forked from pytorch/pytorch.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodels.html
1042 lines (857 loc) · 51.3 KB
/
models.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>torchvision.models — PyTorch master documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/torchvision/models.html"/>
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="../_static/katex-math.css" type="text/css" />
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="torchvision.transforms" href="transforms.html" />
<link rel="prev" title="torchvision.datasets" href="datasets.html" />
<script src="../_static/js/modernizr.min.js"></script>
</head>
<div class="container-fluid header-holder tutorials-header" id="header-holder">
<div class="container">
<div class="header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.org/get-started">Get Started</a>
</li>
<li>
<a href="https://pytorch.org/features">Features</a>
</li>
<li>
<a href="https://pytorch.org/ecosystem">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>
<li>
<a href="https://pytorch.org/tutorials">Tutorials</a>
</li>
<li class="active">
<a href="https://pytorch.org/docs/stable/index.html">Docs</a>
</li>
<li>
<a href="https://pytorch.org/resources">Resources</a>
</li>
<li>
<a href="https://github.com/pytorch/pytorch">Github</a>
</li>
</ul>
</div>
<a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
</div>
</div>
</div>
<body class="pytorch-body">
<div class="table-of-contents-link-wrapper">
<span>Table of Contents</span>
<a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
</div>
<nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
<div class="pytorch-side-scroll">
<div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<div class="pytorch-left-menu-search">
<div class="version">
<a href='http://pytorch.org/docs/versions.html'>1.0.1 ▼</a>
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
<input type="text" name="q" placeholder="Search Docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>
<p class="caption"><span class="caption-text">Package Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html#torch-nn-functional">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html#torch-nn-init">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../multiprocessing.html">torch.multiprocessing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="../checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="../data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ffi.html">torch.utils.ffi</a></li>
<li class="toctree-l1"><a class="reference internal" href="../hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="../model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../distributed_deprecated.html">torch.distributed.deprecated</a></li>
<li class="toctree-l1"><a class="reference internal" href="../legacy.html">torch.legacy</a></li>
</ul>
<p class="caption"><span class="caption-text">torchvision Reference</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">torchvision</a></li>
</ul>
</div>
</div>
</nav>
<div class="pytorch-container">
<div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
<div class="pytorch-breadcrumbs-wrapper">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="pytorch-breadcrumbs">
<li>
<a href="../index.html">
Docs
</a> >
</li>
<li><a href="index.html">torchvision</a> ></li>
<li>torchvision.models</li>
<li class="pytorch-breadcrumbs-aside">
<a href="../_sources/torchvision/models.rst.txt" rel="nofollow"><img src="../_static/images/view-page-source-icon.svg"></a>
</li>
</ul>
</div>
</div>
<div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
Shortcuts
</div>
</div>
<section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
<div class="pytorch-content-left">
<div class="rst-content">
<div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<div class="section" id="torchvision-models">
<h1>torchvision.models<a class="headerlink" href="#torchvision-models" title="Permalink to this headline">¶</a></h1>
<p>The models subpackage contains definitions for the following model
architectures:</p>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></li>
<li><a class="reference external" href="https://arxiv.org/abs/1512.00567">Inception</a> v3</li>
<li><a class="reference external" href="https://arxiv.org/abs/1409.4842">GoogLeNet</a></li>
</ul>
<p>You can construct a model with random weights by calling its constructor:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
<span class="n">alexnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">()</span>
<span class="n">vgg16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</span><span class="p">()</span>
<span class="n">squeezenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">()</span>
<span class="n">densenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet161</span><span class="p">()</span>
<span class="n">inception</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</span><span class="p">()</span>
<span class="n">googlenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">googlenet</span><span class="p">()</span>
</pre></div>
</div>
<p>We provide pre-trained models, using the PyTorch <a class="reference internal" href="../model_zoo.html#module-torch.utils.model_zoo" title="torch.utils.model_zoo"><code class="xref py py-mod docutils literal"><span class="pre">torch.utils.model_zoo</span></code></a>.
These can be constructed by passing <code class="docutils literal"><span class="pre">pretrained=True</span></code>:</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">resnet18</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">alexnet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">squeezenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">vgg16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">densenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet161</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">inception</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">googlenet</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">googlenet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Instancing a pre-trained model will download its weights to a cache directory.
