

@@ -0,0 +1,71 @@
+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

data/yago/about.txt

@@ -0,0 +1,15 @@
+# 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

data/yago/relations.dict

@@ -0,0 +1,177 @@
+0	<wasBornIn>[0-2]
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+2	<wasBornIn>[5-7]
+3	<wasBornIn>[7-10]
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data/yago/time_map.dict

@@ -0,0 +1,60 @@
+0	-431	1782
+1	1783	1848
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+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
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+34	1990	1991
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+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

data/yago11k/indices_test.txt

@@ -0,0 +1,793 @@
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icews14_both.log

@@ -1,3 +0,0 @@
-nohup: ignoring input
-2023-06-20 09:22:51,618 - [INFO] - {'dataset': 'icews14_both', 'name': 'icews14_both', 'gpu': '2', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
-2023-06-20 09:22:57,979 - [INFO] - [E:0| 0]: Train Loss:0.70005,  Val MRR:0.0, 	icews14_both

log/icews14_128

@@ -1 +0,0 @@
-2023-05-13 03:52:44,141 - icews14_128 - [INFO] - {'dataset': 'icews14', 'name': 'icews14_128', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True, 'filtered': False}

log/poofnet.log

@@ -1,2 +0,0 @@
-nohup: ignoring input
-python: can't open file 'run.py': [Errno 2] No such file or directory

log/testrun_227cb2f9

@@ -1 +0,0 @@
-2023-05-17 06:54:57,988 - testrun_227cb2f9 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_227cb2f9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_30d70322

@@ -1 +0,0 @@
-2023-05-17 06:23:34,181 - testrun_30d70322 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_30d70322', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_3212b281

@@ -1 +0,0 @@
-2023-05-17 06:53:01,668 - testrun_3212b281 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_3212b281', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_3dbc9e89

@@ -1 +0,0 @@
-2023-05-06 08:35:38,753 - testrun_3dbc9e89 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_3dbc9e89', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}

log/testrun_43389ddf

@@ -1 +0,0 @@
-2023-05-17 06:38:00,469 - testrun_43389ddf - [INFO] - {'dataset': 'icews14', 'name': 'testrun_43389ddf', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_47ede3b9

@@ -1 +0,0 @@
-2023-05-17 06:13:02,952 - testrun_47ede3b9 - [INFO] - {'dataset': 'FB15k-237', 'name': 'testrun_47ede3b9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_49495af8

@@ -1 +0,0 @@
-2023-05-06 08:37:18,939 - testrun_49495af8 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_49495af8', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}

log/testrun_4f5d8391

@@ -1 +0,0 @@
-2023-05-06 08:35:13,356 - testrun_4f5d8391 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_4f5d8391', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}

log/testrun_540f6a03

@@ -1 +0,0 @@
-2023-05-06 08:34:55,992 - testrun_540f6a03 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_540f6a03', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}

log/testrun_5a901712

@@ -1 +0,0 @@
-2023-05-17 07:04:56,051 - testrun_5a901712 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_5a901712', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_5cafe61a

@@ -1,44 +0,0 @@
-2023-05-17 06:48:57,396 - testrun_5cafe61a - [INFO] - {'dataset': 'icews14', 'name': 'testrun_5cafe61a', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
-2023-05-17 06:49:44,802 - concurrent.futures - [ERROR] - exception calling callback for <Future at 0x7efb51b74160 state=finished raised BrokenProcessPool>
-joblib.externals.loky.process_executor._RemoteTraceback: 
-"""
-Traceback (most recent call last):
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 391, in _process_worker
-    call_item = call_queue.get(block=True, timeout=timeout)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/multiprocessing/queues.py", line 116, in get
-    return _ForkingPickler.loads(res)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/storage.py", line 222, in _load_from_bytes
-    return torch.load(io.BytesIO(b))
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 713, in load
-    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 930, in _legacy_load
-    result = unpickler.load()
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 876, in persistent_load
-    wrap_storage=restore_location(obj, location),
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 175, in default_restore_location
-    result = fn(storage, location)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 155, in _cuda_deserialize
-    return torch._UntypedStorage(obj.nbytes(), device=torch.device(location))
-RuntimeError: CUDA out of memory. Tried to allocate 678.00 MiB (GPU 0; 31.72 GiB total capacity; 0 bytes already allocated; 593.94 MiB free; 0 bytes reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
-"""
-
-The above exception was the direct cause of the following exception:
-
-Traceback (most recent call last):
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/_base.py", line 26, in _invoke_callbacks
-    callback(self)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 385, in __call__
-    self.parallel.dispatch_next()
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 834, in dispatch_next
-    if not self.dispatch_one_batch(self._original_iterator):
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 901, in dispatch_one_batch
-    self._dispatch(tasks)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 819, in _dispatch
-    job = self._backend.apply_async(batch, callback=cb)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 556, in apply_async
-    future = self._workers.submit(SafeFunction(func))
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/reusable_executor.py", line 176, in submit
-    return super().submit(fn, *args, **kwargs)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 1129, in submit
-    raise self._flags.broken
-joblib.externals.loky.process_executor.BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.

log/testrun_6fd94d59

@@ -1 +0,0 @@
-2023-05-06 08:34:33,652 - testrun_6fd94d59 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_6fd94d59', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}

log/testrun_7c096a18

@@ -1 +0,0 @@
-2023-05-17 06:56:35,124 - testrun_7c096a18 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_7c096a18', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_7fb885ee

@@ -1 +0,0 @@
-2023-05-17 07:13:14,777 - testrun_7fb885ee - [INFO] - {'dataset': 'icews14', 'name': 'testrun_7fb885ee', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_8f32040f

@@ -1 +0,0 @@
-2023-05-17 06:59:35,220 - testrun_8f32040f - [INFO] - {'dataset': 'icews14', 'name': 'testrun_8f32040f', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_958ef154

@@ -1 +0,0 @@
-2023-05-17 06:16:45,427 - testrun_958ef154 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_958ef154', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_9acdfb58

@@ -1,2 +0,0 @@
-2023-05-06 08:36:46,668 - testrun_9acdfb58 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_9acdfb58', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
-2023-05-06 08:36:57,409 - testrun_9acdfb58 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	testrun_9acdfb58

log/testrun_a051cf32

@@ -1 +0,0 @@
-2023-05-17 06:36:14,606 - testrun_a051cf32 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_a051cf32', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_a06d39d0

@@ -1 +0,0 @@
-2023-05-17 06:13:16,274 - testrun_a06d39d0 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_a06d39d0', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_aca2b734

@@ -1 +0,0 @@
-2023-05-17 06:41:20,654 - testrun_aca2b734 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_aca2b734', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_ad7a0edb

@@ -1 +0,0 @@
-2023-05-17 06:45:54,332 - testrun_ad7a0edb - [INFO] - {'dataset': 'icews14', 'name': 'testrun_ad7a0edb', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_b381870f

@@ -1 +0,0 @@
-2023-05-30 17:54:20,857 - testrun_b381870f - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b381870f', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}

log/testrun_b396dcde

@@ -1,2 +0,0 @@
-2023-05-30 17:56:25,430 - testrun_b396dcde - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b396dcde', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
-2023-05-30 17:57:00,673 - testrun_b396dcde - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b396dcde', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False, 'num_ent': 12554, 'num_rel': 423}

log/testrun_bbf65ab5

@@ -1 +0,0 @@
-2023-05-17 06:21:14,228 - testrun_bbf65ab5 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_bbf65ab5', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_bfaa042b

