[
{
"path": "LICENSE",
"content": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n Preamble\n\n The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works. By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users. We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors. You can apply it to\nyour programs, too.\n\n When we speak of free software, we are referring to freedom, not\nprice. Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights. Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received. You must make sure that they, too, receive\nor can get the source code. And you must show them these terms so they\nknow their rights.\n\n Developers that use the GNU GPL protect your rights with two steps:\n(1) assert copyright on the software, and (2) offer you this License\ngiving you legal permission to copy, distribute and/or modify it.\n\n For the developers' and authors' protection, the GPL clearly explains\nthat there is no warranty for this free software. For both users' and\nauthors' sake, the GPL requires that modified versions be marked as\nchanged, so that their problems will not be attributed erroneously to\nauthors of previous versions.\n\n Some devices are designed to deny users access to install or run\nmodified versions of the software inside them, although the manufacturer\ncan do so. This is fundamentally incompatible with the aim of\nprotecting users' freedom to change the software. The systematic\npattern of such abuse occurs in the area of products for individuals to\nuse, which is precisely where it is most unacceptable. Therefore, we\nhave designed this version of the GPL to prohibit the practice for those\nproducts. If such problems arise substantially in other domains, we\nstand ready to extend this provision to those domains in future versions\nof the GPL, as needed to protect the freedom of users.\n\n Finally, every program is threatened constantly by software patents.\nStates should not allow patents to restrict development and use of\nsoftware on general-purpose computers, but in those that do, we wish to\navoid the special danger that patents applied to a free program could\nmake it effectively proprietary. To prevent this, the GPL assures that\npatents cannot be used to render the program non-free.\n\n The precise terms and conditions for copying, distribution and\nmodification follow.\n\n TERMS AND CONDITIONS\n\n 0. Definitions.\n\n \"This License\" refers to version 3 of the GNU General Public License.\n\n \"Copyright\" also means copyright-like laws that apply to other kinds of\nworks, such as semiconductor masks.\n\n \"The Program\" refers to any copyrightable work licensed under this\nLicense. Each licensee is addressed as \"you\". \"Licensees\" and\n\"recipients\" may be individuals or organizations.\n\n To \"modify\" a work means to copy from or adapt all or part of the work\nin a fashion requiring copyright permission, other than the making of an\nexact copy. The resulting work is called a \"modified version\" of the\nearlier work or a work \"based on\" the earlier work.\n\n A \"covered work\" means either the unmodified Program or a work based\non the Program.\n\n To \"propagate\" a work means to do anything with it that, without\npermission, would make you directly or secondarily liable for\ninfringement under applicable copyright law, except executing it on a\ncomputer or modifying a private copy. Propagation includes copying,\ndistribution (with or without modification), making available to the\npublic, and in some countries other activities as well.\n\n To \"convey\" a work means any kind of propagation that enables other\nparties to make or receive copies. Mere interaction with a user through\na computer network, with no transfer of a copy, is not conveying.\n\n An interactive user interface displays \"Appropriate Legal Notices\"\nto the extent that it includes a convenient and prominently visible\nfeature that (1) displays an appropriate copyright notice, and (2)\ntells the user that there is no warranty for the work (except to the\nextent that warranties are provided), that licensees may convey the\nwork under this License, and how to view a copy of this License. If\nthe interface presents a list of user commands or options, such as a\nmenu, a prominent item in the list meets this criterion.\n\n 1. Source Code.\n\n The \"source code\" for a work means the preferred form of the work\nfor making modifications to it. \"Object code\" means any non-source\nform of a work.\n\n A \"Standard Interface\" means an interface that either is an official\nstandard defined by a recognized standards body, or, in the case of\ninterfaces specified for a particular programming language, one that\nis widely used among developers working in that language.\n\n The \"System Libraries\" of an executable work include anything, other\nthan the work as a whole, that (a) is included in the normal form of\npackaging a Major Component, but which is not part of that Major\nComponent, and (b) serves only to enable use of the work with that\nMajor Component, or to implement a Standard Interface for which an\nimplementation is available to the public in source code form. A\n\"Major Component\", in this context, means a major essential component\n(kernel, window system, and so on) of the specific operating system\n(if any) on which the executable work runs, or a compiler used to\nproduce the work, or an object code interpreter used to run it.\n\n The \"Corresponding Source\" for a work in object code form means all\nthe source code needed to generate, install, and (for an executable\nwork) run the object code and to modify the work, including scripts to\ncontrol those activities. However, it does not include the work's\nSystem Libraries, or general-purpose tools or generally available free\nprograms which are used unmodified in performing those activities but\nwhich are not part of the work. For example, Corresponding Source\nincludes interface definition files associated with source files for\nthe work, and the source code for shared libraries and dynamically\nlinked subprograms that the work is specifically designed to require,\nsuch as by intimate data communication or control flow between those\nsubprograms and other parts of the work.\n\n The Corresponding Source need not include anything that users\ncan regenerate automatically from other parts of the Corresponding\nSource.\n\n The Corresponding Source for a work in source code form is that\nsame work.\n\n 2. Basic Permissions.\n\n All rights granted under this License are granted for the term of\ncopyright on the Program, and are irrevocable provided the stated\nconditions are met. This License explicitly affirms your unlimited\npermission to run the unmodified Program. The output from running a\ncovered work is covered by this License only if the output, given its\ncontent, constitutes a covered work. This License acknowledges your\nrights of fair use or other equivalent, as provided by copyright law.\n\n You may make, run and propagate covered works that you do not\nconvey, without conditions so long as your license otherwise remains\nin force. You may convey covered works to others for the sole purpose\nof having them make modifications exclusively for you, or provide you\nwith facilities for running those works, provided that you comply with\nthe terms of this License in conveying all material for which you do\nnot control copyright. Those thus making or running the covered works\nfor you must do so exclusively on your behalf, under your direction\nand control, on terms that prohibit them from making any copies of\nyour copyrighted material outside their relationship with you.\n\n Conveying under any other circumstances is permitted solely under\nthe conditions stated below. Sublicensing is not allowed; section 10\nmakes it unnecessary.\n\n 3. 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Conveying Verbatim Copies.\n\n You may convey verbatim copies of the Program's source code as you\nreceive it, in any medium, provided that you conspicuously and\nappropriately publish on each copy an appropriate copyright notice;\nkeep intact all notices stating that this License and any\nnon-permissive terms added in accord with section 7 apply to the code;\nkeep intact all notices of the absence of any warranty; and give all\nrecipients a copy of this License along with the Program.\n\n You may charge any price or no price for each copy that you convey,\nand you may offer support or warranty protection for a fee.\n\n 5. 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This License gives no\n permission to license the work in any other way, but it does not\n invalidate such permission if you have separately received it.\n\n d) If the work has interactive user interfaces, each must display\n Appropriate Legal Notices; however, if the Program has interactive\n interfaces that do not display Appropriate Legal Notices, your\n work need not make them do so.\n\n A compilation of a covered work with other separate and independent\nworks, which are not by their nature extensions of the covered work,\nand which are not combined with it such as to form a larger program,\nin or on a volume of a storage or distribution medium, is called an\n\"aggregate\" if the compilation and its resulting copyright are not\nused to limit the access or legal rights of the compilation's users\nbeyond what the individual works permit. Inclusion of a covered work\nin an aggregate does not cause this License to apply to the other\nparts of the aggregate.\n\n 6. Conveying Non-Source Forms.\n\n You may convey a covered work in object code form under the terms\nof sections 4 and 5, provided that you also convey the\nmachine-readable Corresponding Source under the terms of this License,\nin one of these ways:\n\n a) Convey the object code in, or embodied in, a physical product\n (including a physical distribution medium), accompanied by the\n Corresponding Source fixed on a durable physical medium\n customarily used for software interchange.\n\n b) Convey the object code in, or embodied in, a physical product\n (including a physical distribution medium), accompanied by a\n written offer, valid for at least three years and valid for as\n long as you offer spare parts or customer support for that product\n model, to give anyone who possesses the object code either (1) a\n copy of the Corresponding Source for all the software in the\n product that is covered by this License, on a durable physical\n medium customarily used for software interchange, for a price no\n more than your reasonable cost of physically performing this\n conveying of source, or (2) access to copy the\n Corresponding Source from a network server at no charge.\n\n c) Convey individual copies of the object code with a copy of the\n written offer to provide the Corresponding Source. This\n alternative is allowed only occasionally and noncommercially, and\n only if you received the object code with such an offer, in accord\n with subsection 6b.\n\n d) Convey the object code by offering access from a designated\n place (gratis or for a charge), and offer equivalent access to the\n Corresponding Source in the same way through the same place at no\n further charge. You need not require recipients to copy the\n Corresponding Source along with the object code. If the place to\n copy the object code is a network server, the Corresponding Source\n may be on a different server (operated by you or a third party)\n that supports equivalent copying facilities, provided you maintain\n clear directions next to the object code saying where to find the\n Corresponding Source. Regardless of what server hosts the\n Corresponding Source, you remain obligated to ensure that it is\n available for as long as needed to satisfy these requirements.\n\n e) Convey the object code using peer-to-peer transmission, provided\n you inform other peers where the object code and Corresponding\n Source of the work are being offered to the general public at no\n charge under subsection 6d.\n\n A separable portion of the object code, whose source code is excluded\nfrom the Corresponding Source as a System Library, need not be\nincluded in conveying the object code work.\n\n A \"User Product\" is either (1) a \"consumer product\", which means any\ntangible personal property which is normally used for personal, family,\nor household purposes, or (2) anything designed or sold for incorporation\ninto a dwelling. In determining whether a product is a consumer product,\ndoubtful cases shall be resolved in favor of coverage. For a particular\nproduct received by a particular user, \"normally used\" refers to a\ntypical or common use of that class of product, regardless of the status\nof the particular user or of the way in which the particular user\nactually uses, or expects or is expected to use, the product. A product\nis a consumer product regardless of whether the product has substantial\ncommercial, industrial or non-consumer uses, unless such uses represent\nthe only significant mode of use of the product.\n\n \"Installation Information\" for a User Product means any methods,\nprocedures, authorization keys, or other information required to install\nand execute modified versions of a covered work in that User Product from\na modified version of its Corresponding Source. The information must\nsuffice to ensure that the continued functioning of the modified object\ncode is in no case prevented or interfered with solely because\nmodification has been made.\n\n If you convey an object code work under this section in, or with, or\nspecifically for use in, a User Product, and the conveying occurs as\npart of a transaction in which the right of possession and use of the\nUser Product is transferred to the recipient in perpetuity or for a\nfixed term (regardless of how the transaction is characterized), the\nCorresponding Source conveyed under this section must be accompanied\nby the Installation Information. But this requirement does not apply\nif neither you nor any third party retains the ability to install\nmodified object code on the User Product (for example, the work has\nbeen installed in ROM).\n\n The requirement to provide Installation Information does not include a\nrequirement to continue to provide support service, warranty, or updates\nfor a work that has been modified or installed by the recipient, or for\nthe User Product in which it has been modified or installed. Access to a\nnetwork may be denied when the modification itself materially and\nadversely affects the operation of the network or violates the rules and\nprotocols for communication across the network.\n\n Corresponding Source conveyed, and Installation Information provided,\nin accord with this section must be in a format that is publicly\ndocumented (and with an implementation available to the public in\nsource code form), and must require no special password or key for\nunpacking, reading or copying.\n\n 7. Additional Terms.\n\n \"Additional permissions\" are terms that supplement the terms of this\nLicense by making exceptions from one or more of its conditions.\nAdditional permissions that are applicable to the entire Program shall\nbe treated as though they were included in this License, to the extent\nthat they are valid under applicable law. If additional permissions\napply only to part of the Program, that part may be used separately\nunder those permissions, but the entire Program remains governed by\nthis License without regard to the additional permissions.\n\n When you convey a copy of a covered work, you may at your option\nremove any additional permissions from that copy, or from any part of\nit. (Additional permissions may be written to require their own\nremoval in certain cases when you modify the work.) You may place\nadditional permissions on material, added by you to a covered work,\nfor which you have or can give appropriate copyright permission.\n\n Notwithstanding any other provision of this License, for material you\nadd to a covered work, you may (if authorized by the copyright holders of\nthat material) supplement the terms of this License with terms:\n\n a) Disclaiming warranty or limiting liability differently from the\n terms of sections 15 and 16 of this License; or\n\n b) Requiring preservation of specified reasonable legal notices or\n author attributions in that material or in the Appropriate Legal\n Notices displayed by works containing it; or\n\n c) Prohibiting misrepresentation of the origin of that material, or\n requiring that modified versions of such material be marked in\n reasonable ways as different from the original version; or\n\n d) Limiting the use for publicity purposes of names of licensors or\n authors of the material; or\n\n e) Declining to grant rights under trademark law for use of some\n trade names, trademarks, or service marks; or\n\n f) Requiring indemnification of licensors and authors of that\n material by anyone who conveys the material (or modified versions of\n it) with contractual assumptions of liability to the recipient, for\n any liability that these contractual assumptions directly impose on\n those licensors and authors.\n\n All other non-permissive additional terms are considered \"further\nrestrictions\" within the meaning of section 10. If the Program as you\nreceived it, or any part of it, contains a notice stating that it is\ngoverned by this License along with a term that is a further\nrestriction, you may remove that term. If a license document contains\na further restriction but permits relicensing or conveying under this\nLicense, you may add to a covered work material governed by the terms\nof that license document, provided that the further restriction does\nnot survive such relicensing or conveying.\n\n If you add terms to a covered work in accord with this section, you\nmust place, in the relevant source files, a statement of the\nadditional terms that apply to those files, or a notice indicating\nwhere to find the applicable terms.\n\n Additional terms, permissive or non-permissive, may be stated in the\nform of a separately written license, or stated as exceptions;\nthe above requirements apply either way.\n\n 8. Termination.\n\n You may not propagate or modify a covered work except as expressly\nprovided under this License. Any attempt otherwise to propagate or\nmodify it is void, and will automatically terminate your rights under\nthis License (including any patent licenses granted under the third\nparagraph of section 11).\n\n However, if you cease all violation of this License, then your\nlicense from a particular copyright holder is reinstated (a)\nprovisionally, unless and until the copyright holder explicitly and\nfinally terminates your license, and (b) permanently, if the copyright\nholder fails to notify you of the violation by some reasonable means\nprior to 60 days after the cessation.\n\n Moreover, your license from a particular copyright holder is\nreinstated permanently if the copyright holder notifies you of the\nviolation by some reasonable means, this is the first time you have\nreceived notice of violation of this License (for any work) from that\ncopyright holder, and you cure the violation prior to 30 days after\nyour receipt of the notice.\n\n Termination of your rights under this section does not terminate the\nlicenses of parties who have received copies or rights from you under\nthis License. If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n 9. Acceptance Not Required for Having Copies.\n\n You are not required to accept this License in order to receive or\nrun a copy of the Program. Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance. However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work. These actions infringe copyright if you do\nnot accept this License. Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n 10. Automatic Licensing of Downstream Recipients.\n\n Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License. You are not responsible\nfor enforcing compliance by third parties with this License.\n\n An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations. If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License. For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n 11. Patents.\n\n A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based. The\nwork thus licensed is called the contributor's \"contributor version\".\n\n A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version. For\npurposes of this definition, \"control\" includes the right to grant\npatent sublicenses in a manner consistent with the requirements of\nthis License.\n\n Each contributor grants you a non-exclusive, worldwide, royalty-free\npatent license under the contributor's essential patent claims, to\nmake, use, sell, offer for sale, import and otherwise run, modify and\npropagate the contents of its contributor version.\n\n In the following three paragraphs, a \"patent license\" is any express\nagreement or commitment, however denominated, not to enforce a patent\n(such as an express permission to practice a patent or covenant not to\nsue for patent infringement). To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients. \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License. You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n 12. No Surrender of Others' Freedom.\n\n If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License. If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all. For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n 13. Use with the GNU Affero General Public License.\n\n Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work. The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n 14. Revised Versions of this License.\n\n The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time. Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n Each version is given a distinguishing version number. If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation. If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n Later license versions may give you additional or different\npermissions. However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n 15. Disclaimer of Warranty.\n\n THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n 16. Limitation of Liability.\n\n IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n 17. Interpretation of Sections 15 and 16.\n\n If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n END OF TERMS AND CONDITIONS\n\n How to Apply These Terms to Your New Programs\n\n If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n To do so, attach the following notices to the program. It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n \n Copyright (C) \n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\nAlso add information on how to contact you by electronic and paper mail.\n\n If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n Copyright (C) \n This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n This is free software, and you are welcome to redistribute it\n under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License. Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n.\n\n The GNU General Public License does not permit incorporating your program\ninto proprietary programs. If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library. If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License. But first, please read\n.\n"
},
{
"path": "README.md",
"content": "# LAVT: Language-Aware Vision Transformer for Referring Image Segmentation\nWelcome to the official repository for the method presented in\n\"LAVT: Language-Aware Vision Transformer for Referring Image Segmentation.\"\n\n\n\n\nCode in this repository is written using [PyTorch](https://pytorch.org/) and is organized in the following way (assuming the working directory is the root directory of this repository):\n* `./lib` contains files implementing the main network.\n* Inside `./lib`, `_utils.py` defines the highest-level model, which incorporates the backbone network\ndefined in `backbone.py` and the simple mask decoder defined in `mask_predictor.py`.\n`segmentation.py` provides the model interface and initialization functions.\n* `./bert` contains files migrated from [Hugging Face Transformers v3.0.2](https://huggingface.co/transformers/v3.0.2/quicktour.html),\nwhich implement the BERT language model.\nWe used Transformers v3.0.2 during development but it had a bug that would appear when using `DistributedDataParallel`.\nTherefore we maintain a copy of the relevant source files in this repository.\nThis way, the bug is fixed and code in this repository is self-contained.\n* `./train.py` is invoked to train the model.\n* `./test.py` is invoked to run inference on the evaluation subsets after training.\n* `./refer` contains data pre-processing code and is also where data should be placed, including the images and all annotations.\nIt is cloned from [refer](https://github.com/lichengunc/refer). \n* `./data/dataset_refer_bert.py` is where the dataset class is defined.\n* `./utils.py` defines functions that track training statistics and setup\nfunctions for `DistributedDataParallel`.\n\n\n## Updates\n**April 13th, 2023**. Using the Dice loss instead of the cross-entropy loss can improve results. Will add code and release weights later when get a chance.\n\n**June 21st, 2022**. Uploaded the training logs and trained\nmodel weights of lavt_one.\n\n**June 9th, 2022**.\nAdded a more efficient implementation of LAVT.\n* To train this new model, specify `--model` as `lavt_one`\n(and `lavt` is still valid for specifying the old model).\nThe rest of the configuration stays unchanged.\n* The difference between this version and the previous one\nis that the language model has been moved inside the overall model,\nso that `DistributedDataParallel` needs to be applied only once.\nApplying it twice (on the standalone language model and the main branch)\nas done in the old implementation led to low GPU utility,\nwhich slowed down training.\nWe recommend training this model on 8 GPUs\n(and same as before with batch size 32).\n\n## Setting Up\n### Preliminaries\nThe code has been verified to work with PyTorch v1.7.1 and Python 3.7.\n1. Clone this repository.\n2. Change directory to root of this repository.\n### Package Dependencies\n1. Create a new Conda environment with Python 3.7 then activate it:\n```shell\nconda create -n lavt python==3.7\nconda activate lavt\n```\n\n2. Install PyTorch v1.7.1 with a CUDA version that works on your cluster/machine (CUDA 10.2 is used in this example):\n```shell\nconda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch\n```\n\n3. Install the packages in `requirements.txt` via `pip`:\n```shell\npip install -r requirements.txt\n```\n\n### Datasets\n1. Follow instructions in the `./refer` directory to set up subdirectories\nand download annotations.\nThis directory is a git clone (minus two data files that we do not need)\nfrom the [refer](https://github.com/lichengunc/refer) public API.\n\n2. Download images from [COCO](https://cocodataset.org/#download).\nPlease use the first downloading link *2014 Train images [83K/13GB]*, and extract\nthe downloaded `train_2014.zip` file to `./refer/data/images/mscoco/images`.\n\n### The Initialization Weights for Training\n1. Create the `./pretrained_weights` directory where we will be storing the weights.\n```shell\nmkdir ./pretrained_weights\n```\n2. Download [pre-trained classification weights of\nthe Swin Transformer](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth),\nand put the `pth` file in `./pretrained_weights`.\nThese weights are needed for training to initialize the model.\n\n### Trained Weights of LAVT for Testing\n1. Create the `./checkpoints` directory where we will be storing the weights.\n```shell\nmkdir ./checkpoints\n```\n2. Download LAVT model weights (which are stored on Google Drive) using links below and put them in `./checkpoints`.\n\n| [RefCOCO](https://drive.google.com/file/d/13D-OeEOijV8KTC3BkFP-gOJymc6DLwVT/view?usp=sharing) | [RefCOCO+](https://drive.google.com/file/d/1B8Q44ZWsc8Pva2xD_M-KFh7-LgzeH2-2/view?usp=sharing) | [G-Ref (UMD)](https://drive.google.com/file/d/1BjUnPVpALurkGl7RXXvQiAHhA-gQYKvK/view?usp=sharing) | [G-Ref (Google)](https://drive.google.com/file/d/1weiw5UjbPfo3tCBPfB8tu6xFXCUG16yS/view?usp=sharing) |\n|---|---|---|---|\n\n3. Model weights and training logs of the new lavt_one implementation are below.\n\n| RefCOCO | RefCOCO+ | G-Ref (UMD) | G-Ref (Google) |\n|:-----:|:-----:|:-----:|:-----:|\n|[log](https://drive.google.com/file/d/1YIojIHqe3bxxsWOltifa2U9jH67hPHLM/view?usp=sharing) | [weights](https://drive.google.com/file/d/1xFMEXr6AGU97Ypj1yr8oo00uObbeIQvJ/view?usp=sharing)|[log](https://drive.google.com/file/d/1Z34T4gEnWlvcSUQya7txOuM0zdLK7MRT/view?usp=sharing) | [weights](https://drive.google.com/file/d/1HS8ZnGaiPJr-OmoUn4-4LVnVtD_zHY6w/view?usp=sharing)|[log](https://drive.google.com/file/d/14VAgahngOV8NA6noLZCqDoqaUrlW14v8/view?usp=sharing) | [weights](https://drive.google.com/file/d/14g8NzgZn6HzC6tP_bsQuWmh5LnOcovsE/view?usp=sharing)|[log](https://drive.google.com/file/d/1JBXfmlwemWSvs92Rky0TlHcVuuLpt4Da/view?usp=sharing) | [weights](https://drive.google.com/file/d/1IJeahFVLgKxu_BVmWacZs3oUzgTCeWcz/view?usp=sharing)|\n\n* The Prec@K, overall IoU and mean IoU numbers in the training logs will differ\nfrom the final results obtained by running `test.py`,\nbecause only one out of multiple annotated expressions is\nrandomly selected and evaluated for each object during training.\nBut these numbers give a good idea about the test performance.\nThe two should be fairly close.\n\n\n## Training\nWe use `DistributedDataParallel` from PyTorch.\nThe released `lavt` weights were trained using 4 x 32G V100 cards (max mem on each card was about 26G).\nThe released `lavt_one` weights were trained using 8 x 32G V100 cards (max mem on each card was about 13G).\nUsing more cards was to accelerate training.\nTo run on 4 GPUs (with IDs 0, 1, 2, and 3) on a single node:\n```shell\nmkdir ./models\n\nmkdir ./models/refcoco\nCUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcoco --model_id refcoco --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/refcoco/output\n\nmkdir ./models/refcoco+\nCUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcoco+ --model_id refcoco+ --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/refcoco+/output\n\nmkdir ./models/gref_umd\nCUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcocog --splitBy umd --model_id gref_umd --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/gref_umd/output\n\nmkdir ./models/gref_google\nCUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 train.py --model lavt --dataset refcocog --splitBy google --model_id gref_google --batch-size 8 --lr 0.00005 --wd 1e-2 --swin_type base --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth --epochs 40 --img_size 480 2>&1 | tee ./models/gref_google/output\n```\n* *--model* is a pre-defined model name. Options include `lavt` and `lavt_one`. See [Updates](#updates).\n* *--dataset* is the dataset name. One can choose from `refcoco`, `refcoco+`, and `refcocog`.\n* *--splitBy* needs to be specified if and only if the dataset is G-Ref (which is also called RefCOCOg).\n`umd` identifies the UMD partition and `google` identifies the Google partition.\n* *--model_id* is the model name one should define oneself (*e.g.*, customize it to contain training/model configurations, dataset information, experiment IDs, *etc*.).\nIt is used in two ways: Training log will be saved as `./models/[args.model_id]/output` and the best checkpoint will be saved as `./checkpoints/model_best_[args.model_id].pth`.\n* *--swin_type* specifies the version of the Swin Transformer.\nOne can choose from `tiny`, `small`, `base`, and `large`. The default is `base`.\n* *--pretrained_swin_weights* specifies the path to pre-trained Swin Transformer weights used for model initialization.\n* Note that currently we need to manually create the `./models/[args.model_id]` directory via `mkdir` before running `train.py`.\nThis is because we use `tee` to redirect `stdout` and `stderr` to `./models/[args.model_id]/output` for logging.\nThis is a nuisance and should be resolved in the future, *i.e.*, using a proper logger or a bash script for initiating training.\n\n## Testing\nFor RefCOCO/RefCOCO+, run one of\n```shell\npython test.py --model lavt --swin_type base --dataset refcoco --split val --resume ./checkpoints/refcoco.pth --workers 4 --ddp_trained_weights --window12 --img_size 480\npython test.py --model lavt --swin_type base --dataset refcoco+ --split val --resume ./checkpoints/refcoco+.pth --workers 4 --ddp_trained_weights --window12 --img_size 480\n```\n* *--split* is the subset to evaluate, and one can choose from `val`, `testA`, and `testB`.\n* *--resume* is the path to the weights of a trained model.\n\nFor G-Ref (UMD)/G-Ref (Google), run one of\n```shell\npython test.py --model lavt --swin_type base --dataset refcocog --splitBy umd --split val --resume ./checkpoints/gref_umd.pth --workers 4 --ddp_trained_weights --window12 --img_size 480\npython test.py --model lavt --swin_type base --dataset refcocog --splitBy google --split val --resume ./checkpoints/gref_google.pth --workers 4 --ddp_trained_weights --window12 --img_size 480\n```\n* *--splitBy* specifies the partition to evaluate.\nOne can choose from `umd` or `google`.\n* *--split* is the subset (according to the specified partition) to evaluate, and one can choose from `val` and `test` for the UMD partition, and only `val` for the Google partition..\n* *--resume* is the path to the weights of a trained model.\n\n## Results\n1. The evaluation results (those reported in the paper) of LAVT trained with a cross-entropy loss and based on our original implementation are summarized as follows:\n\n| Dataset | P@0.5 | P@0.6 | P@0.7 | P@0.8 | P@0.9 | Overall IoU | Mean IoU |\n|:---------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----------:|:--------:|\n| RefCOCO val | 84.46 | 80.90 | 75.28 | 64.71 | 34.30 | 72.73 | 74.46 |\n| RefCOCO test A | 88.07 | 85.17 | 79.90 | 68.52 | 35.69 | 75.82 | 76.89 |\n| RefCOCO test B | 79.12 | 74.94 | 69.17 | 59.37 | 34.45 | 68.79 | 70.94 |\n| RefCOCO+ val | 74.44 | 70.91 | 65.58 | 56.34 | 30.23 | 62.14 | 65.81 |\n| RefCOCO+ test A | 80.68 | 77.96 | 72.90 | 62.21 | 32.36 | 68.38 | 70.97 |\n| RefCOCO+ test B | 65.66 | 61.85 | 55.94 | 47.56 | 27.24 | 55.10 | 59.23 |\n| G-Ref val (UMD) | 70.81 | 65.28 | 58.60 | 47.49 | 22.73 | 61.24 | 63.34 |\n| G-Ref test (UMD)| 71.54 | 66.38 | 59.00 | 48.21 | 23.10 | 62.09 | 63.62 |\n|G-Ref val (Goog.)| 71.16 | 67.21 | 61.76 | 51.98 | 27.30 | 60.50 | 63.66 |\n\n - We have validated LAVT on RefCOCO with multiple runs. The overall IoU on the val set generally lies in the range of 72.73±0.5%.\n\n2. In the following, we report the results of LAVT trained with a multi-class Dice loss and based on the new implementation (`lavt_one`).\n\n| Dataset | P@0.5 | P@0.6 | P@0.7 | P@0.8 | P@0.9 | Overall IoU | Mean IoU |\n|:---------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----------:|:--------:|\n| RefCOCO val | 85.87 | 82.13 | 76.64 | 65.45 | 35.30 | 73.50 | 75.41 |\n| RefCOCO test A | 88.47 | 85.63 | 80.57 | 68.84 | 35.71 | 75.97 | 77.31 |\n| RefCOCO test B | 80.20 | 76.49 | 70.34 | 60.12 | 34.94 | 69.33 | 71.86 |\n| RefCOCO+ val | 76.19 | 72.27 | 66.82 | 56.87 | 30.15 | 63.79 | 67.65 |\n| RefCOCO+ test A | 82.50 | 79.44 | 74.00 | 63.27 | 31.99 | 69.79 | 72.53 |\n| RefCOCO+ test B | 68.03 | 63.35 | 57.29 | 47.92 | 26.98 | 56.49 | 61.22 |\n| G-Ref val (UMD) | 75.82 | 71.06 | 63.99 | 52.98 | 27.31 | 64.02 | 67.41 |\n| G-Ref test (UMD)| 76.12 | 71.13 | 64.58 | 53.62 | 28.03 | 64.49 | 67.45 |\n|G-Ref val (Goog.)| 72.57 | 68.65 | 63.09 | 53.33 | 28.14 | 61.31 | 64.84 |\n\n\n## Demo: Try LAVT on Your Own Image-Text Pairs\nYou can run inference on any image-text pair\nand visualize the result by running the script `./demo_inference.py`.\nHave fun!\n\n\n## Citing LAVT\n```\n@inproceedings{yang2022lavt,\n title={LAVT: Language-Aware Vision Transformer for Referring Image Segmentation},\n author={Yang, Zhao and Wang, Jiaqi and Tang, Yansong and Chen, Kai and Zhao, Hengshuang and Torr, Philip HS},\n booktitle={CVPR},\n year={2022}\n}\n```\n\n\n## Contributing\nWe appreciate all contributions.\nIt helps the project if you could\n- report issues you are facing,\n- give a :+1: on issues reported by others that are relevant to you,\n- answer issues reported by others for which you have found solutions,\n- and implement helpful new features or improve the code otherwise with pull requests.\n\n## Acknowledgements\nCode in this repository is built upon several public repositories.\nSpecifically,\n* data pre-processing leverages the [refer](https://github.com/lichengunc/refer) repository,\n* the backbone model is implemented based on code from [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation),\n* the training and testing pipelines are adapted from [RefVOS](https://github.com/miriambellver/refvos),\n* and implementation of the BERT model (files in the bert directory) is from [Hugging Face Transformers v3.0.2](https://github.com/huggingface/transformers/tree/v3.0.2)\n(we migrated over the relevant code to fix a bug and simplify the installation process).\n\nSome of these repositories in turn adapt code from [OpenMMLab](https://github.com/open-mmlab) and [TorchVision](https://github.com/pytorch/vision).\nWe'd like to thank the authors/organizations of these repositories for open sourcing their projects.\n\n\n## License\nGNU GPLv3\n"
},
{
"path": "args.py",
"content": "import argparse\n\n\ndef get_parser():\n parser = argparse.ArgumentParser(description='LAVT training and testing')\n parser.add_argument('--amsgrad', action='store_true',\n help='if true, set amsgrad to True in an Adam or AdamW optimizer.')\n parser.add_argument('-b', '--batch-size', default=8, type=int)\n parser.add_argument('--bert_tokenizer', default='bert-base-uncased', help='BERT tokenizer')\n parser.add_argument('--ck_bert', default='bert-base-uncased', help='pre-trained BERT weights')\n parser.add_argument('--dataset', default='refcoco', help='refcoco, refcoco+, or refcocog')\n parser.add_argument('--ddp_trained_weights', action='store_true',\n help='Only needs specified when testing,'\n 'whether the weights to be loaded are from a DDP-trained model')\n parser.add_argument('--device', default='cuda:0', help='device') # only used when testing on a single machine\n parser.add_argument('--epochs', default=40, type=int, metavar='N', help='number of total epochs to run')\n parser.add_argument('--fusion_drop', default=0.0, type=float, help='dropout rate for PWAMs')\n parser.add_argument('--img_size', default=480, type=int, help='input image size')\n parser.add_argument(\"--local_rank\", type=int, help='local rank for DistributedDataParallel')\n parser.add_argument('--lr', default=0.00005, type=float, help='the initial learning rate')\n parser.add_argument('--mha', default='', help='If specified, should be in the format of a-b-c-d, e.g., 4-4-4-4,'\n 'where a, b, c, and d refer to the numbers of heads in stage-1,'\n 'stage-2, stage-3, and stage-4 PWAMs')\n parser.add_argument('--model', default='lavt', help='model: lavt, lavt_one')\n parser.add_argument('--model_id', default='lavt', help='name to identify the model')\n parser.add_argument('--output-dir', default='./checkpoints/', help='path where to save checkpoint weights')\n parser.add_argument('--pin_mem', action='store_true',\n help='If true, pin memory when using the data loader.')\n parser.add_argument('--pretrained_swin_weights', default='',\n help='path to pre-trained Swin backbone weights')\n parser.add_argument('--print-freq', default=10, type=int, help='print frequency')\n parser.add_argument('--refer_data_root', default='./refer/data/', help='REFER dataset root directory')\n parser.add_argument('--resume', default='', help='resume from checkpoint')\n parser.add_argument('--split', default='test', help='only used when testing')\n parser.add_argument('--splitBy', default='unc', help='change to umd or google when the dataset is G-Ref (RefCOCOg)')\n parser.add_argument('--swin_type', default='base',\n help='tiny, small, base, or large variants of the Swin Transformer')\n parser.add_argument('--wd', '--weight-decay', default=1e-2, type=float, metavar='W', help='weight decay',\n dest='weight_decay')\n parser.add_argument('--window12', action='store_true',\n help='only needs specified when testing,'\n 'when training, window size is inferred from pre-trained weights file name'\n '(containing \\'window12\\'). Initialize Swin with window size 12 instead of the default 7.')\n parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', help='number of data loading workers')\n\n return parser\n\n\nif __name__ == \"__main__\":\n parser = get_parser()\n args_dict = parser.parse_args()\n"
},
{
"path": "bert/activations.py",
"content": "import logging\nimport math\n\nimport torch\nimport torch.nn.functional as F\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef swish(x):\n return x * torch.sigmoid(x)\n\n\ndef _gelu_python(x):\n \"\"\" Original Implementation of the gelu activation function in Google Bert repo when initially created.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n This is now written in C in torch.nn.functional\n Also see https://arxiv.org/abs/1606.08415\n \"\"\"\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\n\n\ndef gelu_new(x):\n \"\"\" Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).\n Also see https://arxiv.org/abs/1606.08415\n \"\"\"\n return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))\n\n\nif torch.__version__ < \"1.4.0\":\n gelu = _gelu_python\nelse:\n gelu = F.gelu\n\n\ndef gelu_fast(x):\n return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))\n\n\nACT2FN = {\n \"relu\": F.relu,\n \"swish\": swish,\n \"gelu\": gelu,\n \"tanh\": torch.tanh,\n \"gelu_new\": gelu_new,\n \"gelu_fast\": gelu_fast,\n}\n\n\ndef get_activation(activation_string):\n if activation_string in ACT2FN:\n return ACT2FN[activation_string]\n else:\n raise KeyError(\"function {} not found in ACT2FN mapping {}\".format(activation_string, list(ACT2FN.keys())))\n"
},
{