This directory can be set using the <cite>TORCH_MODEL_ZOO</cite> environment variable. See
<a class="reference internal" href="../model_zoo.html#torch.utils.model_zoo.load_url" title="torch.utils.model_zoo.load_url"><code class="xref py py-func docutils literal"><span class="pre">torch.utils.model_zoo.load_url()</span></code></a> for details.</p>
<p>Some models use modules which have different training and evaluation
behavior, such as batch normalization. To switch between these modes, use
<code class="docutils literal"><span class="pre">model.train()</span></code> or <code class="docutils literal"><span class="pre">model.eval()</span></code> as appropriate. See
<a class="reference internal" href="../nn.html#torch.nn.Module.train" title="torch.nn.Module.train"><code class="xref py py-meth docutils literal"><span class="pre">train()</span></code></a> or <a class="reference internal" href="../nn.html#torch.nn.Module.eval" title="torch.nn.Module.eval"><code class="xref py py-meth docutils literal"><span class="pre">eval()</span></code></a> for details.</p>
<p>All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using <code class="docutils literal"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>.
You can use the following transform to normalize:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">normalize</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
<span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
</pre></div>
</div>
<p>An example of such normalization can be found in the imagenet example
<a class="reference external" href="https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101">here</a></p>
<p>ImageNet 1-crop error rates (224x224)</p>
<table border="1" class="docutils">
<colgroup>
<col width="55%" />
<col width="22%" />
<col width="22%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Network</th>
<th class="head">Top-1 error</th>
<th class="head">Top-5 error</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>AlexNet</td>
<td>43.45</td>
<td>20.91</td>
</tr>
<tr class="row-odd"><td>VGG-11</td>
<td>30.98</td>
<td>11.37</td>
</tr>
<tr class="row-even"><td>VGG-13</td>
<td>30.07</td>
<td>10.75</td>
</tr>
<tr class="row-odd"><td>VGG-16</td>
<td>28.41</td>
<td>9.62</td>
</tr>
<tr class="row-even"><td>VGG-19</td>
<td>27.62</td>
<td>9.12</td>
</tr>
<tr class="row-odd"><td>VGG-11 with batch normalization</td>
<td>29.62</td>
<td>10.19</td>
</tr>
<tr class="row-even"><td>VGG-13 with batch normalization</td>
<td>28.45</td>
<td>9.63</td>
</tr>
<tr class="row-odd"><td>VGG-16 with batch normalization</td>
<td>26.63</td>
<td>8.50</td>
</tr>
<tr class="row-even"><td>VGG-19 with batch normalization</td>
<td>25.76</td>
<td>8.15</td>
</tr>
<tr class="row-odd"><td>ResNet-18</td>
<td>30.24</td>
<td>10.92</td>
</tr>
<tr class="row-even"><td>ResNet-34</td>
<td>26.70</td>
<td>8.58</td>
</tr>
<tr class="row-odd"><td>ResNet-50</td>
<td>23.85</td>
<td>7.13</td>
</tr>
<tr class="row-even"><td>ResNet-101</td>
<td>22.63</td>
<td>6.44</td>
</tr>
<tr class="row-odd"><td>ResNet-152</td>
<td>21.69</td>
<td>5.94</td>
</tr>
<tr class="row-even"><td>SqueezeNet 1.0</td>
<td>41.90</td>
<td>19.58</td>
</tr>
<tr class="row-odd"><td>SqueezeNet 1.1</td>
<td>41.81</td>
<td>19.38</td>
</tr>
<tr class="row-even"><td>Densenet-121</td>
<td>25.35</td>
<td>7.83</td>
</tr>
<tr class="row-odd"><td>Densenet-169</td>
<td>24.00</td>
<td>7.00</td>
</tr>
<tr class="row-even"><td>Densenet-201</td>
<td>22.80</td>
<td>6.43</td>
</tr>
<tr class="row-odd"><td>Densenet-161</td>
<td>22.35</td>
<td>6.20</td>
</tr>
<tr class="row-even"><td>Inception v3</td>
<td>22.55</td>
<td>6.44</td>
</tr>
<tr class="row-odd"><td>GoogleNet</td>
<td>30.22</td>
<td>10.47</td>
</tr>
</tbody>
</table>
<div class="section" id="id1">
<h2>Alexnet<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.alexnet">
<code class="descclassname">torchvision.models.</code><code class="descname">alexnet</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.alexnet" title="Permalink to this definition">¶</a></dt>
<dd><p>AlexNet model architecture from the
<a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="id2">
<h2>VGG<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.vgg11">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg11</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg11" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 11-layer model (configuration “A”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg11_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg11_bn</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg11_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 11-layer model (configuration “A”) with batch normalization</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg13</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg13" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 13-layer model (configuration “B”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg13_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg13_bn</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg13_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 13-layer model (configuration “B”) with batch normalization</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg16</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg16" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 16-layer model (configuration “D”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg16_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg16_bn</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg16_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 16-layer model (configuration “D”) with batch normalization</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg19</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg19" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 19-layer model (configuration “E”)</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.vgg19_bn">