@@ -1 +0,0 @@
-2023-05-17 06:50:58,251 - testrun_bfaa042b - [INFO] - {'dataset': 'icews14', 'name': 'testrun_bfaa042b', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_c77a8ec3

@@ -1 +0,0 @@
-2023-05-17 06:37:11,288 - testrun_c77a8ec3 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_c77a8ec3', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_cb3528f3

@@ -1 +0,0 @@
-2023-05-17 07:08:13,688 - testrun_cb3528f3 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_cb3528f3', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_cd333c33

@@ -1 +0,0 @@
-2023-05-17 06:25:12,047 - testrun_cd333c33 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_cd333c33', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_d0367b19

@@ -1,2 +0,0 @@
-2023-05-06 08:37:25,129 - testrun_d0367b19 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_d0367b19', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
-2023-05-06 08:37:36,239 - testrun_d0367b19 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	testrun_d0367b19

log/testrun_f0394b3c

@@ -1 +0,0 @@
-2023-05-17 06:47:48,537 - testrun_f0394b3c - [INFO] - {'dataset': 'icews14', 'name': 'testrun_f0394b3c', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/testrun_f42f568c

@@ -1 +0,0 @@
-2023-05-30 17:55:52,461 - testrun_f42f568c - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_f42f568c', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}

log/testrun_fdb0e82c

@@ -1 +0,0 @@
-2023-05-17 06:39:01,301 - testrun_fdb0e82c - [INFO] - {'dataset': 'icews14', 'name': 'testrun_fdb0e82c', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}

log/wikidata12k_0.00003

@@ -1,2 +0,0 @@
-2023-06-04 17:05:45,012 - wikidata12k_0.00003 - [INFO] - {'dataset': 'wikidata12k', 'name': 'wikidata12k_0.00003', 'gpu': '2', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
-2023-06-04 17:06:06,702 - wikidata12k_0.00003 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	wikidata12k_0.00003

main.py

@@ -3,12 +3,10 @@ import uuid
 import argparse
 import logging
 import logging.config
-import pandas as pd
+import time
-import sys
 
 import torch
 import numpy as np
-import time
 
 from collections import defaultdict as ddict
 from pprint import pprint
@@ -20,12 +18,11 @@ from data_loader import TrainDataset, TestDataset
 from utils import get_logger, get_combined_results, set_gpu, prepare_env, set_seed
 
 from models import ComplEx, ConvE, HypER, InteractE, FouriER, TuckER
-import traceback
 
 
 class Main(object):
 
-    def __init__(self, params, logger):
+    def __init__(self, params):
         """
         Constructor of the runner class
         Parameters
@@ -38,9 +35,11 @@ class Main(object):
 
         """
         self.p = params
-        self.logger = logger
+        self.logger = get_logger(
+            self.p.name, self.p.log_dir, self.p.config_dir)
 
         self.logger.info(vars(self.p))
+        pprint(vars(self.p))
 
         if self.p.gpu != '-1' and torch.cuda.is_available():
             self.device = torch.device('cuda')
@@ -85,7 +84,7 @@ class Main(object):
         
         self.ent2id = {}
         for line in open('./data/{}/{}'.format(self.p.dataset, "entities.dict")):
-            id, ent = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
+            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")):
@@ -109,14 +108,20 @@ class Main(object):
         sr2o = ddict(set)
 
         for split in ['train', 'test', 'valid']:
-            for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
+            samples = 0
-                sub, rel, obj, *_ = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
+            for i, line in enumerate(open('./data/{}/{}.txt'.format(self.p.dataset, split))):
+                sub, rel, obj, rel_type, *_ = map(str.lower, line.strip().split('\t'))
+                if (split == 'test' and self.p.rel_type is not None):
+                    if rel_type != self.p.rel_type:
+                        continue
                 sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
                 self.data[split].append((sub, rel, obj))
 
                 if split == 'train':
                     sr2o[(sub, rel)].add(obj)
                     sr2o[(obj, rel+self.p.num_rel)].add(sub)
+                samples += 1
+            print(split.capitalize() + ': ' + str(samples) + ' samples')
         self.data = dict(self.data)
 
         self.sr2o = {k: list(v) for k, v in sr2o.items()}
@@ -154,6 +159,8 @@ class Main(object):
                     {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
 
         self.triples = dict(self.triples)
+        print(len(self.triples['test_head']))
+        print(len(self.triples['test_tail']))
 
         def get_data_loader(dataset_class, split, batch_size, shuffle=True):
             return DataLoader(
@@ -408,13 +415,6 @@ class Main(object):
             train_iter = iter(
                 self.data_iter['{}_{}'.format(split, mode.split('_')[0])])
 
-            sub_all = []
-            obj_all = []
-            rel_all = []
-            target_score = []
-            target_rank = []
-            obj_pred = []
-            obj_pred_score = []
             for step, batch in enumerate(train_iter):
                 sub, rel, obj, label = self.read_batch(batch, split)
                 pred = self.model.forward(sub, rel, None, 'one_to_n')
@@ -422,21 +422,9 @@ class Main(object):
                 target_pred = pred[b_range, obj]
                 pred = torch.where(label.byte(), torch.zeros_like(pred), pred)
                 pred[b_range, obj] = target_pred
-
-                highest = torch.argsort(pred, dim=1, descending=True)[:,0]
-                highest_score = pred[b_range, highest]
-
                 ranks = 1 + torch.argsort(torch.argsort(pred, dim=1,
                                           descending=True), dim=1, descending=False)[b_range, obj]
 
-                sub_all.extend(sub.cpu().numpy())
-                obj_all.extend(obj.cpu().numpy())
-                rel_all.extend(rel.cpu().numpy())
-                target_score.extend(target_pred.cpu().numpy())
-                target_rank.extend(ranks.cpu().numpy())
-                obj_pred.extend(highest.cpu().numpy())
-                obj_pred_score.extend(highest_score.cpu().numpy())
-
                 ranks = ranks.float()
                 results['count'] = torch.numel(
                     ranks) + results.get('count', 0.0)
@@ -451,8 +439,7 @@ class Main(object):
                 if step % 100 == 0:
                     self.logger.info('[{}, {} Step {}]\t{}'.format(
                         split.title(), mode.title(), step, self.p.name))
-        df = pd.DataFrame({"sub":sub_all,"rel":rel_all,"obj":obj_all, "rank": target_rank,"score":target_score, "pred":obj_pred,"pred_score":obj_pred_score})
+
-        df.to_csv(f"{self.p.name}_result.csv",header=True, index=False)
         return results
 
     def run_epoch(self, epoch):
@@ -478,11 +465,7 @@ class Main(object):
                 batch, 'train')
 
             pred = self.model.forward(sub, rel, neg_ent, self.p.train_strategy)
-            try:
+            loss = self.model.loss(pred, label, sub_samp)
-                loss = self.model.loss(pred, label, sub_samp)
-            except Exception as e:
-                print(pred)
-                raise e
 
             loss.backward()
             self.optimizer.step()
@@ -652,6 +635,7 @@ if __name__ == "__main__":
     
     parser.add_argument('--test_only', action='store_true', default=False)
     parser.add_argument('--grid_search', action='store_true', default=False)
+    parser.add_argument('--rel_type', default=None, type=str)
 
     args = parser.parse_args()
 