"path": "bert/configuration_bert.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" BERT model configuration \"\"\"\n\n\nimport logging\n\nfrom .configuration_utils import PretrainedConfig\n\n\nlogger = logging.getLogger(__name__)\n\nBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {\n \"bert-base-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json\",\n \"bert-large-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json\",\n \"bert-base-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json\",\n \"bert-large-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json\",\n \"bert-base-multilingual-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json\",\n \"bert-base-multilingual-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json\",\n \"bert-base-chinese\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json\",\n \"bert-base-german-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json\",\n \"bert-large-uncased-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json\",\n \"bert-large-cased-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json\",\n \"bert-large-uncased-whole-word-masking-finetuned-squad\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json\",\n \"bert-large-cased-whole-word-masking-finetuned-squad\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json\",\n \"bert-base-cased-finetuned-mrpc\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json\",\n \"bert-base-german-dbmdz-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json\",\n \"bert-base-german-dbmdz-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json\",\n \"cl-tohoku/bert-base-japanese\": \"https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese/config.json\",\n \"cl-tohoku/bert-base-japanese-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking/config.json\",\n \"cl-tohoku/bert-base-japanese-char\": \"https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char/config.json\",\n \"cl-tohoku/bert-base-japanese-char-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking/config.json\",\n \"TurkuNLP/bert-base-finnish-cased-v1\": \"https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json\",\n \"TurkuNLP/bert-base-finnish-uncased-v1\": \"https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json\",\n \"wietsedv/bert-base-dutch-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json\",\n # See all BERT models at https://huggingface.co/models?filter=bert\n}\n\n\nclass BertConfig(PretrainedConfig):\n r\"\"\"\n This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.\n It is used to instantiate an BERT model according to the specified arguments, defining the model\n architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of\n the BERT `bert-base-uncased `__ architecture.\n\n Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used\n to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`\n for more information.\n\n\n Args:\n vocab_size (:obj:`int`, optional, defaults to 30522):\n Vocabulary size of the BERT model. Defines the different tokens that\n can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.\n hidden_size (:obj:`int`, optional, defaults to 768):\n Dimensionality of the encoder layers and the pooler layer.\n num_hidden_layers (:obj:`int`, optional, defaults to 12):\n Number of hidden layers in the Transformer encoder.\n num_attention_heads (:obj:`int`, optional, defaults to 12):\n Number of attention heads for each attention layer in the Transformer encoder.\n intermediate_size (:obj:`int`, optional, defaults to 3072):\n Dimensionality of the \"intermediate\" (i.e., feed-forward) layer in the Transformer encoder.\n hidden_act (:obj:`str` or :obj:`function`, optional, defaults to \"gelu\"):\n The non-linear activation function (function or string) in the encoder and pooler.\n If string, \"gelu\", \"relu\", \"swish\" and \"gelu_new\" are supported.\n hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):\n The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.\n attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):\n The dropout ratio for the attention probabilities.\n max_position_embeddings (:obj:`int`, optional, defaults to 512):\n The maximum sequence length that this model might ever be used with.\n Typically set this to something large just in case (e.g., 512 or 1024 or 2048).\n type_vocab_size (:obj:`int`, optional, defaults to 2):\n The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.\n initializer_range (:obj:`float`, optional, defaults to 0.02):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):\n The epsilon used by the layer normalization layers.\n gradient_checkpointing (:obj:`bool`, optional, defaults to False):\n If True, use gradient checkpointing to save memory at the expense of slower backward pass.\n\n Example::\n\n >>> from transformers import BertModel, BertConfig\n\n >>> # Initializing a BERT bert-base-uncased style configuration\n >>> configuration = BertConfig()\n\n >>> # Initializing a model from the bert-base-uncased style configuration\n >>> model = BertModel(configuration)\n\n >>> # Accessing the model configuration\n >>> configuration = model.config\n \"\"\"\n model_type = \"bert\"\n\n def __init__(\n self,\n vocab_size=30522,\n hidden_size=768,\n num_hidden_layers=12,\n num_attention_heads=12,\n intermediate_size=3072,\n hidden_act=\"gelu\",\n hidden_dropout_prob=0.1,\n attention_probs_dropout_prob=0.1,\n max_position_embeddings=512,\n type_vocab_size=2,\n initializer_range=0.02,\n layer_norm_eps=1e-12,\n pad_token_id=0,\n gradient_checkpointing=False,\n **kwargs\n ):\n super().__init__(pad_token_id=pad_token_id, **kwargs)\n\n self.vocab_size = vocab_size\n self.hidden_size = hidden_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n self.hidden_act = hidden_act\n self.intermediate_size = intermediate_size\n self.hidden_dropout_prob = hidden_dropout_prob\n self.attention_probs_dropout_prob = attention_probs_dropout_prob\n self.max_position_embeddings = max_position_embeddings\n self.type_vocab_size = type_vocab_size\n self.initializer_range = initializer_range\n self.layer_norm_eps = layer_norm_eps\n self.gradient_checkpointing = gradient_checkpointing\n"
},
{
"path": "bert/configuration_utils.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Configuration base class and utilities.\"\"\"\n\n\nimport copy\nimport json\nimport logging\nimport os\nfrom typing import Dict, Tuple\n\nfrom .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass PretrainedConfig(object):\n r\"\"\" Base class for all configuration classes.\n Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.\n\n Note:\n A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.\n It only affects the model's configuration.\n\n Class attributes (overridden by derived classes):\n - ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.\n\n Args:\n finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):\n Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.\n num_labels (:obj:`int`, `optional`, defaults to `2`):\n Number of classes to use when the model is a classification model (sequences/tokens)\n output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Should the model returns all hidden-states.\n output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Should the model returns all attentions.\n torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Is the model used with Torchscript (for PyTorch models).\n \"\"\"\n model_type: str = \"\"\n\n def __init__(self, **kwargs):\n # Attributes with defaults\n self.output_hidden_states = kwargs.pop(\"output_hidden_states\", False)\n self.output_attentions = kwargs.pop(\"output_attentions\", False)\n self.use_cache = kwargs.pop(\"use_cache\", True) # Not used by all models\n self.torchscript = kwargs.pop(\"torchscript\", False) # Only used by PyTorch models\n self.use_bfloat16 = kwargs.pop(\"use_bfloat16\", False)\n self.pruned_heads = kwargs.pop(\"pruned_heads\", {})\n\n # Is decoder is used in encoder-decoder models to differentiate encoder from decoder\n self.is_encoder_decoder = kwargs.pop(\"is_encoder_decoder\", False)\n self.is_decoder = kwargs.pop(\"is_decoder\", False)\n\n # Parameters for sequence generation\n self.max_length = kwargs.pop(\"max_length\", 20)\n self.min_length = kwargs.pop(\"min_length\", 0)\n self.do_sample = kwargs.pop(\"do_sample\", False)\n self.early_stopping = kwargs.pop(\"early_stopping\", False)\n self.num_beams = kwargs.pop(\"num_beams\", 1)\n self.temperature = kwargs.pop(\"temperature\", 1.0)\n self.top_k = kwargs.pop(\"top_k\", 50)\n self.top_p = kwargs.pop(\"top_p\", 1.0)\n self.repetition_penalty = kwargs.pop(\"repetition_penalty\", 1.0)\n self.length_penalty = kwargs.pop(\"length_penalty\", 1.0)\n self.no_repeat_ngram_size = kwargs.pop(\"no_repeat_ngram_size\", 0)\n self.bad_words_ids = kwargs.pop(\"bad_words_ids\", None)\n self.num_return_sequences = kwargs.pop(\"num_return_sequences\", 1)\n\n # Fine-tuning task arguments\n self.architectures = kwargs.pop(\"architectures\", None)\n self.finetuning_task = kwargs.pop(\"finetuning_task\", None)\n self.id2label = kwargs.pop(\"id2label\", None)\n self.label2id = kwargs.pop(\"label2id\", None)\n if self.id2label is not None:\n kwargs.pop(\"num_labels\", None)\n self.id2label = dict((int(key), value) for key, value in self.id2label.items())\n # Keys are always strings in JSON so convert ids to int here.\n else:\n self.num_labels = kwargs.pop(\"num_labels\", 2)\n\n # Tokenizer arguments TODO: eventually tokenizer and models should share the same config\n self.prefix = kwargs.pop(\"prefix\", None)\n self.bos_token_id = kwargs.pop(\"bos_token_id\", None)\n self.pad_token_id = kwargs.pop(\"pad_token_id\", None)\n self.eos_token_id = kwargs.pop(\"eos_token_id\", None)\n self.decoder_start_token_id = kwargs.pop(\"decoder_start_token_id\", None)\n\n # task specific arguments\n self.task_specific_params = kwargs.pop(\"task_specific_params\", None)\n\n # TPU arguments\n self.xla_device = kwargs.pop(\"xla_device\", None)\n\n # Additional attributes without default values\n for key, value in kwargs.items():\n try:\n setattr(self, key, value)\n except AttributeError as err:\n logger.error(\"Can't set {} with value {} for {}\".format(key, value, self))\n raise err\n\n @property\n def num_labels(self):\n return len(self.id2label)\n\n @num_labels.setter\n def num_labels(self, num_labels):\n self.id2label = {i: \"LABEL_{}\".format(i) for i in range(num_labels)}\n self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))\n\n def save_pretrained(self, save_directory):\n \"\"\"\n Save a configuration object to the directory `save_directory`, so that it\n can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.\n\n Args:\n save_directory (:obj:`string`):\n Directory where the configuration JSON file will be saved.\n \"\"\"\n if os.path.isfile(save_directory):\n raise AssertionError(\"Provided path ({}) should be a directory, not a file\".format(save_directory))\n os.makedirs(save_directory, exist_ok=True)\n # If we save using the predefined names, we can load using `from_pretrained`\n output_config_file = os.path.join(save_directory, CONFIG_NAME)\n\n self.to_json_file(output_config_file, use_diff=True)\n logger.info(\"Configuration saved in {}\".format(output_config_file))\n\n @classmethod\n def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> \"PretrainedConfig\":\n r\"\"\"\n\n Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.\n\n Args:\n pretrained_model_name_or_path (:obj:`string`):\n either:\n - a string with the `shortcut name` of a pre-trained model configuration to load from cache or\n download, e.g.: ``bert-base-uncased``.\n - a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to\n our S3, e.g.: ``dbmdz/bert-base-german-cased``.\n - a path to a `directory` containing a configuration file saved using the\n :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.\n - a path or url to a saved configuration JSON `file`, e.g.:\n ``./my_model_directory/configuration.json``.\n cache_dir (:obj:`string`, `optional`):\n Path to a directory in which a downloaded pre-trained model\n configuration should be cached if the standard cache should not be used.\n kwargs (:obj:`Dict[str, any]`, `optional`):\n The values in kwargs of any keys which are configuration attributes will be used to override the loaded\n values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is\n controlled by the `return_unused_kwargs` keyword parameter.\n force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Force to (re-)download the model weights and configuration files and override the cached versions if they exist.\n resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.\n proxies (:obj:`Dict`, `optional`):\n A dictionary of proxy servers to use by protocol or endpoint, e.g.:\n :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`\n The proxies are used on each request.\n return_unused_kwargs: (`optional`) bool:\n If False, then this function returns just the final configuration object.\n If True, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs` is a\n dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part\n of kwargs which has not been used to update `config` and is otherwise ignored.\n\n Returns:\n :class:`PretrainedConfig`: An instance of a configuration object\n\n Examples::\n\n # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a\n # derived class: BertConfig\n config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.\n config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`\n config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')\n config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)\n assert config.output_attention == True\n config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,\n foo=False, return_unused_kwargs=True)\n assert config.output_attention == True\n assert unused_kwargs == {'foo': False}\n\n \"\"\"\n config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)\n return cls.from_dict(config_dict, **kwargs)\n\n @classmethod\n def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict, Dict]:\n \"\"\"\n From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used\n for instantiating a Config using `from_dict`.\n\n Parameters:\n pretrained_model_name_or_path (:obj:`string`):\n The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.\n\n Returns:\n :obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.\n\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n\n if os.path.isdir(pretrained_model_name_or_path):\n config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)\n elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):\n config_file = pretrained_model_name_or_path\n else:\n config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)\n\n try:\n # Load from URL or cache if already cached\n resolved_config_file = cached_path(\n config_file,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n )\n # Load config dict\n if resolved_config_file is None:\n raise EnvironmentError\n config_dict = cls._dict_from_json_file(resolved_config_file)\n\n except EnvironmentError:\n msg = (\n f\"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\\n\\n\"\n f\"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\\n\\n\"\n f\"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\\n\\n\"\n )\n raise EnvironmentError(msg)\n\n except json.JSONDecodeError:\n msg = (\n \"Couldn't reach server at '{}' to download configuration file or \"\n \"configuration file is not a valid JSON file. \"\n \"Please check network or file content here: {}.\".format(config_file, resolved_config_file)\n )\n raise EnvironmentError(msg)\n\n if resolved_config_file == config_file:\n logger.info(\"loading configuration file {}\".format(config_file))\n else:\n logger.info(\"loading configuration file {} from cache at {}\".format(config_file, resolved_config_file))\n\n return config_dict, kwargs\n\n @classmethod\n def from_dict(cls, config_dict: Dict, **kwargs) -> \"PretrainedConfig\":\n \"\"\"\n Constructs a `Config` from a Python dictionary of parameters.\n\n Args:\n config_dict (:obj:`Dict[str, any]`):\n Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved\n from a pre-trained checkpoint by leveraging the :func:`~transformers.PretrainedConfig.get_config_dict`\n method.\n kwargs (:obj:`Dict[str, any]`):\n Additional parameters from which to initialize the configuration object.\n\n Returns:\n :class:`PretrainedConfig`: An instance of a configuration object\n \"\"\"\n return_unused_kwargs = kwargs.pop(\"return_unused_kwargs\", False)\n\n config = cls(**config_dict)\n\n if hasattr(config, \"pruned_heads\"):\n config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())\n\n # Update config with kwargs if needed\n to_remove = []\n for key, value in kwargs.items():\n if hasattr(config, key):\n setattr(config, key, value)\n to_remove.append(key)\n for key in to_remove:\n kwargs.pop(key, None)\n\n logger.info(\"Model config %s\", str(config))\n if return_unused_kwargs:\n return config, kwargs\n else:\n return config\n\n @classmethod\n def from_json_file(cls, json_file: str) -> \"PretrainedConfig\":\n \"\"\"\n Constructs a `Config` from the path to a json file of parameters.\n\n Args:\n json_file (:obj:`string`):\n Path to the JSON file containing the parameters.\n\n Returns:\n :class:`PretrainedConfig`: An instance of a configuration object\n\n \"\"\"\n config_dict = cls._dict_from_json_file(json_file)\n return cls(**config_dict)\n\n @classmethod\n def _dict_from_json_file(cls, json_file: str):\n with open(json_file, \"r\", encoding=\"utf-8\") as reader:\n text = reader.read()\n return json.loads(text)\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n def __repr__(self):\n return \"{} {}\".format(self.__class__.__name__, self.to_json_string())\n\n def to_diff_dict(self):\n \"\"\"\n Removes all attributes from config which correspond to the default\n config attributes for better readability and serializes to a Python\n dictionary.\n\n Returns:\n :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,\n \"\"\"\n config_dict = self.to_dict()\n\n # get the default config dict\n default_config_dict = PretrainedConfig().to_dict()\n\n serializable_config_dict = {}\n\n # only serialize values that differ from the default config\n for key, value in config_dict.items():\n if key not in default_config_dict or value != default_config_dict[key]:\n serializable_config_dict[key] = value\n\n return serializable_config_dict\n\n def to_dict(self):\n \"\"\"\n Serializes this instance to a Python dictionary.\n\n Returns:\n :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,\n \"\"\"\n output = copy.deepcopy(self.__dict__)\n if hasattr(self.__class__, \"model_type\"):\n output[\"model_type\"] = self.__class__.model_type\n return output\n\n def to_json_string(self, use_diff=True):\n \"\"\"\n Serializes this instance to a JSON string.\n\n Args:\n use_diff (:obj:`bool`):\n If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON string.\n\n Returns:\n :obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.\n \"\"\"\n if use_diff is True:\n config_dict = self.to_diff_dict()\n else:\n config_dict = self.to_dict()\n return json.dumps(config_dict, indent=2, sort_keys=True) + \"\\n\"\n\n def to_json_file(self, json_file_path, use_diff=True):\n \"\"\"\n Save this instance to a json file.\n\n Args:\n json_file_path (:obj:`string`):\n Path to the JSON file in which this configuration instance's parameters will be saved.\n use_diff (:obj:`bool`):\n If set to True, only the difference between the config instance and the default PretrainedConfig() is serialized to JSON file.\n \"\"\"\n with open(json_file_path, \"w\", encoding=\"utf-8\") as writer:\n writer.write(self.to_json_string(use_diff=use_diff))\n\n def update(self, config_dict: Dict):\n \"\"\"\n Updates attributes of this class\n with attributes from `config_dict`.\n\n Args:\n :obj:`Dict[str, any]`: Dictionary of attributes that shall be updated for this class.\n \"\"\"\n for key, value in config_dict.items():\n setattr(self, key, value)\n"
},
{
"path": "bert/file_utils.py",
"content": "\"\"\"\nUtilities for working with the local dataset cache.\nThis file is adapted from the AllenNLP library at https://github.com/allenai/allennlp\nCopyright by the AllenNLP authors.\n\"\"\"\n\nimport fnmatch\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport tempfile\nfrom contextlib import contextmanager\nfrom functools import partial, wraps\nfrom hashlib import sha256\nfrom pathlib import Path\nfrom typing import Dict, Optional, Union\nfrom urllib.parse import urlparse\nfrom zipfile import ZipFile, is_zipfile\n\nimport requests\nfrom filelock import FileLock\nfrom tqdm.auto import tqdm\n\n#from . import __version__\n__version__ = \"3.0.2\"\n\nlogger = logging.getLogger(__name__) # pylint: disable=invalid-name\n\ntry:\n USE_TF = os.environ.get(\"USE_TF\", \"AUTO\").upper()\n USE_TORCH = os.environ.get(\"USE_TORCH\", \"AUTO\").upper()\n if USE_TORCH in (\"1\", \"ON\", \"YES\", \"AUTO\") and USE_TF not in (\"1\", \"ON\", \"YES\"):\n import torch\n\n _torch_available = True # pylint: disable=invalid-name\n logger.info(\"PyTorch version {} available.\".format(torch.__version__))\n else:\n logger.info(\"Disabling PyTorch because USE_TF is set\")\n _torch_available = False\nexcept ImportError:\n _torch_available = False # pylint: disable=invalid-name\n\ntry:\n USE_TF = os.environ.get(\"USE_TF\", \"AUTO\").upper()\n USE_TORCH = os.environ.get(\"USE_TORCH\", \"AUTO\").upper()\n\n if USE_TF in (\"1\", \"ON\", \"YES\", \"AUTO\") and USE_TORCH not in (\"1\", \"ON\", \"YES\"):\n import tensorflow as tf\n\n assert hasattr(tf, \"__version__\") and int(tf.__version__[0]) >= 2\n _tf_available = True # pylint: disable=invalid-name\n logger.info(\"TensorFlow version {} available.\".format(tf.__version__))\n else:\n logger.info(\"Disabling Tensorflow because USE_TORCH is set\")\n _tf_available = False\nexcept (ImportError, AssertionError):\n _tf_available = False # pylint: disable=invalid-name\n\n\ntry:\n from torch.hub import _get_torch_home\n\n torch_cache_home = _get_torch_home()\nexcept ImportError:\n torch_cache_home = os.path.expanduser(\n os.getenv(\"TORCH_HOME\", os.path.join(os.getenv(\"XDG_CACHE_HOME\", \"~/.cache\"), \"torch\"))\n )\n\n\ntry:\n import torch_xla.core.xla_model as xm # noqa: F401\n\n if _torch_available:\n _torch_tpu_available = True # pylint: disable=\n else:\n _torch_tpu_available = False\nexcept ImportError:\n _torch_tpu_available = False\n\n\ntry:\n import psutil # noqa: F401\n\n _psutil_available = True\n\nexcept ImportError:\n _psutil_available = False\n\n\ntry:\n import py3nvml # noqa: F401\n\n _py3nvml_available = True\n\nexcept ImportError:\n _py3nvml_available = False\n\n\ntry:\n from apex import amp # noqa: F401\n\n _has_apex = True\nexcept ImportError:\n _has_apex = False\n\ndefault_cache_path = os.path.join(torch_cache_home, \"transformers\")\n\n\nPYTORCH_PRETRAINED_BERT_CACHE = os.getenv(\"PYTORCH_PRETRAINED_BERT_CACHE\", default_cache_path)\nPYTORCH_TRANSFORMERS_CACHE = os.getenv(\"PYTORCH_TRANSFORMERS_CACHE\", PYTORCH_PRETRAINED_BERT_CACHE)\nTRANSFORMERS_CACHE = os.getenv(\"TRANSFORMERS_CACHE\", PYTORCH_TRANSFORMERS_CACHE)\n\nWEIGHTS_NAME = \"pytorch_model.bin\"\nTF2_WEIGHTS_NAME = \"tf_model.h5\"\nTF_WEIGHTS_NAME = \"model.ckpt\"\nCONFIG_NAME = \"config.json\"\nMODEL_CARD_NAME = \"modelcard.json\"\n\n\nMULTIPLE_CHOICE_DUMMY_INPUTS = [[[0], [1]], [[0], [1]]]\nDUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]\nDUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]\n\nS3_BUCKET_PREFIX = \"https://s3.amazonaws.com/models.huggingface.co/bert\"\nCLOUDFRONT_DISTRIB_PREFIX = \"https://cdn.huggingface.co\"\n\n\ndef is_torch_available():\n return _torch_available\n\n\ndef is_tf_available():\n return _tf_available\n\n\ndef is_torch_tpu_available():\n return _torch_tpu_available\n\n\ndef is_psutil_available():\n return _psutil_available\n\n\ndef is_py3nvml_available():\n return _py3nvml_available\n\n\ndef is_apex_available():\n return _has_apex\n\n\ndef add_start_docstrings(*docstr):\n def docstring_decorator(fn):\n fn.__doc__ = \"\".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else \"\")\n return fn\n\n return docstring_decorator\n\n\ndef add_start_docstrings_to_callable(*docstr):\n def docstring_decorator(fn):\n class_name = \":class:`~transformers.{}`\".format(fn.__qualname__.split(\".\")[0])\n intro = \" The {} forward method, overrides the :func:`__call__` special method.\".format(class_name)\n note = r\"\"\"\n\n .. note::\n Although the recipe for forward pass needs to be defined within\n this function, one should call the :class:`Module` instance afterwards\n instead of this since the former takes care of running the\n pre and post processing steps while the latter silently ignores them.\n \"\"\"\n fn.__doc__ = intro + note + \"\".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else \"\")\n return fn\n\n return docstring_decorator\n\n\ndef add_end_docstrings(*docstr):\n def docstring_decorator(fn):\n fn.__doc__ = fn.__doc__ + \"\".join(docstr)\n return fn\n\n return docstring_decorator\n\n\nPT_TOKEN_CLASSIFICATION_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> labels = torch.tensor([1] * inputs[\"input_ids\"].size(1)).unsqueeze(0) # Batch size 1\n\n >>> outputs = model(**inputs, labels=labels)\n >>> loss, scores = outputs[:2]\n\"\"\"\n\nPT_QUESTION_ANSWERING_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> start_positions = torch.tensor([1])\n >>> end_positions = torch.tensor([3])\n\n >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)\n >>> loss, start_scores, end_scores = outputs[:3]\n\"\"\"\n\nPT_SEQUENCE_CLASSIFICATION_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1\n >>> outputs = model(**inputs, labels=labels)\n >>> loss, logits = outputs[:2]\n\"\"\"\n\nPT_MASKED_LM_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> input_ids = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")[\"input_ids\"]\n\n >>> outputs = model(input_ids, labels=input_ids)\n >>> loss, prediction_scores = outputs[:2]\n\"\"\"\n\nPT_BASE_MODEL_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> outputs = model(**inputs)\n\n >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple\n\"\"\"\n\nPT_MULTIPLE_CHOICE_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import torch\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> prompt = \"In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.\"\n >>> choice0 = \"It is eaten with a fork and a knife.\"\n >>> choice1 = \"It is eaten while held in the hand.\"\n >>> labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1\n\n >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)\n >>> outputs = model(**{{k: v.unsqueeze(0) for k,v in encoding.items()}}, labels=labels) # batch size is 1\n\n >>> # the linear classifier still needs to be trained\n >>> loss, logits = outputs[:2]\n\"\"\"\n\nPT_CAUSAL_LM_SAMPLE = r\"\"\"\n Example::\n\n >>> import torch\n >>> from transformers import {tokenizer_class}, {model_class}\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> outputs = model(**inputs, labels=inputs[\"input_ids\"])\n >>> loss, logits = outputs[:2]\n\"\"\"\n\nTF_TOKEN_CLASSIFICATION_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n >>> input_ids = inputs[\"input_ids\"]\n >>> inputs[\"labels\"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1\n\n >>> outputs = model(inputs)\n >>> loss, scores = outputs[:2]\n\"\"\"\n\nTF_QUESTION_ANSWERING_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> question, text = \"Who was Jim Henson?\", \"Jim Henson was a nice puppet\"\n >>> input_dict = tokenizer(question, text, return_tensors='tf')\n >>> start_scores, end_scores = model(input_dict)\n\n >>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict[\"input_ids\"].numpy()[0])\n >>> answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])\n\"\"\"\n\nTF_SEQUENCE_CLASSIFICATION_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n >>> inputs[\"labels\"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1\n\n >>> outputs = model(inputs)\n >>> loss, logits = outputs[:2]\n\"\"\"\n\nTF_MASKED_LM_SAMPLE = r\"\"\"\n Example::\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> input_ids = tf.constant(tokenizer.encode(\"Hello, my dog is cute\", add_special_tokens=True))[None, :] # Batch size 1\n\n >>> outputs = model(input_ids)\n >>> prediction_scores = outputs[0]\n\"\"\"\n\nTF_BASE_MODEL_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n >>> outputs = model(inputs)\n\n >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple\n\"\"\"\n\nTF_MULTIPLE_CHOICE_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> prompt = \"In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.\"\n >>> choice0 = \"It is eaten with a fork and a knife.\"\n >>> choice1 = \"It is eaten while held in the hand.\"\n\n >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='tf', padding=True)\n >>> inputs = {{k: tf.expand_dims(v, 0) for k, v in encoding.items()}}\n >>> outputs = model(inputs) # batch size is 1\n\n >>> # the linear classifier still needs to be trained\n >>> logits = outputs[0]\n\"\"\"\n\nTF_CAUSAL_LM_SAMPLE = r\"\"\"\n Example::\n\n >>> from transformers import {tokenizer_class}, {model_class}\n >>> import tensorflow as tf\n\n >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')\n >>> model = {model_class}.from_pretrained('{checkpoint}')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n >>> outputs = model(inputs)\n >>> logits = outputs[0]\n\"\"\"\n\n\ndef add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None):\n def docstring_decorator(fn):\n model_class = fn.__qualname__.split(\".\")[0]\n is_tf_class = model_class[:2] == \"TF\"\n\n if \"SequenceClassification\" in model_class:\n code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE\n elif \"QuestionAnswering\" in model_class:\n code_sample = TF_QUESTION_ANSWERING_SAMPLE if is_tf_class else PT_QUESTION_ANSWERING_SAMPLE\n elif \"TokenClassification\" in model_class:\n code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE\n elif \"MultipleChoice\" in model_class:\n code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE\n elif \"MaskedLM\" in model_class:\n code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE\n elif \"LMHead\" in model_class:\n code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE\n elif \"Model\" in model_class:\n code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE\n else:\n raise ValueError(f\"Docstring can't be built for model {model_class}\")\n\n built_doc = code_sample.format(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)\n fn.__doc__ = (fn.__doc__ or \"\") + \"\".join(docstr) + built_doc\n return fn\n\n return docstring_decorator\n\n\ndef is_remote_url(url_or_filename):\n parsed = urlparse(url_or_filename)\n return parsed.scheme in (\"http\", \"https\")\n\n\ndef hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:\n \"\"\"\n Resolve a model identifier, and a file name, to a HF-hosted url\n on either S3 or Cloudfront (a Content Delivery Network, or CDN).\n\n Cloudfront is replicated over the globe so downloads are way faster\n for the end user (and it also lowers our bandwidth costs). However, it\n is more aggressively cached by default, so may not always reflect the\n latest changes to the underlying file (default TTL is 24 hours).\n\n In terms of client-side caching from this library, even though\n Cloudfront relays the ETags from S3, using one or the other\n (or switching from one to the other) will affect caching: cached files\n are not shared between the two because the cached file's name contains\n a hash of the url.\n \"\"\"\n endpoint = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX\n legacy_format = \"/\" not in model_id\n if legacy_format:\n return f\"{endpoint}/{model_id}-{filename}\"\n else:\n return f\"{endpoint}/{model_id}/{filename}\"\n\n\ndef url_to_filename(url, etag=None):\n \"\"\"\n Convert `url` into a hashed filename in a repeatable way.\n If `etag` is specified, append its hash to the url's, delimited\n by a period.\n If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name\n so that TF 2.0 can identify it as a HDF5 file\n (see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)\n \"\"\"\n url_bytes = url.encode(\"utf-8\")\n url_hash = sha256(url_bytes)\n filename = url_hash.hexdigest()\n\n if etag:\n etag_bytes = etag.encode(\"utf-8\")\n etag_hash = sha256(etag_bytes)\n filename += \".\" + etag_hash.hexdigest()\n\n if url.endswith(\".h5\"):\n filename += \".h5\"\n\n return filename\n\n\ndef filename_to_url(filename, cache_dir=None):\n \"\"\"\n Return the url and etag (which may be ``None``) stored for `filename`.\n Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.\n \"\"\"\n if cache_dir is None:\n cache_dir = TRANSFORMERS_CACHE\n if isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n\n cache_path = os.path.join(cache_dir, filename)\n if not os.path.exists(cache_path):\n raise EnvironmentError(\"file {} not found\".format(cache_path))\n\n meta_path = cache_path + \".json\"\n if not os.path.exists(meta_path):\n raise EnvironmentError(\"file {} not found\".format(meta_path))\n\n with open(meta_path, encoding=\"utf-8\") as meta_file:\n metadata = json.load(meta_file)\n url = metadata[\"url\"]\n etag = metadata[\"etag\"]\n\n return url, etag\n\n\ndef cached_path(\n url_or_filename,\n cache_dir=None,\n force_download=False,\n proxies=None,\n resume_download=False,\n user_agent: Union[Dict, str, None] = None,\n extract_compressed_file=False,\n force_extract=False,\n local_files_only=False,\n) -> Optional[str]:\n \"\"\"\n Given something that might be a URL (or might be a local path),\n determine which. If it's a URL, download the file and cache it, and\n return the path to the cached file. If it's already a local path,\n make sure the file exists and then return the path.\n Args:\n cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).\n force_download: if True, re-dowload the file even if it's already cached in the cache dir.\n resume_download: if True, resume the download if incompletly recieved file is found.\n user_agent: Optional string or dict that will be appended to the user-agent on remote requests.\n extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed\n file in a folder along the archive.\n force_extract: if True when extract_compressed_file is True and the archive was already extracted,\n re-extract the archive and overide the folder where it was extracted.\n\n Return:\n None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).\n Local path (string) otherwise\n \"\"\"\n if cache_dir is None:\n cache_dir = TRANSFORMERS_CACHE\n if isinstance(url_or_filename, Path):\n url_or_filename = str(url_or_filename)\n if isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n\n if is_remote_url(url_or_filename):\n # URL, so get it from the cache (downloading if necessary)\n output_path = get_from_cache(\n url_or_filename,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n user_agent=user_agent,\n local_files_only=local_files_only,\n )\n elif os.path.exists(url_or_filename):\n # File, and it exists.\n output_path = url_or_filename\n elif urlparse(url_or_filename).scheme == \"\":\n # File, but it doesn't exist.\n raise EnvironmentError(\"file {} not found\".format(url_or_filename))\n else:\n # Something unknown\n raise ValueError(\"unable to parse {} as a URL or as a local path\".format(url_or_filename))\n\n if extract_compressed_file:\n if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\n return output_path\n\n # Path where we extract compressed archives\n # We avoid '.' in dir name and add \"-extracted\" at the end: \"./model.zip\" => \"./model-zip-extracted/\"\n output_dir, output_file = os.path.split(output_path)\n output_extract_dir_name = output_file.replace(\".\", \"-\") + \"-extracted\"\n output_path_extracted = os.path.join(output_dir, output_extract_dir_name)\n\n if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:\n return output_path_extracted\n\n # Prevent parallel extractions\n lock_path = output_path + \".lock\"\n with FileLock(lock_path):\n shutil.rmtree(output_path_extracted, ignore_errors=True)\n os.makedirs(output_path_extracted)\n if is_zipfile(output_path):\n with ZipFile(output_path, \"r\") as zip_file:\n zip_file.extractall(output_path_extracted)\n zip_file.close()\n elif tarfile.is_tarfile(output_path):\n tar_file = tarfile.open(output_path)\n tar_file.extractall(output_path_extracted)\n tar_file.close()\n else:\n raise EnvironmentError(\"Archive format of {} could not be identified\".format(output_path))\n\n return output_path_extracted\n\n return output_path\n\n\ndef http_get(url, temp_file, proxies=None, resume_size=0, user_agent: Union[Dict, str, None] = None):\n ua = \"transformers/{}; python/{}\".format(__version__, sys.version.split()[0])\n if is_torch_available():\n ua += \"; torch/{}\".format(torch.__version__)\n if is_tf_available():\n ua += \"; tensorflow/{}\".format(tf.__version__)\n if isinstance(user_agent, dict):\n ua += \"; \" + \"; \".join(\"{}/{}\".format(k, v) for k, v in user_agent.items())\n elif isinstance(user_agent, str):\n ua += \"; \" + user_agent\n headers = {\"user-agent\": ua}\n if resume_size > 0:\n headers[\"Range\"] = \"bytes=%d-\" % (resume_size,)\n response = requests.get(url, stream=True, proxies=proxies, headers=headers)\n if response.status_code == 416: # Range not satisfiable\n return\n content_length = response.headers.get(\"Content-Length\")\n total = resume_size + int(content_length) if content_length is not None else None\n progress = tqdm(\n unit=\"B\",\n unit_scale=True,\n total=total,\n initial=resume_size,\n desc=\"Downloading\",\n disable=bool(logger.getEffectiveLevel() == logging.NOTSET),\n )\n for chunk in response.iter_content(chunk_size=1024):\n if chunk: # filter out keep-alive new chunks\n progress.update(len(chunk))\n temp_file.write(chunk)\n progress.close()\n\n\ndef get_from_cache(\n url,\n cache_dir=None,\n force_download=False,\n proxies=None,\n etag_timeout=10,\n resume_download=False,\n user_agent: Union[Dict, str, None] = None,\n local_files_only=False,\n) -> Optional[str]:\n \"\"\"\n Given a URL, look for the corresponding file in the local cache.\n If it's not there, download it. Then return the path to the cached file.\n\n Return:\n None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).\n Local path (string) otherwise\n \"\"\"\n if cache_dir is None:\n cache_dir = TRANSFORMERS_CACHE\n if isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n\n os.makedirs(cache_dir, exist_ok=True)\n\n etag = None\n if not local_files_only:\n try:\n response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)\n if response.status_code == 200:\n etag = response.headers.get(\"ETag\")\n except (EnvironmentError, requests.exceptions.Timeout):\n # etag is already None\n pass\n\n filename = url_to_filename(url, etag)\n\n # get cache path to put the file\n cache_path = os.path.join(cache_dir, filename)\n\n # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.\n # try to get the last downloaded one\n if etag is None:\n if os.path.exists(cache_path):\n return cache_path\n else:\n matching_files = [\n file\n for file in fnmatch.filter(os.listdir(cache_dir), filename + \".*\")\n if not file.endswith(\".json\") and not file.endswith(\".lock\")\n ]\n if len(matching_files) > 0:\n return os.path.join(cache_dir, matching_files[-1])\n else:\n # If files cannot be found and local_files_only=True,\n # the models might've been found if local_files_only=False\n # Notify the user about that\n if local_files_only:\n raise ValueError(\n \"Cannot find the requested files in the cached path and outgoing traffic has been\"\n \" disabled. To enable model look-ups and downloads online, set 'local_files_only'\"\n \" to False.\"\n )\n return None\n\n # From now on, etag is not None.\n if os.path.exists(cache_path) and not force_download:\n return cache_path\n\n # Prevent parallel downloads of the same file with a lock.\n lock_path = cache_path + \".lock\"\n with FileLock(lock_path):\n\n # If the download just completed while the lock was activated.\n if os.path.exists(cache_path) and not force_download:\n # Even if returning early like here, the lock will be released.\n return cache_path\n\n if resume_download:\n incomplete_path = cache_path + \".incomplete\"\n\n @contextmanager\n def _resumable_file_manager():\n with open(incomplete_path, \"a+b\") as f:\n yield f\n\n temp_file_manager = _resumable_file_manager\n if os.path.exists(incomplete_path):\n resume_size = os.stat(incomplete_path).st_size\n else:\n resume_size = 0\n else:\n temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)\n resume_size = 0\n\n # Download to temporary file, then copy to cache dir once finished.\n # Otherwise you get corrupt cache entries if the download gets interrupted.\n with temp_file_manager() as temp_file:\n logger.info(\"%s not found in cache or force_download set to True, downloading to %s\", url, temp_file.name)\n\n http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)\n\n logger.info(\"storing %s in cache at %s\", url, cache_path)\n os.replace(temp_file.name, cache_path)\n\n logger.info(\"creating metadata file for %s\", cache_path)\n meta = {\"url\": url, \"etag\": etag}\n meta_path = cache_path + \".json\"\n with open(meta_path, \"w\") as meta_file:\n json.dump(meta, meta_file)\n\n return cache_path\n\n\nclass cached_property(property):\n \"\"\"\n Descriptor that mimics @property but caches output in member variable.\n\n From tensorflow_datasets\n\n Built-in in functools from Python 3.8.\n \"\"\"\n\n def __get__(self, obj, objtype=None):\n # See docs.python.org/3/howto/descriptor.html#properties\n if obj is None:\n return self\n if self.fget is None:\n raise AttributeError(\"unreadable attribute\")\n attr = \"__cached_\" + self.fget.__name__\n cached = getattr(obj, attr, None)\n if cached is None:\n cached = self.fget(obj)\n setattr(obj, attr, cached)\n return cached\n\n\ndef torch_required(func):\n # Chose a different decorator name than in tests so it's clear they are not the same.\n @wraps(func)\n def wrapper(*args, **kwargs):\n if is_torch_available():\n return func(*args, **kwargs)\n else:\n raise ImportError(f\"Method `{func.__name__}` requires PyTorch.\")\n\n return wrapper\n\n\ndef tf_required(func):\n # Chose a different decorator name than in tests so it's clear they are not the same.\n @wraps(func)\n def wrapper(*args, **kwargs):\n if is_tf_available():\n return func(*args, **kwargs)\n else:\n raise ImportError(f\"Method `{func.__name__}` requires TF.\")\n\n return wrapper\n"
},
{