<code class="descclassname">torchvision.models.</code><code class="descname">vgg19_bn</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.vgg19_bn" title="Permalink to this definition">¶</a></dt>
<dd><p>VGG 19-layer model (configuration ‘E’) with batch normalization</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="id3">
<h2>ResNet<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.resnet18">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet18</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet18" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-18 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet34">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet34</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet34" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-34 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet50">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet50</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet50" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-50 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet101">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet101</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet101" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-101 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.resnet152">
<code class="descclassname">torchvision.models.</code><code class="descname">resnet152</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.resnet152" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a ResNet-152 model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="id4">
<h2>SqueezeNet<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.squeezenet1_0">
<code class="descclassname">torchvision.models.</code><code class="descname">squeezenet1_0</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.squeezenet1_0" title="Permalink to this definition">¶</a></dt>
<dd><p>SqueezeNet model architecture from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.squeezenet1_1">
<code class="descclassname">torchvision.models.</code><code class="descname">squeezenet1_1</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.squeezenet1_1" title="Permalink to this definition">¶</a></dt>
<dd><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="id5">
<h2>DenseNet<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.densenet121">
<code class="descclassname">torchvision.models.</code><code class="descname">densenet121</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.densenet121" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-121 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet169">
<code class="descclassname">torchvision.models.</code><code class="descname">densenet169</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.densenet169" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-169 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet161">
<code class="descclassname">torchvision.models.</code><code class="descname">densenet161</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.densenet161" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-161 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torchvision.models.densenet201">
<code class="descclassname">torchvision.models.</code><code class="descname">densenet201</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.densenet201" title="Permalink to this definition">¶</a></dt>
<dd><p>Densenet-201 model from
<a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="inception-v3">
<h2>Inception v3<a class="headerlink" href="#inception-v3" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.inception_v3">
<code class="descclassname">torchvision.models.</code><code class="descname">inception_v3</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.inception_v3" title="Permalink to this definition">¶</a></dt>
<dd><p>Inception v3 model architecture from
<a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><strong>Important</strong>: In contrast to the other models the inception_v3 expects tensors with a size of
N x 3 x 299 x 299, so ensure your images are sized accordingly.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>pretrained</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, returns a model pre-trained on ImageNet</li>
<li><strong>transform_input</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, preprocesses the input according to the method with which it</li>
<li><strong>trained on ImageNet. Default</strong> (<em>was</em>) – <em>False</em></li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="id9">
<h2>GoogLeNet<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.models.googlenet">
<code class="descclassname">torchvision.models.</code><code class="descname">googlenet</code><span class="sig-paren">(</span><em>pretrained=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torchvision.models.googlenet" title="Permalink to this definition">¶</a></dt>
<dd><p>GoogLeNet (Inception v1) model architecture from
<a class="reference external" href="http://arxiv.org/abs/1409.4842">“Going Deeper with Convolutions”</a>.
:param pretrained: If True, returns a model pre-trained on ImageNet
:type pretrained: bool
:param transform_input: If True, preprocesses the input according to the method with which it
:type transform_input: bool
:param was trained on ImageNet. Default: <em>False</em></p>
</dd></dl>
</div>
</div>
</article>
</div>
<footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="transforms.html" class="btn btn-neutral float-right" title="torchvision.transforms" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
<a href="datasets.html" class="btn btn-neutral" title="torchvision.datasets" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
</div>
<hr/>
<div role="contentinfo">
<p>
© Copyright 2018, Torch Contributors.