@@ -660,10 +644,9 @@ if __name__ == "__main__":
     set_gpu(args.gpu)
     set_seed(args.seed)
 
+    model = Main(args)
 
     if (args.grid_search):
-        
-        model = Main(args)
         from sklearn.model_selection import GridSearchCV
         from skorch import NeuralNet
 
@@ -712,27 +695,18 @@ if __name__ == "__main__":
             search = grid.fit(inputs, label)
             print("BEST SCORE: ", search.best_score_)
             print("BEST PARAMS: ", search.best_params_)
-    logger = get_logger(
-            args.name, args.log_dir, args.config_dir)
     if (args.test_only):
-        model = Main(args, logger)
         save_path = os.path.join('./torch_saved', args.name)
         model.load_model(save_path)
         model.evaluate('test')
     else:
-        model = Main(args, logger)
+        while True:
-        model.fit()
+            try:
-        # while True:
+                model.fit()
-        #     try:
+            except Exception as e:
-        #         model = Main(args, logger)
+                print(e)
-        #         model.fit()
+                time.sleep(30)
-        #     except Exception as e:
+                del model
-        #         print(e)
+                model = Main(args)
-        #         traceback.print_exc()
+                continue
-        #         try:
+            break
-        #             del model
-        #         except Exception:
-        #             pass
-        #         time.sleep(30)
-        #         continue
-        #     break

models.py

@@ -1,16 +1,15 @@
 import torch
-from torch import nn, einsum
+from torch import nn
 import torch.nn.functional as F
 import numpy as np
 from functools import partial
 from einops.layers.torch import Rearrange, Reduce
-from einops import rearrange, repeat
 from utils import *
 from layers import *
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.models.layers import DropPath, trunc_normal_
 from timm.models.registry import register_model
-from timm.layers.helpers import to_2tuple
+from timm.models.layers.helpers import to_2tuple
 
 
 class ConvE(torch.nn.Module):
@@ -558,8 +557,6 @@ class FouriER(torch.nn.Module):
         z = self.forward_embeddings(y)
         z = self.forward_tokens(z)
         z = z.mean([-2, -1])
-        if np.count_nonzero(np.isnan(z)) > 0:
-            print("ZZZ")
         z = self.norm(z)
         x = self.head(z)
         x = self.hidden_drop(x)
@@ -710,363 +707,6 @@ def basic_blocks(dim, index, layers,
 
     return blocks
 
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, C, H, W = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
-                                     nn.ReLU(inplace=True),
-                                     nn.Linear(512, num_heads, bias=False))
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
-        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(
-            torch.meshgrid([relative_coords_h,
-                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
-        else:
-            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))
-            self.v_bias = nn.Parameter(torch.zeros(dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
-        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).cuda()).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        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
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, ' \
-               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-    
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, -1, H, W)
-    return x
-
-def cast_tuple(val, length = 1):
-    return val if isinstance(val, tuple) else ((val,) * length)
-
-# helper classes
-
-class ChanLayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
-        super().__init__()
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
-        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
-
-    def forward(self, x):
-        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
-        mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
-
-class OverlappingPatchEmbed(nn.Module):
-    def __init__(self, dim_in, dim_out, stride = 2):
-        super().__init__()
-        kernel_size = stride * 2 - 1
-        padding = kernel_size // 2
-        self.conv = nn.Conv2d(dim_in, dim_out, kernel_size, stride = stride, padding = padding)
-
-    def forward(self, x):
-        return self.conv(x)
-
-class PEG(nn.Module):
-    def __init__(self, dim, kernel_size = 3):
-        super().__init__()
-        self.proj = nn.Conv2d(dim, dim, kernel_size = kernel_size, padding = kernel_size // 2, groups = dim, stride = 1)
-
-    def forward(self, x):
-        return self.proj(x) + x
-
-# feedforward
-
-class FeedForwardDSSA(nn.Module):
-    def __init__(self, dim, mult = 4, dropout = 0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            ChanLayerNorm(dim),
-            nn.Conv2d(dim, inner_dim, 1),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Conv2d(inner_dim, dim, 1),
-            nn.Dropout(dropout)
-        )
-    def forward(self, x):
-        return self.net(x)
-
-# attention
-
-class DSSA(nn.Module):
-    def __init__(
-        self,
-        dim,
-        heads = 8,
-        dim_head = 32,
-        dropout = 0.,
-        window_size = 7
-    ):
-        super().__init__()
-        self.heads = heads
-        self.scale = dim_head ** -0.5
-        self.window_size = window_size
-        inner_dim = dim_head * heads
-
-        self.norm = ChanLayerNorm(dim)
-
-        self.attend = nn.Sequential(
-            nn.Softmax(dim = -1),
-            nn.Dropout(dropout)
-        )
-
-        self.to_qkv = nn.Conv1d(dim, inner_dim * 3, 1, bias = False)
-
-        # window tokens
-
-        self.window_tokens = nn.Parameter(torch.randn(dim))
-
-        # prenorm and non-linearity for window tokens
-        # then projection to queries and keys for window tokens
-
-        self.window_tokens_to_qk = nn.Sequential(
-            nn.LayerNorm(dim_head),
-            nn.GELU(),
-            Rearrange('b h n c -> b (h c) n'),
-            nn.Conv1d(inner_dim, inner_dim * 2, 1),
-            Rearrange('b (h c) n -> b h n c', h = heads),
-        )
-
-        # window attention
-
-        self.window_attend = nn.Sequential(
-            nn.Softmax(dim = -1),
-            nn.Dropout(dropout)
-        )
-
-        self.to_out = nn.Sequential(
-            nn.Conv2d(inner_dim, dim, 1),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x):
-        """
-        einstein notation
-
-        b - batch
-        c - channels
-        w1 - window size (height)
-        w2 - also window size (width)
-        i - sequence dimension (source)
-        j - sequence dimension (target dimension to be reduced)
-        h - heads
-        x - height of feature map divided by window size
-        y - width of feature map divided by window size
-        """
-
-        batch, height, width, heads, wsz = x.shape[0], *x.shape[-2:], self.heads, self.window_size
-        assert (height % wsz) == 0 and (width % wsz) == 0, f'height {height} and width {width} must be divisible by window size {wsz}'
-        num_windows = (height // wsz) * (width // wsz)
-
-        x = self.norm(x)
-
-        # fold in windows for "depthwise" attention - not sure why it is named depthwise when it is just "windowed" attention
-
-        x = rearrange(x, 'b c (h w1) (w w2) -> (b h w) c (w1 w2)', w1 = wsz, w2 = wsz)
-
-        # add windowing tokens
-
-        w = repeat(self.window_tokens, 'c -> b c 1', b = x.shape[0])
-        x = torch.cat((w, x), dim = -1)
-
-        # project for queries, keys, value
-
-        q, k, v = self.to_qkv(x).chunk(3, dim = 1)
-
-        # split out heads
-
-        q, k, v = map(lambda t: rearrange(t, 'b (h d) ... -> b h (...) d', h = heads), (q, k, v))
-
-        # scale
-
-        q = q * self.scale
-
-        # similarity
-
-        dots = einsum('b h i d, b h j d -> b h i j', q, k)
-
-        # attention
-
-        attn = self.attend(dots)
-
-        # aggregate values
-
-        out = torch.matmul(attn, v)
-
-        # split out windowed tokens
-
-        window_tokens, windowed_fmaps = out[:, :, 0], out[:, :, 1:]
-
-        # early return if there is only 1 window
-
-        if num_windows == 1:
-            fmap = rearrange(windowed_fmaps, '(b x y) h (w1 w2) d -> b (h d) (x w1) (y w2)', x = height // wsz, y = width // wsz, w1 = wsz, w2 = wsz)
-            return self.to_out(fmap)
-
-        # carry out the pointwise attention, the main novelty in the paper
-
-        window_tokens = rearrange(window_tokens, '(b x y) h d -> b h (x y) d', x = height // wsz, y = width // wsz)
-        windowed_fmaps = rearrange(windowed_fmaps, '(b x y) h n d -> b h (x y) n d', x = height // wsz, y = width // wsz)
-
-        # windowed queries and keys (preceded by prenorm activation)
-
-        w_q, w_k = self.window_tokens_to_qk(window_tokens).chunk(2, dim = -1)
-
-        # scale
-
-        w_q = w_q * self.scale
-
-        # similarities
-
-        w_dots = einsum('b h i d, b h j d -> b h i j', w_q, w_k)
-
-        w_attn = self.window_attend(w_dots)
-
-        # aggregate the feature maps from the "depthwise" attention step (the most interesting part of the paper, one i haven't seen before)
-
-        aggregated_windowed_fmap = einsum('b h i j, b h j w d -> b h i w d', w_attn, windowed_fmaps)
-
-        # fold back the windows and then combine heads for aggregation
-
-        fmap = rearrange(aggregated_windowed_fmap, 'b h (x y) (w1 w2) d -> b (h d) (x w1) (y w2)', x = height // wsz, y = width // wsz, w1 = wsz, w2 = wsz)
-        return self.to_out(fmap)
 