"path": "bert/generation_utils.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nfrom typing import Iterable, Optional, Tuple\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn import functional as F\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass GenerationMixin:\n \"\"\"\n A class contraining all of the functions supporting generation, to be used as a mixin in PreTrainedModel.\n \"\"\"\n\n def prepare_inputs_for_generation(self, input_ids, **kwargs):\n return {\"input_ids\": input_ids}\n\n def adjust_logits_during_generation(self, logits, **kwargs):\n return logits\n\n def _use_cache(self, outputs, use_cache):\n \"\"\"During generation, decide whether to pass the `past` variable to the next forward pass.\"\"\"\n if len(outputs) <= 1 or use_cache is False:\n return False\n if hasattr(self.config, \"mem_len\") and self.config.mem_len == 0:\n return False\n return True\n\n def enforce_repetition_penalty_(self, lprobs, batch_size, num_beams, prev_output_tokens, repetition_penalty):\n \"\"\"repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). \"\"\"\n for i in range(batch_size * num_beams):\n for previous_token in set(prev_output_tokens[i].tolist()):\n # if score < 0 then repetition penalty has to multiplied to reduce the previous token probability\n if lprobs[i, previous_token] < 0:\n lprobs[i, previous_token] *= repetition_penalty\n else:\n lprobs[i, previous_token] /= repetition_penalty\n\n def postprocess_next_token_scores(\n self,\n scores,\n input_ids,\n no_repeat_ngram_size,\n bad_words_ids,\n cur_len,\n min_length,\n max_length,\n eos_token_id,\n repetition_penalty,\n batch_size,\n num_beams,\n ):\n # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)\n if repetition_penalty != 1.0:\n self.enforce_repetition_penalty_(\n scores, batch_size, num_beams, input_ids, repetition_penalty,\n )\n\n # set eos token prob to zero if min_length is not reached\n if eos_token_id is not None and cur_len < min_length:\n scores[:, eos_token_id] = -float(\"inf\")\n\n if no_repeat_ngram_size > 0:\n # calculate a list of banned tokens to prevent repetitively generating the same ngrams\n num_batch_hypotheses = batch_size * num_beams\n # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345\n banned_batch_tokens = calc_banned_ngram_tokens(\n input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len\n )\n for i, banned_tokens in enumerate(banned_batch_tokens):\n scores[i, banned_tokens] = -float(\"inf\")\n\n if bad_words_ids is not None:\n # calculate a list of banned tokens according to bad words\n banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)\n\n for i, banned_tokens in enumerate(banned_tokens):\n scores[i, banned_tokens] = -float(\"inf\")\n\n return scores\n\n @torch.no_grad()\n def generate(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n max_length: Optional[int] = None,\n min_length: Optional[int] = None,\n do_sample: Optional[bool] = None,\n early_stopping: Optional[bool] = None,\n num_beams: Optional[int] = None,\n temperature: Optional[float] = None,\n top_k: Optional[int] = None,\n top_p: Optional[float] = None,\n repetition_penalty: Optional[float] = None,\n bad_words_ids: Optional[Iterable[int]] = None,\n bos_token_id: Optional[int] = None,\n pad_token_id: Optional[int] = None,\n eos_token_id: Optional[int] = None,\n length_penalty: Optional[float] = None,\n no_repeat_ngram_size: Optional[int] = None,\n num_return_sequences: Optional[int] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n decoder_start_token_id: Optional[int] = None,\n use_cache: Optional[bool] = None,\n **model_specific_kwargs\n ) -> torch.LongTensor:\n r\"\"\" Generates sequences for models with a LM head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.\n\n Adapted in part from `Facebook's XLM beam search code`_.\n\n .. _`Facebook's XLM beam search code`:\n https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529\n\n\n Parameters:\n\n input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)`\n The sequence used as a prompt for the generation. If `None` the method initializes\n it as an empty `torch.LongTensor` of shape `(1,)`.\n\n max_length: (`optional`) int\n The max length of the sequence to be generated. Between `min_length` and infinity. Default to 20.\n\n min_length: (`optional`) int\n The min length of the sequence to be generated. Between 0 and infinity. Default to 0.\n\n do_sample: (`optional`) bool\n If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.\n\n early_stopping: (`optional`) bool\n if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.\n\n num_beams: (`optional`) int\n Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.\n\n temperature: (`optional`) float\n The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.\n\n top_k: (`optional`) int\n The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.\n\n top_p: (`optional`) float\n The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.\n\n repetition_penalty: (`optional`) float\n The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.\n\n pad_token_id: (`optional`) int\n Padding token. Default to specicic model pad_token_id or None if it does not exist.\n\n bos_token_id: (`optional`) int\n BOS token. Defaults to `bos_token_id` as defined in the models config.\n\n eos_token_id: (`optional`) int\n EOS token. Defaults to `eos_token_id` as defined in the models config.\n\n length_penalty: (`optional`) float\n Exponential penalty to the length. Default to 1.\n\n no_repeat_ngram_size: (`optional`) int\n If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.\n bad_words_ids: (`optional`) list of lists of int\n `bad_words_ids` contains tokens that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.\n\n num_return_sequences: (`optional`) int\n The number of independently computed returned sequences for each element in the batch. Default to 1.\n\n attention_mask (`optional`) obj: `torch.LongTensor` of same shape as `input_ids`\n Mask to avoid performing attention on padding token indices.\n Mask values selected in ``[0, 1]``:\n ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.\n Defaults to `None`.\n\n `What are attention masks? <../glossary.html#attention-mask>`__\n\n decoder_start_token_id=None: (`optional`) int\n If an encoder-decoder model starts decoding with a different token than BOS.\n Defaults to `None` and is changed to `BOS` later.\n\n use_cache: (`optional`) bool\n If `use_cache` is True, past key values are used to speed up decoding if applicable to model. Defaults to `True`.\n\n model_specific_kwargs: (`optional`) dict\n Additional model specific kwargs will be forwarded to the `forward` function of the model.\n\n Return:\n\n output: `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`\n sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`\n\n Examples::\n\n tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer\n model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.\n outputs = model.generate(max_length=40) # do greedy decoding\n print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))\n\n tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer\n model = AutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache.\n input_context = 'The dog'\n input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context\n outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'\n for i in range(3): # 3 output sequences were generated\n print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))\n\n tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer\n model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.\n input_context = 'The dog'\n input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context\n outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3) # 3 generate sequences using by sampling\n for i in range(3): # 3 output sequences were generated\n print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))\n\n tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer\n model = AutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache.\n input_context = 'Legal My neighbor is' # \"Legal\" is one of the control codes for ctrl\n input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context\n outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences\n print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))\n\n tokenizer = AutoTokenizer.from_pretrained('gpt2') # Initialize tokenizer\n model = AutoModelWithLMHead.from_pretrained('gpt2') # Download model and configuration from S3 and cache.\n input_context = 'My cute dog' # \"Legal\" is one of the control codes for ctrl\n bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]\n input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context\n outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids) # generate sequences without allowing bad_words to be generated\n \"\"\"\n\n # We cannot generate if the model does not have a LM head\n if self.get_output_embeddings() is None:\n raise AttributeError(\n \"You tried to generate sequences with a model that does not have a LM Head.\"\n \"Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )\"\n )\n\n max_length = max_length if max_length is not None else self.config.max_length\n min_length = min_length if min_length is not None else self.config.min_length\n do_sample = do_sample if do_sample is not None else self.config.do_sample\n early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n num_beams = num_beams if num_beams is not None else self.config.num_beams\n temperature = temperature if temperature is not None else self.config.temperature\n top_k = top_k if top_k is not None else self.config.top_k\n top_p = top_p if top_p is not None else self.config.top_p\n repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty\n bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id\n pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id\n eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id\n length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty\n no_repeat_ngram_size = (\n no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size\n )\n bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids\n num_return_sequences = (\n num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences\n )\n decoder_start_token_id = (\n decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id\n )\n\n if input_ids is not None:\n batch_size = input_ids.shape[0] # overriden by the input batch_size\n else:\n batch_size = 1\n\n assert isinstance(max_length, int) and max_length > 0, \"`max_length` should be a strictly positive integer.\"\n assert isinstance(min_length, int) and min_length >= 0, \"`min_length` should be a positive integer.\"\n assert isinstance(do_sample, bool), \"`do_sample` should be a boolean.\"\n assert isinstance(early_stopping, bool), \"`early_stopping` should be a boolean.\"\n assert isinstance(use_cache, bool), \"`use_cache` should be a boolean.\"\n assert isinstance(num_beams, int) and num_beams > 0, \"`num_beams` should be a strictly positive integer.\"\n assert temperature > 0, \"`temperature` should be strictly positive.\"\n assert isinstance(top_k, int) and top_k >= 0, \"`top_k` should be a positive integer.\"\n assert 0 <= top_p <= 1, \"`top_p` should be between 0 and 1.\"\n assert repetition_penalty >= 1.0, \"`repetition_penalty` should be >= 1.\"\n assert input_ids is not None or (\n isinstance(bos_token_id, int) and bos_token_id >= 0\n ), \"If input_ids is not defined, `bos_token_id` should be a positive integer.\"\n assert pad_token_id is None or (\n isinstance(pad_token_id, int) and (pad_token_id >= 0)\n ), \"`pad_token_id` should be a positive integer.\"\n assert (eos_token_id is None) or (\n isinstance(eos_token_id, int) and (eos_token_id >= 0)\n ), \"`eos_token_id` should be a positive integer.\"\n assert length_penalty > 0, \"`length_penalty` should be strictly positive.\"\n assert (\n isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0\n ), \"`no_repeat_ngram_size` should be a positive integer.\"\n assert (\n isinstance(num_return_sequences, int) and num_return_sequences > 0\n ), \"`num_return_sequences` should be a strictly positive integer.\"\n assert (\n bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)\n ), \"`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated\"\n\n if input_ids is None:\n assert isinstance(bos_token_id, int) and bos_token_id >= 0, (\n \"you should either supply a context to complete as `input_ids` input \"\n \"or a `bos_token_id` (integer >= 0) as a first token to start the generation.\"\n )\n input_ids = torch.full(\n (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,\n )\n else:\n assert input_ids.dim() == 2, \"Input prompt should be of shape (batch_size, sequence length).\"\n\n # not allow to duplicate outputs when greedy decoding\n if do_sample is False:\n if num_beams == 1:\n # no_beam_search greedy generation conditions\n assert (\n num_return_sequences == 1\n ), \"Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1\"\n\n else:\n # beam_search greedy generation conditions\n assert (\n num_beams >= num_return_sequences\n ), \"Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences\"\n\n # create attention mask if necessary\n # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140\n if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):\n attention_mask = input_ids.ne(pad_token_id).long()\n elif attention_mask is None:\n attention_mask = input_ids.new_ones(input_ids.shape)\n\n # set pad_token_id to eos_token_id if not set. Important that this is done after\n # attention_mask is created\n if pad_token_id is None and eos_token_id is not None:\n logger.warning(\n \"Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence\".format(eos_token_id)\n )\n pad_token_id = eos_token_id\n\n # current position and vocab size\n if hasattr(self.config, \"vocab_size\"):\n vocab_size = self.config.vocab_size\n elif (\n self.config.is_encoder_decoder\n and hasattr(self.config, \"decoder\")\n and hasattr(self.config.decoder, \"vocab_size\")\n ):\n vocab_size = self.config.decoder.vocab_size\n\n # set effective batch size and effective batch multiplier according to do_sample\n if do_sample:\n effective_batch_size = batch_size * num_return_sequences\n effective_batch_mult = num_return_sequences\n else:\n effective_batch_size = batch_size\n effective_batch_mult = 1\n\n if self.config.is_encoder_decoder:\n if decoder_start_token_id is None:\n decoder_start_token_id = bos_token_id\n\n assert (\n decoder_start_token_id is not None\n ), \"decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation\"\n assert hasattr(self, \"get_encoder\"), \"{} should have a 'get_encoder' function defined\".format(self)\n assert callable(self.get_encoder), \"{} should be a method\".format(self.get_encoder)\n\n # get encoder and store encoder outputs\n encoder = self.get_encoder()\n\n encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask)\n\n # Expand input ids if num_beams > 1 or num_return_sequences > 1\n if num_return_sequences > 1 or num_beams > 1:\n input_ids_len = input_ids.shape[-1]\n input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)\n attention_mask = attention_mask.unsqueeze(1).expand(\n batch_size, effective_batch_mult * num_beams, input_ids_len\n )\n\n input_ids = input_ids.contiguous().view(\n effective_batch_size * num_beams, input_ids_len\n ) # shape: (batch_size * num_return_sequences * num_beams, cur_len)\n attention_mask = attention_mask.contiguous().view(\n effective_batch_size * num_beams, input_ids_len\n ) # shape: (batch_size * num_return_sequences * num_beams, cur_len)\n\n if self.config.is_encoder_decoder:\n # create empty decoder_input_ids\n input_ids = torch.full(\n (effective_batch_size * num_beams, 1),\n decoder_start_token_id,\n dtype=torch.long,\n device=next(self.parameters()).device,\n )\n cur_len = 1\n\n assert (\n batch_size == encoder_outputs[0].shape[0]\n ), f\"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} \"\n\n # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)\n expanded_batch_idxs = (\n torch.arange(batch_size)\n .view(-1, 1)\n .repeat(1, num_beams * effective_batch_mult)\n .view(-1)\n .to(input_ids.device)\n )\n # expand encoder_outputs\n encoder_outputs = (encoder_outputs[0].index_select(0, expanded_batch_idxs), *encoder_outputs[1:])\n\n else:\n encoder_outputs = None\n cur_len = input_ids.shape[-1]\n\n assert (\n cur_len < max_length\n ), f\"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`\"\n\n if num_beams > 1:\n output = self._generate_beam_search(\n input_ids,\n cur_len=cur_len,\n max_length=max_length,\n min_length=min_length,\n do_sample=do_sample,\n early_stopping=early_stopping,\n temperature=temperature,\n top_k=top_k,\n top_p=top_p,\n repetition_penalty=repetition_penalty,\n no_repeat_ngram_size=no_repeat_ngram_size,\n bad_words_ids=bad_words_ids,\n pad_token_id=pad_token_id,\n eos_token_id=eos_token_id,\n batch_size=effective_batch_size,\n num_return_sequences=num_return_sequences,\n length_penalty=length_penalty,\n num_beams=num_beams,\n vocab_size=vocab_size,\n encoder_outputs=encoder_outputs,\n attention_mask=attention_mask,\n use_cache=use_cache,\n model_specific_kwargs=model_specific_kwargs,\n )\n else:\n output = self._generate_no_beam_search(\n input_ids,\n cur_len=cur_len,\n max_length=max_length,\n min_length=min_length,\n do_sample=do_sample,\n temperature=temperature,\n top_k=top_k,\n top_p=top_p,\n repetition_penalty=repetition_penalty,\n no_repeat_ngram_size=no_repeat_ngram_size,\n bad_words_ids=bad_words_ids,\n pad_token_id=pad_token_id,\n eos_token_id=eos_token_id,\n batch_size=effective_batch_size,\n encoder_outputs=encoder_outputs,\n attention_mask=attention_mask,\n use_cache=use_cache,\n model_specific_kwargs=model_specific_kwargs,\n )\n\n return output\n\n def _generate_no_beam_search(\n self,\n input_ids,\n cur_len,\n max_length,\n min_length,\n do_sample,\n temperature,\n top_k,\n top_p,\n repetition_penalty,\n no_repeat_ngram_size,\n bad_words_ids,\n pad_token_id,\n eos_token_id,\n batch_size,\n encoder_outputs,\n attention_mask,\n use_cache,\n model_specific_kwargs,\n ):\n \"\"\" Generate sequences for each example without beam search (num_beams == 1).\n All returned sequence are generated independantly.\n \"\"\"\n # length of generated sentences / unfinished sentences\n unfinished_sents = input_ids.new(batch_size).fill_(1)\n sent_lengths = input_ids.new(batch_size).fill_(max_length)\n\n past = (encoder_outputs, None) if encoder_outputs is not None else None\n\n while cur_len < max_length:\n model_inputs = self.prepare_inputs_for_generation(\n input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs\n )\n\n outputs = self(**model_inputs)\n next_token_logits = outputs[0][:, -1, :]\n\n scores = self.postprocess_next_token_scores(\n scores=next_token_logits,\n input_ids=input_ids,\n no_repeat_ngram_size=no_repeat_ngram_size,\n bad_words_ids=bad_words_ids,\n cur_len=cur_len,\n min_length=min_length,\n max_length=max_length,\n eos_token_id=eos_token_id,\n repetition_penalty=repetition_penalty,\n batch_size=batch_size,\n num_beams=1,\n )\n\n # if model has past, then set the past variable to speed up decoding\n if self._use_cache(outputs, use_cache):\n past = outputs[1]\n\n if do_sample:\n # Temperature (higher temperature => more likely to sample low probability tokens)\n if temperature != 1.0:\n scores = scores / temperature\n # Top-p/top-k filtering\n next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p)\n # Sample\n probs = F.softmax(next_token_logscores, dim=-1)\n next_token = torch.multinomial(probs, num_samples=1).squeeze(1)\n else:\n # Greedy decoding\n next_token = torch.argmax(next_token_logits, dim=-1)\n\n # update generations and finished sentences\n if eos_token_id is not None:\n # pad finished sentences if eos_token_id exist\n tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)\n else:\n tokens_to_add = next_token\n\n # add token and increase length by one\n input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)\n cur_len = cur_len + 1\n\n if eos_token_id is not None:\n eos_in_sents = tokens_to_add == eos_token_id\n # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length\n is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()\n sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)\n # unfinished_sents is set to zero if eos in sentence\n unfinished_sents.mul_((~eos_in_sents).long())\n\n # stop when there is a in each sentence, or if we exceed the maximul length\n if unfinished_sents.max() == 0:\n break\n\n # extend attention_mask for new generated input if only decoder\n if self.config.is_encoder_decoder is False:\n attention_mask = torch.cat(\n [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1\n )\n\n return input_ids\n\n def _generate_beam_search(\n self,\n input_ids,\n cur_len,\n max_length,\n min_length,\n do_sample,\n early_stopping,\n temperature,\n top_k,\n top_p,\n repetition_penalty,\n no_repeat_ngram_size,\n bad_words_ids,\n pad_token_id,\n eos_token_id,\n batch_size,\n num_return_sequences,\n length_penalty,\n num_beams,\n vocab_size,\n encoder_outputs,\n attention_mask,\n use_cache,\n model_specific_kwargs,\n ):\n \"\"\" Generate sequences for each example with beam search.\n \"\"\"\n\n # generated hypotheses\n generated_hyps = [\n BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)\n for _ in range(batch_size)\n ]\n\n # scores for each sentence in the beam\n beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)\n\n # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times\n if do_sample is False:\n beam_scores[:, 1:] = -1e9\n beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,)\n\n # cache compute states\n past = (encoder_outputs, None) if encoder_outputs is not None else None\n\n # done sentences\n done = [False for _ in range(batch_size)]\n\n while cur_len < max_length:\n model_inputs = self.prepare_inputs_for_generation(\n input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs\n )\n outputs = self(**model_inputs) # (batch_size * num_beams, cur_len, vocab_size)\n next_token_logits = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size)\n\n # if model has past, then set the past variable to speed up decoding\n if self._use_cache(outputs, use_cache):\n past = outputs[1]\n if self.config.is_encoder_decoder and do_sample is False:\n # TODO (PVP) still a bit hacky here - there might be a better solution\n next_token_logits = self.adjust_logits_during_generation(\n next_token_logits, cur_len=cur_len, max_length=max_length\n )\n\n scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)\n\n scores = self.postprocess_next_token_scores(\n scores=scores,\n input_ids=input_ids,\n no_repeat_ngram_size=no_repeat_ngram_size,\n bad_words_ids=bad_words_ids,\n cur_len=cur_len,\n min_length=min_length,\n max_length=max_length,\n eos_token_id=eos_token_id,\n repetition_penalty=repetition_penalty,\n batch_size=batch_size,\n num_beams=num_beams,\n )\n\n assert scores.shape == (batch_size * num_beams, vocab_size), \"Shapes of scores: {} != {}\".format(\n scores.shape, (batch_size * num_beams, vocab_size)\n )\n\n if do_sample:\n _scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size)\n # Temperature\n if temperature != 1.0:\n _scores = _scores / temperature\n # Top-p/top-k filtering\n _scores = top_k_top_p_filtering(\n _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2\n ) # (batch_size * num_beams, vocab_size)\n # re-organize to group the beam together to sample from all beam_idxs\n _scores = _scores.contiguous().view(\n batch_size, num_beams * vocab_size\n ) # (batch_size, num_beams * vocab_size)\n\n # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)\n probs = F.softmax(_scores, dim=-1)\n next_tokens = torch.multinomial(probs, num_samples=2 * num_beams) # (batch_size, num_beams * 2)\n # Compute next scores\n next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2)\n # sort the sampled vector to make sure that the first num_beams samples are the best\n next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)\n next_tokens = torch.gather(next_tokens, -1, next_scores_indices) # (batch_size, num_beams * 2)\n\n else:\n next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size)\n\n # re-organize to group the beam together (we are keeping top hypothesis accross beams)\n next_scores = next_scores.view(\n batch_size, num_beams * vocab_size\n ) # (batch_size, num_beams * vocab_size)\n\n next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True)\n\n assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)\n\n # next batch beam content\n next_batch_beam = []\n\n # for each sentence\n for batch_idx in range(batch_size):\n\n # if we are done with this sentence, add a pad token\n if done[batch_idx]:\n assert (\n len(generated_hyps[batch_idx]) >= num_beams\n ), \"Batch can only be done if at least {} beams have been generated\".format(num_beams)\n assert (\n eos_token_id is not None and pad_token_id is not None\n ), \"generated beams >= num_beams -> eos_token_id and pad_token have to be defined\"\n next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch\n continue\n\n # next sentence beam content, this will get added to next_batch_beam\n next_sent_beam = []\n\n # next tokens for this sentence\n for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(\n zip(next_tokens[batch_idx], next_scores[batch_idx])\n ):\n # get beam and token IDs\n beam_id = beam_token_id // vocab_size\n token_id = beam_token_id % vocab_size\n\n effective_beam_id = batch_idx * num_beams + beam_id\n # add to generated hypotheses if end of sentence\n if (eos_token_id is not None) and (token_id.item() == eos_token_id):\n # if beam_token does not belong to top num_beams tokens, it should not be added\n is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams\n if is_beam_token_worse_than_top_num_beams:\n continue\n generated_hyps[batch_idx].add(\n input_ids[effective_beam_id].clone(), beam_token_score.item(),\n )\n else:\n # add next predicted token since it is not eos_token\n next_sent_beam.append((beam_token_score, token_id, effective_beam_id))\n\n # once the beam for next step is full, don't add more tokens to it.\n if len(next_sent_beam) == num_beams:\n break\n\n # Check if we are done so that we can save a pad step if all(done)\n done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(\n next_scores[batch_idx].max().item(), cur_len\n )\n\n # update next beam content\n assert len(next_sent_beam) == num_beams, \"Beam should always be full\"\n next_batch_beam.extend(next_sent_beam)\n assert len(next_batch_beam) == num_beams * (batch_idx + 1), \"We should have added num_beams each step\"\n\n # stop when we are done with each sentence\n if all(done):\n break\n\n # sanity check / prepare next batch\n assert len(next_batch_beam) == batch_size * num_beams\n beam_scores = beam_scores.new([x[0] for x in next_batch_beam])\n beam_tokens = input_ids.new([x[1] for x in next_batch_beam])\n beam_idx = input_ids.new([x[2] for x in next_batch_beam])\n\n # re-order batch and update current length\n input_ids = input_ids[beam_idx, :]\n input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)\n cur_len = cur_len + 1\n\n # re-order internal states\n if past is not None:\n past = self._reorder_cache(past, beam_idx)\n\n # extend attention_mask for new generated input if only decoder\n if self.config.is_encoder_decoder is False:\n attention_mask = torch.cat(\n [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1\n )\n\n # finalize all open beam hypotheses and add to generated hypotheses\n for batch_idx in range(batch_size):\n if done[batch_idx]:\n continue\n\n # test that beam scores match previously calculated scores if not eos and batch_idx not done\n if eos_token_id is not None and all(\n (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx]\n ):\n assert torch.all(\n next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]\n ), \"If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}\".format(\n next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],\n )\n\n # need to add best num_beams hypotheses to generated hyps\n for beam_id in range(num_beams):\n effective_beam_id = batch_idx * num_beams + beam_id\n final_score = beam_scores[effective_beam_id].item()\n final_tokens = input_ids[effective_beam_id]\n generated_hyps[batch_idx].add(final_tokens, final_score)\n\n # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch\n output_batch_size = batch_size if do_sample else batch_size * num_return_sequences\n output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences\n\n # select the best hypotheses\n sent_lengths = input_ids.new(output_batch_size)\n best = []\n\n # retrieve best hypotheses\n for i, hypotheses in enumerate(generated_hyps):\n sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])\n for j in range(output_num_return_sequences_per_batch):\n effective_batch_idx = output_num_return_sequences_per_batch * i + j\n best_hyp = sorted_hyps.pop()[1]\n sent_lengths[effective_batch_idx] = len(best_hyp)\n best.append(best_hyp)\n\n # shorter batches are padded\n if sent_lengths.min().item() != sent_lengths.max().item():\n assert pad_token_id is not None, \"`Pad_token_id` has to be defined\"\n sent_max_len = min(sent_lengths.max().item() + 1, max_length)\n decoded = input_ids.new(output_batch_size, sent_max_len).fill_(pad_token_id)\n\n # fill with hypothesis and eos_token_id if necessary\n for i, hypo in enumerate(best):\n decoded[i, : sent_lengths[i]] = hypo\n if sent_lengths[i] < max_length:\n decoded[i, sent_lengths[i]] = eos_token_id\n else:\n # none of the hypotheses have an eos_token\n assert (len(hypo) == max_length for hypo in best)\n decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)\n\n return decoded\n\n @staticmethod\n def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:\n return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)\n\n\ndef calc_banned_ngram_tokens(prev_input_ids: Tensor, num_hypos: int, no_repeat_ngram_size: int, cur_len: int) -> None:\n \"\"\"Copied from fairseq for no_repeat_ngram in beam_search\"\"\"\n if cur_len + 1 < no_repeat_ngram_size:\n # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet\n return [[] for _ in range(num_hypos)]\n generated_ngrams = [{} for _ in range(num_hypos)]\n for idx in range(num_hypos):\n gen_tokens = prev_input_ids[idx].tolist()\n generated_ngram = generated_ngrams[idx]\n for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):\n prev_ngram_tuple = tuple(ngram[:-1])\n generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]\n\n def _get_generated_ngrams(hypo_idx):\n # Before decoding the next token, prevent decoding of ngrams that have already appeared\n start_idx = cur_len + 1 - no_repeat_ngram_size\n ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())\n return generated_ngrams[hypo_idx].get(ngram_idx, [])\n\n banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]\n return banned_tokens\n\n\ndef calc_banned_bad_words_ids(prev_input_ids: Iterable[int], bad_words_ids: Iterable[int]) -> Iterable[int]:\n banned_tokens = []\n\n def _tokens_match(prev_tokens, tokens):\n if len(tokens) == 0:\n # if bad word tokens is just one token always ban it\n return True\n if len(tokens) > len(prev_input_ids):\n # if bad word tokens are longer then prev input_ids they can't be equal\n return False\n\n if prev_tokens[-len(tokens) :] == tokens:\n # if tokens match\n return True\n else:\n return False\n\n for prev_input_ids_slice in prev_input_ids:\n banned_tokens_slice = []\n\n for banned_token_seq in bad_words_ids:\n assert len(banned_token_seq) > 0, \"Banned words token sequences {} cannot have an empty list\".format(\n bad_words_ids\n )\n\n if _tokens_match(prev_input_ids_slice.tolist(), banned_token_seq[:-1]) is False:\n # if tokens do not match continue\n continue\n\n banned_tokens_slice.append(banned_token_seq[-1])\n\n banned_tokens.append(banned_tokens_slice)\n\n return banned_tokens\n\n\ndef top_k_top_p_filtering(\n logits: Tensor,\n top_k: int = 0,\n top_p: float = 1.0,\n filter_value: float = -float(\"Inf\"),\n min_tokens_to_keep: int = 1,\n) -> Tensor:\n \"\"\" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering\n Args:\n logits: logits distribution shape (batch size, vocabulary size)\n if top_k > 0: keep only top k tokens with highest probability (top-k filtering).\n if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).\n Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)\n Make sure we keep at least min_tokens_to_keep per batch example in the output\n From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317\n \"\"\"\n if top_k > 0:\n top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check\n # Remove all tokens with a probability less than the last token of the top-k\n indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n logits[indices_to_remove] = filter_value\n\n if top_p < 1.0:\n sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)\n\n # Remove tokens with cumulative probability above the threshold (token with 0 are kept)\n sorted_indices_to_remove = cumulative_probs > top_p\n if min_tokens_to_keep > 1:\n # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)\n sorted_indices_to_remove[..., :min_tokens_to_keep] = 0\n # Shift the indices to the right to keep also the first token above the threshold\n sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()\n sorted_indices_to_remove[..., 0] = 0\n\n # scatter sorted tensors to original indexing\n indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)\n logits[indices_to_remove] = filter_value\n return logits\n\n\nclass BeamHypotheses(object):\n def __init__(self, num_beams, max_length, length_penalty, early_stopping):\n \"\"\"\n Initialize n-best list of hypotheses.\n \"\"\"\n self.max_length = max_length - 1 # ignoring bos_token\n self.length_penalty = length_penalty\n self.early_stopping = early_stopping\n self.num_beams = num_beams\n self.beams = []\n self.worst_score = 1e9\n\n def __len__(self):\n \"\"\"\n Number of hypotheses in the list.\n \"\"\"\n return len(self.beams)\n\n def add(self, hyp, sum_logprobs):\n \"\"\"\n Add a new hypothesis to the list.\n \"\"\"\n score = sum_logprobs / len(hyp) ** self.length_penalty\n if len(self) < self.num_beams or score > self.worst_score:\n self.beams.append((score, hyp))\n if len(self) > self.num_beams:\n sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])\n del self.beams[sorted_scores[0][1]]\n self.worst_score = sorted_scores[1][0]\n else:\n self.worst_score = min(score, self.worst_score)\n\n def is_done(self, best_sum_logprobs, cur_len):\n \"\"\"\n If there are enough hypotheses and that none of the hypotheses being generated\n can become better than the worst one in the heap, then we are done with this sentence.\n \"\"\"\n\n if len(self) < self.num_beams:\n return False\n elif self.early_stopping:\n return True\n else:\n cur_score = best_sum_logprobs / cur_len ** self.length_penalty\n ret = self.worst_score >= cur_score\n return ret\n"
},
{
"path": "bert/modeling_bert.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"PyTorch BERT model. \"\"\"\n\n\nimport logging\nimport math\nimport os\nimport warnings\n\nimport torch\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss, MSELoss\n\nfrom .activations import gelu, gelu_new, swish\nfrom .configuration_bert import BertConfig\nfrom .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable\nfrom .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer\n\n\nlogger = logging.getLogger(__name__)\n\n_TOKENIZER_FOR_DOC = \"BertTokenizer\"\n\nBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [\n \"bert-base-uncased\",\n \"bert-large-uncased\",\n \"bert-base-cased\",\n \"bert-large-cased\",\n \"bert-base-multilingual-uncased\",\n \"bert-base-multilingual-cased\",\n \"bert-base-chinese\",\n \"bert-base-german-cased\",\n \"bert-large-uncased-whole-word-masking\",\n \"bert-large-cased-whole-word-masking\",\n \"bert-large-uncased-whole-word-masking-finetuned-squad\",\n \"bert-large-cased-whole-word-masking-finetuned-squad\",\n \"bert-base-cased-finetuned-mrpc\",\n \"bert-base-german-dbmdz-cased\",\n \"bert-base-german-dbmdz-uncased\",\n \"cl-tohoku/bert-base-japanese\",\n \"cl-tohoku/bert-base-japanese-whole-word-masking\",\n \"cl-tohoku/bert-base-japanese-char\",\n \"cl-tohoku/bert-base-japanese-char-whole-word-masking\",\n \"TurkuNLP/bert-base-finnish-cased-v1\",\n \"TurkuNLP/bert-base-finnish-uncased-v1\",\n \"wietsedv/bert-base-dutch-cased\",\n # See all BERT models at https://huggingface.co/models?filter=bert\n]\n\n\ndef load_tf_weights_in_bert(model, config, tf_checkpoint_path):\n \"\"\" Load tf checkpoints in a pytorch model.\n \"\"\"\n try:\n import re\n import numpy as np\n import tensorflow as tf\n except ImportError:\n logger.error(\n \"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see \"\n \"https://www.tensorflow.org/install/ for installation instructions.\"\n )\n raise\n tf_path = os.path.abspath(tf_checkpoint_path)\n logger.info(\"Converting TensorFlow checkpoint from {}\".format(tf_path))\n # Load weights from TF model\n init_vars = tf.train.list_variables(tf_path)\n names = []\n arrays = []\n for name, shape in init_vars:\n logger.info(\"Loading TF weight {} with shape {}\".format(name, shape))\n array = tf.train.load_variable(tf_path, name)\n names.append(name)\n arrays.append(array)\n\n for name, array in zip(names, arrays):\n name = name.split(\"/\")\n # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v\n # which are not required for using pretrained model\n if any(\n n in [\"adam_v\", \"adam_m\", \"AdamWeightDecayOptimizer\", \"AdamWeightDecayOptimizer_1\", \"global_step\"]\n for n in name\n ):\n logger.info(\"Skipping {}\".format(\"/\".join(name)))\n continue\n pointer = model\n for m_name in name:\n if re.fullmatch(r\"[A-Za-z]+_\\d+\", m_name):\n scope_names = re.split(r\"_(\\d+)\", m_name)\n else:\n scope_names = [m_name]\n if scope_names[0] == \"kernel\" or scope_names[0] == \"gamma\":\n pointer = getattr(pointer, \"weight\")\n elif scope_names[0] == \"output_bias\" or scope_names[0] == \"beta\":\n pointer = getattr(pointer, \"bias\")\n elif scope_names[0] == \"output_weights\":\n pointer = getattr(pointer, \"weight\")\n elif scope_names[0] == \"squad\":\n pointer = getattr(pointer, \"classifier\")\n else:\n try:\n pointer = getattr(pointer, scope_names[0])\n except AttributeError:\n logger.info(\"Skipping {}\".format(\"/\".join(name)))\n continue\n if len(scope_names) >= 2:\n num = int(scope_names[1])\n pointer = pointer[num]\n if m_name[-11:] == \"_embeddings\":\n pointer = getattr(pointer, \"weight\")\n elif m_name == \"kernel\":\n array = np.transpose(array)\n try:\n assert pointer.shape == array.shape\n except AssertionError as e:\n e.args += (pointer.shape, array.shape)\n raise\n logger.info(\"Initialize PyTorch weight {}\".format(name))\n pointer.data = torch.from_numpy(array)\n return model\n\n\ndef mish(x):\n return x * torch.tanh(nn.functional.softplus(x))\n\n\nACT2FN = {\"gelu\": gelu, \"relu\": torch.nn.functional.relu, \"swish\": swish, \"gelu_new\": gelu_new, \"mish\": mish}\n\n\nBertLayerNorm = torch.nn.LayerNorm\n\n\nclass BertEmbeddings(nn.Module):\n \"\"\"Construct the embeddings from word, position and token_type embeddings.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)\n\n # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n # any TensorFlow checkpoint file\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):\n if input_ids is not None:\n input_shape = input_ids.size()\n else:\n input_shape = inputs_embeds.size()[:-1]\n\n seq_length = input_shape[1]\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n if position_ids is None:\n position_ids = torch.arange(seq_length, dtype=torch.long, device=device)\n position_ids = position_ids.unsqueeze(0).expand(input_shape)\n if token_type_ids is None:\n token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)\n\n if inputs_embeds is None:\n inputs_embeds = self.word_embeddings(input_ids)\n position_embeddings = self.position_embeddings(position_ids)\n token_type_embeddings = self.token_type_embeddings(token_type_ids)\n\n embeddings = inputs_embeds + position_embeddings + token_type_embeddings\n embeddings = self.LayerNorm(embeddings)\n embeddings = self.dropout(embeddings)\n return embeddings\n\n\nclass BertSelfAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, \"embedding_size\"):\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads)\n )\n\n self.num_attention_heads = config.num_attention_heads\n self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n self.query = nn.Linear(config.hidden_size, self.all_head_size)\n self.key = nn.Linear(config.hidden_size, self.all_head_size)\n self.value = nn.Linear(config.hidden_size, self.all_head_size)\n\n self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n\n def transpose_for_scores(self, x):\n new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=False,\n ):\n mixed_query_layer = self.query(hidden_states)\n\n # If this is instantiated as a cross-attention module, the keys\n # and values come from an encoder; the attention mask needs to be\n # such that the encoder's padding tokens are not attended to.\n if encoder_hidden_states is not None:\n mixed_key_layer = self.key(encoder_hidden_states)\n mixed_value_layer = self.value(encoder_hidden_states)\n attention_mask = encoder_attention_mask\n else:\n mixed_key_layer = self.key(hidden_states)\n mixed_value_layer = self.value(hidden_states)\n\n query_layer = self.transpose_for_scores(mixed_query_layer)\n key_layer = self.transpose_for_scores(mixed_key_layer)\n value_layer = self.transpose_for_scores(mixed_value_layer)\n\n # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n if attention_mask is not None:\n # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n attention_scores = attention_scores + attention_mask\n\n # Normalize the attention scores to probabilities.\n attention_probs = nn.Softmax(dim=-1)(attention_scores)\n\n # This is actually dropping out entire tokens to attend to, which might\n # seem a bit unusual, but is taken from the original Transformer paper.