</p>
</div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
<div class="pytorch-content-right" id="pytorch-content-right">
<div class="pytorch-right-menu" id="pytorch-right-menu">
<div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
<ul>
<li><a class="reference internal" href="#">torchvision.models</a><ul>
<li><a class="reference internal" href="#id1">Alexnet</a></li>
<li><a class="reference internal" href="#id2">VGG</a></li>
<li><a class="reference internal" href="#id3">ResNet</a></li>
<li><a class="reference internal" href="#id4">SqueezeNet</a></li>
<li><a class="reference internal" href="#id5">DenseNet</a></li>
<li><a class="reference internal" href="#inception-v3">Inception v3</a></li>
<li><a class="reference internal" href="#id9">GoogLeNet</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</section>
</div>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../',
VERSION:'master',
LANGUAGE:'None',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../_static/jquery.js"></script>
<script type="text/javascript" src="../_static/underscore.js"></script>
<script type="text/javascript" src="../_static/doctools.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js"></script>
<script type="text/javascript" src="../_static/katex_autorenderer.js"></script>
<script type="text/javascript" src="../_static/js/vendor/popper.min.js"></script>
<script type="text/javascript" src="../_static/js/vendor/bootstrap.min.js"></script>
<script type="text/javascript" src="../_static/js/theme.js"></script>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
<!-- Begin Footer -->
<div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
<div class="container">
<div class="row">
<div class="col-md-4 text-center">
<h2>Docs</h2>
<p>Access comprehensive developer documentation for PyTorch</p>
<a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
</div>
<div class="col-md-4 text-center">
<h2>Tutorials</h2>
<p>Get in-depth tutorials for beginners and advanced developers</p>
<a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
</div>
<div class="col-md-4 text-center">
<h2>Resources</h2>
<p>Find development resources and get your questions answered</p>
<a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
</div>
</div>
</div>
</div>
<footer class="site-footer">
<div class="container footer-container">
<div class="footer-logo-wrapper">
<a href="https://pytorch.org/" class="footer-logo"></a>
</div>
<div class="footer-links-wrapper">
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
<li><a href="https://pytorch.org/get-started">Get Started</a></li>
<li><a href="https://pytorch.org/features">Features</a></li>
<li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
<li><a href="https://pytorch.org/blog/">Blog</a></li>
<li><a href="https://pytorch.org/resources">Resources</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/support">Support</a></li>
<li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
<li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
<li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
<li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
<li><a href="https://pytorch.slack.com" target="_blank">Slack</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md" target="_blank">Contributing</a></li>
</ul>
</div>
<div class="footer-links-col follow-us-col">
<ul>
<li class="list-title">Follow Us</li>
<li>
<div id="mc_embed_signup">
<form
action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
method="post"
id="mc-embedded-subscribe-form"
name="mc-embedded-subscribe-form"
class="email-subscribe-form validate"
target="_blank"
novalidate>
<div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
<div class="mc-field-group">
<label for="mce-EMAIL" style="display:none;">Email Address</label>
<input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
</div>
<div id="mce-responses" class="clear">
<div class="response" id="mce-error-response" style="display:none"></div>
<div class="response" id="mce-success-response" style="display:none"></div>
</div> <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->
<div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>
<div class="clear">
<input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
</div>
</div>
</form>
</div>
</li>
</ul>
<div class="footer-social-icons">
<a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
<a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
</div>
</div>
</div>
</div>
</footer>
<div class="cookie-banner-wrapper">
<div class="container">
<p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
<img class="close-button" src="../_static/images/pytorch-x.svg">
</div>
</div>
<!-- End Footer -->
<!-- Begin Mobile Menu -->
<div class="mobile-main-menu">
<div class="container-fluid">
<div class="container">
<div class="mobile-main-menu-header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
</div>
</div>
</div>
<div class="mobile-main-menu-links-container">
<div class="main-menu">
<ul>
<li>
<a href="#">Get Started</a>
</li>
<li>
<a href="#">Features</a>
</li>
<li>
<a href="#">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>