 class PoolFormerBlock(nn.Module):
     """
@@ -1091,15 +731,7 @@ class PoolFormerBlock(nn.Module):
 
         self.norm1 = norm_layer(dim)
         #self.token_mixer = Pooling(pool_size=pool_size)
-        # self.token_mixer = FNetBlock()
+        self.token_mixer = FNetBlock()
-        self.window_size = 4
-        self.attn_heads = 4
-        self.attn_mask = None
-        # self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=4)
-        self.token_mixer = nn.ModuleList([
-            DSSA(dim, heads=self.attn_heads, window_size=self.window_size),
-            FeedForwardDSSA(dim)
-        ])
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, 
@@ -1116,26 +748,16 @@ class PoolFormerBlock(nn.Module):
                 layer_scale_init_value * torch.ones((dim)), requires_grad=True)
 
     def forward(self, x):
-        B, C, H, W = x.shape
-        # x_windows = window_partition(x, self.window_size)
-        # x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
-        # attn_windows = self.token_mixer(x_windows, mask=self.attn_mask)
-        # attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        # x_attn = window_reverse(attn_windows, self.window_size, H, W)
-        x_attn = self.token_mixer(x)
         if self.use_layer_scale:
             x = x + self.drop_path(
                 self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
-                * x_attn)
+                * self.token_mixer(self.norm1(x)))
             x = x + self.drop_path(
                 self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                 * self.mlp(self.norm2(x)))
         else:
-            x = x + self.drop_path(x_attn)
+            x = x + self.drop_path(self.token_mixer(self.norm1(x)))
             x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        if np.count_nonzero(np.isnan(x)) > 0:
-            print("PFBlock")
         return x
 class PatchEmbed(nn.Module):
     """
@@ -1221,7 +843,7 @@ class LayerNormChannel(nn.Module):
             + self.bias.unsqueeze(-1).unsqueeze(-1)
         return x
 
-class FeedForwardFNet(nn.Module):
+class FeedForward(nn.Module):
     def __init__(self, dim, hidden_dim, dropout = 0.):
         super().__init__()
         self.net = nn.Sequential(
@@ -1257,7 +879,7 @@ class FNet(nn.Module):
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
                 PreNorm(dim, FNetBlock()),
-                PreNorm(dim, FeedForwardFNet(dim, mlp_dim, dropout = dropout))
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
             ]))
     def forward(self, x):
         for attn, ff in self.layers:

requirements.txt

@@ -1,6 +1,4 @@
 torch==1.12.1+cu116
 ordered-set==4.1.0
 numpy==1.21.5
-einops==0.4.1
+einops==0.4.1
-pandas
-timm==0.9.16

run.sh

@@ -37,7 +37,4 @@ nohup python main.py --name ice00001 --lr 0.00001 --data icews14 --gpu 2 >run_lo
 PID:
 
 ___
-nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
+nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
-___
-nohup python main.py --name iceboth --data icews14_both --gpu 0 >run_log/iceboth.log 2>&1 &
-PID: 21984