\n attention_probs = self.dropout(attention_probs)\n\n # Mask heads if we want to\n if head_mask is not None:\n attention_probs = attention_probs * head_mask\n\n context_layer = torch.matmul(attention_probs, value_layer)\n\n context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n context_layer = context_layer.view(*new_context_layer_shape)\n\n outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)\n return outputs\n\n\nclass BertSelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\nclass BertAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.self = BertSelfAttention(config)\n self.output = BertSelfOutput(config)\n self.pruned_heads = set()\n\n def prune_heads(self, heads):\n if len(heads) == 0:\n return\n heads, index = find_pruneable_heads_and_indices(\n heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads\n )\n\n # Prune linear layers\n self.self.query = prune_linear_layer(self.self.query, index)\n self.self.key = prune_linear_layer(self.self.key, index)\n self.self.value = prune_linear_layer(self.self.value, index)\n self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)\n\n # Update hyper params and store pruned heads\n self.self.num_attention_heads = self.self.num_attention_heads - len(heads)\n self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads\n self.pruned_heads = self.pruned_heads.union(heads)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=False,\n ):\n self_outputs = self.self(\n hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions,\n )\n attention_output = self.output(self_outputs[0], hidden_states)\n outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them\n return outputs\n\n\nclass BertIntermediate(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n return hidden_states\n\n\nclass BertOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\nclass BertLayer(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.attention = BertAttention(config)\n self.is_decoder = config.is_decoder\n if self.is_decoder:\n self.crossattention = BertAttention(config)\n self.intermediate = BertIntermediate(config)\n self.output = BertOutput(config)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=False,\n ):\n self_attention_outputs = self.attention(\n hidden_states, attention_mask, head_mask, output_attentions=output_attentions,\n )\n attention_output = self_attention_outputs[0]\n outputs = self_attention_outputs[1:] # add self attentions if we output attention weights\n\n if self.is_decoder and encoder_hidden_states is not None:\n cross_attention_outputs = self.crossattention(\n attention_output,\n attention_mask,\n head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n output_attentions,\n )\n attention_output = cross_attention_outputs[0]\n outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights\n\n intermediate_output = self.intermediate(attention_output)\n layer_output = self.output(intermediate_output, attention_output)\n outputs = (layer_output,) + outputs\n return outputs\n\n\nclass BertEncoder(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=False,\n output_hidden_states=False,\n ):\n all_hidden_states = ()\n all_attentions = ()\n for i, layer_module in enumerate(self.layer):\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n if getattr(self.config, \"gradient_checkpointing\", False):\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs, output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(layer_module),\n hidden_states,\n attention_mask,\n head_mask[i],\n encoder_hidden_states,\n encoder_attention_mask,\n )\n else:\n layer_outputs = layer_module(\n hidden_states,\n attention_mask,\n head_mask[i],\n encoder_hidden_states,\n encoder_attention_mask,\n output_attentions,\n )\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n # Add last layer\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n outputs = (hidden_states,)\n if output_hidden_states:\n outputs = outputs + (all_hidden_states,)\n if output_attentions:\n outputs = outputs + (all_attentions,)\n return outputs # last-layer hidden state, (all hidden states), (all attentions)\n\n\nclass BertPooler(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.activation = nn.Tanh()\n\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n first_token_tensor = hidden_states[:, 0]\n pooled_output = self.dense(first_token_tensor)\n pooled_output = self.activation(pooled_output)\n return pooled_output\n\n\nclass BertPredictionHeadTransform(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n if isinstance(config.hidden_act, str):\n self.transform_act_fn = ACT2FN[config.hidden_act]\n else:\n self.transform_act_fn = config.hidden_act\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.transform_act_fn(hidden_states)\n hidden_states = self.LayerNorm(hidden_states)\n return hidden_states\n\n\nclass BertLMPredictionHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.transform = BertPredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n\n\nclass BertOnlyMLMHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.predictions = BertLMPredictionHead(config)\n\n def forward(self, sequence_output):\n prediction_scores = self.predictions(sequence_output)\n return prediction_scores\n\n\nclass BertOnlyNSPHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.seq_relationship = nn.Linear(config.hidden_size, 2)\n\n def forward(self, pooled_output):\n seq_relationship_score = self.seq_relationship(pooled_output)\n return seq_relationship_score\n\n\nclass BertPreTrainingHeads(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.predictions = BertLMPredictionHead(config)\n self.seq_relationship = nn.Linear(config.hidden_size, 2)\n\n def forward(self, sequence_output, pooled_output):\n prediction_scores = self.predictions(sequence_output)\n seq_relationship_score = self.seq_relationship(pooled_output)\n return prediction_scores, seq_relationship_score\n\n\nclass BertPreTrainedModel(PreTrainedModel):\n \"\"\" An abstract class to handle weights initialization and\n a simple interface for downloading and loading pretrained models.\n \"\"\"\n\n config_class = BertConfig\n load_tf_weights = load_tf_weights_in_bert\n base_model_prefix = \"bert\"\n\n def _init_weights(self, module):\n \"\"\" Initialize the weights \"\"\"\n if isinstance(module, (nn.Linear, nn.Embedding)):\n # Slightly different from the TF version which uses truncated_normal for initialization\n # cf https://github.com/pytorch/pytorch/pull/5617\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n elif isinstance(module, BertLayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n if isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n\n\nBERT_START_DOCSTRING = r\"\"\"\n This model is a PyTorch `torch.nn.Module `_ sub-class.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general\n usage and behavior.\n\n Parameters:\n config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the configuration.\n Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.\n\"\"\"\n\nBERT_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using :class:`transformers.BertTokenizer`.\n See :func:`transformers.PreTrainedTokenizer.encode` and\n :func:`transformers.PreTrainedTokenizer.__call__` for details.\n\n `What are input IDs? <../glossary.html#input-ids>`__\n attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):\n Mask to avoid performing attention on padding token indices.\n Mask values selected in ``[0, 1]``:\n ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.\n\n `What are attention masks? <../glossary.html#attention-mask>`__\n token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):\n Segment token indices to indicate first and second portions of the inputs.\n Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``\n corresponds to a `sentence B` token\n\n `What are token type IDs? <../glossary.html#token-type-ids>`_\n position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):\n Indices of positions of each input sequence tokens in the position embeddings.\n Selected in the range ``[0, config.max_position_embeddings - 1]``.\n\n `What are position IDs? <../glossary.html#position-ids>`_\n head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):\n Mask to nullify selected heads of the self-attention modules.\n Mask values selected in ``[0, 1]``:\n :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.\n inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):\n Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):\n Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention\n if the model is configured as a decoder.\n encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):\n Mask to avoid performing attention on the padding token indices of the encoder input. This mask\n is used in the cross-attention if the model is configured as a decoder.\n Mask values selected in ``[0, 1]``:\n ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.\n output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):\n If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.\n\"\"\"\n\n\n@add_start_docstrings(\n \"The bare Bert Model transformer outputting raw hidden-states without any specific head on top.\",\n BERT_START_DOCSTRING,\n)\nclass BertModel(BertPreTrainedModel):\n \"\"\"\n\n The model can behave as an encoder (with only self-attention) as well\n as a decoder, in which case a layer of cross-attention is added between\n the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,\n Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.\n\n To behave as an decoder the model needs to be initialized with the\n :obj:`is_decoder` argument of the configuration set to :obj:`True`; an\n :obj:`encoder_hidden_states` is expected as an input to the forward pass.\n\n .. _`Attention is all you need`:\n https://arxiv.org/abs/1706.03762\n\n \"\"\"\n\n def __init__(self, config):\n super().__init__(config)\n self.config = config\n\n self.embeddings = BertEmbeddings(config)\n self.encoder = BertEncoder(config)\n self.pooler = BertPooler(config)\n\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.embeddings.word_embeddings\n\n def set_input_embeddings(self, value):\n self.embeddings.word_embeddings = value\n\n def _prune_heads(self, heads_to_prune):\n \"\"\" Prunes heads of the model.\n heads_to_prune: dict of {layer_num: list of heads to prune in this layer}\n See base class PreTrainedModel\n \"\"\"\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n Return:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):\n Sequence of hidden-states at the output of the last layer of the model.\n pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):\n Last layer hidden-state of the first token of the sequence (classification token)\n further processed by a Linear layer and a Tanh activation function. The Linear\n layer weights are trained from the next sentence prediction (classification)\n objective during pre-training.\n\n This output is usually *not* a good summary\n of the semantic content of the input, you're often better with averaging or pooling\n the sequence of hidden-states for the whole input sequence.\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n input_shape = input_ids.size()\n elif inputs_embeds is not None:\n input_shape = inputs_embeds.size()[:-1]\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n\n if attention_mask is None:\n attention_mask = torch.ones(input_shape, device=device)\n if token_type_ids is None:\n token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)\n\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)\n\n # If a 2D ou 3D attention mask is provided for the cross-attention\n # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]\n if self.config.is_decoder and encoder_hidden_states is not None:\n encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n if encoder_attention_mask is None:\n encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n else:\n encoder_extended_attention_mask = None\n\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n # attention_probs has shape bsz x n_heads x N x N\n # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n\n embedding_output = self.embeddings(\n input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds\n )\n encoder_outputs = self.encoder(\n embedding_output,\n attention_mask=extended_attention_mask,\n head_mask=head_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_extended_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n sequence_output = encoder_outputs[0]\n pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n outputs = (sequence_output, pooled_output,) + encoder_outputs[\n 1:\n ] # add hidden_states and attentions if they are here\n return outputs # sequence_output, pooled_output, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with two heads on top as done during the pre-training: a `masked language modeling` head and\n a `next sentence prediction (classification)` head. \"\"\",\n BERT_START_DOCSTRING,\n)\nclass BertForPreTraining(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n self.bert = BertModel(config)\n self.cls = BertPreTrainingHeads(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n next_sentence_label=None,\n output_attentions=None,\n output_hidden_states=None,\n **kwargs\n ):\n r\"\"\"\n labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):\n Labels for computing the masked language modeling loss.\n Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)\n Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels\n in ``[0, ..., config.vocab_size]``\n next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`, defaults to :obj:`None`):\n Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`input_ids` docstring)\n Indices should be in ``[0, 1]``.\n ``0`` indicates sequence B is a continuation of sequence A,\n ``1`` indicates sequence B is a random sequence.\n kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):\n Used to hide legacy arguments that have been deprecated.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.\n prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)\n Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).\n seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):\n Prediction scores of the next sequence prediction (classification) head (scores of True/False\n continuation before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n\n\n Examples::\n\n >>> from transformers import BertTokenizer, BertForPreTraining\n >>> import torch\n\n >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> outputs = model(**inputs)\n\n >>> prediction_scores, seq_relationship_scores = outputs[:2]\n\n \"\"\"\n if \"masked_lm_labels\" in kwargs:\n warnings.warn(\n \"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.\",\n DeprecationWarning,\n )\n labels = kwargs.pop(\"masked_lm_labels\")\n assert kwargs == {}, f\"Unexpected keyword arguments: {list(kwargs.keys())}.\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n sequence_output, pooled_output = outputs[:2]\n prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)\n\n outputs = (prediction_scores, seq_relationship_score,) + outputs[\n 2:\n ] # add hidden states and attention if they are here\n\n if labels is not None and next_sentence_label is not None:\n loss_fct = CrossEntropyLoss()\n masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))\n total_loss = masked_lm_loss + next_sentence_loss\n outputs = (total_loss,) + outputs\n\n return outputs # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with a `language modeling` head on top for CLM fine-tuning. \"\"\", BERT_START_DOCSTRING\n)\nclass BertLMHeadModel(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n assert config.is_decoder, \"If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True`.\"\n\n self.bert = BertModel(config)\n self.cls = BertOnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=None,\n output_hidden_states=None,\n **kwargs\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):\n Labels for computing the left-to-right language modeling loss (next word prediction).\n Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)\n Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels\n in ``[0, ..., config.vocab_size]``\n kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):\n Used to hide legacy arguments that have been deprecated.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):\n Next token prediction loss.\n prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)\n Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n\n Example::\n\n >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig\n >>> import torch\n\n >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n >>> config = BertConfig.from_pretrained(\"bert-base-cased\")\n >>> config.is_decoder = True\n >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> outputs = model(**inputs)\n\n >>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple\n \"\"\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n sequence_output = outputs[0]\n prediction_scores = self.cls(sequence_output)\n\n outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here\n\n if labels is not None:\n # we are doing next-token prediction; shift prediction scores and input ids by one\n prediction_scores = prediction_scores[:, :-1, :].contiguous()\n labels = labels[:, 1:].contiguous()\n loss_fct = CrossEntropyLoss()\n ltr_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n outputs = (ltr_lm_loss,) + outputs\n\n return outputs # (ltr_lm_loss), prediction_scores, (hidden_states), (attentions)\n\n def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):\n input_shape = input_ids.shape\n\n # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\n if attention_mask is None:\n attention_mask = input_ids.new_ones(input_shape)\n\n return {\"input_ids\": input_ids, \"attention_mask\": attention_mask}\n\n\n@add_start_docstrings(\"\"\"Bert Model with a `language modeling` head on top. \"\"\", BERT_START_DOCSTRING)\nclass BertForMaskedLM(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n assert (\n not config.is_decoder\n ), \"If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.\"\n\n self.bert = BertModel(config)\n self.cls = BertOnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=None,\n output_hidden_states=None,\n **kwargs\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):\n Labels for computing the masked language modeling loss.\n Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)\n Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels\n in ``[0, ..., config.vocab_size]``\n kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):\n Used to hide legacy arguments that have been deprecated.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Masked language modeling loss.\n prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)\n Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n if \"masked_lm_labels\" in kwargs:\n warnings.warn(\n \"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.\",\n DeprecationWarning,\n )\n labels = kwargs.pop(\"masked_lm_labels\")\n assert \"lm_labels\" not in kwargs, \"Use `BertWithLMHead` for autoregressive language modeling task.\"\n assert kwargs == {}, f\"Unexpected keyword arguments: {list(kwargs.keys())}.\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n sequence_output = outputs[0]\n prediction_scores = self.cls(sequence_output)\n\n outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here\n\n if labels is not None:\n loss_fct = CrossEntropyLoss() # -100 index = padding token\n masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n outputs = (masked_lm_loss,) + outputs\n\n return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)\n\n def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):\n input_shape = input_ids.shape\n effective_batch_size = input_shape[0]\n\n # add a dummy token\n assert self.config.pad_token_id is not None, \"The PAD token should be defined for generation\"\n attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)\n dummy_token = torch.full(\n (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device\n )\n input_ids = torch.cat([input_ids, dummy_token], dim=1)\n\n return {\"input_ids\": input_ids, \"attention_mask\": attention_mask}\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with a `next sentence prediction (classification)` head on top. \"\"\", BERT_START_DOCSTRING,\n)\nclass BertForNextSentencePrediction(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n self.bert = BertModel(config)\n self.cls = BertOnlyNSPHead(config)\n\n self.init_weights()\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n next_sentence_label=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):\n Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring)\n Indices should be in ``[0, 1]``.\n ``0`` indicates sequence B is a continuation of sequence A,\n ``1`` indicates sequence B is a random sequence.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):\n Next sequence prediction (classification) loss.\n seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):\n Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n\n Examples::\n\n >>> from transformers import BertTokenizer, BertForNextSentencePrediction\n >>> import torch\n\n >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')\n\n >>> prompt = \"In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.\"\n >>> next_sentence = \"The sky is blue due to the shorter wavelength of blue light.\"\n >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')\n\n >>> loss, logits = model(**encoding, next_sentence_label=torch.LongTensor([1]))\n >>> assert logits[0, 0] < logits[0, 1] # next sentence was random\n \"\"\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n pooled_output = outputs[1]\n\n seq_relationship_score = self.cls(pooled_output)\n\n outputs = (seq_relationship_score,) + outputs[2:] # add hidden states and attention if they are here\n if next_sentence_label is not None:\n loss_fct = CrossEntropyLoss()\n next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))\n outputs = (next_sentence_loss,) + outputs\n\n return outputs # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. \"\"\",\n BERT_START_DOCSTRING,\n)\nclass BertForSequenceClassification(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.bert = BertModel(config)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):\n Labels for computing the sequence classification/regression loss.\n Indices should be in :obj:`[0, ..., config.num_labels - 1]`.\n If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),\n If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):\n Classification (or regression if config.num_labels==1) loss.\n logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):\n Classification (or regression if config.num_labels==1) scores (before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n pooled_output = outputs[1]\n\n pooled_output = self.dropout(pooled_output)\n logits = self.classifier(pooled_output)\n\n outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here\n\n if labels is not None:\n if self.num_labels == 1:\n # We are doing regression\n loss_fct = MSELoss()\n loss = loss_fct(logits.view(-1), labels.view(-1))\n else:\n loss_fct = CrossEntropyLoss()\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n outputs = (loss,) + outputs\n\n return outputs # (loss), logits, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with a multiple choice classification head on top (a linear layer on top of\n the pooled output and a softmax) e.g. for RocStories/SWAG tasks. \"\"\",\n BERT_START_DOCSTRING,\n)\nclass BertForMultipleChoice(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n self.bert = BertModel(config)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n self.classifier = nn.Linear(config.hidden_size, 1)\n\n self.init_weights()\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, num_choices, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):\n Labels for computing the multiple choice classification loss.\n Indices should be in ``[0, ..., num_choices-1]`` where `num_choices` is the size of the second dimension\n of the input tensors. (see `input_ids` above)\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):\n Classification loss.\n classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):\n `num_choices` is the second dimension of the input tensors. (see `input_ids` above).\n\n Classification scores (before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]\n\n input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None\n attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None\n token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None\n position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None\n inputs_embeds = (\n inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))\n if inputs_embeds is not None\n else None\n )\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n pooled_output = outputs[1]\n\n pooled_output = self.dropout(pooled_output)\n logits = self.classifier(pooled_output)\n reshaped_logits = logits.view(-1, num_choices)\n\n outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here\n\n if labels is not None:\n loss_fct = CrossEntropyLoss()\n loss = loss_fct(reshaped_logits, labels)\n outputs = (loss,) + outputs\n\n return outputs # (loss), reshaped_logits, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with a token classification head on top (a linear layer on top of\n the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. \"\"\",\n BERT_START_DOCSTRING,\n)\nclass BertForTokenClassification(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.bert = BertModel(config)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):\n Labels for computing the token classification loss.\n Indices should be in ``[0, ..., config.num_labels - 1]``.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :\n Classification loss.\n scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)\n Classification scores (before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n sequence_output = outputs[0]\n\n sequence_output = self.dropout(sequence_output)\n logits = self.classifier(sequence_output)\n\n outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here\n if labels is not None:\n loss_fct = CrossEntropyLoss()\n # Only keep active parts of the loss\n if attention_mask is not None:\n active_loss = attention_mask.view(-1) == 1\n active_logits = logits.view(-1, self.num_labels)\n active_labels = torch.where(\n active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)\n )\n loss = loss_fct(active_logits, active_labels)\n else:\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n outputs = (loss,) + outputs\n\n return outputs # (loss), scores, (hidden_states), (attentions)\n\n\n@add_start_docstrings(\n \"\"\"Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear\n layers on top of the hidden-states output to compute `span start logits` and `span end logits`). \"\"\",\n BERT_START_DOCSTRING,\n)\nclass BertForQuestionAnswering(BertPreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.bert = BertModel(config)\n self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format(\"(batch_size, sequence_length)\"))\n @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=\"bert-base-uncased\")\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n start_positions=None,\n end_positions=None,\n output_attentions=None,\n output_hidden_states=None,\n ):\n r\"\"\"\n start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):\n Labels for position (index) of the start of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (`sequence_length`).\n Position outside of the sequence are not taken into account for computing the loss.\n end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):\n Labels for position (index) of the end of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (`sequence_length`).\n Position outside of the sequence are not taken into account for computing the loss.\n\n Returns:\n :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:\n loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):\n Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.\n start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):\n Span-start scores (before SoftMax).\n end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):\n Span-end scores (before SoftMax).\n hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):\n Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)\n of shape :obj:`(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the initial embedding outputs.\n attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):\n Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape\n :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n outputs = self.bert(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n )\n\n sequence_output = outputs[0]\n\n logits = self.qa_outputs(sequence_output)\n start_logits, end_logits = logits.split(1, dim=-1)\n start_logits = start_logits.squeeze(-1)\n end_logits = end_logits.squeeze(-1)\n\n outputs = (start_logits, end_logits,) + outputs[2:]\n if start_positions is not None and end_positions is not None:\n # If we are on multi-GPU, split add a dimension\n if len(start_positions.size()) > 1:\n start_positions = start_positions.squeeze(-1)\n if len(end_positions.size()) > 1:\n end_positions = end_positions.squeeze(-1)\n # sometimes the start/end positions are outside our model inputs, we ignore these terms\n ignored_index = start_logits.size(1)\n start_positions.clamp_(0, ignored_index)\n end_positions.clamp_(0, ignored_index)\n\n loss_fct = CrossEntropyLoss(ignore_index=ignored_index)\n start_loss = loss_fct(start_logits, start_positions)\n end_loss = loss_fct(end_logits, end_positions)\n total_loss = (start_loss + end_loss) / 2\n outputs = (total_loss,) + outputs\n\n return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)\n"
},
{
"path": "bert/modeling_utils.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport logging\nimport os\nfrom typing import Callable, Dict, List, Optional, Tuple\n\nimport torch\nfrom torch import Tensor, device, dtype, nn\nfrom torch.nn import CrossEntropyLoss\nfrom torch.nn import functional as F\n\nfrom .activations import get_activation\nfrom .configuration_utils import PretrainedConfig\nfrom .file_utils import (\n DUMMY_INPUTS,\n TF2_WEIGHTS_NAME,\n TF_WEIGHTS_NAME,\n WEIGHTS_NAME,\n cached_path,\n hf_bucket_url,\n is_remote_url,\n)\nfrom .generation_utils import GenerationMixin\n\n\nlogger = logging.getLogger(__name__)\n\n\ntry:\n from torch.nn import Identity\nexcept ImportError:\n # Older PyTorch compatibility\n class Identity(nn.Module):\n r\"\"\"A placeholder identity operator that is argument-insensitive.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n\n def forward(self, input):\n return input\n\n\ndef find_pruneable_heads_and_indices(\n heads: List, n_heads: int, head_size: int, already_pruned_heads: set\n) -> Tuple[set, \"torch.LongTensor\"]:\n mask = torch.ones(n_heads, head_size)\n heads = set(heads) - already_pruned_heads # Convert to set and remove already pruned heads\n for head in heads:\n # Compute how many pruned heads are before the head and move the index accordingly\n head = head - sum(1 if h < head else 0 for h in already_pruned_heads)\n mask[head] = 0\n mask = mask.view(-1).contiguous().eq(1)\n index: torch.LongTensor = torch.arange(len(mask))[mask].long()\n return heads, index\n\n\nclass ModuleUtilsMixin:\n \"\"\"\n A few utilities for torch.nn.Modules, to be used as a mixin.\n \"\"\"\n\n def num_parameters(self, only_trainable: bool = False) -> int:\n \"\"\"\n Get number of (optionally, trainable) parameters in the module.\n \"\"\"\n params = filter(lambda x: x.requires_grad, self.parameters()) if only_trainable else self.parameters()\n return sum(p.numel() for p in params)\n\n @staticmethod\n def _hook_rss_memory_pre_forward(module, *args, **kwargs):\n try:\n import psutil\n except (ImportError):\n raise ImportError(\"You need to install psutil (pip install psutil) to use memory tracing.\")\n\n process = psutil.Process(os.getpid())\n mem = process.memory_info()\n module.mem_rss_pre_forward = mem.rss\n return None\n\n @staticmethod\n def _hook_rss_memory_post_forward(module, *args, **kwargs):\n try:\n import psutil\n except (ImportError):\n raise ImportError(\"You need to install psutil (pip install psutil) to use memory tracing.\")\n\n process = psutil.Process(os.getpid())\n mem = process.memory_info()\n module.mem_rss_post_forward = mem.rss\n mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward\n module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, \"mem_rss_diff\") else 0)\n return None\n\n def add_memory_hooks(self):\n \"\"\" Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.\n Increase in memory consumption is stored in a `mem_rss_diff` attribute for each module and can be reset to zero with `model.reset_memory_hooks_state()`\n \"\"\"\n for module in self.modules():\n module.register_forward_pre_hook(self._hook_rss_memory_pre_forward)\n module.register_forward_hook(self._hook_rss_memory_post_forward)\n self.reset_memory_hooks_state()\n\n def reset_memory_hooks_state(self):\n for module in self.modules():\n module.mem_rss_diff = 0\n module.mem_rss_post_forward = 0\n module.mem_rss_pre_forward = 0\n\n @property\n def device(self) -> device:\n \"\"\"\n Get torch.device from module, assuming that the whole module has one device.\n \"\"\"\n try:\n return next(self.parameters()).device\n except StopIteration:\n # For nn.DataParallel compatibility in PyTorch 1.5\n\n def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:\n tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]\n return tuples\n\n gen = self._named_members(get_members_fn=find_tensor_attributes)\n first_tuple = next(gen)\n return first_tuple[1].device\n\n @property\n def dtype(self) -> dtype:\n \"\"\"\n Get torch.dtype from module, assuming that the whole module has one dtype.\n \"\"\"\n try:\n return next(self.parameters()).dtype\n except StopIteration:\n # For nn.DataParallel compatibility in PyTorch 1.5\n\n def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:\n tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]\n return tuples\n\n gen = self._named_members(get_members_fn=find_tensor_attributes)\n first_tuple = next(gen)\n return first_tuple[1].dtype\n\n def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:\n \"\"\"type: torch.Tensor -> torch.Tensor\"\"\"\n if encoder_attention_mask.dim() == 3:\n encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]\n if encoder_attention_mask.dim() == 2:\n encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]\n # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition\n # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow\n # /transformer/transformer_layers.py#L270\n # encoder_extended_attention_mask = (encoder_extended_attention_mask ==\n # encoder_extended_attention_mask.transpose(-1, -2))\n encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility\n\n if self.dtype == torch.float16:\n encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4\n elif self.dtype == torch.float32:\n encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9\n else:\n raise ValueError(\n \"{} not recognized. `dtype` should be set to either `torch.float32` or `torch.float16`\".format(\n self.dtype\n )\n )\n\n return encoder_extended_attention_mask\n\n def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple, device: device) -> Tensor:\n \"\"\"Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored.\n\n Arguments:\n attention_mask: torch.Tensor with 1 indicating tokens to ATTEND to\n input_shape: tuple, shape of input_ids\n device: torch.Device, usually self.device\n\n Returns:\n torch.Tensor with dtype of attention_mask.dtype\n \"\"\"\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n if attention_mask.dim() == 3:\n extended_attention_mask = attention_mask[:, None, :, :]\n elif attention_mask.dim() == 2:\n # Provided a padding mask of dimensions [batch_size, seq_length]\n # - if the model is a decoder, apply a causal mask in addition to the padding mask\n # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]\n if self.config.is_decoder:\n batch_size, seq_length = input_shape\n seq_ids = torch.arange(seq_length, device=device)\n causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]\n # causal and attention masks must have same type with pytorch version < 1.3\n causal_mask = causal_mask.to(attention_mask.dtype)\n extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]\n else:\n extended_attention_mask = attention_mask[:, None, None, :]\n else:\n raise ValueError(\n \"Wrong shape for input_ids (shape {}) or attention_mask (shape {})\".format(\n input_shape, attention_mask.shape\n )\n )\n\n # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n # masked positions, this operation will create a tensor which is 0.0 for\n # positions we want to attend and -10000.0 for masked positions.\n # Since we are adding it to the raw scores before the softmax, this is\n # effectively the same as removing these entirely.\n extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility\n extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n return extended_attention_mask\n\n def get_head_mask(self, head_mask: Tensor, num_hidden_layers: int, is_attention_chunked: bool = False) -> Tensor:\n \"\"\"\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n attention_probs has shape bsz x n_heads x N x N\n Arguments:\n head_mask: torch.Tensor or None: has shape [num_heads] or [num_hidden_layers x num_heads]\n num_hidden_layers: int\n Returns:\n Tensor of shape shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n or list with [None] for each layer\n \"\"\"\n if head_mask is not None:\n head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)\n if is_attention_chunked is True:\n head_mask = head_mask.unsqueeze(-1)\n else:\n head_mask = [None] * num_hidden_layers\n\n return head_mask\n\n def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):\n \"\"\"-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]\"\"\"\n if head_mask.dim() == 1:\n head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)\n head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)\n elif head_mask.dim() == 2:\n head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer\n assert head_mask.dim() == 5, f\"head_mask.dim != 5, instead {head_mask.dim()}\"\n head_mask = head_mask.to(dtype=self.dtype) # switch to fload if need + fp16 compatibility\n return head_mask\n\n\nclass PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):\n r\"\"\" Base class for all models.\n\n :class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models\n as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.\n\n Class attributes (overridden by derived classes):\n - ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.\n - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:\n\n - ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,\n - ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,\n - ``path``: a path (string) to the TensorFlow checkpoint.\n\n - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.\n \"\"\"\n config_class = None\n base_model_prefix = \"\"\n\n @property\n def dummy_inputs(self):\n \"\"\" Dummy inputs to do a forward pass in the network.\n\n Returns:\n torch.Tensor with dummy inputs\n \"\"\"\n return {\"input_ids\": torch.tensor(DUMMY_INPUTS)}\n\n def __init__(self, config, *inputs, **kwargs):\n super().__init__()\n if not isinstance(config, PretrainedConfig):\n raise ValueError(\n \"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. \"\n \"To create a model from a pretrained model use \"\n \"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`\".format(\n self.__class__.__name__, self.__class__.__name__\n )\n )\n # Save config in model\n self.config = config\n\n @property\n def base_model(self):\n return getattr(self, self.base_model_prefix, self)\n\n def get_input_embeddings(self):\n \"\"\"\n Returns the model's input embeddings.\n\n Returns:\n :obj:`nn.Module`:\n A torch module mapping vocabulary to hidden states.\n \"\"\"\n base_model = getattr(self, self.base_model_prefix, self)\n if base_model is not self:\n return base_model.get_input_embeddings()\n else:\n raise NotImplementedError\n\n def set_input_embeddings(self, value: nn.Module):\n \"\"\"\n Set model's input embeddings\n\n Args:\n value (:obj:`nn.Module`):\n A module mapping vocabulary to hidden states.\n \"\"\"\n base_model = getattr(self, self.base_model_prefix, self)\n if base_model is not self:\n base_model.set_input_embeddings(value)\n else:\n raise NotImplementedError\n\n def get_output_embeddings(self):\n \"\"\"\n Returns the model's output embeddings.\n\n Returns:\n :obj:`nn.Module`:\n A torch module mapping hidden states to vocabulary.\n \"\"\"\n return None # Overwrite for models with output embeddings\n\n def tie_weights(self):\n \"\"\"\n Tie the weights between the input embeddings and the output embeddings.\n If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning\n the weights instead.\n \"\"\"\n output_embeddings = self.get_output_embeddings()\n if output_embeddings is not None:\n self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())\n\n def _tie_or_clone_weights(self, output_embeddings, input_embeddings):\n \"\"\" Tie or clone module weights depending of whether we are using TorchScript or not\n \"\"\"\n if self.config.torchscript:\n output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())\n else:\n output_embeddings.weight = input_embeddings.weight\n\n if getattr(output_embeddings, \"bias\", None) is not None:\n output_embeddings.bias.data = torch.nn.functional.pad(\n output_embeddings.bias.data,\n (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),\n \"constant\",\n 0,\n )\n if hasattr(output_embeddings, \"out_features\") and hasattr(input_embeddings, \"num_embeddings\"):\n output_embeddings.out_features = input_embeddings.num_embeddings\n\n def resize_token_embeddings(self, new_num_tokens: Optional[int] = None):\n \"\"\" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.\n Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.\n\n Arguments:\n\n new_num_tokens: (`optional`) int:\n New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.\n If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.\n\n Return: ``torch.nn.Embeddings``\n Pointer to the input tokens Embeddings Module of the model\n \"\"\"\n base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed\n model_embeds = base_model._resize_token_embeddings(new_num_tokens)\n if new_num_tokens is None:\n return model_embeds\n\n # Update base model and current model config\n self.config.vocab_size = new_num_tokens\n base_model.vocab_size = new_num_tokens\n\n # Tie weights again if needed\n self.tie_weights()\n\n return model_embeds\n\n def _resize_token_embeddings(self, new_num_tokens):\n old_embeddings = self.get_input_embeddings()\n new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)\n self.set_input_embeddings(new_embeddings)\n return self.get_input_embeddings()\n\n def _get_resized_embeddings(\n self, old_embeddings: torch.nn.Embedding, new_num_tokens: Optional[int] = None\n ) -> torch.nn.Embedding:\n \"\"\" Build a resized Embedding Module from a provided token Embedding Module.\n Increasing the size will add newly initialized vectors at the end\n Reducing the size will remove vectors from the end\n\n Args:\n old_embeddings: ``torch.nn.Embedding``\n Old embeddings to be resized.\n new_num_tokens: (`optional`) int\n New number of tokens in the embedding matrix.\n Increasing the size will add newly initialized vectors at the end\n Reducing the size will remove vectors from the end\n If not provided or None: return the provided token Embedding Module.\n Return: ``torch.nn.Embedding``\n Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None\n \"\"\"\n if new_num_tokens is None:\n return old_embeddings\n\n old_num_tokens, old_embedding_dim = old_embeddings.weight.size()\n if old_num_tokens == new_num_tokens:\n return old_embeddings\n\n # Build new embeddings\n new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)\n new_embeddings.to(old_embeddings.weight.device)\n\n # initialize all new embeddings (in particular added tokens)\n self._init_weights(new_embeddings)\n\n # Copy token embeddings from the previous weights\n num_tokens_to_copy = min(old_num_tokens, new_num_tokens)\n new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]\n\n return new_embeddings\n\n def init_weights(self):\n \"\"\" Initialize and prunes weights if needed. \"\"\"\n # Initialize weights\n self.apply(self._init_weights)\n\n # Prune heads if needed\n if self.config.pruned_heads:\n self.prune_heads(self.config.pruned_heads)\n\n # Tie weights if needed\n self.tie_weights()\n\n def prune_heads(self, heads_to_prune: Dict):\n \"\"\" Prunes heads of the base model.\n\n Arguments:\n\n heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).\n E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.\n \"\"\"\n # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads\n for layer, heads in heads_to_prune.items():\n union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)\n self.config.pruned_heads[layer] = list(union_heads) # Unfortunately we have to store it as list for JSON\n\n self.base_model._prune_heads(heads_to_prune)\n\n def save_pretrained(self, save_directory):\n \"\"\" Save a model and its configuration file to a directory, so that it\n can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.\n\n Arguments:\n save_directory: directory to which to save.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(\"Provided path ({}) should be a directory, not a file\".format(save_directory))\n return\n os.makedirs(save_directory, exist_ok=True)\n\n # Only save the model itself if we are using distributed training\n model_to_save = self.module if hasattr(self, \"module\") else self\n\n # Attach architecture to the config\n model_to_save.config.architectures = [model_to_save.__class__.__name__]\n\n # If we save using the predefined names, we can load using `from_pretrained`\n output_model_file = os.path.join(save_directory, WEIGHTS_NAME)\n\n if getattr(self.config, \"xla_device\", False):\n import torch_xla.core.xla_model as xm\n\n if xm.is_master_ordinal():\n # Save configuration file\n model_to_save.config.save_pretrained(save_directory)\n # xm.save takes care of saving only from master\n xm.save(model_to_save.state_dict(), output_model_file)\n else:\n model_to_save.config.save_pretrained(save_directory)\n torch.save(model_to_save.state_dict(), output_model_file)\n\n logger.info(\"Model weights saved in {}\".format(output_model_file))\n\n @classmethod\n def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):\n r\"\"\"Instantiate a pretrained pytorch model from a pre-trained model configuration.\n\n The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)\n To train the model, you should first set it back in training mode with ``model.train()``\n\n The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.\n It is up to you to train those weights with a downstream fine-tuning task.\n\n The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.\n\n Parameters:\n pretrained_model_name_or_path: either:\n - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.\n - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.\n - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.\n - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.\n - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)\n\n model_args: (`optional`) Sequence of positional arguments:\n All remaning positional arguments will be passed to the underlying model's ``__init__`` method\n\n config: (`optional`) one of:\n - an instance of a class derived from :class:`~transformers.PretrainedConfig`, or\n - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`\n\n Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:\n - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or\n - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.\n - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.\n\n state_dict: (`optional`) dict:\n an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.\n This option can be used if you want to create a model from a pretrained configuration but load your own weights.\n In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.\n\n cache_dir: (`optional`) string:\n Path to a directory in which a downloaded pre-trained model\n configuration should be cached if the standard cache should not be used.\n\n force_download: (`optional`) boolean, default False:\n Force to (re-)download the model weights and configuration files and override the cached versions if they exists.\n\n resume_download: (`optional`) boolean, default False:\n Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.\n\n proxies: (`optional`) dict, default None:\n A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.\n The proxies are used on each request.\n\n output_loading_info: (`optional`) boolean:\n Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.\n\n kwargs: (`optional`) Remaining dictionary of keyword arguments:\n Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:\n\n - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)\n - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.\n\n Examples::\n\n # For example purposes. Not runnable.\n model = BertModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.\n model = BertModel.from_pretrained('./test/saved_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`\n model = BertModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading\n assert model.config.output_attention == True\n # Loading from a TF checkpoint file instead of a PyTorch model (slower)\n config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')\n model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)\n\n \"\"\"\n config = kwargs.pop(\"config\", None)\n state_dict = kwargs.pop(\"state_dict\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n from_tf = kwargs.pop(\"from_tf\", False)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n output_loading_info = kwargs.pop(\"output_loading_info\", False)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n use_cdn = kwargs.pop(\"use_cdn\", True)\n\n # Load config if we don't provide a configuration\n if not isinstance(config, PretrainedConfig):\n config_path = config if config is not None else pretrained_model_name_or_path\n config, model_kwargs = cls.config_class.from_pretrained(\n config_path,\n *model_args,\n cache_dir=cache_dir,\n return_unused_kwargs=True,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n **kwargs,\n )\n else:\n model_kwargs = kwargs\n\n # Load model\n if pretrained_model_name_or_path is not None:\n if os.path.isdir(pretrained_model_name_or_path):\n if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + \".index\")):\n # Load from a TF 1.0 checkpoint\n archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + \".index\")\n elif from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):\n # Load from a TF 2.0 checkpoint\n archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)\n elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):\n # Load from a PyTorch checkpoint\n archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)\n else:\n raise EnvironmentError(\n \"Error no file named {} found in directory {} or `from_tf` set to False\".format(\n [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + \".index\"],\n pretrained_model_name_or_path,\n )\n )\n elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):\n archive_file = pretrained_model_name_or_path\n elif os.path.isfile(pretrained_model_name_or_path + \".index\"):\n assert (\n from_tf\n ), \"We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint\".format(\n pretrained_model_name_or_path + \".index\"\n )\n archive_file = pretrained_model_name_or_path + \".index\"\n else:\n archive_file = hf_bucket_url(\n pretrained_model_name_or_path,\n filename=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),\n use_cdn=use_cdn,\n )\n\n try:\n # Load from URL or cache if already cached\n resolved_archive_file = cached_path(\n archive_file,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n )\n if resolved_archive_file is None:\n raise EnvironmentError\n except EnvironmentError:\n msg = (\n f\"Can't load weights for '{pretrained_model_name_or_path}'. Make sure that:\\n\\n\"\n f\"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\\n\\n\"\n f\"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a file named one of {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME}.\\n\\n\"\n )\n raise EnvironmentError(msg)\n\n if resolved_archive_file == archive_file:\n logger.info(\"loading weights file {}\".format(archive_file))\n else:\n logger.info(\"loading weights file {} from cache at {}\".format(archive_file, resolved_archive_file))\n else:\n resolved_archive_file = None\n\n # Instantiate model.\n model = cls(config, *model_args, **model_kwargs)\n\n if state_dict is None and not from_tf:\n try:\n state_dict = torch.load(resolved_archive_file, map_location=\"cpu\")\n except Exception:\n raise OSError(\n \"Unable to load weights from pytorch checkpoint file. \"\n \"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. \"\n )\n\n missing_keys = []\n unexpected_keys = []\n error_msgs = []\n\n if from_tf:\n if resolved_archive_file.endswith(\".index\"):\n # Load from a TensorFlow 1.X checkpoint - provided by original authors\n model = cls.load_tf_weights(model, config, resolved_archive_file[:-6]) # Remove the '.index'\n else:\n # Load from our TensorFlow 2.0 checkpoints\n try:\n from transformers import load_tf2_checkpoint_in_pytorch_model\n\n model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)\n except ImportError:\n logger.error(\n \"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see \"\n \"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.\"\n )\n raise\n else:\n # Convert old format to new format if needed from a PyTorch state_dict\n old_keys = []\n new_keys = []\n for key in state_dict.keys():\n new_key = None\n if \"gamma\" in key:\n new_key = key.replace(\"gamma\", \"weight\")\n if \"beta\" in key:\n new_key = key.replace(\"beta\", \"bias\")\n if new_key:\n old_keys.append(key)\n new_keys.append(new_key)\n for old_key, new_key in zip(old_keys, new_keys):\n state_dict[new_key] = state_dict.pop(old_key)\n\n # copy state_dict so _load_from_state_dict can modify it\n metadata = getattr(state_dict, \"_metadata\", None)\n state_dict = state_dict.copy()\n if metadata is not None:\n state_dict._metadata = metadata\n\n ##############################################################################################\n # Print out state_dict's contents: keys\n '''\n for key, _ in state_dict.items():\n print(key)\n '''\n ##############################################################################################\n\n\n # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants\n # so we need to apply the function recursively.\n def load(module: nn.Module, prefix=\"\"):\n local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})\n module._load_from_state_dict(\n state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,\n )\n for name, child in module._modules.items():\n if child is not None:\n load(child, prefix + name + \".\")\n\n # Make sure we are able to load base models as well as derived models (with heads)\n start_prefix = \"\"\n model_to_load = model\n has_prefix_module = any(s.startswith(cls.base_model_prefix) for s in state_dict.keys())\n if not hasattr(model, cls.base_model_prefix) and has_prefix_module:\n start_prefix = cls.base_model_prefix + \".\"\n if hasattr(model, cls.base_model_prefix) and not has_prefix_module:\n model_to_load = getattr(model, cls.base_model_prefix)\n\n load(model_to_load, prefix=start_prefix)\n\n if model.__class__.__name__ != model_to_load.__class__.__name__:\n base_model_state_dict = model_to_load.state_dict().keys()\n head_model_state_dict_without_base_prefix = [\n key.split(cls.base_model_prefix + \".\")[-1] for key in model.state_dict().keys()\n ]\n\n missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when \"\n f\"initializing {model.__class__.__name__}: {unexpected_keys}\\n\"\n f\"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task \"\n f\"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\\n\"\n f\"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect \"\n f\"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\"\n )\n else:\n logger.info(f\"All model checkpoint weights were used when initializing {model.__class__.__name__}.\\n\")\n if len(missing_keys) > 0:\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} \"\n f\"and are newly initialized: {missing_keys}\\n\"\n f\"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\"\n )\n else:\n logger.info(\n f\"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\\n\"\n f\"If your task is similar to the task the model of the ckeckpoint was trained on, \"\n f\"you can already use {model.__class__.__name__} for predictions without further training.\"\n )\n if len(error_msgs) > 0:\n raise RuntimeError(\n \"Error(s) in loading state_dict for {}:\\n\\t{}\".format(\n model.__class__.__name__, \"\\n\\t\".join(error_msgs)\n )\n )\n model.tie_weights() # make sure token embedding weights are still tied if needed\n\n # Set model in evaluation mode to deactivate DropOut modules by default\n model.eval()\n\n if output_loading_info:\n loading_info = {\n \"missing_keys\": missing_keys,\n \"unexpected_keys\": unexpected_keys,\n \"error_msgs\": error_msgs,\n }\n return model, loading_info\n\n if hasattr(config, \"xla_device\") and config.xla_device:\n import torch_xla.core.xla_model as xm\n\n model = xm.send_cpu_data_to_device(model, xm.xla_device())\n model.to(xm.xla_device())\n\n return model\n\n\nclass Conv1D(nn.Module):\n def __init__(self, nf, nx):\n \"\"\" Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)\n Basically works like a Linear layer but the weights are transposed\n \"\"\"\n super().__init__()\n self.nf = nf\n w = torch.empty(nx, nf)\n nn.init.normal_(w, std=0.02)\n self.weight = nn.Parameter(w)\n self.bias = nn.Parameter(torch.zeros(nf))\n\n def forward(self, x):\n size_out = x.size()[:-1] + (self.nf,)\n x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)\n x = x.view(*size_out)\n return x\n\n\nclass PoolerStartLogits(nn.Module):\n \"\"\" Compute SQuAD start_logits from sequence hidden states. \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, 1)\n\n def forward(self, hidden_states, p_mask=None):\n \"\"\" Args:\n **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`\n invalid position mask such as query and special symbols (PAD, SEP, CLS)\n 1.0 means token should be masked.\n \"\"\"\n x = self.dense(hidden_states).squeeze(-1)\n\n if p_mask is not None:\n if next(self.parameters()).dtype == torch.float16:\n x = x * (1 - p_mask) - 65500 * p_mask\n else:\n x = x * (1 - p_mask) - 1e30 * p_mask\n\n return x\n\n\nclass PoolerEndLogits(nn.Module):\n \"\"\" Compute SQuAD end_logits from sequence hidden states and start token hidden state.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)\n self.activation = nn.Tanh()\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dense_1 = nn.Linear(config.hidden_size, 1)\n\n def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):\n \"\"\" Args:\n One of ``start_states``, ``start_positions`` should be not None.\n If both are set, ``start_positions`` overrides ``start_states``.\n\n **start_states**: ``torch.LongTensor`` of shape identical to hidden_states\n hidden states of the first tokens for the labeled span.\n **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``\n position of the first token for the labeled span:\n **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``\n Mask of invalid position such as query and special symbols (PAD, SEP, CLS)\n 1.0 means token should be masked.\n \"\"\"\n assert (\n start_states is not None or start_positions is not None\n ), \"One of start_states, start_positions should be not None\"\n if start_positions is not None:\n slen, hsz = hidden_states.shape[-2:]\n start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)\n start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz)\n start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz)\n\n x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))\n x = self.activation(x)\n x = self.LayerNorm(x)\n x = self.dense_1(x).squeeze(-1)\n\n if p_mask is not None:\n if next(self.parameters()).dtype == torch.float16:\n x = x * (1 - p_mask) - 65500 * p_mask\n else:\n x = x * (1 - p_mask) - 1e30 * p_mask\n\n return x\n\n\nclass PoolerAnswerClass(nn.Module):\n \"\"\" Compute SQuAD 2.0 answer class from classification and start tokens hidden states. \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)\n self.activation = nn.Tanh()\n self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)\n\n def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):\n \"\"\"\n Args:\n One of ``start_states``, ``start_positions`` should be not None.\n If both are set, ``start_positions`` overrides ``start_states``.\n\n **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.\n hidden states of the first tokens for the labeled span.\n **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``\n position of the first token for the labeled span.\n **cls_index**: torch.LongTensor of shape ``(batch_size,)``\n position of the CLS token. If None, take the last token.\n\n note(Original repo):\n no dependency on end_feature so that we can obtain one single `cls_logits`\n for each sample\n \"\"\"\n hsz = hidden_states.shape[-1]\n assert (\n start_states is not None or start_positions is not None\n ), \"One of start_states, start_positions should be not None\"\n if start_positions is not None:\n start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)\n start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz)\n\n if cls_index is not None:\n cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)\n cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz)\n else:\n cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz)\n\n x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))\n x = self.activation(x)\n x = self.dense_1(x).squeeze(-1)\n\n return x\n\n\nclass SQuADHead(nn.Module):\n r\"\"\" A SQuAD head inspired by XLNet.\n\n Parameters:\n config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.\n\n Inputs:\n **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``\n hidden states of sequence tokens\n **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``\n position of the first token for the labeled span.\n **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``\n position of the last token for the labeled span.\n **cls_index**: torch.LongTensor of shape ``(batch_size,)``\n position of the CLS token. If None, take the last token.\n **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``\n Whether the question has a possible answer in the paragraph or not.\n **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``\n Mask of invalid position such as query and special symbols (PAD, SEP, CLS)\n 1.0 means token should be masked.\n\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.\n **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)\n ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``\n Log probabilities for the top config.start_n_top start token possibilities (beam-search).\n **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)\n ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``\n Indices for the top config.start_n_top start token possibilities (beam-search).\n **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)\n ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``\n Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).\n **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)\n ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``\n Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).\n **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)\n ``torch.FloatTensor`` of shape ``(batch_size,)``\n Log probabilities for the ``is_impossible`` label of the answers.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.start_n_top = config.start_n_top\n self.end_n_top = config.end_n_top\n\n self.start_logits = PoolerStartLogits(config)\n self.end_logits = PoolerEndLogits(config)\n self.answer_class = PoolerAnswerClass(config)\n\n def forward(\n self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,\n ):\n outputs = ()\n\n start_logits = self.start_logits(hidden_states, p_mask=p_mask)\n\n if start_positions is not None and end_positions is not None:\n # If we are on multi-GPU, let's remove the dimension added by batch splitting\n for x in (start_positions, end_positions, cls_index, is_impossible):\n if x is not None and x.dim() > 1:\n x.squeeze_(-1)\n\n # during training, compute the end logits based on the ground truth of the start position\n end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)\n\n loss_fct = CrossEntropyLoss()\n start_loss = loss_fct(start_logits, start_positions)\n end_loss = loss_fct(end_logits, end_positions)\n total_loss = (start_loss + end_loss) / 2\n\n if cls_index is not None and is_impossible is not None:\n # Predict answerability from the representation of CLS and START\n cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)\n loss_fct_cls = nn.BCEWithLogitsLoss()\n cls_loss = loss_fct_cls(cls_logits, is_impossible)\n\n # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss\n total_loss += cls_loss * 0.5\n\n outputs = (total_loss,) + outputs\n\n else:\n # during inference, compute the end logits based on beam search\n bsz, slen, hsz = hidden_states.size()\n start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)\n\n start_top_log_probs, start_top_index = torch.topk(\n start_log_probs, self.start_n_top, dim=-1\n ) # shape (bsz, start_n_top)\n start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)\n start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)\n start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)\n\n hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(\n start_states\n ) # shape (bsz, slen, start_n_top, hsz)\n p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None\n end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)\n end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)\n\n end_top_log_probs, end_top_index = torch.topk(\n end_log_probs, self.end_n_top, dim=1\n ) # shape (bsz, end_n_top, start_n_top)\n end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)\n end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)\n\n start_states = torch.einsum(\"blh,bl->bh\", hidden_states, start_log_probs)\n cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)\n\n outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs\n\n # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits\n # or (if labels are provided) (total_loss,)\n return outputs\n\n\nclass SequenceSummary(nn.Module):\n r\"\"\" Compute a single vector summary of a sequence hidden states according to various possibilities:\n Args of the config class:\n summary_type:\n - 'last' => [default] take the last token hidden state (like XLNet)\n - 'first' => take the first token hidden state (like Bert)\n - 'mean' => take the mean of all tokens hidden states\n - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)\n - 'attn' => Not implemented now, use multi-head attention\n summary_use_proj: Add a projection after the vector extraction\n summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.\n summary_activation: 'tanh' or another string => add an activation to the output, Other => no activation. Default\n summary_first_dropout: Add a dropout before the projection and activation\n summary_last_dropout: Add a dropout after the projection and activation\n \"\"\"\n\n def __init__(self, config: PretrainedConfig):\n super().__init__()\n\n self.summary_type = getattr(config, \"summary_type\", \"last\")\n if self.summary_type == \"attn\":\n # We should use a standard multi-head attention module with absolute positional embedding for that.\n # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276\n # We can probably just use the multi-head attention module of PyTorch >=1.1.0\n raise NotImplementedError\n\n self.summary = Identity()\n if hasattr(config, \"summary_use_proj\") and config.summary_use_proj:\n if hasattr(config, \"summary_proj_to_labels\") and config.summary_proj_to_labels and config.num_labels > 0:\n num_classes = config.num_labels\n else:\n num_classes = config.hidden_size\n self.summary = nn.Linear(config.hidden_size, num_classes)\n\n activation_string = getattr(config, \"summary_activation\", None)\n self.activation: Callable = (get_activation(activation_string) if activation_string else Identity())\n\n self.first_dropout = Identity()\n if hasattr(config, \"summary_first_dropout\") and config.summary_first_dropout > 0:\n self.first_dropout = nn.Dropout(config.summary_first_dropout)\n\n self.last_dropout = Identity()\n if hasattr(config, \"summary_last_dropout\") and config.summary_last_dropout > 0:\n self.last_dropout = nn.Dropout(config.summary_last_dropout)\n\n def forward(self, hidden_states, cls_index=None):\n \"\"\" hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer.\n cls_index: [optional] position of the classification token if summary_type == 'cls_index',\n shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.\n if summary_type == 'cls_index' and cls_index is None:\n we take the last token of the sequence as classification token\n \"\"\"\n if self.summary_type == \"last\":\n output = hidden_states[:, -1]\n elif self.summary_type == \"first\":\n output = hidden_states[:, 0]\n elif self.summary_type == \"mean\":\n output = hidden_states.mean(dim=1)\n elif self.summary_type == \"cls_index\":\n if cls_index is None:\n cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long,)\n else:\n cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)\n cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))\n # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states\n output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)\n elif self.summary_type == \"attn\":\n raise NotImplementedError\n\n output = self.first_dropout(output)\n output = self.summary(output)\n output = self.activation(output)\n output = self.last_dropout(output)\n\n return output\n\n\ndef prune_linear_layer(layer, index, dim=0):\n \"\"\" Prune a linear layer (a model parameters) to keep only entries in index.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n index = index.to(layer.weight.device)\n W = layer.weight.index_select(dim, index).clone().detach()\n if layer.bias is not None:\n if dim == 1:\n b = layer.bias.clone().detach()\n else:\n b = layer.bias[index].clone().detach()\n new_size = list(layer.weight.size())\n new_size[dim] = len(index)\n new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)\n new_layer.weight.requires_grad = False\n new_layer.weight.copy_(W.contiguous())\n new_layer.weight.requires_grad = True\n if layer.bias is not None:\n new_layer.bias.requires_grad = False\n new_layer.bias.copy_(b.contiguous())\n new_layer.bias.requires_grad = True\n return new_layer\n\n\ndef prune_conv1d_layer(layer, index, dim=1):\n \"\"\" Prune a Conv1D layer (a model parameters) to keep only entries in index.\n A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n index = index.to(layer.weight.device)\n W = layer.weight.index_select(dim, index).clone().detach()\n if dim == 0:\n b = layer.bias.clone().detach()\n else:\n b = layer.bias[index].clone().detach()\n new_size = list(layer.weight.size())\n new_size[dim] = len(index)\n new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)\n new_layer.weight.requires_grad = False\n new_layer.weight.copy_(W.contiguous())\n new_layer.weight.requires_grad = True\n new_layer.bias.requires_grad = False\n new_layer.bias.copy_(b.contiguous())\n new_layer.bias.requires_grad = True\n return new_layer\n\n\ndef prune_layer(layer, index, dim=None):\n \"\"\" Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n if isinstance(layer, nn.Linear):\n return prune_linear_layer(layer, index, dim=0 if dim is None else dim)\n elif isinstance(layer, Conv1D):\n return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)\n else:\n raise ValueError(\"Can't prune layer of class {}\".format(layer.__class__))\n\n\ndef apply_chunking_to_forward(\n chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors\n) -> torch.Tensor:\n \"\"\"\n This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension `chunk_dim`.\n It then applies a layer `forward_fn` to each chunk independently to save memory.\n If the `forward_fn` is independent across the `chunk_dim` this function will yield the\n same result as not applying it.\n\n Args:\n chunk_size: int - the chunk size of a chunked tensor. `num_chunks` = `len(input_tensors[0]) / chunk_size`\n chunk_dim: int - the dimension over which the input_tensors should be chunked\n forward_fn: fn - the forward fn of the model\n input_tensors: tuple(torch.Tensor) - the input tensors of `forward_fn` which are chunked\n Returns:\n a Tensor with the same shape the foward_fn would have given if applied\n\n\n Examples::\n\n # rename the usual forward() fn to forward_chunk()\n def forward_chunk(self, hidden_states):\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n\n # implement a chunked forward function\n def forward(self, hidden_states):\n return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states)\n \"\"\"\n\n assert len(input_tensors) > 0, \"{} has to be a tuple/list of tensors\".format(input_tensors)\n tensor_shape = input_tensors[0].shape\n assert all(\n input_tensor.shape == tensor_shape for input_tensor in input_tensors\n ), \"All input tenors have to be of the same shape\"\n\n # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compability\n num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)\n assert num_args_in_forward_chunk_fn == len(\n input_tensors\n ), \"forward_chunk_fn expects {} arguments, but only {} input tensors are given\".format(\n num_args_in_forward_chunk_fn, len(input_tensors)\n )\n\n if chunk_size > 0:\n assert (\n input_tensors[0].shape[chunk_dim] % chunk_size == 0\n ), \"The dimension to be chunked {} has to be a multiple of the chunk size {}\".format(\n input_tensors[0].shape[chunk_dim], chunk_size\n )\n\n num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size\n\n # chunk input tensor into tuples\n input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, dim=chunk_dim) for input_tensor in input_tensors)\n # apply forward fn to every tuple\n output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))\n # concatenate output at same dimension\n return torch.cat(output_chunks, dim=chunk_dim)\n\n return forward_fn(*input_tensors)\n"
},
{
"path": "bert/tokenization_bert.py",
"content": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tokenization classes.\"\"\"\n\n\nimport collections\nimport logging\nimport os\nimport unicodedata\nfrom typing import List, Optional\n\nfrom .tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace\n\n\nlogger = logging.getLogger(__name__)\n\nVOCAB_FILES_NAMES = {\"vocab_file\": \"vocab.txt\"}\n\nPRETRAINED_VOCAB_FILES_MAP = {\n \"vocab_file\": {\n \"bert-base-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt\",\n \"bert-large-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt\",\n \"bert-base-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt\",\n \"bert-large-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt\",\n \"bert-base-multilingual-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt\",\n \"bert-base-multilingual-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt\",\n \"bert-base-chinese\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt\",\n \"bert-base-german-cased\": \"https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt\",\n \"bert-large-uncased-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt\",\n \"bert-large-cased-whole-word-masking\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt\",\n \"bert-large-uncased-whole-word-masking-finetuned-squad\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt\",\n \"bert-large-cased-whole-word-masking-finetuned-squad\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt\",\n \"bert-base-cased-finetuned-mrpc\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt\",\n \"bert-base-german-dbmdz-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt\",\n \"bert-base-german-dbmdz-uncased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt\",\n \"TurkuNLP/bert-base-finnish-cased-v1\": \"https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/vocab.txt\",\n \"TurkuNLP/bert-base-finnish-uncased-v1\": \"https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/vocab.txt\",\n \"wietsedv/bert-base-dutch-cased\": \"https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/vocab.txt\",\n }\n}\n\nPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {\n \"bert-base-uncased\": 512,\n \"bert-large-uncased\": 512,\n \"bert-base-cased\": 512,\n \"bert-large-cased\": 512,\n \"bert-base-multilingual-uncased\": 512,\n \"bert-base-multilingual-cased\": 512,\n \"bert-base-chinese\": 512,\n \"bert-base-german-cased\": 512,\n \"bert-large-uncased-whole-word-masking\": 512,\n \"bert-large-cased-whole-word-masking\": 512,\n \"bert-large-uncased-whole-word-masking-finetuned-squad\": 512,\n \"bert-large-cased-whole-word-masking-finetuned-squad\": 512,\n \"bert-base-cased-finetuned-mrpc\": 512,\n \"bert-base-german-dbmdz-cased\": 512,\n \"bert-base-german-dbmdz-uncased\": 512,\n \"TurkuNLP/bert-base-finnish-cased-v1\": 512,\n \"TurkuNLP/bert-base-finnish-uncased-v1\": 512,\n \"wietsedv/bert-base-dutch-cased\": 512,\n}\n\nPRETRAINED_INIT_CONFIGURATION = {\n \"bert-base-uncased\": {\"do_lower_case\": True},\n \"bert-large-uncased\": {\"do_lower_case\": True},\n \"bert-base-cased\": {\"do_lower_case\": False},\n \"bert-large-cased\": {\"do_lower_case\": False},\n \"bert-base-multilingual-uncased\": {\"do_lower_case\": True},\n \"bert-base-multilingual-cased\": {\"do_lower_case\": False},\n \"bert-base-chinese\": {\"do_lower_case\": False},\n \"bert-base-german-cased\": {\"do_lower_case\": False},\n \"bert-large-uncased-whole-word-masking\": {\"do_lower_case\": True},\n \"bert-large-cased-whole-word-masking\": {\"do_lower_case\": False},\n \"bert-large-uncased-whole-word-masking-finetuned-squad\": {\"do_lower_case\": True},\n \"bert-large-cased-whole-word-masking-finetuned-squad\": {\"do_lower_case\": False},\n \"bert-base-cased-finetuned-mrpc\": {\"do_lower_case\": False},\n \"bert-base-german-dbmdz-cased\": {\"do_lower_case\": False},\n \"bert-base-german-dbmdz-uncased\": {\"do_lower_case\": True},\n \"TurkuNLP/bert-base-finnish-cased-v1\": {\"do_lower_case\": False},\n \"TurkuNLP/bert-base-finnish-uncased-v1\": {\"do_lower_case\": True},\n \"wietsedv/bert-base-dutch-cased\": {\"do_lower_case\": False},\n}\n\n\ndef load_vocab(vocab_file):\n \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n vocab = collections.OrderedDict()\n with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n tokens = reader.readlines()\n for index, token in enumerate(tokens):\n token = token.rstrip(\"\\n\")\n vocab[token] = index\n return vocab\n\n\ndef whitespace_tokenize(text):\n \"\"\"Runs basic whitespace cleaning and splitting on a piece of text.\"\"\"\n text = text.strip()\n if not text:\n return []\n tokens = text.split()\n return tokens\n\n\nclass BertTokenizer(PreTrainedTokenizer):\n r\"\"\"\n Constructs a BERT tokenizer. Based on WordPiece.\n\n This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users\n should refer to the superclass for more information regarding methods.\n\n Args:\n vocab_file (:obj:`string`):\n File containing the vocabulary.\n do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):\n Whether to lowercase the input when tokenizing.\n do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):\n Whether to do basic tokenization before WordPiece.\n never_split (:obj:`Iterable`, `optional`, defaults to :obj:`None`):\n Collection of tokens which will never be split during tokenization. Only has an effect when\n :obj:`do_basic_tokenize=True`\n unk_token (:obj:`string`, `optional`, defaults to \"[UNK]\"):\n The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this\n token instead.\n sep_token (:obj:`string`, `optional`, defaults to \"[SEP]\"):\n The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences\n for sequence classification or for a text and a question for question answering.\n It is also used as the last token of a sequence built with special tokens.\n pad_token (:obj:`string`, `optional`, defaults to \"[PAD]\"):\n The token used for padding, for example when batching sequences of different lengths.\n cls_token (:obj:`string`, `optional`, defaults to \"[CLS]\"):\n The classifier token which is used when doing sequence classification (classification of the whole\n sequence instead of per-token classification). It is the first token of the sequence when built with\n special tokens.\n mask_token (:obj:`string`, `optional`, defaults to \"[MASK]\"):\n The token used for masking values. This is the token used when training this model with masked language\n modeling. This is the token which the model will try to predict.\n tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):\n Whether to tokenize Chinese characters.\n This should likely be deactivated for Japanese:\n see: https://github.com/huggingface/transformers/issues/328\n \"\"\"\n\n vocab_files_names = VOCAB_FILES_NAMES\n pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP\n pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION\n max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\n\n def __init__(\n self,\n vocab_file,\n do_lower_case=True,\n do_basic_tokenize=True,\n never_split=None,\n unk_token=\"[UNK]\",\n sep_token=\"[SEP]\",\n pad_token=\"[PAD]\",\n cls_token=\"[CLS]\",\n mask_token=\"[MASK]\",\n tokenize_chinese_chars=True,\n **kwargs\n ):\n super().__init__(\n unk_token=unk_token,\n sep_token=sep_token,\n pad_token=pad_token,\n cls_token=cls_token,\n mask_token=mask_token,\n **kwargs,\n )\n\n if not os.path.isfile(vocab_file):\n raise ValueError(\n \"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained \"\n \"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`\".format(vocab_file)\n )\n self.vocab = load_vocab(vocab_file)\n self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])\n self.do_basic_tokenize = do_basic_tokenize\n if do_basic_tokenize:\n self.basic_tokenizer = BasicTokenizer(\n do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars\n )\n self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)\n\n @property\n def vocab_size(self):\n return len(self.vocab)\n\n def get_vocab(self):\n return dict(self.vocab, **self.added_tokens_encoder)\n\n def _tokenize(self, text):\n split_tokens = []\n if self.do_basic_tokenize:\n for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):\n\n # If the token is part of the never_split set\n if token in self.basic_tokenizer.never_split:\n split_tokens.append(token)\n else:\n split_tokens += self.wordpiece_tokenizer.tokenize(token)\n else:\n split_tokens = self.wordpiece_tokenizer.tokenize(text)\n return split_tokens\n\n def _convert_token_to_id(self, token):\n \"\"\" Converts a token (str) in an id using the vocab. \"\"\"\n return self.vocab.get(token, self.vocab.get(self.unk_token))\n\n def _convert_id_to_token(self, index):\n \"\"\"Converts an index (integer) in a token (str) using the vocab.\"\"\"\n return self.ids_to_tokens.get(index, self.unk_token)\n\n def convert_tokens_to_string(self, tokens):\n \"\"\" Converts a sequence of tokens (string) in a single string. \"\"\"\n out_string = \" \".join(tokens).replace(\" ##\", \"\").strip()\n return out_string\n\n def build_inputs_with_special_tokens(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n \"\"\"\n Build model inputs from a sequence or a pair of sequence for sequence classification tasks\n by concatenating and adding special tokens.\n A BERT sequence has the following format:\n\n - single sequence: ``[CLS] X [SEP]``\n - pair of sequences: ``[CLS] A [SEP] B [SEP]``\n\n Args:\n token_ids_0 (:obj:`List[int]`):\n List of IDs to which the special tokens will be added\n token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):\n Optional second list of IDs for sequence pairs.\n\n Returns:\n :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.\n \"\"\"\n if token_ids_1 is None:\n return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]\n cls = [self.cls_token_id]\n sep = [self.sep_token_id]\n return cls + token_ids_0 + sep + token_ids_1 + sep\n\n def get_special_tokens_mask(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False\n ) -> List[int]:\n \"\"\"\n Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding\n special tokens using the tokenizer ``prepare_for_model`` method.\n\n Args:\n token_ids_0 (:obj:`List[int]`):\n List of ids.\n token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):\n Optional second list of IDs for sequence pairs.\n already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Set to True if the token list is already formatted with special tokens for the model\n\n Returns:\n :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\n \"\"\"\n\n if already_has_special_tokens:\n if token_ids_1 is not None:\n raise ValueError(\n \"You should not supply a second sequence if the provided sequence of \"\n \"ids is already formated with special tokens for the model.\"\n )\n return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))\n\n if token_ids_1 is not None:\n return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]\n return [1] + ([0] * len(token_ids_0)) + [1]\n\n def create_token_type_ids_from_sequences(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n \"\"\"\n Creates a mask from the two sequences passed to be used in a sequence-pair classification task.\n A BERT sequence pair mask has the following format:\n\n ::\n\n 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1\n | first sequence | second sequence |\n\n if token_ids_1 is None, only returns the first portion of the mask (0's).\n\n Args:\n token_ids_0 (:obj:`List[int]`):\n List of ids.\n token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):\n Optional second list of IDs for sequence pairs.\n\n Returns:\n :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given\n sequence(s).\n \"\"\"\n sep = [self.sep_token_id]\n cls = [self.cls_token_id]\n if token_ids_1 is None:\n return len(cls + token_ids_0 + sep) * [0]\n return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]\n\n def save_vocabulary(self, vocab_path):\n \"\"\"\n Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory.\n\n Args:\n vocab_path (:obj:`str`):\n The directory in which to save the vocabulary.\n\n Returns:\n :obj:`Tuple(str)`: Paths to the files saved.\n \"\"\"\n index = 0\n if os.path.isdir(vocab_path):\n vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES[\"vocab_file\"])\n else:\n vocab_file = vocab_path\n with open(vocab_file, \"w\", encoding=\"utf-8\") as writer:\n for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):\n if index != token_index:\n logger.warning(\n \"Saving vocabulary to {}: vocabulary indices are not consecutive.\"\n \" Please check that the vocabulary is not corrupted!\".format(vocab_file)\n )\n index = token_index\n writer.write(token + \"\\n\")\n index += 1\n return (vocab_file,)\n\n\nclass BasicTokenizer(object):\n \"\"\"Runs basic tokenization (punctuation splitting, lower casing, etc.).\"\"\"\n\n def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True):\n \"\"\" Constructs a BasicTokenizer.\n\n Args:\n **do_lower_case**: Whether to lower case the input.\n **never_split**: (`optional`) list of str\n Kept for backward compatibility purposes.\n Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)\n List of token not to split.\n **tokenize_chinese_chars**: (`optional`) boolean (default True)\n Whether to tokenize Chinese characters.\n This should likely be deactivated for Japanese:\n see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328\n \"\"\"\n if never_split is None:\n never_split = []\n self.do_lower_case = do_lower_case\n self.never_split = set(never_split)\n self.tokenize_chinese_chars = tokenize_chinese_chars\n\n def tokenize(self, text, never_split=None):\n \"\"\" Basic Tokenization of a piece of text.\n Split on \"white spaces\" only, for sub-word tokenization, see WordPieceTokenizer.\n\n Args:\n **never_split**: (`optional`) list of str\n Kept for backward compatibility purposes.\n Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)\n List of token not to split.\n \"\"\"\n # union() returns a new set by concatenating the two sets.\n never_split = self.never_split.union(set(never_split)) if never_split else self.never_split\n\n # This was added on November 1st, 2018 for the multilingual and Chinese\n # models. This is also applied to the English models now, but it doesn't\n # matter since the English models were not trained on any Chinese data\n # and generally don't have any Chinese data in them (there are Chinese\n # characters in the vocabulary because Wikipedia does have some Chinese\n # words in the English Wikipedia.).\n if self.tokenize_chinese_chars:\n text = self._tokenize_chinese_chars(text)\n orig_tokens = whitespace_tokenize(text)\n split_tokens = []\n for token in orig_tokens:\n if self.do_lower_case and token not in never_split:\n token = token.lower()\n token = self._run_strip_accents(token)\n split_tokens.extend(self._run_split_on_punc(token, never_split))\n\n output_tokens = whitespace_tokenize(\" \".join(split_tokens))\n return output_tokens\n\n def _run_strip_accents(self, text):\n \"\"\"Strips accents from a piece of text.\"\"\"\n text = unicodedata.normalize(\"NFD\", text)\n output = []\n for char in text:\n cat = unicodedata.category(char)\n if cat == \"Mn\":\n continue\n output.append(char)\n return \"\".join(output)\n\n def _run_split_on_punc(self, text, never_split=None):\n \"\"\"Splits punctuation on a piece of text.\"\"\"\n if never_split is not None and text in never_split:\n return [text]\n chars = list(text)\n i = 0\n start_new_word = True\n output = []\n while i < len(chars):\n char = chars[i]\n if _is_punctuation(char):\n output.append([char])\n start_new_word = True\n else:\n if start_new_word:\n output.append([])\n start_new_word = False\n output[-1].append(char)\n i += 1\n\n return [\"\".join(x) for x in output]\n\n def _tokenize_chinese_chars(self, text):\n \"\"\"Adds whitespace around any CJK character.\"\"\"\n output = []\n for char in text:\n cp = ord(char)\n if self._is_chinese_char(cp):\n output.append(\" \")\n output.append(char)\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)\n\n def _is_chinese_char(self, cp):\n \"\"\"Checks whether CP is the codepoint of a CJK character.\"\"\"\n # This defines a \"chinese character\" as anything in the CJK Unicode block:\n # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)\n #\n # Note that the CJK Unicode block is NOT all Japanese and Korean characters,\n # despite its name. The modern Korean Hangul alphabet is a different block,\n # as is Japanese Hiragana and Katakana. Those alphabets are used to write\n # space-separated words, so they are not treated specially and handled\n # like the all of the other languages.\n if (\n (cp >= 0x4E00 and cp <= 0x9FFF)\n or (cp >= 0x3400 and cp <= 0x4DBF) #\n or (cp >= 0x20000 and cp <= 0x2A6DF) #\n or (cp >= 0x2A700 and cp <= 0x2B73F) #\n or (cp >= 0x2B740 and cp <= 0x2B81F) #\n or (cp >= 0x2B820 and cp <= 0x2CEAF) #\n or (cp >= 0xF900 and cp <= 0xFAFF)\n or (cp >= 0x2F800 and cp <= 0x2FA1F) #\n ): #\n return True\n\n return False\n\n def _clean_text(self, text):\n \"\"\"Performs invalid character removal and whitespace cleanup on text.\"\"\"\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xFFFD or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)\n\n\nclass WordpieceTokenizer(object):\n \"\"\"Runs WordPiece tokenization.\"\"\"\n\n def __init__(self, vocab, unk_token, max_input_chars_per_word=100):\n self.vocab = vocab\n self.unk_token = unk_token\n self.max_input_chars_per_word = max_input_chars_per_word\n\n def tokenize(self, text):\n \"\"\"Tokenizes a piece of text into its word pieces.\n\n This uses a greedy longest-match-first algorithm to perform tokenization\n using the given vocabulary.\n\n For example:\n input = \"unaffable\"\n output = [\"un\", \"##aff\", \"##able\"]\n\n Args:\n text: A single token or whitespace separated tokens. This should have\n already been passed through `BasicTokenizer`.\n\n Returns:\n A list of wordpiece tokens.\n \"\"\"\n\n output_tokens = []\n for token in whitespace_tokenize(text):\n chars = list(token)\n if len(chars) > self.max_input_chars_per_word:\n output_tokens.append(self.unk_token)\n continue\n\n is_bad = False\n start = 0\n sub_tokens = []\n while start < len(chars):\n end = len(chars)\n cur_substr = None\n while start < end:\n substr = \"\".join(chars[start:end])\n if start > 0:\n substr = \"##\" + substr\n if substr in self.vocab:\n cur_substr = substr\n break\n end -= 1\n if cur_substr is None:\n is_bad = True\n break\n sub_tokens.append(cur_substr)\n start = end\n\n if is_bad:\n output_tokens.append(self.unk_token)\n else:\n output_tokens.extend(sub_tokens)\n return output_tokens\n\n"