run_log/wikidata12k/0.001.log

@@ -0,0 +1,425 @@
+nohup: ignoring input
+2023-05-27 04:41:18,497 - [INFO] - {'dataset': 'wikidata12k', 'name': 'wikidata12k_0.001', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
+{'batch_size': 128,
+ 'bias': False,
+ 'config_dir': './config/',
+ 'dataset': 'wikidata12k',
+ 'drop': 0.0,
+ 'drop_path': 0.0,
+ 'embed_dim': 400,
+ 'ent_vec_dim': 400,
+ 'expansion_factor': 4,
+ 'expansion_factor_token': 0.5,
+ 'feat_drop': 0.2,
+ 'filt_h': 1,
+ 'filt_w': 9,
+ 'form': 'plain',
+ 'gpu': '3',
+ 'grid_search': False,
+ 'hid_drop': 0.5,
+ 'image_h': 128,
+ 'image_w': 128,
+ 'in_channels': 1,
+ 'inp_drop': 0.2,
+ 'k_h': 20,
+ 'k_w': 10,
+ 'ker_sz': 9,
+ 'l2': 0.0,
+ 'lbl_smooth': 0.1,
+ 'log_dir': './log/',
+ 'lr': 0.001,
+ 'max_epochs': 500,
+ 'mixer_depth': 16,
+ 'mixer_dim': 256,
+ 'mixer_dropout': 0.2,
+ 'name': 'wikidata12k_0.001',
+ 'neg_num': 1000,
+ 'num_filt': 96,
+ 'num_workers': 0,
+ 'opt': 'adam',
+ 'out_channels': 32,
+ 'patch_size': 8,
+ 'perm': 1,
+ 'rel_vec_dim': 400,
+ 'restore': False,
+ 'seed': 42,
+ 'test_only': False,
+ 'train_strategy': 'one_to_n'}
+2023-05-27 04:41:28,635 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:42:32,570 - [INFO] - [E:0| 100]: Train Loss:0.053587,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:43:36,618 - [INFO] - [E:0| 200]: Train Loss:0.028724,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:44:40,687 - [INFO] - [E:0| 300]: Train Loss:0.020033,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:45:44,799 - [INFO] - [E:0| 400]: Train Loss:0.015589,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:46:48,901 - [INFO] - [E:0| 500]: Train Loss:0.012878,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:47:53,124 - [INFO] - [E:0| 600]: Train Loss:0.011054,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:48:57,224 - [INFO] - [E:0| 700]: Train Loss:0.0097532,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:50:01,352 - [INFO] - [E:0| 800]: Train Loss:0.008763,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:51:05,445 - [INFO] - [E:0| 900]: Train Loss:0.0079929,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:52:09,559 - [INFO] - [E:0| 1000]: Train Loss:0.0073745,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:53:13,624 - [INFO] - [E:0| 1100]: Train Loss:0.0068693,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:54:17,823 - [INFO] - [E:0| 1200]: Train Loss:0.0064497,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:55:21,967 - [INFO] - [E:0| 1300]: Train Loss:0.0060945,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:56:26,129 - [INFO] - [E:0| 1400]: Train Loss:0.0057879,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:57:30,256 - [INFO] - [E:0| 1500]: Train Loss:0.0055195,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:58:34,350 - [INFO] - [E:0| 1600]: Train Loss:0.0052845,  Val MRR:0.0, 	wikidata12k_0.001
+2023-05-27 04:59:16,259 - [INFO] - [Epoch:0]:  Training Loss:0.005147
+
+2023-05-27 04:59:16,481 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 04:59:38,187 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 04:59:50,745 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:00:12,609 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:00:25,062 - [INFO] - [Evaluating Epoch 0 valid]: 
+	MRR: Tail : 0.08049, Head : 0.01947, Avg : 0.04998
+
+2023-05-27 05:00:26,469 - [INFO] - [Epoch 0]:  Training Loss: 0.0051469,  Valid MRR: 0.04998, 
+
+
+
+2023-05-27 05:00:27,127 - [INFO] - [E:1| 0]: Train Loss:0.0016275,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:01:31,277 - [INFO] - [E:1| 100]: Train Loss:0.0017991,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:02:35,390 - [INFO] - [E:1| 200]: Train Loss:0.0017846,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:03:39,590 - [INFO] - [E:1| 300]: Train Loss:0.0017789,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:04:43,748 - [INFO] - [E:1| 400]: Train Loss:0.001772,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:05:47,967 - [INFO] - [E:1| 500]: Train Loss:0.0017692,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:06:52,036 - [INFO] - [E:1| 600]: Train Loss:0.0017597,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:07:56,215 - [INFO] - [E:1| 700]: Train Loss:0.0017589,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:09:00,363 - [INFO] - [E:1| 800]: Train Loss:0.0017555,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:10:04,516 - [INFO] - [E:1| 900]: Train Loss:0.0017507,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:11:08,719 - [INFO] - [E:1| 1000]: Train Loss:0.0017476,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:12:12,940 - [INFO] - [E:1| 1100]: Train Loss:0.0017427,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:13:17,076 - [INFO] - [E:1| 1200]: Train Loss:0.0017384,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:14:21,295 - [INFO] - [E:1| 1300]: Train Loss:0.0017345,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:15:25,462 - [INFO] - [E:1| 1400]: Train Loss:0.0017307,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:16:29,614 - [INFO] - [E:1| 1500]: Train Loss:0.0017243,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:17:33,705 - [INFO] - [E:1| 1600]: Train Loss:0.001719,  Val MRR:0.04998, 	wikidata12k_0.001
+2023-05-27 05:18:15,618 - [INFO] - [Epoch:1]:  Training Loss:0.001714
+
+2023-05-27 05:18:15,839 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:18:37,583 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:18:50,191 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:19:12,067 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:19:24,503 - [INFO] - [Evaluating Epoch 1 valid]: 
+	MRR: Tail : 0.1748, Head : 0.04108, Avg : 0.10794
+
+2023-05-27 05:19:25,566 - [INFO] - [Epoch 1]:  Training Loss: 0.0017143,  Valid MRR: 0.10794, 
+
+
+
+2023-05-27 05:19:26,219 - [INFO] - [E:2| 0]: Train Loss:0.0016961,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:20:30,344 - [INFO] - [E:2| 100]: Train Loss:0.0016227,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:21:34,535 - [INFO] - [E:2| 200]: Train Loss:0.0016161,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:22:38,770 - [INFO] - [E:2| 300]: Train Loss:0.0016161,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:23:43,004 - [INFO] - [E:2| 400]: Train Loss:0.0016106,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:24:47,137 - [INFO] - [E:2| 500]: Train Loss:0.0016058,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:25:51,362 - [INFO] - [E:2| 600]: Train Loss:0.0016067,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:26:55,499 - [INFO] - [E:2| 700]: Train Loss:0.0016013,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:27:59,761 - [INFO] - [E:2| 800]: Train Loss:0.0015978,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:29:03,935 - [INFO] - [E:2| 900]: Train Loss:0.0015935,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:30:08,210 - [INFO] - [E:2| 1000]: Train Loss:0.0015896,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:31:12,398 - [INFO] - [E:2| 1100]: Train Loss:0.0015856,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:32:16,608 - [INFO] - [E:2| 1200]: Train Loss:0.0015814,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:33:20,836 - [INFO] - [E:2| 1300]: Train Loss:0.0015758,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:34:25,014 - [INFO] - [E:2| 1400]: Train Loss:0.001571,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:35:29,265 - [INFO] - [E:2| 1500]: Train Loss:0.001565,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:36:33,450 - [INFO] - [E:2| 1600]: Train Loss:0.0015589,  Val MRR:0.10794, 	wikidata12k_0.001
+2023-05-27 05:37:15,383 - [INFO] - [Epoch:2]:  Training Loss:0.001556
+
+2023-05-27 05:37:15,603 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:37:37,308 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:37:49,874 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:38:11,738 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:38:24,173 - [INFO] - [Evaluating Epoch 2 valid]: 
+	MRR: Tail : 0.28305, Head : 0.07818, Avg : 0.18062
+
+2023-05-27 05:38:25,157 - [INFO] - [Epoch 2]:  Training Loss: 0.001556,  Valid MRR: 0.18062, 
+
+
+
+2023-05-27 05:38:25,813 - [INFO] - [E:3| 0]: Train Loss:0.0013897,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:39:30,024 - [INFO] - [E:3| 100]: Train Loss:0.0014599,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:40:34,122 - [INFO] - [E:3| 200]: Train Loss:0.0014516,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:41:38,261 - [INFO] - [E:3| 300]: Train Loss:0.0014552,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:42:42,459 - [INFO] - [E:3| 400]: Train Loss:0.0014541,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:43:46,707 - [INFO] - [E:3| 500]: Train Loss:0.0014521,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:44:50,829 - [INFO] - [E:3| 600]: Train Loss:0.0014476,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:45:54,979 - [INFO] - [E:3| 700]: Train Loss:0.0014439,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:46:59,115 - [INFO] - [E:3| 800]: Train Loss:0.0014396,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:48:03,341 - [INFO] - [E:3| 900]: Train Loss:0.0014367,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:49:07,419 - [INFO] - [E:3| 1000]: Train Loss:0.0014329,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:50:11,647 - [INFO] - [E:3| 1100]: Train Loss:0.0014308,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:51:15,783 - [INFO] - [E:3| 1200]: Train Loss:0.0014276,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:52:19,915 - [INFO] - [E:3| 1300]: Train Loss:0.0014245,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:53:24,121 - [INFO] - [E:3| 1400]: Train Loss:0.0014212,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:54:28,236 - [INFO] - [E:3| 1500]: Train Loss:0.0014184,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:55:32,482 - [INFO] - [E:3| 1600]: Train Loss:0.0014147,  Val MRR:0.18062, 	wikidata12k_0.001
+2023-05-27 05:56:14,438 - [INFO] - [Epoch:3]:  Training Loss:0.001413
+
+2023-05-27 05:56:14,658 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:56:36,372 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:56:48,954 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 05:57:10,881 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 05:57:23,328 - [INFO] - [Evaluating Epoch 3 valid]: 
+	MRR: Tail : 0.31549, Head : 0.09979, Avg : 0.20764
+
+2023-05-27 05:57:24,420 - [INFO] - [Epoch 3]:  Training Loss: 0.001413,  Valid MRR: 0.20764, 