},
{
"path": "bert/tokenization_utils.py",
"content": "# coding=utf-8\n# Copyright 2020 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Tokenization classes for python tokenizers.\n For fast tokenizers (provided by HuggingFace's tokenizers library) see tokenization_utils_fast.py\n\"\"\"\n\nimport itertools\nimport logging\nimport re\nimport unicodedata\nfrom typing import Dict, List, Optional, Tuple, Union\n\nfrom .file_utils import add_end_docstrings\nfrom .tokenization_utils_base import (\n ENCODE_KWARGS_DOCSTRING,\n ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,\n AddedToken,\n BatchEncoding,\n EncodedInput,\n EncodedInputPair,\n PaddingStrategy,\n PreTokenizedInput,\n PreTokenizedInputPair,\n PreTrainedTokenizerBase,\n TensorType,\n TextInput,\n TextInputPair,\n TruncationStrategy,\n)\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef _is_whitespace(char):\n \"\"\"Checks whether `chars` is a whitespace character.\"\"\"\n # \\t, \\n, and \\r are technically contorl characters but we treat them\n # as whitespace since they are generally considered as such.\n if char == \" \" or char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return True\n cat = unicodedata.category(char)\n if cat == \"Zs\":\n return True\n return False\n\n\ndef _is_control(char):\n \"\"\"Checks whether `chars` is a control character.\"\"\"\n # These are technically control characters but we count them as whitespace\n # characters.\n if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n return False\n cat = unicodedata.category(char)\n if cat.startswith(\"C\"):\n return True\n return False\n\n\ndef _is_punctuation(char):\n \"\"\"Checks whether `chars` is a punctuation character.\"\"\"\n cp = ord(char)\n # We treat all non-letter/number ASCII as punctuation.\n # Characters such as \"^\", \"$\", and \"`\" are not in the Unicode\n # Punctuation class but we treat them as punctuation anyways, for\n # consistency.\n if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):\n return True\n cat = unicodedata.category(char)\n if cat.startswith(\"P\"):\n return True\n return False\n\n\ndef _is_end_of_word(text):\n \"\"\"Checks whether the last character in text is one of a punctuation, control or whitespace character.\"\"\"\n last_char = text[-1]\n return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char))\n\n\ndef _is_start_of_word(text):\n \"\"\"Checks whether the first character in text is one of a punctuation, control or whitespace character.\"\"\"\n first_char = text[0]\n return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))\n\n\nclass PreTrainedTokenizer(PreTrainedTokenizerBase):\n \"\"\" Base class for all slow tokenizers.\n\n Handle all the shared methods for tokenization and special tokens as well as methods\n downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.\n\n This class also contain the added tokens in a unified way on top of all tokenizers so we don't\n have to handle the specific vocabulary augmentation methods of the various underlying\n dictionary structures (BPE, sentencepiece...).\n\n Class attributes (overridden by derived classes):\n\n - ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file\n required by the model, and as associated values, the filename for saving the associated file (string).\n - ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys\n being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the\n `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the\n associated pretrained vocabulary file.\n - ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained\n models, and as associated values, the maximum length of the sequence inputs of this model, or None if the\n model has no maximum input size.\n - ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the\n pretrained models, and as associated values, a dictionnary of specific arguments to pass to the\n ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the\n ``from_pretrained()`` method.\n\n Args:\n - ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.\n When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated\n model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).\n no associated max_length can be found in ``max_model_input_sizes``.\n - ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.\n Should be selected between ['right', 'left']\n - ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the\n model (\"token_type_ids\", \"attention_mask\"...).\n - ``bos_token``: (`Optional`) string: a beginning of sentence token.\n Will be associated to ``self.bos_token`` and ``self.bos_token_id``\n - ``eos_token``: (`Optional`) string: an end of sentence token.\n Will be associated to ``self.eos_token`` and ``self.eos_token_id``\n - ``unk_token``: (`Optional`) string: an unknown token.\n Will be associated to ``self.unk_token`` and ``self.unk_token_id``\n - ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).\n Will be associated to ``self.sep_token`` and ``self.sep_token_id``\n - ``pad_token``: (`Optional`) string: a padding token.\n Will be associated to ``self.pad_token`` and ``self.pad_token_id``\n - ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence\n leveraging self-attention along the full depth of the model).\n Will be associated to ``self.cls_token`` and ``self.cls_token_id``\n - ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language\n modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``\n - ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.\n Adding all special tokens here ensure they won't be split by the tokenization process.\n Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``\n\n\n .. automethod:: __call__\n \"\"\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n # Added tokens - We store this for both slow and fast tokenizers\n # until the serialization of Fast tokenizers is updated\n self.added_tokens_encoder: Dict[str, int] = {}\n self.added_tokens_decoder: Dict[int, str] = {}\n self.unique_no_split_tokens: List[str] = []\n\n @property\n def is_fast(self) -> bool:\n return False\n\n @property\n def vocab_size(self) -> int:\n \"\"\" Size of the base vocabulary (without the added tokens) \"\"\"\n raise NotImplementedError\n\n def get_vocab(self):\n \"\"\" Returns the vocabulary as a dict of {token: index} pairs. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. \"\"\"\n raise NotImplementedError()\n\n def get_added_vocab(self) -> Dict[str, int]:\n return self.added_tokens_encoder\n\n def __len__(self):\n \"\"\" Size of the full vocabulary with the added tokens \"\"\"\n return self.vocab_size + len(self.added_tokens_encoder)\n\n def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens=False) -> int:\n \"\"\"\n Add a list of new tokens to the tokenizer class. If the new tokens are not in the\n vocabulary, they are added to it with indices starting from length of the current vocabulary.\n\n Args:\n new_tokens: string or list of string. Each string is a token to add. Tokens are only added if they are not\n already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).\n\n Returns:\n Number of tokens added to the vocabulary.\n\n Examples::\n\n # Let's see how to increase the vocabulary of Bert model and tokenizer\n tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n model = BertModel.from_pretrained('bert-base-uncased')\n\n num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])\n print('We have added', num_added_toks, 'tokens')\n model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.\n \"\"\"\n new_tokens = [str(tok) for tok in new_tokens]\n\n tokens_to_add = []\n for token in new_tokens:\n assert isinstance(token, str)\n if not special_tokens and self.init_kwargs.get(\"do_lower_case\", False):\n token = token.lower()\n if (\n token != self.unk_token\n and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)\n and token not in tokens_to_add\n ):\n tokens_to_add.append(token)\n if self.verbose:\n logger.info(\"Adding %s to the vocabulary\", token)\n\n added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))\n added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}\n self.added_tokens_encoder.update(added_tok_encoder)\n self.added_tokens_decoder.update(added_tok_decoder)\n\n # Make sure we don't split on any special tokens (even they were already in the vocab before e.g. for Albert)\n if special_tokens:\n self.unique_no_split_tokens = list(set(self.unique_no_split_tokens).union(set(new_tokens)))\n else:\n # Or on the newly added tokens\n self.unique_no_split_tokens = list(set(self.unique_no_split_tokens).union(set(tokens_to_add)))\n\n return len(tokens_to_add)\n\n def num_special_tokens_to_add(self, pair=False):\n \"\"\"\n Returns the number of added tokens when encoding a sequence with special tokens.\n\n Note:\n This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this\n inside your training loop.\n\n Args:\n pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the\n number of added tokens in the case of a single sequence if set to False.\n\n Returns:\n Number of tokens added to sequences\n \"\"\"\n token_ids_0 = []\n token_ids_1 = []\n return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))\n\n def tokenize(self, text: TextInput, **kwargs):\n \"\"\" Converts a string in a sequence of tokens (string), using the tokenizer.\n Split in words for word-based vocabulary or sub-words for sub-word-based\n vocabularies (BPE/SentencePieces/WordPieces).\n\n Take care of added tokens.\n\n Args:\n text (:obj:`string`): The sequence to be encoded.\n **kwargs (:obj: `dict`): Arguments passed to the model-specific `prepare_for_tokenization` preprocessing method.\n \"\"\"\n # Simple mapping string => AddedToken for special tokens with specific tokenization behaviors\n all_special_tokens_extended = dict(\n (str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)\n )\n\n text, kwargs = self.prepare_for_tokenization(text, **kwargs)\n\n if kwargs:\n logger.warning(f\"Keyword arguments {kwargs} not recognized.\")\n\n # TODO: should this be in the base class?\n if self.init_kwargs.get(\"do_lower_case\", False):\n # convert non-special tokens to lowercase\n escaped_special_toks = [re.escape(s_tok) for s_tok in self.all_special_tokens]\n pattern = r\"(\" + r\"|\".join(escaped_special_toks) + r\")|\" + r\"(.+?)\"\n text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)\n\n def split_on_token(tok, text):\n result = []\n tok_extended = all_special_tokens_extended.get(tok, None)\n split_text = text.split(tok)\n full_word = \"\"\n for i, sub_text in enumerate(split_text):\n # AddedToken can control whitespace stripping around them.\n # We use them for GPT2 and Roberta to have different behavior depending on the special token\n # Cf. https://github.com/huggingface/transformers/pull/2778\n # and https://github.com/huggingface/transformers/issues/3788\n if isinstance(tok_extended, AddedToken):\n if tok_extended.single_word:\n # Try to avoid splitting on token\n if (\n i < len(split_text) - 1\n and not _is_end_of_word(sub_text)\n and not _is_start_of_word(split_text[i + 1])\n ):\n # Don't extract the special token\n full_word += sub_text + tok\n elif full_word:\n full_word += sub_text\n result += [full_word]\n full_word = \"\"\n continue\n # Strip white spaces on the right\n if tok_extended.rstrip and i > 0:\n # A bit counter-intuitive but we strip the left of the string\n # since tok_extended.rstrip means the special token is eating all white spaces on its right\n sub_text = sub_text.lstrip()\n # Strip white spaces on the left\n if tok_extended.lstrip and i < len(split_text) - 1:\n sub_text = sub_text.rstrip() # Opposite here\n else:\n # We strip left and right by default\n if i < len(split_text) - 1:\n sub_text = sub_text.rstrip()\n if i > 0:\n sub_text = sub_text.lstrip()\n\n if i == 0 and not sub_text:\n result += [tok]\n elif i == len(split_text) - 1:\n if sub_text:\n result += [sub_text]\n else:\n pass\n else:\n if sub_text:\n result += [sub_text]\n result += [tok]\n return result\n\n def split_on_tokens(tok_list, text):\n if not text.strip():\n return []\n if not tok_list:\n return self._tokenize(text)\n\n tokenized_text = []\n text_list = [text]\n for tok in tok_list:\n tokenized_text = []\n for sub_text in text_list:\n if sub_text not in self.unique_no_split_tokens:\n tokenized_text += split_on_token(tok, sub_text)\n else:\n tokenized_text += [sub_text]\n text_list = tokenized_text\n\n return list(\n itertools.chain.from_iterable(\n (\n self._tokenize(token) if token not in self.unique_no_split_tokens else [token]\n for token in tokenized_text\n )\n )\n )\n\n no_split_token = self.unique_no_split_tokens\n tokenized_text = split_on_tokens(no_split_token, text)\n return tokenized_text\n\n def _tokenize(self, text, **kwargs):\n \"\"\" Converts a string in a sequence of tokens (string), using the tokenizer.\n Split in words for word-based vocabulary or sub-words for sub-word-based\n vocabularies (BPE/SentencePieces/WordPieces).\n\n Do NOT take care of added tokens.\n \"\"\"\n raise NotImplementedError\n\n def convert_tokens_to_ids(self, tokens):\n \"\"\" Converts a token string (or a sequence of tokens) in a single integer id\n (or a sequence of ids), using the vocabulary.\n \"\"\"\n if tokens is None:\n return None\n\n if isinstance(tokens, str):\n return self._convert_token_to_id_with_added_voc(tokens)\n\n ids = []\n for token in tokens:\n ids.append(self._convert_token_to_id_with_added_voc(token))\n return ids\n\n def _convert_token_to_id_with_added_voc(self, token):\n if token is None:\n return None\n\n if token in self.added_tokens_encoder:\n return self.added_tokens_encoder[token]\n return self._convert_token_to_id(token)\n\n def _convert_token_to_id(self, token):\n raise NotImplementedError\n\n def _encode_plus(\n self,\n text: Union[TextInput, PreTokenizedInput, EncodedInput],\n text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,\n add_special_tokens: bool = True,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n def get_input_ids(text):\n if isinstance(text, str):\n tokens = self.tokenize(text, **kwargs)\n return self.convert_tokens_to_ids(tokens)\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):\n if is_pretokenized:\n tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))\n return self.convert_tokens_to_ids(tokens)\n else:\n return self.convert_tokens_to_ids(text)\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):\n return text\n else:\n if is_pretokenized:\n raise ValueError(\n f\"Input {text} is not valid. Should be a string or a list/tuple of strings when `is_pretokenized=True`.\"\n )\n else:\n raise ValueError(\n f\"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers.\"\n )\n\n if return_offsets_mapping:\n raise NotImplementedError(\n \"return_offset_mapping is not available when using Python tokenizers.\"\n \"To use this feature, change your tokenizer to one deriving from \"\n \"transformers.PreTrainedTokenizerFast.\"\n \"More information on available tokenizers at \"\n \"https://github.com/huggingface/transformers/pull/2674\"\n )\n\n first_ids = get_input_ids(text)\n second_ids = get_input_ids(text_pair) if text_pair is not None else None\n\n return self.prepare_for_model(\n first_ids,\n pair_ids=second_ids,\n add_special_tokens=add_special_tokens,\n padding=padding_strategy.value,\n truncation=truncation_strategy.value,\n max_length=max_length,\n stride=stride,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=return_tensors,\n prepend_batch_axis=True,\n return_attention_mask=return_attention_mask,\n return_token_type_ids=return_token_type_ids,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_length=return_length,\n verbose=verbose,\n )\n\n def _batch_encode_plus(\n self,\n batch_text_or_text_pairs: Union[\n List[TextInput],\n List[TextInputPair],\n List[PreTokenizedInput],\n List[PreTokenizedInputPair],\n List[EncodedInput],\n List[EncodedInputPair],\n ],\n add_special_tokens: bool = True,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n def get_input_ids(text):\n if isinstance(text, str):\n tokens = self.tokenize(text, **kwargs)\n return self.convert_tokens_to_ids(tokens)\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):\n if is_pretokenized:\n tokens = list(itertools.chain(*(self.tokenize(t, is_pretokenized=True, **kwargs) for t in text)))\n return self.convert_tokens_to_ids(tokens)\n else:\n return self.convert_tokens_to_ids(text)\n elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):\n return text\n else:\n raise ValueError(\n \"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers.\"\n )\n\n if return_offsets_mapping:\n raise NotImplementedError(\n \"return_offset_mapping is not available when using Python tokenizers.\"\n \"To use this feature, change your tokenizer to one deriving from \"\n \"transformers.PreTrainedTokenizerFast.\"\n )\n\n input_ids = []\n for ids_or_pair_ids in batch_text_or_text_pairs:\n if not isinstance(ids_or_pair_ids, (list, tuple)):\n ids, pair_ids = ids_or_pair_ids, None\n elif is_pretokenized and not isinstance(ids_or_pair_ids[0], (list, tuple)):\n ids, pair_ids = ids_or_pair_ids, None\n else:\n ids, pair_ids = ids_or_pair_ids\n\n first_ids = get_input_ids(ids)\n second_ids = get_input_ids(pair_ids) if pair_ids is not None else None\n input_ids.append((first_ids, second_ids))\n\n batch_outputs = self._batch_prepare_for_model(\n input_ids,\n add_special_tokens=add_special_tokens,\n padding_strategy=padding_strategy,\n truncation_strategy=truncation_strategy,\n max_length=max_length,\n stride=stride,\n pad_to_multiple_of=pad_to_multiple_of,\n return_attention_mask=return_attention_mask,\n return_token_type_ids=return_token_type_ids,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_length=return_length,\n return_tensors=return_tensors,\n verbose=verbose,\n )\n\n return BatchEncoding(batch_outputs)\n\n @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)\n def _batch_prepare_for_model(\n self,\n batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],\n add_special_tokens: bool = True,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,\n max_length: Optional[int] = None,\n stride: int = 0,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[str] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n ) -> BatchEncoding:\n \"\"\" Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.\n It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and\n manages a moving window (with user defined stride) for overflowing tokens\n\n Args:\n batch_ids_pairs: list of tokenized input ids or input ids pairs\n \"\"\"\n\n batch_outputs = {}\n for first_ids, second_ids in batch_ids_pairs:\n outputs = self.prepare_for_model(\n first_ids,\n second_ids,\n add_special_tokens=add_special_tokens,\n padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward\n truncation=truncation_strategy.value,\n max_length=max_length,\n stride=stride,\n pad_to_multiple_of=None, # we pad in batch afterward\n return_attention_mask=False, # we pad in batch afterward\n return_token_type_ids=return_token_type_ids,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_length=return_length,\n return_tensors=None, # We convert the whole batch to tensors at the end\n prepend_batch_axis=False,\n verbose=verbose,\n )\n\n for key, value in outputs.items():\n if key not in batch_outputs:\n batch_outputs[key] = []\n batch_outputs[key].append(value)\n\n batch_outputs = self.pad(\n batch_outputs,\n padding=padding_strategy.value,\n max_length=max_length,\n pad_to_multiple_of=pad_to_multiple_of,\n return_attention_mask=return_attention_mask,\n )\n\n batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)\n\n return batch_outputs\n\n def prepare_for_tokenization(self, text: str, is_pretokenized=False, **kwargs) -> (str, dict):\n \"\"\" Performs any necessary transformations before tokenization.\n\n This method should pop the arguments from kwargs and return kwargs as well.\n We test kwargs at the end of the encoding process to be sure all the arguments have been used.\n \"\"\"\n return (text, kwargs)\n\n def get_special_tokens_mask(\n self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False\n ) -> List[int]:\n \"\"\"\n Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding\n special tokens using the tokenizer ``prepare_for_model`` method.\n\n Args:\n token_ids_0: list of ids (must not contain special tokens)\n token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids\n for sequence pairs\n already_has_special_tokens: (default False) Set to True if the token list is already formated with\n special tokens for the model\n\n Returns:\n A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\n \"\"\"\n return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))\n\n def convert_ids_to_tokens(\n self, ids: Union[int, List[int]], skip_special_tokens: bool = False\n ) -> Union[str, List[str]]:\n \"\"\" Converts a single index or a sequence of indices (integers) in a token \"\n (resp.) a sequence of tokens (str), using the vocabulary and added tokens.\n\n Args:\n skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False\n \"\"\"\n if isinstance(ids, int):\n if ids in self.added_tokens_decoder:\n return self.added_tokens_decoder[ids]\n else:\n return self._convert_id_to_token(ids)\n tokens = []\n for index in ids:\n index = int(index)\n if skip_special_tokens and index in self.all_special_ids:\n continue\n if index in self.added_tokens_decoder:\n tokens.append(self.added_tokens_decoder[index])\n else:\n tokens.append(self._convert_id_to_token(index))\n return tokens\n\n def _convert_id_to_token(self, index: int) -> str:\n raise NotImplementedError\n\n def convert_tokens_to_string(self, tokens: List[str]) -> str:\n \"\"\" Converts a sequence of tokens (string) in a single string.\n The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))\n but we often want to remove sub-word tokenization artifacts at the same time.\n \"\"\"\n return \" \".join(self.convert_ids_to_tokens(tokens))\n\n def decode(\n self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True\n ) -> str:\n filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)\n\n # To avoid mixing byte-level and unicode for byte-level BPT\n # we need to build string separatly for added tokens and byte-level tokens\n # cf. https://github.com/huggingface/transformers/issues/1133\n sub_texts = []\n current_sub_text = []\n for token in filtered_tokens:\n if skip_special_tokens and token in self.all_special_ids:\n continue\n if token in self.added_tokens_encoder:\n if current_sub_text:\n sub_texts.append(self.convert_tokens_to_string(current_sub_text))\n current_sub_text = []\n sub_texts.append(token)\n else:\n current_sub_text.append(token)\n if current_sub_text:\n sub_texts.append(self.convert_tokens_to_string(current_sub_text))\n text = \" \".join(sub_texts)\n\n if clean_up_tokenization_spaces:\n clean_text = self.clean_up_tokenization(text)\n return clean_text\n else:\n return text\n\n def save_vocabulary(self, save_directory) -> Tuple[str]:\n \"\"\" Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens\n and special token mappings.\n\n Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full\n Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained`\n class method.\n \"\"\"\n raise NotImplementedError\n"
},
{
"path": "bert/tokenization_utils_base.py",
"content": "# coding=utf-8\n# Copyright 2020 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Base classes common to both the slow and the fast tokenization classes:\n PreTrainedTokenizerBase (host all the user fronting encoding methodes)\n Special token mixing (host the special tokens logic) and\n BatchEncoding (wrap the dictionnary of output with special method for the Fast tokenizers)\n\"\"\"\n\nimport copy\nimport json\nimport logging\nimport os\nimport warnings\nfrom collections import UserDict\nfrom enum import Enum\nfrom typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nfrom tokenizers import AddedToken\nfrom tokenizers import Encoding as EncodingFast\n\nfrom .file_utils import (\n add_end_docstrings,\n cached_path,\n hf_bucket_url,\n is_remote_url,\n is_tf_available,\n is_torch_available,\n torch_required,\n)\n\n\nif is_tf_available():\n import tensorflow as tf\nif is_torch_available():\n import torch\n\n\nlogger = logging.getLogger(__name__)\n\nVERY_LARGE_INTEGER = int(1e30) # This is used to set the max input length for a model with infinite size input\nLARGE_INTEGER = int(1e20) # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER\n\n# Define type aliases and NamedTuples\nTextInput = str\nPreTokenizedInput = List[str]\nEncodedInput = List[int]\nTextInputPair = Tuple[str, str]\nPreTokenizedInputPair = Tuple[List[str], List[str]]\nEncodedInputPair = Tuple[List[int], List[int]]\n\n\n# Slow tokenizers used to be saved in three separated files\nSPECIAL_TOKENS_MAP_FILE = \"special_tokens_map.json\"\nADDED_TOKENS_FILE = \"added_tokens.json\"\nTOKENIZER_CONFIG_FILE = \"tokenizer_config.json\"\n\n# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file\nFULL_TOKENIZER_FILE = \"tokenizer.json\"\n\n\nclass ExplicitEnum(Enum):\n \"\"\" Enum with more explicit error message for missing values.\n \"\"\"\n\n @classmethod\n def _missing_(cls, value):\n raise ValueError(\n \"%r is not a valid %s, please select one of %s\"\n % (value, cls.__name__, str(list(cls._value2member_map_.keys())))\n )\n\n\nclass TruncationStrategy(ExplicitEnum):\n ONLY_FIRST = \"only_first\"\n ONLY_SECOND = \"only_second\"\n LONGEST_FIRST = \"longest_first\"\n DO_NOT_TRUNCATE = \"do_not_truncate\"\n\n\nclass PaddingStrategy(ExplicitEnum):\n LONGEST = \"longest\"\n MAX_LENGTH = \"max_length\"\n DO_NOT_PAD = \"do_not_pad\"\n\n\nclass TensorType(ExplicitEnum):\n PYTORCH = \"pt\"\n TENSORFLOW = \"tf\"\n NUMPY = \"np\"\n\n\nclass CharSpan(NamedTuple):\n \"\"\" Character span in the original string\n\n Args:\n start: index of the first character in the original string\n end: index of the character following the last character in the original string\n \"\"\"\n\n start: int\n end: int\n\n\nclass TokenSpan(NamedTuple):\n \"\"\" Token span in an encoded string (list of tokens)\n\n Args:\n start: index of the first token in the span\n end: index of the token following the last token in the span\n \"\"\"\n\n start: int\n end: int\n\n\nclass BatchEncoding(UserDict):\n \"\"\" BatchEncoding hold the output of the encode and batch_encode methods (tokens, attention_masks, etc).\n This class is derived from a python Dictionary and can be used as a dictionnary.\n In addition, this class expose utility methods to map from word/char space to token space.\n\n Args:\n data (:obj:`dict`): Dictionary of lists/arrays returned by the encode/batch_encode methods ('input_ids', 'attention_mask'...)\n encoding (:obj:`EncodingFast`, :obj:`list(EncodingFast)`, `optional`, defaults to :obj:`None`):\n If the tokenizer is a fast tokenizer which outputs additional informations like mapping from word/char space to token space\n the `EncodingFast` instance or list of instance (for batches) hold these informations.\n tensor_type (:obj:`Union[None, str, TensorType]`, `optional`, defaults to :obj:`None`):\n You can give a tensor_type here to convert the lists of integers in PyTorch/TF/Numpy Tensors at initialization\n prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Set to True to add a batch axis when converting in Tensors (see :obj:`tensor_type` above)\n \"\"\"\n\n def __init__(\n self,\n data: Optional[Dict[str, Any]] = None,\n encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,\n tensor_type: Union[None, str, TensorType] = None,\n prepend_batch_axis: bool = False,\n ):\n super().__init__(data)\n\n if isinstance(encoding, EncodingFast):\n encoding = [encoding]\n\n self._encodings = encoding\n\n self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)\n\n @property\n def is_fast(self):\n \"\"\"\n Indicate if this BatchEncoding was generated from the result of a PreTrainedTokenizerFast\n Returns: True if generated from subclasses of PreTrainedTokenizerFast, else otherwise\n \"\"\"\n return self._encodings is not None\n\n def __getitem__(self, item: Union[int, str]) -> EncodingFast:\n \"\"\" If the key is a string, get the value of the dict associated to `key` ('input_ids', 'attention_mask'...)\n If the key is an integer, get the EncodingFast for batch item with index `key`\n \"\"\"\n if isinstance(item, str):\n return self.data[item]\n elif self._encodings is not None:\n return self._encodings[item]\n else:\n raise KeyError(\n \"Indexing with integers (to access backend Encoding for a given batch index) \"\n \"is not available when using Python based tokenizers\"\n )\n\n def __getattr__(self, item: str):\n try:\n return self.data[item]\n except KeyError:\n raise AttributeError\n\n def __getstate__(self):\n return {\"data\": self.data, \"encodings\": self._encodings}\n\n def __setstate__(self, state):\n if \"data\" in state:\n self.data = state[\"data\"]\n\n if \"encodings\" in state:\n self._encodings = state[\"encodings\"]\n\n def keys(self):\n return self.data.keys()\n\n def values(self):\n return self.data.values()\n\n def items(self):\n return self.data.items()\n\n # After this point:\n # Extended properties and methods only available for fast (Rust-based) tokenizers\n # provided by HuggingFace tokenizers library.\n\n @property\n def encodings(self) -> Optional[List[EncodingFast]]:\n \"\"\"\n Return the list all encoding from the tokenization process\n\n Returns: List[EncodingFast] or None if input was tokenized through Python (i.e. not fast) tokenizer\n \"\"\"\n return self._encodings\n\n def tokens(self, batch_index: int = 0) -> List[str]:\n if not self._encodings:\n raise ValueError(\"tokens() is not available when using Python based tokenizers\")\n return self._encodings[batch_index].tokens\n\n def words(self, batch_index: int = 0) -> List[Optional[int]]:\n if not self._encodings:\n raise ValueError(\"words() is not available when using Python based tokenizers\")\n return self._encodings[batch_index].words\n\n def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:\n \"\"\"\n Get the index of the word corresponding (i.e. comprising) to an encoded token\n in a sequence of the batch.\n\n Can be called as:\n\n - ``self.token_to_word(token_index)`` if batch size is 1\n - ``self.token_to_word(batch_index, token_index)`` if batch size is greater than 1\n\n This method is particularly suited when the input sequences are provided as\n pre-tokenized sequences (i.e. words are defined by the user). In this case it allows\n to easily associate encoded tokens with provided tokenized words.\n\n Args:\n batch_or_token_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprise one sequence,\n this can be the index of the token in the sequence\n token_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the token in the sequence.\n\n Returns:\n :obj:`int`:\n index of the word in the input sequence.\n\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"token_to_word() is not available when using Python based tokenizers\")\n if token_index is not None:\n batch_index = batch_or_token_index\n else:\n batch_index = 0\n token_index = batch_or_token_index\n if batch_index < 0:\n batch_index = self._batch_size + batch_index\n if token_index < 0:\n token_index = self._seq_len + token_index\n return self._encodings[batch_index].token_to_word(token_index)\n\n def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None) -> TokenSpan:\n \"\"\"\n Get the encoded token span corresponding to a word in the sequence of the batch.\n\n Token spans are returned as a TokenSpan NamedTuple with:\n\n - start: index of the first token\n - end: index of the token following the last token\n\n Can be called as:\n\n - ``self.word_to_tokens(word_index)`` if batch size is 1\n - ``self.word_to_tokens(batch_index, word_index)`` if batch size is greater or equal to 1\n\n This method is particularly suited when the input sequences are provided as\n pre-tokenized sequences (i.e. words are defined by the user). In this case it allows\n to easily associate encoded tokens with provided tokenized words.\n\n Args:\n batch_or_word_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprises one sequence,\n this can be the index of the word in the sequence\n word_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the word in the sequence.\n\n Returns:\n :obj:`TokenSpan`:\n Span of tokens in the encoded sequence.\n\n :obj:`TokenSpan` are NamedTuple with:\n\n - start: index of the first token\n - end: index of the token following the last token\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"word_to_tokens() is not available when using Python based tokenizers\")\n if word_index is not None:\n batch_index = batch_or_word_index\n else:\n batch_index = 0\n word_index = batch_or_word_index\n if batch_index < 0:\n batch_index = self._batch_size + batch_index\n if word_index < 0:\n word_index = self._seq_len + word_index\n return TokenSpan(*(self._encodings[batch_index].word_to_tokens(word_index)))\n\n def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:\n \"\"\"\n Get the character span corresponding to an encoded token in a sequence of the batch.\n\n Character spans are returned as a CharSpan NamedTuple with:\n\n - start: index of the first character in the original string associated to the token\n - end: index of the character following the last character in the original string associated to the token\n\n Can be called as:\n\n - ``self.token_to_chars(token_index)`` if batch size is 1\n - ``self.token_to_chars(batch_index, token_index)`` if batch size is greater or equal to 1\n\n Args:\n batch_or_token_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprise one sequence,\n this can be the index of the token in the sequence\n token_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the token or tokens in the sequence.\n\n Returns:\n :obj:`CharSpan`:\n Span of characters in the original string.\n\n :obj:`CharSpan` are NamedTuple with:\n\n - start: index of the first character in the original string\n - end: index of the character following the last character in the original string\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"token_to_chars() is not available when using Python based tokenizers\")\n if token_index is not None:\n batch_index = batch_or_token_index\n else:\n batch_index = 0\n token_index = batch_or_token_index\n return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))\n\n def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:\n \"\"\"\n Get the index of the token in the encoded output comprising a character\n in the original string for a sequence of the batch.\n\n Can be called as:\n\n - ``self.char_to_token(char_index)`` if batch size is 1\n - ``self.char_to_token(batch_index, char_index)`` if batch size is greater or equal to 1\n\n This method is particularly suited when the input sequences are provided as\n pre-tokenized sequences (i.e. words are defined by the user). In this case it allows\n to easily associate encoded tokens with provided tokenized words.\n\n Args:\n batch_or_char_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprise one sequence,\n this can be the index of the word in the sequence\n char_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the word in the sequence.\n\n\n Returns:\n :obj:`int`: Index of the token.\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"char_to_token() is not available when using Python based tokenizers\")\n if char_index is not None:\n batch_index = batch_or_char_index\n else:\n batch_index = 0\n char_index = batch_or_char_index\n return self._encodings[batch_index].char_to_token(char_index)\n\n def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None) -> CharSpan:\n \"\"\"\n Get the character span in the original string corresponding to given word in a sequence\n of the batch.\n\n Character spans are returned as a CharSpan NamedTuple with:\n\n - start: index of the first character in the original string\n - end: index of the character following the last character in the original string\n\n Can be called as:\n\n - ``self.word_to_chars(word_index)`` if batch size is 1\n - ``self.word_to_chars(batch_index, word_index)`` if batch size is greater or equal to 1\n\n Args:\n batch_or_word_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprise one sequence,\n this can be the index of the word in the sequence\n word_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the word in the sequence.\n\n Returns:\n :obj:`CharSpan` or :obj:`List[CharSpan]`:\n Span(s) of the associated character or characters in the string.\n CharSpan are NamedTuple with:\n\n - start: index of the first character associated to the token in the original string\n - end: index of the character following the last character associated to the token in the original string\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"word_to_chars() is not available when using Python based tokenizers\")\n if word_index is not None:\n batch_index = batch_or_word_index\n else:\n batch_index = 0\n word_index = batch_or_word_index\n return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index)))\n\n def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:\n \"\"\"\n Get the word in the original string corresponding to a character in the original string of\n a sequence of the batch.\n\n Can be called as:\n\n - ``self.char_to_word(char_index)`` if batch size is 1\n - ``self.char_to_word(batch_index, char_index)`` if batch size is greater than 1\n\n This method is particularly suited when the input sequences are provided as\n pre-tokenized sequences (i.e. words are defined by the user). In this case it allows\n to easily associate encoded tokens with provided tokenized words.\n\n Args:\n batch_or_char_index (:obj:`int`):\n Index of the sequence in the batch. If the batch only comprise one sequence,\n this can be the index of the character in the orginal string.\n char_index (:obj:`int`, `optional`):\n If a batch index is provided in `batch_or_token_index`, this can be the index\n of the character in the orginal string.\n\n\n Returns:\n :obj:`int` or :obj:`List[int]`:\n Index or indices of the associated encoded token(s).\n \"\"\"\n\n if not self._encodings:\n raise ValueError(\"char_to_word() is not available when using Python based tokenizers\")\n if char_index is not None:\n batch_index = batch_or_char_index\n else:\n batch_index = 0\n char_index = batch_or_char_index\n return self._encodings[batch_index].char_to_word(char_index)\n\n def convert_to_tensors(self, tensor_type: Union[None, str, TensorType], prepend_batch_axis: bool = False):\n if tensor_type is None:\n return self\n\n # Convert to TensorType\n if not isinstance(tensor_type, TensorType):\n tensor_type = TensorType(tensor_type)\n\n # Get a function reference for the correct framework\n if tensor_type == TensorType.TENSORFLOW and is_tf_available():\n as_tensor = tf.constant\n elif tensor_type == TensorType.PYTORCH and is_torch_available():\n as_tensor = torch.tensor\n elif tensor_type == TensorType.NUMPY:\n as_tensor = np.asarray\n else:\n raise ImportError(\n \"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.\".format(\n tensor_type\n )\n )\n\n # Do the tensor conversion in batch\n for key, value in self.items():\n try:\n if prepend_batch_axis:\n value = [value]\n\n tensor = as_tensor(value)\n\n # at-least2d\n if tensor.ndim > 2:\n tensor = tensor.squeeze(0)\n elif tensor.ndim < 2:\n tensor = tensor[None, :]\n\n self[key] = tensor\n except: # noqa E722\n raise ValueError(\n \"Unable to create tensor, you should probably activate truncation and/or padding \"\n \"with 'padding=True' 'truncation=True' to have batched tensors with the same length.\"\n )\n\n return self\n\n @torch_required\n def to(self, device: str):\n \"\"\"Send all values to device by calling v.to(device)\"\"\"\n self.data = {k: v.to(device) for k, v in self.data.items()}\n return self\n\n\n# class AddedToken(UserString):\n# \"\"\" AddedToken represents a token to be added to a Tokenizer\n\n# An AddedToken can have special options defining the way it should behave.\n\n# Args:\n# content: str:\n# The content of the token\n\n# single_word: bool\n# Whether this token should only match against single word. If True,\n# this token will never match inside of a word.\n\n# lstrip: bool\n# Whether this token should strip all potential whitespaces on the left side.\n# If True, this token will greedily match any whitespace on the left and then strip\n# them out.\n\n# rstrip: bool\n# Whether this token should strip all potential whitespaces on the right side.\n# If True, this token will greedily match any whitespace on the right and then strip\n# them out.\n# \"\"\"\n\n# def __init__(\n# self, data: str, single_word: bool = False, lstrip: bool = False, rstrip: bool = False,\n# ):\n# super().__init__(data)\n\n# self._single_word = single_word\n# self._lstrip = lstrip\n# self._rstrip = rstrip\n\n# def lower(self):\n# return AddedToken(self.data.lower(), self._single_word, self._lstrip, self._rstrip)\n\n\nclass SpecialTokensMixin:\n \"\"\" SpecialTokensMixin is derived by ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` and\n handles specific behaviors related to special tokens. In particular, this class hold the\n attributes which can be used to directly access to these special tokens in a\n model-independant manner and allow to set and update the special tokens.\n \"\"\"\n\n SPECIAL_TOKENS_ATTRIBUTES = [\n \"bos_token\",\n \"eos_token\",\n \"unk_token\",\n \"sep_token\",\n \"pad_token\",\n \"cls_token\",\n \"mask_token\",\n \"additional_special_tokens\",\n ]\n\n def __init__(self, verbose=True, **kwargs):\n self._bos_token = None\n self._eos_token = None\n self._unk_token = None\n self._sep_token = None\n self._pad_token = None\n self._cls_token = None\n self._mask_token = None\n self._pad_token_type_id = 0\n self._additional_special_tokens = []\n self.verbose = verbose\n\n # We directly set the hidden value to allow initialization with special tokens\n # which are not yet in the vocabulary. Necesssary for serialization/de-serialization\n # TODO clean this up at some point (probably by sitching to fast tokenizers)\n for key, value in kwargs.items():\n if key in self.SPECIAL_TOKENS_ATTRIBUTES:\n if key == \"additional_special_tokens\":\n assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)\n setattr(self, key, value)\n elif isinstance(value, (str, AddedToken)):\n setattr(self, key, value)\n else:\n raise TypeError(\n \"special token {} has to be either str or AddedToken but got: {}\".format(key, type(value))\n )\n\n def sanitize_special_tokens(self) -> int:\n \"\"\" Make sure that all the special tokens attributes of the tokenizer (tokenizer.mask_token, tokenizer.cls_token, ...)\n are in the vocabulary. Add the missing ones to the vocabulary if needed.\n\n Return:\n Number of tokens added in the vocaulary during the operation.\n \"\"\"\n return self.add_tokens(self.all_special_tokens_extended, special_tokens=True)\n\n def add_special_tokens(self, special_tokens_dict: Dict[str, Union[str, AddedToken]]) -> int:\n \"\"\"\n Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them\n to class attributes. If special tokens are NOT in the vocabulary, they are added\n to it (indexed starting from the last index of the current vocabulary).\n\n Using `add_special_tokens` will ensure your special tokens can be used in several ways:\n\n - special tokens are carefully handled by the tokenizer (they are never split)\n - you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.\n\n When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '')\n\n Args:\n special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:\n [``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,\n ``additional_special_tokens``].\n\n Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).\n\n Returns:\n Number of tokens added to the vocabulary.\n\n Examples::\n\n # Let's see how to add a new classification token to GPT-2\n tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n model = GPT2Model.from_pretrained('gpt2')\n\n special_tokens_dict = {'cls_token': ''}\n\n num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n print('We have added', num_added_toks, 'tokens')\n model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.\n\n assert tokenizer.cls_token == ''\n \"\"\"\n if not special_tokens_dict:\n return 0\n\n added_tokens = 0\n for key, value in special_tokens_dict.items():\n assert key in self.SPECIAL_TOKENS_ATTRIBUTES\n\n if self.verbose:\n logger.info(\"Assigning %s to the %s key of the tokenizer\", value, key)\n setattr(self, key, value)\n\n if key == \"additional_special_tokens\":\n assert isinstance(value, (list, tuple)) and all(\n isinstance(t, (str, AddedToken)) for t in value\n ), f\"Tokens {value} for key {key} should all be str or AddedToken instances\"\n added_tokens += self.add_tokens(value, special_tokens=True)\n else:\n assert isinstance(\n value, (str, AddedToken)\n ), f\"Token {value} for key {key} should be a str or an AddedToken instance\"\n added_tokens += self.add_tokens([value], special_tokens=True)\n\n return added_tokens\n\n def add_tokens(self, new_tokens: Union[str, AddedToken, List[str], List[AddedToken]], special_tokens=False) -> int:\n \"\"\"\n Add a list of new tokens to the tokenizer class. If the new tokens are not in the\n vocabulary, they are added to it with indices starting from length of the current vocabulary.\n\n Args:\n new_tokens: string or list of string or :class:`~transformers.AddedToken`. Each string is a token to add.\n Tokens are only added if they are not already in the vocabulary. AddedToken wrap a string token to\n let you personnalize it's behavior (Whether this token should only match against single word, whether\n this token should strip all potential whitespaces on the left side, Whether this token should strip\n all potential whitespaces on the right side...).\n special_token: can be used to specify if the token is a special token. This mostly change the normalization\n behavior (special tokens like CLS or [MASK] are usually not lower-cased for instance)\n\n See details for :class:`~transformers.AddedToken` in HuggingFace tokenizers library.\n\n Returns:\n Number of tokens added to the vocabulary.\n\n Examples::\n\n # Let's see how to increase the vocabulary of Bert model and tokenizer\n tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n model = BertModel.from_pretrained('bert-base-uncased')\n\n num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])\n print('We have added', num_added_toks, 'tokens')\n model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.\n \"\"\"\n if not new_tokens:\n return 0\n\n if not isinstance(new_tokens, (list, tuple)):\n new_tokens = [new_tokens]\n\n return self._add_tokens(new_tokens, special_tokens=special_tokens)\n\n @property\n def bos_token(self):\n \"\"\" Beginning of sentence token (string). Log an error if used while not having been set. \"\"\"\n if self._bos_token is None and self.verbose:\n logger.error(\"Using bos_token, but it is not set yet.\")\n return None\n return str(self._bos_token)\n\n @property\n def eos_token(self):\n \"\"\" End of sentence token (string). Log an error if used while not having been set. \"\"\"\n if self._eos_token is None and self.verbose:\n logger.error(\"Using eos_token, but it is not set yet.\")\n return None\n return str(self._eos_token)\n\n @property\n def unk_token(self):\n \"\"\" Unknown token (string). Log an error if used while not having been set. \"\"\"\n if self._unk_token is None and self.verbose:\n logger.error(\"Using unk_token, but it is not set yet.\")\n return None\n return str(self._unk_token)\n\n @property\n def sep_token(self):\n \"\"\" Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. \"\"\"\n if self._sep_token is None and self.verbose:\n logger.error(\"Using sep_token, but it is not set yet.\")\n return None\n return str(self._sep_token)\n\n @property\n def pad_token(self):\n \"\"\" Padding token (string). Log an error if used while not having been set. \"\"\"\n if self._pad_token is None and self.verbose:\n logger.error(\"Using pad_token, but it is not set yet.\")\n return None\n return str(self._pad_token)\n\n @property\n def cls_token(self):\n \"\"\" Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. \"\"\"\n if self._cls_token is None and self.verbose:\n logger.error(\"Using cls_token, but it is not set yet.\")\n return None\n return str(self._cls_token)\n\n @property\n def mask_token(self):\n \"\"\" Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. \"\"\"\n if self._mask_token is None and self.verbose:\n logger.error(\"Using mask_token, but it is not set yet.\")\n return None\n return str(self._mask_token)\n\n @property\n def additional_special_tokens(self):\n \"\"\" All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. \"\"\"\n if self._additional_special_tokens is None and self.verbose:\n logger.error(\"Using additional_special_tokens, but it is not set yet.\")\n return None\n return [str(tok) for tok in self._additional_special_tokens]\n\n @bos_token.setter\n def bos_token(self, value):\n self._bos_token = value\n\n @eos_token.setter\n def eos_token(self, value):\n self._eos_token = value\n\n @unk_token.setter\n def unk_token(self, value):\n self._unk_token = value\n\n @sep_token.setter\n def sep_token(self, value):\n self._sep_token = value\n\n @pad_token.setter\n def pad_token(self, value):\n self._pad_token = value\n\n @cls_token.setter\n def cls_token(self, value):\n self._cls_token = value\n\n @mask_token.setter\n def mask_token(self, value):\n self._mask_token = value\n\n @additional_special_tokens.setter\n def additional_special_tokens(self, value):\n self._additional_special_tokens = value\n\n @property\n def bos_token_id(self):\n \"\"\" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. \"\"\"\n if self._bos_token is None:\n return None\n return self.convert_tokens_to_ids(self.bos_token)\n\n @property\n def eos_token_id(self):\n \"\"\" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. \"\"\"\n if self._eos_token is None:\n return None\n return self.convert_tokens_to_ids(self.eos_token)\n\n @property\n def unk_token_id(self):\n \"\"\" Id of the unknown token in the vocabulary. Log an error if used while not having been set. \"\"\"\n if self._unk_token is None:\n return None\n return self.convert_tokens_to_ids(self.unk_token)\n\n @property\n def sep_token_id(self):\n \"\"\" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. \"\"\"\n if self._sep_token is None:\n return None\n return self.convert_tokens_to_ids(self.sep_token)\n\n @property\n def pad_token_id(self):\n \"\"\" Id of the padding token in the vocabulary. Log an error if used while not having been set. \"\"\"\n if self._pad_token is None:\n return None\n return self.convert_tokens_to_ids(self.pad_token)\n\n @property\n def pad_token_type_id(self):\n \"\"\" Id of the padding token type in the vocabulary.\"\"\"\n return self._pad_token_type_id\n\n @property\n def cls_token_id(self):\n \"\"\" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. \"\"\"\n if self._cls_token is None:\n return None\n return self.convert_tokens_to_ids(self.cls_token)\n\n @property\n def mask_token_id(self):\n \"\"\" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. \"\"\"\n if self._mask_token is None:\n return None\n return self.convert_tokens_to_ids(self.mask_token)\n\n @property\n def additional_special_tokens_ids(self):\n \"\"\" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. \"\"\"\n return self.convert_tokens_to_ids(self.additional_special_tokens)\n\n @property\n def special_tokens_map(self):\n \"\"\" A dictionary mapping special token class attribute (cls_token, unk_token...) to their\n values ('', ''...)\n Convert tokens of AddedToken type in string.\n All returned tokens are strings\n \"\"\"\n set_attr = {}\n for attr in self.SPECIAL_TOKENS_ATTRIBUTES:\n attr_value = getattr(self, \"_\" + attr)\n if attr_value:\n set_attr[attr] = str(attr_value)\n return set_attr\n\n @property\n def special_tokens_map_extended(self):\n \"\"\" A dictionary mapping special token class attribute (cls_token, unk_token...) to their\n values ('', ''...)\n Keep the tokens as AddedToken if they are of this type.\n\n AddedToken can be used to control more finely how special tokens are tokenized.\n \"\"\"\n set_attr = {}\n for attr in self.SPECIAL_TOKENS_ATTRIBUTES:\n attr_value = getattr(self, \"_\" + attr)\n if attr_value:\n set_attr[attr] = attr_value\n return set_attr\n\n @property\n def all_special_tokens(self):\n \"\"\" List all the special tokens ('', ''...) mapped to class attributes\n Convert tokens of AddedToken type in string.\n All returned tokens are strings\n (cls_token, unk_token...).\n \"\"\"\n all_toks = [str(s) for s in self.all_special_tokens_extended]\n return all_toks\n\n @property\n def all_special_tokens_extended(self):\n \"\"\" List all the special tokens ('', ''...) mapped to class attributes\n Keep the tokens as AddedToken if they are of this type.\n\n AddedToken can be used to control more finely how special tokens are tokenized.\n \"\"\"\n all_toks = []\n set_attr = self.special_tokens_map_extended\n for attr_value in set_attr.values():\n all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])\n all_toks = list(set(all_toks))\n return all_toks\n\n @property\n def all_special_ids(self):\n \"\"\" List the vocabulary indices of the special tokens ('', ''...) mapped to\n class attributes (cls_token, unk_token...).\n \"\"\"\n all_toks = self.all_special_tokens\n all_ids = self.convert_tokens_to_ids(all_toks)\n return all_ids\n\n\nENCODE_KWARGS_DOCSTRING = r\"\"\"\n add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):\n If set to ``True``, the sequences will be encoded with the special tokens relative\n to their model.\n `padding` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):\n Activate and control padding. Accepts the following values:\n\n * `True` or `'longest'`: pad to the longest sequence in the batch (or no padding if only a single sequence if provided),\n * `'max_length'`: pad to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`)\n * `False` or `'do_not_pad'` (default): No padding (i.e. can output batch with sequences of uneven lengths)\n `truncation` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):\n Activate and control truncation. Accepts the following values:\n\n * `True` or `'longest_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided,\n * `'only_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided,\n * `'only_second'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided,\n * `False` or `'do_not_truncate'` (default): No truncation (i.e. can output batch with sequences length greater than the model max admissible input size)\n `max_length` (:obj:`Union[int, None]`, `optional`, defaults to :obj:`None`):\n Control the length for padding/truncation. Accepts the following values\n\n * `None` (default): This will use the predefined model max length if required by one of the truncation/padding parameters. If the model has no specific max input length (e.g. XLNet) truncation/padding to max length is deactivated.\n * `any integer value` (e.g. `42`): Use this specific maximum length value if required by one of the truncation/padding parameters.\n stride (:obj:`int`, `optional`, defaults to ``0``):\n If set to a number along with max_length, the overflowing tokens returned when `return_overflowing_tokens=True`\n will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflow ing sequences.\n The value of this argument defines the number of overlapping tokens.\n is_pretokenized (:obj:`bool`, defaults to :obj:`False`):\n Set to True to indicate the input is already tokenized\n pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.\n This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability\n >= 7.5 (Volta).\n return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):\n Can be set to 'tf', 'pt' or 'np' to return respectively TensorFlow :obj:`tf.constant`,\n PyTorch :obj:`torch.Tensor` or Numpy :oj: `np.ndarray` instead of a list of python integers.\n\"\"\"\n\nENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r\"\"\"\n return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):\n Whether to return token type IDs. If left to the default, will return the token type IDs according\n to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.\n\n `What are token type IDs? <../glossary.html#token-type-ids>`_\n return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):\n Whether to return the attention mask. If left to the default, will return the attention mask according\n to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.\n\n `What are attention masks? <../glossary.html#attention-mask>`__\n return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Set to True to return overflowing token sequences (default False).\n return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Set to True to return special tokens mask information (default False).\n return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Set to True to return (char_start, char_end) for each token (default False).\n If using Python's tokenizer, this method will raise NotImplementedError.\n This one is only available on fast tokenizers inheriting from PreTrainedTokenizerFast.\n **kwargs: passed to the `self.tokenize()` method\n\n Return:\n A Dictionary of shape::\n\n {\n input_ids: list[int],\n token_type_ids: list[int] if return_token_type_ids is True (default)\n attention_mask: list[int] if return_attention_mask is True (default)\n overflowing_tokens: list[int] if the tokenizer is a slow tokenize, else a List[List[int]] if a ``max_length`` is specified and ``return_overflowing_tokens=True``\n special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``\n and return_special_tokens_mask is True\n }\n\n With the fields:\n\n - ``input_ids``: list of token ids to be fed to a model\n - ``token_type_ids``: list of token type ids to be fed to a model\n - ``attention_mask``: list of indices specifying which tokens should be attended to by the model\n - ``overflowing_tokens``: list of overflowing tokens sequences if a max length is specified and ``return_overflowing_tokens=True``.\n - ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added\n tokens and 1 specifying sequence tokens.\n\"\"\"\n\n\nclass PreTrainedTokenizerBase(SpecialTokensMixin):\n \"\"\" Base class for slow and fast tokenizers.\n\n Handle shared (mostly boiler plate) methods for slow and fast tokenizers.\n \"\"\"\n\n vocab_files_names: Dict[str, str] = {}\n pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}\n pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}\n max_model_input_sizes: Dict[str, int] = {}\n model_input_names: List[str] = [\"token_type_ids\", \"attention_mask\"]\n\n padding_side: str = \"right\"\n\n def __init__(self, **kwargs):\n # inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)\n self.init_inputs = ()\n self.init_kwargs = kwargs\n\n # For backward compatibility we fallback to set model_max_length from max_len if provided\n model_max_length = kwargs.pop(\"model_max_length\", kwargs.pop(\"max_len\", None))\n self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER\n\n # Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.\n self.padding_side = kwargs.pop(\"padding_side\", self.padding_side)\n assert self.padding_side in [\n \"right\",\n \"left\",\n ], f\"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}\"\n self.model_input_names = kwargs.pop(\"model_input_names\", self.model_input_names)\n\n super().__init__(**kwargs)\n\n @property\n def max_len(self) -> int:\n \"\"\" Kept here for backward compatibility.\n Now renamed to `model_max_length` to avoid ambiguity.\n \"\"\"\n return self.model_max_length\n\n @property\n def max_len_single_sentence(self) -> int:\n return self.model_max_length - self.num_special_tokens_to_add(pair=False)\n\n @property\n def max_len_sentences_pair(self) -> int:\n return self.model_max_length - self.num_special_tokens_to_add(pair=True)\n\n @max_len_single_sentence.setter\n def max_len_single_sentence(self, value) -> int:\n \"\"\" For backward compatibility, allow to try to setup 'max_len_single_sentence' \"\"\"\n if value == self.model_max_length - self.num_special_tokens_to_add(pair=False) and self.verbose:\n logger.warning(\n \"Setting 'max_len_single_sentence' is now deprecated. \" \"This value is automatically set up.\"\n )\n else:\n raise ValueError(\n \"Setting 'max_len_single_sentence' is now deprecated. \" \"This value is automatically set up.\"\n )\n\n @max_len_sentences_pair.setter\n def max_len_sentences_pair(self, value) -> int:\n \"\"\" For backward compatibility, allow to try to setup 'max_len_sentences_pair' \"\"\"\n if value == self.model_max_length - self.num_special_tokens_to_add(pair=True) and self.verbose:\n logger.warning(\n \"Setting 'max_len_sentences_pair' is now deprecated. \" \"This value is automatically set up.\"\n )\n else:\n raise ValueError(\n \"Setting 'max_len_sentences_pair' is now deprecated. \" \"This value is automatically set up.\"\n )\n\n @classmethod\n def from_pretrained(cls, *inputs, **kwargs):\n r\"\"\"\n Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.\n\n Args:\n pretrained_model_name_or_path: either:\n\n - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.\n - a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.\n - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.\n - (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.\n\n cache_dir: (`optional`) string:\n Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.\n\n force_download: (`optional`) boolean, default False:\n Force to (re-)download the vocabulary files and override the cached versions if they exists.\n\n resume_download: (`optional`) boolean, default False:\n Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.\n\n proxies: (`optional`) dict, default None:\n A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.\n The proxies are used on each request.\n\n inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.\n\n kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.\n\n Examples::\n\n # We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer\n\n # Download vocabulary from S3 and cache.\n tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n # Download vocabulary from S3 (user-uploaded) and cache.\n tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')\n\n # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)\n tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')\n\n # If the tokenizer uses a single vocabulary file, you can point directly to this file\n tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')\n\n # You can link tokens to special vocabulary when instantiating\n tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='')\n # You should be sure '' is in the vocabulary when doing that.\n # Otherwise use tokenizer.add_special_tokens({'unk_token': ''}) instead)\n assert tokenizer.unk_token == ''\n\n \"\"\"\n return cls._from_pretrained(*inputs, **kwargs)\n\n @classmethod\n def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n\n s3_models = list(cls.max_model_input_sizes.keys())\n vocab_files = {}\n init_configuration = {}\n if pretrained_model_name_or_path in s3_models:\n # Get the vocabulary from AWS S3 bucket\n for file_id, map_list in cls.pretrained_vocab_files_map.items():\n vocab_files[file_id] = map_list[pretrained_model_name_or_path]\n if (\n cls.pretrained_init_configuration\n and pretrained_model_name_or_path in cls.pretrained_init_configuration\n ):\n init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()\n else:\n # Get the vocabulary from local files\n logger.info(\n \"Model name '{}' not found in model shortcut name list ({}). \"\n \"Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.\".format(\n pretrained_model_name_or_path, \", \".join(s3_models), pretrained_model_name_or_path\n )\n )\n\n if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):\n if len(cls.vocab_files_names) > 1:\n raise ValueError(\n \"Calling {}.from_pretrained() with the path to a single file or url is not supported.\"\n \"Use a model identifier or the path to a directory instead.\".format(cls.__name__)\n )\n logger.warning(\n \"Calling {}.from_pretrained() with the path to a single file or url is deprecated\".format(\n cls.__name__\n )\n )\n file_id = list(cls.vocab_files_names.keys())[0]\n vocab_files[file_id] = pretrained_model_name_or_path\n else:\n # At this point pretrained_model_name_or_path is either a directory or a model identifier name\n additional_files_names = {\n \"added_tokens_file\": ADDED_TOKENS_FILE,\n \"special_tokens_map_file\": SPECIAL_TOKENS_MAP_FILE,\n \"tokenizer_config_file\": TOKENIZER_CONFIG_FILE,\n \"full_tokenizer_file\": FULL_TOKENIZER_FILE,\n }\n # Look for the tokenizer files\n for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():\n if os.path.isdir(pretrained_model_name_or_path):\n full_file_name = os.path.join(pretrained_model_name_or_path, file_name)\n if not os.path.exists(full_file_name):\n logger.info(\"Didn't find file {}. We won't load it.\".format(full_file_name))\n full_file_name = None\n else:\n full_file_name = hf_bucket_url(\n pretrained_model_name_or_path, filename=file_name, use_cdn=False\n )\n\n vocab_files[file_id] = full_file_name\n\n # Get files from url, cache, or disk depending on the case\n try:\n resolved_vocab_files = {}\n for file_id, file_path in vocab_files.items():\n if file_path is None:\n resolved_vocab_files[file_id] = None\n else:\n resolved_vocab_files[file_id] = cached_path(\n file_path,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n )\n except EnvironmentError:\n if pretrained_model_name_or_path in s3_models:\n msg = \"Couldn't reach server at '{}' to download vocabulary files.\"\n else:\n msg = (\n \"Model name '{}' was not found in tokenizers model name list ({}). \"\n \"We assumed '{}' was a path or url to a directory containing vocabulary files \"\n \"named {}, but couldn't find such vocabulary files at this path or url.\".format(\n pretrained_model_name_or_path,\n \", \".join(s3_models),\n pretrained_model_name_or_path,\n list(cls.vocab_files_names.values()),\n )\n )\n\n raise EnvironmentError(msg)\n\n if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):\n raise EnvironmentError(\n \"Model name '{}' was not found in tokenizers model name list ({}). \"\n \"We assumed '{}' was a path, a model identifier, or url to a directory containing vocabulary files \"\n \"named {} but couldn't find such vocabulary files at this path or url.\".format(\n pretrained_model_name_or_path,\n \", \".join(s3_models),\n pretrained_model_name_or_path,\n list(cls.vocab_files_names.values()),\n )\n )\n\n for file_id, file_path in vocab_files.items():\n if file_path == resolved_vocab_files[file_id]:\n logger.info(\"loading file {}\".format(file_path))\n else:\n logger.info(\"loading file {} from cache at {}\".format(file_path, resolved_vocab_files[file_id]))\n\n # Prepare tokenizer initialization kwargs\n # Did we saved some inputs and kwargs to reload ?\n tokenizer_config_file = resolved_vocab_files.pop(\"tokenizer_config_file\", None)\n if tokenizer_config_file is not None:\n with open(tokenizer_config_file, encoding=\"utf-8\") as tokenizer_config_handle:\n init_kwargs = json.load(tokenizer_config_handle)\n saved_init_inputs = init_kwargs.pop(\"init_inputs\", ())\n if not init_inputs:\n init_inputs = saved_init_inputs\n else:\n init_kwargs = init_configuration\n\n # Update with newly provided kwargs\n init_kwargs.update(kwargs)\n\n # Set max length if needed\n if pretrained_model_name_or_path in cls.max_model_input_sizes:\n # if we're using a pretrained model, ensure the tokenizer\n # wont index sequences longer than the number of positional embeddings\n model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]\n if model_max_length is not None and isinstance(model_max_length, (int, float)):\n init_kwargs[\"model_max_length\"] = min(init_kwargs.get(\"model_max_length\", int(1e30)), model_max_length)\n\n # Merge resolved_vocab_files arguments in init_kwargs.\n added_tokens_file = resolved_vocab_files.pop(\"added_tokens_file\", None)\n for args_name, file_path in resolved_vocab_files.items():\n if args_name not in init_kwargs:\n init_kwargs[args_name] = file_path\n\n # Instantiate tokenizer.\n try:\n tokenizer = cls(*init_inputs, **init_kwargs)\n except OSError:\n raise OSError(\n \"Unable to load vocabulary from file. \"\n \"Please check that the provided vocabulary is accessible and not corrupted.\"\n )\n\n # Save inputs and kwargs for saving and re-loading with ``save_pretrained``\n tokenizer.init_inputs = init_inputs\n tokenizer.init_kwargs = init_kwargs\n\n # If there is a complementary special token map, load it\n special_tokens_map_file = resolved_vocab_files.pop(\"special_tokens_map_file\", None)\n if special_tokens_map_file is not None:\n with open(special_tokens_map_file, encoding=\"utf-8\") as special_tokens_map_handle:\n special_tokens_map = json.load(special_tokens_map_handle)\n\n for key, value in special_tokens_map.items():\n if isinstance(value, dict):\n value = AddedToken(**value)\n setattr(tokenizer, key, value)\n\n # Add supplementary tokens.\n special_tokens = tokenizer.all_special_tokens\n if added_tokens_file is not None:\n with open(added_tokens_file, encoding=\"utf-8\") as added_tokens_handle:\n added_tok_encoder = json.load(added_tokens_handle)\n\n # Sort added tokens by index\n added_tok_encoder_sorted = list(sorted(added_tok_encoder.items(), key=lambda x: x[1]))\n\n for token, index in added_tok_encoder_sorted:\n assert index == len(tokenizer), (\n f\"Non-consecutive added token '{token}' found. \"\n f\"Should have index {len(tokenizer)} but has index {index} in saved vocabulary.\"\n )\n tokenizer.add_tokens(token, special_tokens=bool(token in special_tokens))\n\n # Check all our special tokens are registrered as \"no split\" token (we don't cut them) and are in the vocab\n added_tokens = tokenizer.sanitize_special_tokens()\n if added_tokens:\n logger.warning(\n \"Special tokens have been added in the vocabulary, make sure the associated word emebedding are fine-tuned or trained.\"\n )\n\n return tokenizer\n\n def save_pretrained(self, save_directory) -> Tuple[str]:\n \"\"\" Save the tokenizer vocabulary files together with:\n - added tokens,\n - special-tokens-to-class-attributes-mapping,\n - tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).\n\n Warning: This won't save modifications you may have applied to the tokenizer after the instantiation\n (e.g. modifying tokenizer.do_lower_case after creation).\n\n This method make sure the full tokenizer can then be re-loaded using the\n :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(\"Provided path ({}) should be a directory, not a file\".format(save_directory))\n return\n os.makedirs(save_directory, exist_ok=True)\n\n special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)\n added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)\n tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)\n\n tokenizer_config = copy.deepcopy(self.init_kwargs)\n if len(self.init_inputs) > 0:\n tokenizer_config[\"init_inputs\"] = copy.deepcopy(self.init_inputs)\n for file_id in self.vocab_files_names.keys():\n tokenizer_config.pop(file_id, None)\n\n with open(tokenizer_config_file, \"w\", encoding=\"utf-8\") as f:\n f.write(json.dumps(tokenizer_config, ensure_ascii=False))\n\n with open(special_tokens_map_file, \"w\", encoding=\"utf-8\") as f:\n write_dict = {}\n for key, value in self.special_tokens_map_extended.items():\n if isinstance(value, AddedToken):\n write_dict[key] = value.__getstate__()\n else:\n write_dict[key] = value\n f.write(json.dumps(write_dict, ensure_ascii=False))\n\n added_vocab = self.get_added_vocab()\n if added_vocab:\n with open(added_tokens_file, \"w\", encoding=\"utf-8\") as f:\n out_str = json.dumps(added_vocab, ensure_ascii=False)\n f.write(out_str)\n\n vocab_files = self.save_vocabulary(save_directory)\n\n return vocab_files + (special_tokens_map_file, added_tokens_file)\n\n @add_end_docstrings(\n ENCODE_KWARGS_DOCSTRING,\n \"\"\"\n **kwargs: passed to the `self.tokenize()` method.\n \"\"\",\n )\n def encode(\n self,\n text: Union[TextInput, PreTokenizedInput, EncodedInput],\n text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,\n add_special_tokens: bool = True,\n padding: Union[bool, str] = False,\n truncation: Union[bool, str] = False,\n max_length: Optional[int] = None,\n stride: int = 0,\n return_tensors: Optional[Union[str, TensorType]] = None,\n **kwargs\n ):\n \"\"\"\n Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.\n\n Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.\n\n Args:\n text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):\n The first sequence to be encoded. This can be a string, a list of strings (tokenized string using\n the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`\n method)\n text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):\n Optional second sequence to be encoded. This can be a string, a list of strings (tokenized\n string using the `tokenize` method) or a list of integers (tokenized string ids using the\n `convert_tokens_to_ids` method)\n \"\"\"\n encoded_inputs = self.encode_plus(\n text,\n text_pair=text_pair,\n add_special_tokens=add_special_tokens,\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n stride=stride,\n return_tensors=return_tensors,\n **kwargs,\n )\n\n return encoded_inputs[\"input_ids\"]\n\n def num_special_tokens_to_add(self, pair: bool = False) -> int:\n raise NotImplementedError\n\n def _get_padding_truncation_strategies(\n self, padding=False, truncation=False, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs\n ):\n \"\"\" Find the correct padding/truncation strategy with backward compatibility\n for old arguments (truncation_strategy and pad_to_max_length) and behaviors.\n \"\"\"\n old_truncation_strategy = kwargs.pop(\"truncation_strategy\", \"do_not_truncate\")\n old_pad_to_max_length = kwargs.pop(\"pad_to_max_length\", False)\n\n # Backward compatibility for previous behavior, maybe we should deprecate it:\n # If you only set max_length, it activates truncation for max_length\n if max_length is not None and padding is False and truncation is False:\n if verbose:\n logger.warning(\n \"Truncation was not explicitely activated but `max_length` is provided a specific value, \"\n \"please use `truncation=True` to explicitely truncate examples to max length. \"\n \"Defaulting to 'longest_first' truncation strategy. \"\n \"If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy \"\n \"more precisely by providing a specific strategy to `truncation`.\"\n )\n truncation = \"longest_first\"\n\n # Get padding strategy\n if padding is False and old_pad_to_max_length:\n if verbose:\n warnings.warn(\n \"The `pad_to_max_length` argument is deprecated and will be removed in a future version, \"\n \"use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or \"\n \"use `padding='max_length'` to pad to a max length. In this case, you can give a specific \"\n \"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the \"\n \"maximal input size of the model (e.g. 512 for Bert).\",\n DeprecationWarning,\n )\n if max_length is None:\n padding_strategy = PaddingStrategy.LONGEST\n else:\n padding_strategy = PaddingStrategy.MAX_LENGTH\n elif padding is not False:\n if padding is True:\n padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch\n elif not isinstance(padding, PaddingStrategy):\n padding_strategy = PaddingStrategy(padding)\n else:\n padding_strategy = PaddingStrategy.DO_NOT_PAD\n\n # Get truncation strategy\n if truncation is False and old_truncation_strategy != \"do_not_truncate\":\n if verbose:\n warnings.warn(\n \"The `truncation_strategy` argument is deprecated and will be removed in a future version, \"\n \"use `truncation=True` to truncate examples to a max length. You can give a specific \"\n \"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the \"\n \"maximal input size of the model (e.g. 512 for Bert). \"\n \" If you have pairs of inputs, you can give a specific truncation strategy selected among \"\n \"`truncation='only_first'` (will only truncate the first sentence in the pairs) \"\n \"`truncation='only_second'` (will only truncate the second sentence in the pairs) \"\n \"or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).\",\n DeprecationWarning,\n )\n truncation_strategy = TruncationStrategy(old_truncation_strategy)\n elif truncation is not False:\n if truncation is True:\n truncation_strategy = (\n TruncationStrategy.LONGEST_FIRST\n ) # Default to truncate the longest sequences in pairs of inputs\n elif not isinstance(truncation, TruncationStrategy):\n truncation_strategy = TruncationStrategy(truncation)\n else:\n truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE\n\n # Set max length if needed\n if max_length is None:\n if padding_strategy == PaddingStrategy.MAX_LENGTH:\n if self.model_max_length > LARGE_INTEGER:\n if verbose:\n logger.warning(\n \"Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. \"\n \"Default to no padding.\"\n )\n padding_strategy = PaddingStrategy.DO_NOT_PAD\n else:\n max_length = self.model_max_length\n\n if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:\n if self.model_max_length > LARGE_INTEGER:\n if verbose:\n logger.warning(\n \"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. \"\n \"Default to no truncation.\"\n )\n truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE\n else:\n max_length = self.model_max_length\n\n # Test if we have a padding token\n if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad_token or self.pad_token_id < 0):\n raise ValueError(\n \"Asking to pad but the tokenizer does not have a padding token. \"\n \"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` \"\n \"or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.\"\n )\n\n # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided\n if (\n truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE\n and padding_strategy != PaddingStrategy.DO_NOT_PAD\n and pad_to_multiple_of is not None\n and max_length is not None\n and (max_length % pad_to_multiple_of != 0)\n ):\n raise ValueError(\n f\"Truncation and padding are both activated but \"\n f\"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of}).\"\n )\n\n return padding_strategy, truncation_strategy, max_length, kwargs\n\n @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)\n def __call__(\n self,\n text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],\n text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,\n add_special_tokens: bool = True,\n padding: Union[bool, str] = False,\n truncation: Union[bool, str] = False,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n \"\"\"\n Returns a dictionary containing the encoded sequence or sequence pair and additional information:\n the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.\n\n Args:\n text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):\n The sequence or batch of sequences to be encoded.\n Each sequence can be a string or a list of strings (pre-tokenized string).\n If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`\n (to lift the ambiguity with a batch of sequences)\n text_pair (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):\n The sequence or batch of sequences to be encoded.\n Each sequence can be a string or a list of strings (pre-tokenized string).