+
+
+
+2023-05-27 05:57:25,077 - [INFO] - [E:4| 0]: Train Loss:0.0014323,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 05:58:29,238 - [INFO] - [E:4| 100]: Train Loss:0.0013524,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 05:59:33,410 - [INFO] - [E:4| 200]: Train Loss:0.0013439,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:00:37,566 - [INFO] - [E:4| 300]: Train Loss:0.0013507,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:01:41,692 - [INFO] - [E:4| 400]: Train Loss:0.0013525,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:02:45,877 - [INFO] - [E:4| 500]: Train Loss:0.0013497,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:03:50,088 - [INFO] - [E:4| 600]: Train Loss:0.0013468,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:04:54,238 - [INFO] - [E:4| 700]: Train Loss:0.0013447,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:05:58,490 - [INFO] - [E:4| 800]: Train Loss:0.0013417,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:07:02,645 - [INFO] - [E:4| 900]: Train Loss:0.001339,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:08:06,755 - [INFO] - [E:4| 1000]: Train Loss:0.0013377,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:09:10,902 - [INFO] - [E:4| 1100]: Train Loss:0.0013348,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:10:15,038 - [INFO] - [E:4| 1200]: Train Loss:0.0013326,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:11:19,143 - [INFO] - [E:4| 1300]: Train Loss:0.0013302,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:12:23,347 - [INFO] - [E:4| 1400]: Train Loss:0.0013283,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:13:27,477 - [INFO] - [E:4| 1500]: Train Loss:0.0013269,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:14:31,542 - [INFO] - [E:4| 1600]: Train Loss:0.0013247,  Val MRR:0.20764, 	wikidata12k_0.001
+2023-05-27 06:15:13,457 - [INFO] - [Epoch:4]:  Training Loss:0.001323
+
+2023-05-27 06:15:13,677 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:15:35,362 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:15:47,916 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:16:09,784 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:16:22,221 - [INFO] - [Evaluating Epoch 4 valid]: 
+	MRR: Tail : 0.36022, Head : 0.1037, Avg : 0.23196
+
+2023-05-27 06:16:23,220 - [INFO] - [Epoch 4]:  Training Loss: 0.0013235,  Valid MRR: 0.23196, 
+
+
+
+2023-05-27 06:16:23,875 - [INFO] - [E:5| 0]: Train Loss:0.0013387,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:17:28,154 - [INFO] - [E:5| 100]: Train Loss:0.0012781,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:18:32,286 - [INFO] - [E:5| 200]: Train Loss:0.0012786,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:19:36,495 - [INFO] - [E:5| 300]: Train Loss:0.0012809,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:20:40,588 - [INFO] - [E:5| 400]: Train Loss:0.0012857,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:21:44,792 - [INFO] - [E:5| 500]: Train Loss:0.0012853,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:22:49,006 - [INFO] - [E:5| 600]: Train Loss:0.0012833,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:23:53,190 - [INFO] - [E:5| 700]: Train Loss:0.0012812,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:24:57,311 - [INFO] - [E:5| 800]: Train Loss:0.0012813,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:26:01,510 - [INFO] - [E:5| 900]: Train Loss:0.0012801,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:27:05,756 - [INFO] - [E:5| 1000]: Train Loss:0.0012789,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:28:09,936 - [INFO] - [E:5| 1100]: Train Loss:0.0012769,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:29:14,145 - [INFO] - [E:5| 1200]: Train Loss:0.0012746,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:30:18,293 - [INFO] - [E:5| 1300]: Train Loss:0.0012721,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:31:22,538 - [INFO] - [E:5| 1400]: Train Loss:0.0012703,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:32:26,694 - [INFO] - [E:5| 1500]: Train Loss:0.0012689,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:33:30,913 - [INFO] - [E:5| 1600]: Train Loss:0.0012677,  Val MRR:0.23196, 	wikidata12k_0.001
+2023-05-27 06:34:12,771 - [INFO] - [Epoch:5]:  Training Loss:0.001267
+
+2023-05-27 06:34:12,992 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:34:34,725 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:34:47,309 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:35:09,233 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:35:21,676 - [INFO] - [Evaluating Epoch 5 valid]: 
+	MRR: Tail : 0.39017, Head : 0.12832, Avg : 0.25924
+
+2023-05-27 06:35:22,811 - [INFO] - [Epoch 5]:  Training Loss: 0.0012668,  Valid MRR: 0.25924, 
+
+
+
+2023-05-27 06:35:23,469 - [INFO] - [E:6| 0]: Train Loss:0.0011894,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:36:27,594 - [INFO] - [E:6| 100]: Train Loss:0.0012342,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:37:31,786 - [INFO] - [E:6| 200]: Train Loss:0.0012378,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:38:35,956 - [INFO] - [E:6| 300]: Train Loss:0.0012388,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:39:40,226 - [INFO] - [E:6| 400]: Train Loss:0.0012378,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:40:44,423 - [INFO] - [E:6| 500]: Train Loss:0.0012438,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:41:48,645 - [INFO] - [E:6| 600]: Train Loss:0.0012421,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:42:52,773 - [INFO] - [E:6| 700]: Train Loss:0.0012408,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:43:56,948 - [INFO] - [E:6| 800]: Train Loss:0.0012416,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:45:01,063 - [INFO] - [E:6| 900]: Train Loss:0.001242,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:46:05,216 - [INFO] - [E:6| 1000]: Train Loss:0.0012397,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:47:09,350 - [INFO] - [E:6| 1100]: Train Loss:0.0012386,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:48:13,445 - [INFO] - [E:6| 1200]: Train Loss:0.0012373,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:49:17,622 - [INFO] - [E:6| 1300]: Train Loss:0.0012363,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:50:21,832 - [INFO] - [E:6| 1400]: Train Loss:0.0012346,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:51:26,056 - [INFO] - [E:6| 1500]: Train Loss:0.0012342,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:52:30,214 - [INFO] - [E:6| 1600]: Train Loss:0.001233,  Val MRR:0.25924, 	wikidata12k_0.001
+2023-05-27 06:53:12,160 - [INFO] - [Epoch:6]:  Training Loss:0.001232
+
+2023-05-27 06:53:12,380 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:53:34,088 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:53:46,650 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 06:54:08,519 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 06:54:20,952 - [INFO] - [Evaluating Epoch 6 valid]: 
+	MRR: Tail : 0.37877, Head : 0.18554, Avg : 0.28215
+
+2023-05-27 06:54:22,025 - [INFO] - [Epoch 6]:  Training Loss: 0.0012324,  Valid MRR: 0.28215, 
+
+
+
+2023-05-27 06:54:22,682 - [INFO] - [E:7| 0]: Train Loss:0.0011315,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 06:55:26,826 - [INFO] - [E:7| 100]: Train Loss:0.001205,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 06:56:30,996 - [INFO] - [E:7| 200]: Train Loss:0.0012037,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 06:57:35,173 - [INFO] - [E:7| 300]: Train Loss:0.0012034,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 06:58:39,365 - [INFO] - [E:7| 400]: Train Loss:0.0012073,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 06:59:43,659 - [INFO] - [E:7| 500]: Train Loss:0.0012094,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:00:47,839 - [INFO] - [E:7| 600]: Train Loss:0.0012093,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:01:51,994 - [INFO] - [E:7| 700]: Train Loss:0.0012077,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:02:56,159 - [INFO] - [E:7| 800]: Train Loss:0.0012085,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:04:00,272 - [INFO] - [E:7| 900]: Train Loss:0.0012086,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:05:04,432 - [INFO] - [E:7| 1000]: Train Loss:0.0012104,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:06:08,565 - [INFO] - [E:7| 1100]: Train Loss:0.00121,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:07:12,766 - [INFO] - [E:7| 1200]: Train Loss:0.0012097,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:08:16,920 - [INFO] - [E:7| 1300]: Train Loss:0.0012101,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:09:21,081 - [INFO] - [E:7| 1400]: Train Loss:0.0012095,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:10:25,247 - [INFO] - [E:7| 1500]: Train Loss:0.0012082,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:11:29,494 - [INFO] - [E:7| 1600]: Train Loss:0.0012075,  Val MRR:0.28215, 	wikidata12k_0.001
+2023-05-27 07:12:11,381 - [INFO] - [Epoch:7]:  Training Loss:0.001208
+
+2023-05-27 07:12:11,602 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:12:33,359 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:12:45,946 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:13:07,852 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:13:20,334 - [INFO] - [Evaluating Epoch 7 valid]: 
+	MRR: Tail : 0.40626, Head : 0.21375, Avg : 0.31001
+
+2023-05-27 07:13:21,326 - [INFO] - [Epoch 7]:  Training Loss: 0.0012077,  Valid MRR: 0.31001, 
+
+
+
+2023-05-27 07:13:21,980 - [INFO] - [E:8| 0]: Train Loss:0.0012363,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:14:26,096 - [INFO] - [E:8| 100]: Train Loss:0.0011868,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:15:30,354 - [INFO] - [E:8| 200]: Train Loss:0.0011847,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:16:34,466 - [INFO] - [E:8| 300]: Train Loss:0.0011814,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:17:38,565 - [INFO] - [E:8| 400]: Train Loss:0.0011847,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:18:42,799 - [INFO] - [E:8| 500]: Train Loss:0.0011887,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:19:46,964 - [INFO] - [E:8| 600]: Train Loss:0.0011901,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:20:51,144 - [INFO] - [E:8| 700]: Train Loss:0.0011897,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:21:55,282 - [INFO] - [E:8| 800]: Train Loss:0.0011913,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:22:59,411 - [INFO] - [E:8| 900]: Train Loss:0.0011918,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:24:03,538 - [INFO] - [E:8| 1000]: Train Loss:0.0011908,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:25:07,761 - [INFO] - [E:8| 1100]: Train Loss:0.0011915,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:26:11,872 - [INFO] - [E:8| 1200]: Train Loss:0.0011925,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:27:16,041 - [INFO] - [E:8| 1300]: Train Loss:0.0011918,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:28:20,210 - [INFO] - [E:8| 1400]: Train Loss:0.0011905,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:29:24,336 - [INFO] - [E:8| 1500]: Train Loss:0.0011898,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:30:28,566 - [INFO] - [E:8| 1600]: Train Loss:0.0011888,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:31:10,538 - [INFO] - [Epoch:8]:  Training Loss:0.001189
+
+2023-05-27 07:31:10,758 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:31:32,478 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:31:45,038 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:32:06,913 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:32:19,354 - [INFO] - [Evaluating Epoch 8 valid]: 
+	MRR: Tail : 0.41408, Head : 0.20141, Avg : 0.30774
+
+2023-05-27 07:32:19,354 - [INFO] - [Epoch 8]:  Training Loss: 0.0011888,  Valid MRR: 0.31001, 
+
+
+
+2023-05-27 07:32:20,011 - [INFO] - [E:9| 0]: Train Loss:0.0011748,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:33:24,159 - [INFO] - [E:9| 100]: Train Loss:0.0011746,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:34:28,351 - [INFO] - [E:9| 200]: Train Loss:0.0011787,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:35:32,472 - [INFO] - [E:9| 300]: Train Loss:0.0011761,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:36:36,656 - [INFO] - [E:9| 400]: Train Loss:0.0011729,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:37:40,796 - [INFO] - [E:9| 500]: Train Loss:0.0011725,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:38:44,981 - [INFO] - [E:9| 600]: Train Loss:0.0011741,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:39:49,133 - [INFO] - [E:9| 700]: Train Loss:0.001173,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:40:53,329 - [INFO] - [E:9| 800]: Train Loss:0.0011736,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:41:57,558 - [INFO] - [E:9| 900]: Train Loss:0.0011731,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:43:01,737 - [INFO] - [E:9| 1000]: Train Loss:0.0011729,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:44:05,854 - [INFO] - [E:9| 1100]: Train Loss:0.001173,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:45:10,080 - [INFO] - [E:9| 1200]: Train Loss:0.0011727,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:46:14,191 - [INFO] - [E:9| 1300]: Train Loss:0.0011718,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:47:18,385 - [INFO] - [E:9| 1400]: Train Loss:0.001171,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:48:22,543 - [INFO] - [E:9| 1500]: Train Loss:0.0011709,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:49:26,748 - [INFO] - [E:9| 1600]: Train Loss:0.0011712,  Val MRR:0.31001, 	wikidata12k_0.001
+2023-05-27 07:50:08,734 - [INFO] - [Epoch:9]:  Training Loss:0.001171
+
+2023-05-27 07:50:08,954 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:50:30,672 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:50:43,251 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 07:51:05,138 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 07:51:17,628 - [INFO] - [Evaluating Epoch 9 valid]: 
+	MRR: Tail : 0.42849, Head : 0.23814, Avg : 0.33331
+	MR: Tail : 655.47, Head : 840.42, Avg : 747.94
+	Hit-1: Tail : 0.35832, Head : 0.15504, Avg : 0.25668
+	Hit-3: Tail : 0.45838, Head : 0.2739, Avg : 0.36614
+	Hit-10: Tail : 0.55785, Head : 0.39074, Avg : 0.47429
+2023-05-27 07:51:18,545 - [INFO] - [Epoch 9]:  Training Loss: 0.0011709,  Valid MRR: 0.33331, 
+
+
+
+2023-05-27 07:51:19,204 - [INFO] - [E:10| 0]: Train Loss:0.00113,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:52:23,358 - [INFO] - [E:10| 100]: Train Loss:0.0011531,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:53:27,523 - [INFO] - [E:10| 200]: Train Loss:0.0011557,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:54:31,758 - [INFO] - [E:10| 300]: Train Loss:0.0011545,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:55:36,004 - [INFO] - [E:10| 400]: Train Loss:0.0011554,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:56:40,140 - [INFO] - [E:10| 500]: Train Loss:0.001154,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:57:44,301 - [INFO] - [E:10| 600]: Train Loss:0.0011525,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:58:48,517 - [INFO] - [E:10| 700]: Train Loss:0.0011538,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 07:59:52,698 - [INFO] - [E:10| 800]: Train Loss:0.0011536,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:00:56,912 - [INFO] - [E:10| 900]: Train Loss:0.0011541,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:02:01,143 - [INFO] - [E:10| 1000]: Train Loss:0.0011546,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:03:05,293 - [INFO] - [E:10| 1100]: Train Loss:0.0011542,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:04:09,471 - [INFO] - [E:10| 1200]: Train Loss:0.0011539,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:05:13,701 - [INFO] - [E:10| 1300]: Train Loss:0.0011531,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:06:17,887 - [INFO] - [E:10| 1400]: Train Loss:0.0011534,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:07:22,089 - [INFO] - [E:10| 1500]: Train Loss:0.0011546,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:08:26,239 - [INFO] - [E:10| 1600]: Train Loss:0.0011552,  Val MRR:0.33331, 	wikidata12k_0.001
+2023-05-27 08:09:08,153 - [INFO] - [Epoch:10]:  Training Loss:0.001156
+
+2023-05-27 08:09:08,373 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 08:09:30,456 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 08:09:43,084 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 08:10:05,005 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 08:10:17,448 - [INFO] - [Evaluating Epoch 10 valid]: 
+	MRR: Tail : 0.45191, Head : 0.21626, Avg : 0.33409
+
+2023-05-27 08:10:18,436 - [INFO] - [Epoch 10]:  Training Loss: 0.0011556,  Valid MRR: 0.33409, 
+
+
+
+2023-05-27 08:10:19,090 - [INFO] - [E:11| 0]: Train Loss:0.0011363,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:11:23,530 - [INFO] - [E:11| 100]: Train Loss:0.0011426,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:12:27,950 - [INFO] - [E:11| 200]: Train Loss:0.0011483,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:13:32,143 - [INFO] - [E:11| 300]: Train Loss:0.0011472,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:14:36,469 - [INFO] - [E:11| 400]: Train Loss:0.0011477,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:15:40,641 - [INFO] - [E:11| 500]: Train Loss:0.0011474,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:16:44,731 - [INFO] - [E:11| 600]: Train Loss:0.0011465,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:17:48,900 - [INFO] - [E:11| 700]: Train Loss:0.0011469,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:18:53,113 - [INFO] - [E:11| 800]: Train Loss:0.0011469,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:19:57,285 - [INFO] - [E:11| 900]: Train Loss:0.0011457,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:21:01,406 - [INFO] - [E:11| 1000]: Train Loss:0.0011445,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:22:05,596 - [INFO] - [E:11| 1100]: Train Loss:0.0011434,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:23:09,693 - [INFO] - [E:11| 1200]: Train Loss:0.0011431,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:24:13,830 - [INFO] - [E:11| 1300]: Train Loss:0.001143,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:25:18,076 - [INFO] - [E:11| 1400]: Train Loss:0.0011426,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:26:22,160 - [INFO] - [E:11| 1500]: Train Loss:0.0011422,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:27:26,373 - [INFO] - [E:11| 1600]: Train Loss:0.0011418,  Val MRR:0.33409, 	wikidata12k_0.001
+2023-05-27 08:28:08,368 - [INFO] - [Epoch:11]:  Training Loss:0.001142
+
+2023-05-27 08:28:08,589 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
+2023-05-27 08:28:30,301 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
+2023-05-27 08:28:42,888 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
+2023-05-27 08:29:04,760 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
+2023-05-27 08:29:17,200 - [INFO] - [Evaluating Epoch 11 valid]: 
+	MRR: Tail : 0.4433, Head : 0.23916, Avg : 0.34123
+
+2023-05-27 08:29:18,266 - [INFO] - [Epoch 11]:  Training Loss: 0.0011416,  Valid MRR: 0.34123, 
+
+
+
+2023-05-27 08:29:18,927 - [INFO] - [E:12| 0]: Train Loss:0.0010957,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:30:23,063 - [INFO] - [E:12| 100]: Train Loss:0.0011303,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:31:27,243 - [INFO] - [E:12| 200]: Train Loss:0.001132,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:32:31,360 - [INFO] - [E:12| 300]: Train Loss:0.0011321,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:33:35,484 - [INFO] - [E:12| 400]: Train Loss:0.0011313,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:34:39,656 - [INFO] - [E:12| 500]: Train Loss:0.0011302,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:35:43,783 - [INFO] - [E:12| 600]: Train Loss:0.0011318,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:36:47,900 - [INFO] - [E:12| 700]: Train Loss:0.0011316,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:37:52,082 - [INFO] - [E:12| 800]: Train Loss:0.0011323,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:38:56,174 - [INFO] - [E:12| 900]: Train Loss:0.001132,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:40:00,316 - [INFO] - [E:12| 1000]: Train Loss:0.0011317,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:41:04,530 - [INFO] - [E:12| 1100]: Train Loss:0.0011322,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:42:08,648 - [INFO] - [E:12| 1200]: Train Loss:0.0011318,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:43:12,819 - [INFO] - [E:12| 1300]: Train Loss:0.0011314,  Val MRR:0.34123, 	wikidata12k_0.001
+2023-05-27 08:44:18,052 - [INFO] - [E:12| 1400]: Train Loss:0.0011312,  Val MRR:0.34123, 	wikidata12k_0.001

visualization_util.py

@@ -10,36 +10,15 @@ def extract_learning_curves(args):
     if len(paths) == 1 and os.path.isdir(paths[0]):
         paths = [os.path.join(paths[0], f) for f in os.listdir(paths[0]) if os.path.isfile(os.path.join(paths[0], f))]
     learning_curves = {}
-    print(paths)
     for path in paths:
-        print(path)
         learning_curve = []
         lines = open(path, 'r').readlines()
-        last_epoch = -1
-        stacked_epoch = -1
-        max_epoch = -1
         for line in lines:
             matched = re.match(r'[0-9\- :,]*\[INFO\] - \[Epoch ([0-9]+)\].*Valid MRR: ([0-9\.]+).*', line)
-            # matched = re.match(r'\tMRR: Tail : [0-9\.]+, Head : [0-9\.]+, Avg : ([0-9\.]+)', line)
             if matched:
-                this_epoch = int(matched.group(1))
+                learning_curve.append(float(matched.group(2)))
-                if (this_epoch > max_epoch):
+                if int(matched.group(1)) >= args.num_epochs:
-                    learning_curve.append(float(matched.group(2)))
-                    max_epoch = this_epoch
-                    stacked_epoch = this_epoch
-                elif (this_epoch < max_epoch and this_epoch > last_epoch):
-                    last_epoch = this_epoch
-                    max_epoch = stacked_epoch + 1 + this_epoch
-                    learning_curve.append(float(matched.group(2)))
-                if max_epoch >= args.num_epochs:
                     break
-            # if matched:
-            #     max_epoch += 1
-            #     learning_curve.append(float(matched.group(1)))
-            #     if max_epoch >= args.num_epochs:
-            #         break
-        while len(learning_curve) < args.num_epochs:
-            learning_curve.append(learning_curve[-1])
         learning_curves[os.path.basename(path)] = learning_curve
     return learning_curves
 
@@ -53,7 +32,7 @@ def draw_learning_curves(args, learning_curves):
             label = name
         plt.plot(epochs, learning_curves[name], label = label)
     plt.xlabel("Epochs")
-    plt.ylabel("Best Valid MRR")
+    plt.ylabel("MRR")
     plt.legend(title=args.legend_title)
     plt.savefig(os.path.join(args.out_path, str(round(datetime.utcnow().timestamp() * 1000)) + '.' + args.fig_filetype))