\n If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`\n (to lift the ambiguity with a batch of sequences)\n \"\"\"\n # Input type checking for clearer error\n assert isinstance(text, str) or (\n isinstance(text, (list, tuple))\n and (\n len(text) == 0\n or (\n isinstance(text[0], str)\n or (isinstance(text[0], (list, tuple)) and (len(text[0]) == 0 or isinstance(text[0][0], str)))\n )\n )\n ), (\n \"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\n \"or `List[List[str]]` (batch of pretokenized examples).\"\n )\n\n assert (\n text_pair is None\n or isinstance(text_pair, str)\n or (\n isinstance(text_pair, (list, tuple))\n and (\n len(text_pair) == 0\n or (\n isinstance(text_pair[0], str)\n or (\n isinstance(text_pair[0], (list, tuple))\n and (len(text_pair[0]) == 0 or isinstance(text_pair[0][0], str))\n )\n )\n )\n )\n ), (\n \"text_pair input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\n \"or `List[List[str]]` (batch of pretokenized examples).\"\n )\n\n is_batched = bool(\n (not is_pretokenized and isinstance(text, (list, tuple)))\n or (is_pretokenized and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)))\n )\n\n if is_batched:\n batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text\n return self.batch_encode_plus(\n batch_text_or_text_pairs=batch_text_or_text_pairs,\n add_special_tokens=add_special_tokens,\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n stride=stride,\n is_pretokenized=is_pretokenized,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=return_tensors,\n return_token_type_ids=return_token_type_ids,\n return_attention_mask=return_attention_mask,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_offsets_mapping=return_offsets_mapping,\n return_length=return_length,\n verbose=verbose,\n **kwargs,\n )\n else:\n return self.encode_plus(\n text=text,\n text_pair=text_pair,\n add_special_tokens=add_special_tokens,\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n stride=stride,\n is_pretokenized=is_pretokenized,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=return_tensors,\n return_token_type_ids=return_token_type_ids,\n return_attention_mask=return_attention_mask,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_offsets_mapping=return_offsets_mapping,\n return_length=return_length,\n verbose=verbose,\n **kwargs,\n )\n\n @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)\n def encode_plus(\n self,\n text: Union[TextInput, PreTokenizedInput, EncodedInput],\n text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,\n add_special_tokens: bool = True,\n padding: Union[bool, str] = False,\n truncation: Union[bool, str] = False,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n \"\"\"\n Returns a dictionary containing the encoded sequence or sequence pair and additional information:\n the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.\n\n Args:\n text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the later only for not-fast tokenizers)):\n The first sequence to be encoded. This can be a string, a list of strings (tokenized string using\n the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`\n method)\n text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):\n Optional second sequence to be encoded. This can be a string, a list of strings (tokenized\n string using the `tokenize` method) or a list of integers (tokenized string ids using the\n `convert_tokens_to_ids` method)\n \"\"\"\n\n # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'\n padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n pad_to_multiple_of=pad_to_multiple_of,\n verbose=verbose,\n **kwargs,\n )\n\n return self._encode_plus(\n text=text,\n text_pair=text_pair,\n add_special_tokens=add_special_tokens,\n padding_strategy=padding_strategy,\n truncation_strategy=truncation_strategy,\n max_length=max_length,\n stride=stride,\n is_pretokenized=is_pretokenized,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=return_tensors,\n return_token_type_ids=return_token_type_ids,\n return_attention_mask=return_attention_mask,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_offsets_mapping=return_offsets_mapping,\n return_length=return_length,\n verbose=verbose,\n **kwargs,\n )\n\n def _encode_plus(\n self,\n text: Union[TextInput, PreTokenizedInput, EncodedInput],\n text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,\n add_special_tokens: bool = True,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n raise NotImplementedError\n\n @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)\n def batch_encode_plus(\n self,\n batch_text_or_text_pairs: Union[\n List[TextInput],\n List[TextInputPair],\n List[PreTokenizedInput],\n List[PreTokenizedInputPair],\n List[EncodedInput],\n List[EncodedInputPair],\n ],\n add_special_tokens: bool = True,\n padding: Union[bool, str] = False,\n truncation: Union[bool, str] = False,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n \"\"\"\n Returns a dictionary containing the encoded sequence or sequence pair and additional information:\n the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.\n\n Args:\n batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`,\n :obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`,\n and for not-fast tokenizers, also:\n :obj:`List[List[int]]`, :obj:`List[Tuple[List[int], List[int]]]`):\n Batch of sequences or pair of sequences to be encoded.\n This can be a list of string/string-sequences/int-sequences or a list of pair of\n string/string-sequences/int-sequence (see details in encode_plus)\n \"\"\"\n\n # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'\n padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n pad_to_multiple_of=pad_to_multiple_of,\n verbose=verbose,\n **kwargs,\n )\n\n return self._batch_encode_plus(\n batch_text_or_text_pairs=batch_text_or_text_pairs,\n add_special_tokens=add_special_tokens,\n padding_strategy=padding_strategy,\n truncation_strategy=truncation_strategy,\n max_length=max_length,\n stride=stride,\n is_pretokenized=is_pretokenized,\n pad_to_multiple_of=pad_to_multiple_of,\n return_tensors=return_tensors,\n return_token_type_ids=return_token_type_ids,\n return_attention_mask=return_attention_mask,\n return_overflowing_tokens=return_overflowing_tokens,\n return_special_tokens_mask=return_special_tokens_mask,\n return_offsets_mapping=return_offsets_mapping,\n return_length=return_length,\n verbose=verbose,\n **kwargs,\n )\n\n def _batch_encode_plus(\n self,\n batch_text_or_text_pairs: Union[\n List[TextInput],\n List[TextInputPair],\n List[PreTokenizedInput],\n List[PreTokenizedInputPair],\n List[EncodedInput],\n List[EncodedInputPair],\n ],\n add_special_tokens: bool = True,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,\n max_length: Optional[int] = None,\n stride: int = 0,\n is_pretokenized: bool = False,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n **kwargs\n ) -> BatchEncoding:\n raise NotImplementedError\n\n def pad(\n self,\n encoded_inputs: Union[\n BatchEncoding,\n List[BatchEncoding],\n Dict[str, EncodedInput],\n Dict[str, List[EncodedInput]],\n List[Dict[str, EncodedInput]],\n ],\n padding: Union[bool, str] = True,\n max_length: Optional[int] = None,\n pad_to_multiple_of: Optional[int] = None,\n return_attention_mask: Optional[bool] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n verbose: bool = True,\n ) -> BatchEncoding:\n \"\"\" Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch.\n\n Padding side (left/right) padding token ids are defined at the tokenizer level\n (with ``self.padding_side``, ``self.pad_token_id`` and ``self.pad_token_type_id``)\n\n Args:\n encoded_inputs: Dictionary of tokenized inputs (`Dict[str, List[int]]`) or batch of tokenized inputs.\n Batch of tokenized inputs can be given as dicts of lists or lists of dicts, both work so you can\n use ``tokenizer.pad()`` during pre-processing as well as in a PyTorch Dataloader collate function.\n (`Dict[str, List[List[int]]]` or `List[Dict[str, List[int]]]`).\n padding: Boolean or specific strategy to use for padding.\n Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among:\n - 'longest' (or `True`) Pad to the longest sequence in the batch\n - 'max_length': Pad to the max length (default)\n - 'do_not_pad' (or `False`): Do not pad\n max_length: maximum length of the returned list and optionally padding length (see below).\n Will truncate by taking into account the special tokens.\n pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.\n This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability\n >= 7.5 (Volta).\n return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)\n return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):\n Can be set to 'tf', 'pt' or 'np' to return respectively TensorFlow :obj:`tf.constant`,\n PyTorch :obj:`torch.Tensor` or Numpy :oj: `np.ndarray` instead of a list of python integers.\n verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):\n Set to ``False`` to avoid printing infos and warnings.\n \"\"\"\n # If we have a list of dicts, let's convert it in a dict of lists\n if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)):\n encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}\n\n assert \"input_ids\" in encoded_inputs, (\n \"You should supply an encoding or a list of encodings to this method. \"\n \"An encoding is the output of one the encoding methods of the tokenizer, i.e. \"\n \"__call__/encode_plus/batch_encode_plus. \"\n )\n\n if not encoded_inputs[\"input_ids\"]:\n if return_attention_mask:\n encoded_inputs[\"attention_mask\"] = []\n return encoded_inputs\n\n # Convert padding_strategy in PaddingStrategy\n padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(\n padding=padding, max_length=max_length, verbose=verbose\n )\n\n if encoded_inputs[\"input_ids\"] and not isinstance(encoded_inputs[\"input_ids\"][0], (list, tuple)):\n encoded_inputs = self._pad(\n encoded_inputs,\n max_length=max_length,\n padding_strategy=padding_strategy,\n pad_to_multiple_of=pad_to_multiple_of,\n return_attention_mask=return_attention_mask,\n )\n return BatchEncoding(encoded_inputs, tensor_type=return_tensors)\n\n batch_size = len(encoded_inputs[\"input_ids\"])\n assert all(\n len(v) == batch_size for v in encoded_inputs.values()\n ), \"Some items in the output dictionnary have a different batch size than others.\"\n\n if padding_strategy == PaddingStrategy.LONGEST:\n max_length = max(len(inputs) for inputs in encoded_inputs[\"input_ids\"])\n padding_strategy = PaddingStrategy.MAX_LENGTH\n\n batch_outputs = {}\n for i in range(batch_size):\n inputs = dict((k, v[i]) for k, v in encoded_inputs.items())\n outputs = self._pad(\n inputs,\n max_length=max_length,\n padding_strategy=padding_strategy,\n pad_to_multiple_of=pad_to_multiple_of,\n return_attention_mask=return_attention_mask,\n )\n\n for key, value in outputs.items():\n if key not in batch_outputs:\n batch_outputs[key] = []\n batch_outputs[key].append(value)\n\n return BatchEncoding(batch_outputs, tensor_type=return_tensors)\n\n def create_token_type_ids_from_sequences(self, token_ids_0: List, token_ids_1: Optional[List] = None) -> List[int]:\n if token_ids_1 is None:\n return len(token_ids_0) * [0]\n return [0] * len(token_ids_0) + [1] * len(token_ids_1)\n\n def build_inputs_with_special_tokens(self, token_ids_0: List, token_ids_1: Optional[List] = None) -> List:\n \"\"\"\n Build model inputs from a sequence or a pair of sequence for sequence classification tasks\n by concatenating and adding special tokens. This implementation does not add special tokens.\n \"\"\"\n if token_ids_1 is None:\n return token_ids_0\n return token_ids_0 + token_ids_1\n\n @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)\n def prepare_for_model(\n self,\n ids: List[int],\n pair_ids: Optional[List[int]] = None,\n add_special_tokens: bool = True,\n padding: Union[bool, str] = False,\n truncation: Union[bool, str] = False,\n max_length: Optional[int] = None,\n stride: int = 0,\n pad_to_multiple_of: Optional[int] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n return_token_type_ids: Optional[bool] = None,\n return_attention_mask: Optional[bool] = None,\n return_overflowing_tokens: bool = False,\n return_special_tokens_mask: bool = False,\n return_offsets_mapping: bool = False,\n return_length: bool = False,\n verbose: bool = True,\n prepend_batch_axis: bool = False,\n **kwargs\n ) -> BatchEncoding:\n \"\"\" Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.\n It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and\n manages a moving window (with user defined stride) for overflowing tokens\n\n Args:\n ids: list of tokenized input ids. Can be obtained from a string by chaining the\n `tokenize` and `convert_tokens_to_ids` methods.\n pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the\n `tokenize` and `convert_tokens_to_ids` methods.\n \"\"\"\n\n if \"return_lengths\" in kwargs:\n if verbose:\n warnings.warn(\n \"The PreTrainedTokenizerBase.prepare_for_model `return_lengths` parameter is deprecated. \"\n \"Please use `return_length` instead.\",\n FutureWarning,\n )\n return_length = kwargs[\"return_lengths\"]\n\n # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'\n padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(\n padding=padding,\n truncation=truncation,\n max_length=max_length,\n pad_to_multiple_of=pad_to_multiple_of,\n verbose=verbose,\n **kwargs,\n )\n\n pair = bool(pair_ids is not None)\n len_ids = len(ids)\n len_pair_ids = len(pair_ids) if pair else 0\n\n # Load from model defaults\n if return_token_type_ids is None:\n return_token_type_ids = \"token_type_ids\" in self.model_input_names\n if return_attention_mask is None:\n return_attention_mask = \"attention_mask\" in self.model_input_names\n\n encoded_inputs = {}\n\n # Compute the total size of the returned encodings\n total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)\n\n # Truncation: Handle max sequence length\n if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:\n ids, pair_ids, overflowing_tokens = self.truncate_sequences(\n ids,\n pair_ids=pair_ids,\n num_tokens_to_remove=total_len - max_length,\n truncation_strategy=truncation_strategy,\n stride=stride,\n )\n if return_overflowing_tokens:\n encoded_inputs[\"overflowing_tokens\"] = overflowing_tokens\n encoded_inputs[\"num_truncated_tokens\"] = total_len - max_length\n\n # Add special tokens\n if add_special_tokens:\n sequence = self.build_inputs_with_special_tokens(ids, pair_ids)\n token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)\n else:\n sequence = ids + pair_ids if pair else ids\n token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])\n\n # Build output dictionnary\n encoded_inputs[\"input_ids\"] = sequence\n if return_token_type_ids:\n encoded_inputs[\"token_type_ids\"] = token_type_ids\n if return_special_tokens_mask:\n if add_special_tokens:\n encoded_inputs[\"special_tokens_mask\"] = self.get_special_tokens_mask(ids, pair_ids)\n else:\n encoded_inputs[\"special_tokens_mask\"] = [0] * len(sequence)\n\n # Check lengths\n if max_length is None and len(encoded_inputs[\"input_ids\"]) > self.model_max_length and verbose:\n logger.warning(\n \"Token indices sequence length is longer than the specified maximum sequence length \"\n \"for this model ({} > {}). Running this sequence through the model will result in \"\n \"indexing errors\".format(len(ids), self.model_max_length)\n )\n\n # Padding\n if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:\n encoded_inputs = self.pad(\n encoded_inputs,\n max_length=max_length,\n padding=padding_strategy.value,\n pad_to_multiple_of=pad_to_multiple_of,\n return_attention_mask=return_attention_mask,\n )\n\n if return_length:\n encoded_inputs[\"length\"] = len(encoded_inputs[\"input_ids\"])\n\n batch_outputs = BatchEncoding(\n encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis\n )\n\n return batch_outputs\n\n def truncate_sequences(\n self,\n ids: List[int],\n pair_ids: Optional[List[int]] = None,\n num_tokens_to_remove: int = 0,\n truncation_strategy: Union[str, TruncationStrategy] = \"longest_first\",\n stride: int = 0,\n ) -> Tuple[List[int], List[int], List[int]]:\n \"\"\" Truncates a sequence pair in place to the maximum length.\n\n Args:\n ids: list of tokenized input ids. Can be obtained from a string by chaining the\n `tokenize` and `convert_tokens_to_ids` methods.\n pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the\n `tokenize` and `convert_tokens_to_ids` methods.\n num_tokens_to_remove (:obj:`int`, `optional`, defaults to ``0``):\n number of tokens to remove using the truncation strategy\n truncation_strategy (:obj:`string`, `optional`, defaults to \"longest_first\"):\n String selected in the following options:\n\n - 'longest_first' (default): Iteratively reduce the inputs sequence until the input is under max_length\n starting from the longest one at each token (when there is a pair of input sequences).\n Overflowing tokens only contains overflow from the first sequence.\n - 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.\n - 'only_second': Only truncate the second sequence\n - 'do_not_truncate'\n stride (:obj:`int`, `optional`, defaults to ``0``):\n If set to a number along with max_length, the overflowing tokens returned will contain some tokens\n from the main sequence returned. The value of this argument defines the number of additional tokens.\n \"\"\"\n if num_tokens_to_remove <= 0:\n return ids, pair_ids, []\n\n if not isinstance(truncation_strategy, TruncationStrategy):\n truncation_strategy = TruncationStrategy(truncation_strategy)\n\n overflowing_tokens = []\n if truncation_strategy == TruncationStrategy.LONGEST_FIRST:\n for _ in range(num_tokens_to_remove):\n if pair_ids is None or len(ids) > len(pair_ids):\n if not overflowing_tokens:\n window_len = min(len(ids), stride + 1)\n else:\n window_len = 1\n overflowing_tokens.extend(ids[-window_len:])\n ids = ids[:-1]\n else:\n if not overflowing_tokens:\n window_len = min(len(pair_ids), stride + 1)\n else:\n window_len = 1\n overflowing_tokens.extend(pair_ids[-window_len:])\n pair_ids = pair_ids[:-1]\n elif truncation_strategy == TruncationStrategy.ONLY_FIRST:\n if len(ids) > num_tokens_to_remove:\n window_len = min(len(ids), stride + num_tokens_to_remove)\n overflowing_tokens = ids[-window_len:]\n ids = ids[:-num_tokens_to_remove]\n else:\n logger.error(\n f\"We need to remove {num_tokens_to_remove} to truncate the input\"\n f\"but the first sequence has a length {len(ids)}. \"\n f\"Please select another truncation strategy than {truncation_strategy}, \"\n f\"for instance 'longest_first' or 'only_second'.\"\n )\n elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:\n if len(pair_ids) > num_tokens_to_remove:\n window_len = min(len(pair_ids), stride + num_tokens_to_remove)\n overflowing_tokens = pair_ids[-window_len:]\n pair_ids = pair_ids[:-num_tokens_to_remove]\n else:\n logger.error(\n f\"We need to remove {num_tokens_to_remove} to truncate the input\"\n f\"but the second sequence has a length {len(pair_ids)}. \"\n f\"Please select another truncation strategy than {truncation_strategy}, \"\n f\"for instance 'longest_first' or 'only_first'.\"\n )\n\n return (ids, pair_ids, overflowing_tokens)\n\n def _pad(\n self,\n encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],\n max_length: Optional[int] = None,\n padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,\n pad_to_multiple_of: Optional[int] = None,\n return_attention_mask: Optional[bool] = None,\n ) -> dict:\n \"\"\" Pad encoded inputs (on left/right and up to predefined legnth or max length in the batch)\n\n Args:\n encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).\n max_length: maximum length of the returned list and optionally padding length (see below).\n Will truncate by taking into account the special tokens.\n padding_strategy: PaddingStrategy to use for padding.\n - PaddingStrategy.LONGEST Pad to the longest sequence in the batch\n - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)\n - PaddingStrategy.DO_NOT_PAD: Do not pad\n The tokenizer padding sides are defined in self.padding_side:\n - 'left': pads on the left of the sequences\n - 'right': pads on the right of the sequences\n pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.\n This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability\n >= 7.5 (Volta).\n return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)\n \"\"\"\n # Load from model defaults\n if return_attention_mask is None:\n return_attention_mask = \"attention_mask\" in self.model_input_names\n\n if padding_strategy == PaddingStrategy.LONGEST:\n max_length = len(encoded_inputs[\"input_ids\"])\n\n if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):\n max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of\n\n needs_to_be_padded = (\n padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs[\"input_ids\"]) != max_length\n )\n\n if needs_to_be_padded:\n difference = max_length - len(encoded_inputs[\"input_ids\"])\n if self.padding_side == \"right\":\n if return_attention_mask:\n encoded_inputs[\"attention_mask\"] = [1] * len(encoded_inputs[\"input_ids\"]) + [0] * difference\n if \"token_type_ids\" in encoded_inputs:\n encoded_inputs[\"token_type_ids\"] = (\n encoded_inputs[\"token_type_ids\"] + [self.pad_token_type_id] * difference\n )\n if \"special_tokens_mask\" in encoded_inputs:\n encoded_inputs[\"special_tokens_mask\"] = encoded_inputs[\"special_tokens_mask\"] + [1] * difference\n encoded_inputs[\"input_ids\"] = encoded_inputs[\"input_ids\"] + [self.pad_token_id] * difference\n elif self.padding_side == \"left\":\n if return_attention_mask:\n encoded_inputs[\"attention_mask\"] = [0] * difference + [1] * len(encoded_inputs[\"input_ids\"])\n if \"token_type_ids\" in encoded_inputs:\n encoded_inputs[\"token_type_ids\"] = [self.pad_token_type_id] * difference + encoded_inputs[\n \"token_type_ids\"\n ]\n if \"special_tokens_mask\" in encoded_inputs:\n encoded_inputs[\"special_tokens_mask\"] = [1] * difference + encoded_inputs[\"special_tokens_mask\"]\n encoded_inputs[\"input_ids\"] = [self.pad_token_id] * difference + encoded_inputs[\"input_ids\"]\n else:\n raise ValueError(\"Invalid padding strategy:\" + str(self.padding_side))\n else:\n if return_attention_mask:\n encoded_inputs[\"attention_mask\"] = [1] * len(encoded_inputs[\"input_ids\"])\n\n return encoded_inputs\n\n def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:\n return [self.decode(seq, **kwargs) for seq in sequences]\n\n def decode(\n self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True\n ) -> str:\n \"\"\"\n Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary\n with options to remove special tokens and clean up tokenization spaces.\n Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.\n\n Args:\n token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.\n skip_special_tokens: if set to True, will replace special tokens.\n clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.\n \"\"\"\n raise NotImplementedError\n\n def get_special_tokens_mask(\n self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False\n ) -> List[int]:\n \"\"\"\n Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding\n special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.\n\n Args:\n token_ids_0: list of ids (must not contain special tokens)\n token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids\n for sequence pairs\n already_has_special_tokens: (default False) Set to True if the token list is already formated with\n special tokens for the model\n\n Returns:\n A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\n \"\"\"\n assert already_has_special_tokens and token_ids_1 is None, (\n \"You cannot use ``already_has_special_tokens=False`` with this tokenizer. \"\n \"Please use a slow (full python) tokenizer to activate this argument.\"\n \"Or set `return_special_token_mask=True` when calling the encoding method \"\n \"to get the special tokens mask in any tokenizer. \"\n )\n\n all_special_ids = self.all_special_ids # cache the property\n\n special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]\n\n return special_tokens_mask\n\n @staticmethod\n def clean_up_tokenization(out_string: str) -> str:\n \"\"\" Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.\n \"\"\"\n out_string = (\n out_string.replace(\" .\", \".\")\n .replace(\" ?\", \"?\")\n .replace(\" !\", \"!\")\n .replace(\" ,\", \",\")\n .replace(\" ' \", \"'\")\n .replace(\" n't\", \"n't\")\n .replace(\" 'm\", \"'m\")\n .replace(\" 's\", \"'s\")\n .replace(\" 've\", \"'ve\")\n .replace(\" 're\", \"'re\")\n )\n return out_string\n"
},
{
"path": "data/dataset_refer_bert.py",
"content": "import os\nimport sys\nimport torch.utils.data as data\nimport torch\nfrom torchvision import transforms\nfrom torch.autograd import Variable\nimport numpy as np\nfrom PIL import Image\nimport torchvision.transforms.functional as TF\nimport random\n\nfrom bert.tokenization_bert import BertTokenizer\n\nimport h5py\nfrom refer.refer import REFER\n\nfrom args import get_parser\n\n# Dataset configuration initialization\nparser = get_parser()\nargs = parser.parse_args()\n\n\nclass ReferDataset(data.Dataset):\n\n def __init__(self,\n args,\n image_transforms=None,\n target_transforms=None,\n split='train',\n eval_mode=False):\n\n self.classes = []\n self.image_transforms = image_transforms\n self.target_transform = target_transforms\n self.split = split\n self.refer = REFER(args.refer_data_root, args.dataset, args.splitBy)\n\n self.max_tokens = 20\n\n ref_ids = self.refer.getRefIds(split=self.split)\n img_ids = self.refer.getImgIds(ref_ids)\n\n all_imgs = self.refer.Imgs\n self.imgs = list(all_imgs[i] for i in img_ids)\n self.ref_ids = ref_ids\n\n self.input_ids = []\n self.attention_masks = []\n self.tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer)\n\n self.eval_mode = eval_mode\n # if we are testing on a dataset, test all sentences of an object;\n # o/w, we are validating during training, randomly sample one sentence for efficiency\n for r in ref_ids:\n ref = self.refer.Refs[r]\n\n sentences_for_ref = []\n attentions_for_ref = []\n\n for i, (el, sent_id) in enumerate(zip(ref['sentences'], ref['sent_ids'])):\n sentence_raw = el['raw']\n attention_mask = [0] * self.max_tokens\n padded_input_ids = [0] * self.max_tokens\n\n input_ids = self.tokenizer.encode(text=sentence_raw, add_special_tokens=True)\n\n # truncation of tokens\n input_ids = input_ids[:self.max_tokens]\n\n padded_input_ids[:len(input_ids)] = input_ids\n attention_mask[:len(input_ids)] = [1]*len(input_ids)\n\n sentences_for_ref.append(torch.tensor(padded_input_ids).unsqueeze(0))\n attentions_for_ref.append(torch.tensor(attention_mask).unsqueeze(0))\n\n self.input_ids.append(sentences_for_ref)\n self.attention_masks.append(attentions_for_ref)\n\n def get_classes(self):\n return self.classes\n\n def __len__(self):\n return len(self.ref_ids)\n\n def __getitem__(self, index):\n this_ref_id = self.ref_ids[index]\n this_img_id = self.refer.getImgIds(this_ref_id)\n this_img = self.refer.Imgs[this_img_id[0]]\n\n img = Image.open(os.path.join(self.refer.IMAGE_DIR, this_img['file_name'])).convert(\"RGB\")\n\n ref = self.refer.loadRefs(this_ref_id)\n\n ref_mask = np.array(self.refer.getMask(ref[0])['mask'])\n annot = np.zeros(ref_mask.shape)\n annot[ref_mask == 1] = 1\n\n annot = Image.fromarray(annot.astype(np.uint8), mode=\"P\")\n\n if self.image_transforms is not None:\n # resize, from PIL to tensor, and mean and std normalization\n img, target = self.image_transforms(img, annot)\n\n if self.eval_mode:\n embedding = []\n att = []\n for s in range(len(self.input_ids[index])):\n e = self.input_ids[index][s]\n a = self.attention_masks[index][s]\n embedding.append(e.unsqueeze(-1))\n att.append(a.unsqueeze(-1))\n\n tensor_embeddings = torch.cat(embedding, dim=-1)\n attention_mask = torch.cat(att, dim=-1)\n else:\n choice_sent = np.random.choice(len(self.input_ids[index]))\n tensor_embeddings = self.input_ids[index][choice_sent]\n attention_mask = self.attention_masks[index][choice_sent]\n\n return img, target, tensor_embeddings, attention_mask\n"
},
{
"path": "demo_inference.py",
"content": "image_path = './demo/demo.jpg'\nsentence = 'the most handsome guy'\nweights = './checkpoints/refcoco.pth'\ndevice = 'cuda:0'\n\n# pre-process the input image\nfrom PIL import Image\nimport torchvision.transforms as T\nimport numpy as np\nimg = Image.open(image_path).convert(\"RGB\")\nimg_ndarray = np.array(img) # (orig_h, orig_w, 3); for visualization\noriginal_w, original_h = img.size # PIL .size returns width first and height second\n\nimage_transforms = T.Compose(\n [\n T.Resize(480),\n T.ToTensor(),\n T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ]\n)\n\nimg = image_transforms(img).unsqueeze(0) # (1, 3, 480, 480)\nimg = img.to(device) # for inference (input)\n\n# pre-process the raw sentence\nfrom bert.tokenization_bert import BertTokenizer\nimport torch\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nsentence_tokenized = tokenizer.encode(text=sentence, add_special_tokens=True)\nsentence_tokenized = sentence_tokenized[:20] # if the sentence is longer than 20, then this truncates it to 20 words\n# pad the tokenized sentence\npadded_sent_toks = [0] * 20\npadded_sent_toks[:len(sentence_tokenized)] = sentence_tokenized\n# create a sentence token mask: 1 for real words; 0 for padded tokens\nattention_mask = [0] * 20\nattention_mask[:len(sentence_tokenized)] = [1]*len(sentence_tokenized)\n# convert lists to tensors\npadded_sent_toks = torch.tensor(padded_sent_toks).unsqueeze(0) # (1, 20)\nattention_mask = torch.tensor(attention_mask).unsqueeze(0) # (1, 20)\npadded_sent_toks = padded_sent_toks.to(device) # for inference (input)\nattention_mask = attention_mask.to(device) # for inference (input)\n\n# initialize model and load weights\nfrom bert.modeling_bert import BertModel\nfrom lib import segmentation\n\n# construct a mini args class; like from a config file\n\n\nclass args:\n swin_type = 'base'\n window12 = True\n mha = ''\n fusion_drop = 0.0\n\n\nsingle_model = segmentation.__dict__['lavt'](pretrained='', args=args)\nsingle_model.to(device)\nmodel_class = BertModel\nsingle_bert_model = model_class.from_pretrained('bert-base-uncased')\nsingle_bert_model.pooler = None\n\ncheckpoint = torch.load(weights, map_location='cpu')\nsingle_bert_model.load_state_dict(checkpoint['bert_model'])\nsingle_model.load_state_dict(checkpoint['model'])\nmodel = single_model.to(device)\nbert_model = single_bert_model.to(device)\n\n\n# inference\nimport torch.nn.functional as F\nlast_hidden_states = bert_model(padded_sent_toks, attention_mask=attention_mask)[0]\nembedding = last_hidden_states.permute(0, 2, 1)\noutput = model(img, embedding, l_mask=attention_mask.unsqueeze(-1))\noutput = output.argmax(1, keepdim=True) # (1, 1, 480, 480)\noutput = F.interpolate(output.float(), (original_h, original_w)) # 'nearest'; resize to the original image size\noutput = output.squeeze() # (orig_h, orig_w)\noutput = output.cpu().data.numpy() # (orig_h, orig_w)\n\n\n# show/save results\ndef overlay_davis(image, mask, colors=[[0, 0, 0], [255, 0, 0]], cscale=1, alpha=0.4):\n from scipy.ndimage.morphology import binary_dilation\n\n colors = np.reshape(colors, (-1, 3))\n colors = np.atleast_2d(colors) * cscale\n\n im_overlay = image.copy()\n object_ids = np.unique(mask)\n\n for object_id in object_ids[1:]:\n # Overlay color on binary mask\n foreground = image*alpha + np.ones(image.shape)*(1-alpha) * np.array(colors[object_id])\n binary_mask = mask == object_id\n\n # Compose image\n im_overlay[binary_mask] = foreground[binary_mask]\n\n # countours = skimage.morphology.binary.binary_dilation(binary_mask) - binary_mask\n countours = binary_dilation(binary_mask) ^ binary_mask\n # countours = cv2.dilate(binary_mask, cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))) - binary_mask\n im_overlay[countours, :] = 0\n\n return im_overlay.astype(image.dtype)\n\n\noutput = output.astype(np.uint8) # (orig_h, orig_w), np.uint8\n# Overlay the mask on the image\nvisualization = overlay_davis(img_ndarray, output) # red\nvisualization = Image.fromarray(visualization)\n# show the visualization\n#visualization.show()\n# Save the visualization\nvisualization.save('./demo/demo_result.jpg')\n\n\n\n\n"
},
{
"path": "lib/_utils.py",
"content": "from collections import OrderedDict\nimport sys\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom bert.modeling_bert import BertModel\n\n\nclass _LAVTSimpleDecode(nn.Module):\n def __init__(self, backbone, classifier):\n super(_LAVTSimpleDecode, self).__init__()\n self.backbone = backbone\n self.classifier = classifier\n\n def forward(self, x, l_feats, l_mask):\n input_shape = x.shape[-2:]\n features = self.backbone(x, l_feats, l_mask)\n x_c1, x_c2, x_c3, x_c4 = features\n x = self.classifier(x_c4, x_c3, x_c2, x_c1)\n x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)\n\n return x\n\n\nclass LAVT(_LAVTSimpleDecode):\n pass\n\n\n###############################################\n# LAVT One: put BERT inside the overall model #\n###############################################\nclass _LAVTOneSimpleDecode(nn.Module):\n def __init__(self, backbone, classifier, args):\n super(_LAVTOneSimpleDecode, self).__init__()\n self.backbone = backbone\n self.classifier = classifier\n self.text_encoder = BertModel.from_pretrained(args.ck_bert)\n self.text_encoder.pooler = None\n\n def forward(self, x, text, l_mask):\n input_shape = x.shape[-2:]\n ### language inference ###\n l_feats = self.text_encoder(text, attention_mask=l_mask)[0] # (6, 10, 768)\n l_feats = l_feats.permute(0, 2, 1) # (B, 768, N_l) to make Conv1d happy\n l_mask = l_mask.unsqueeze(dim=-1) # (batch, N_l, 1)\n ##########################\n features = self.backbone(x, l_feats, l_mask)\n x_c1, x_c2, x_c3, x_c4 = features\n x = self.classifier(x_c4, x_c3, x_c2, x_c1)\n x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)\n\n return x\n\n\nclass LAVTOne(_LAVTOneSimpleDecode):\n pass\n"
},
{
"path": "lib/backbone.py",
"content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nimport numpy as np\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\nfrom .mmcv_custom import load_checkpoint\nfrom mmseg.utils import get_root_logger\n\n\nclass Mlp(nn.Module):\n \"\"\" Multilayer perceptron.\"\"\"\n\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\" Window based multi-head self attention (W-MSA) module with relative position bias.\n It supports both of shifted and non-shifted window.\n\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):\n\n super().__init__()\n self.dim = dim\n self.window_size = window_size # Wh, Ww\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim ** -0.5\n\n # define a parameter table of relative position bias\n self.relative_position_bias_table = nn.Parameter(\n torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH\n\n # get pair-wise relative position index for each token inside the window\n coords_h = torch.arange(self.window_size[0])\n coords_w = torch.arange(self.window_size[1])\n coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww\n coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww\n relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww\n relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2\n relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0\n relative_coords[:, :, 1] += self.window_size[1] - 1\n relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww\n self.register_buffer(\"relative_position_index\", relative_position_index)\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n trunc_normal_(self.relative_position_bias_table, std=.02)\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x, mask=None):\n \"\"\" Forward function.\n\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n \"\"\"\n B_, N, C = x.shape\n qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)\n q = q * self.scale\n attn = (q @ k.transpose(-2, -1))\n relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(\n self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH\n relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C) # cat op\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass SwinTransformerBlock(nn.Module):\n \"\"\" Swin Transformer Block.\n\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self, dim, num_heads, window_size=7, shift_size=0,\n mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n act_layer=nn.GELU, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.shift_size = shift_size\n self.mlp_ratio = mlp_ratio\n assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n self.norm1 = norm_layer(dim)\n self.attn = WindowAttention(\n dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,\n qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n\n self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n self.H = None\n self.W = None\n\n def forward(self, x, mask_matrix):\n \"\"\" Forward function.\n\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n mask_matrix: Attention mask for cyclic shift.\n \"\"\"\n B, L, C = x.shape\n H, W = self.H, self.W\n assert L == H * W, \"input feature has wrong size\"\n\n shortcut = x\n x = self.norm1(x)\n x = x.view(B, H, W, C)\n\n # pad feature maps to multiples of window size\n pad_l = pad_t = 0\n pad_r = (self.window_size - W % self.window_size) % self.window_size\n pad_b = (self.window_size - H % self.window_size) % self.window_size\n x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))\n _, Hp, Wp, _ = x.shape\n\n # cyclic shift\n if self.shift_size > 0:\n shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n attn_mask = mask_matrix\n else:\n shifted_x = x\n attn_mask = None\n\n # partition windows\n x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C\n x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C\n\n # W-MSA/SW-MSA\n attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n else:\n x = shifted_x\n\n if pad_r > 0 or pad_b > 0:\n x = x[:, :H, :W, :].contiguous()\n\n x = x.view(B, H * W, C)\n\n # FFN feed-forward network\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n\nclass PatchMerging(nn.Module):\n \"\"\" Patch Merging Layer\n\n Args:\n dim (int): Number of input channels.\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n self.norm = norm_layer(4 * dim)\n\n def forward(self, x, H, W):\n \"\"\" Forward function.\n\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n\n x = x.view(B, H, W, C)\n\n # padding\n pad_input = (H % 2 == 1) or (W % 2 == 1)\n if pad_input:\n x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C\n x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C\n x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C\n x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C\n x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C\n x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C\n\n x = self.norm(x)\n x = self.reduction(x)\n\n return x\n\n\nclass PatchEmbed(nn.Module):\n \"\"\" Image to Patch Embedding\n\n Args:\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\n\nclass MultiModalSwinTransformer(nn.Module):\n def __init__(self,\n pretrain_img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_size=7,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.,\n attn_drop_rate=0.,\n drop_path_rate=0.2,\n norm_layer=nn.LayerNorm,\n ape=False,\n patch_norm=True,\n out_indices=(0, 1, 2, 3),\n frozen_stages=-1,\n use_checkpoint=False,\n num_heads_fusion=[1, 1, 1, 1],\n fusion_drop=0.0\n ):\n super().__init__()\n\n self.pretrain_img_size = pretrain_img_size\n self.num_layers = len(depths)\n self.embed_dim = embed_dim\n self.ape = ape\n self.patch_norm = patch_norm\n self.out_indices = out_indices\n self.frozen_stages = frozen_stages\n\n # split image into non-overlapping patches\n self.patch_embed = PatchEmbed(\n patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,\n norm_layer=norm_layer if self.patch_norm else None)\n\n # absolute position embedding\n if self.ape:\n pretrain_img_size = to_2tuple(pretrain_img_size)\n patch_size = to_2tuple(patch_size)\n patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]\n\n self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))\n trunc_normal_(self.absolute_pos_embed, std=.02)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n # stochastic depth\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule\n\n # build layers\n self.layers = nn.ModuleList()\n for i_layer in range(self.num_layers):\n layer = MMBasicLayer(\n dim=int(embed_dim * 2 ** i_layer),\n depth=depths[i_layer],\n num_heads=num_heads[i_layer],\n window_size=window_size,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop_rate,\n attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],\n norm_layer=norm_layer,\n downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n use_checkpoint=use_checkpoint,\n num_heads_fusion=num_heads_fusion[i_layer],\n fusion_drop=fusion_drop\n )\n self.layers.append(layer)\n\n num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]\n self.num_features = num_features\n\n # add a norm layer for each output\n for i_layer in out_indices:\n layer = norm_layer(num_features[i_layer])\n layer_name = f'norm{i_layer}'\n self.add_module(layer_name, layer)\n\n self._freeze_stages()\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n def init_weights(self, pretrained=None):\n \"\"\"Initialize the weights in backbone.\n\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n \"\"\"\n\n def _init_weights(m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n if isinstance(pretrained, str):\n self.apply(_init_weights)\n logger = get_root_logger()\n load_checkpoint(self, pretrained, strict=('upernet' in pretrained), logger=logger)\n elif pretrained is None:\n self.apply(_init_weights)\n else:\n raise TypeError('pretrained must be a str or None')\n\n def forward(self, x, l, l_mask):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = []\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, l, l_mask)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f'norm{i}')\n x_out = norm_layer(x_out) # output of a Block has shape (B, H*W, dim)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs.append(out)\n\n return tuple(outs)\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(MultiModalSwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\nclass MMBasicLayer(nn.Module):\n def __init__(self,\n dim,\n depth,\n num_heads,\n window_size=7,\n mlp_ratio=4.,\n qkv_bias=True,\n qk_scale=None,\n drop=0.,\n attn_drop=0.,\n drop_path=0.,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False,\n num_heads_fusion=1,\n fusion_drop=0.0\n ):\n super().__init__()\n self.window_size = window_size\n self.shift_size = window_size // 2\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n self.dim = dim\n\n # build blocks\n self.blocks = nn.ModuleList([\n SwinTransformerBlock(\n dim=dim,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer)\n for i in range(depth)])\n\n # fuse before downsampling\n self.fusion = PWAM(dim, # both the visual input and for combining, num of channels\n dim, # v_in\n 768, # l_in\n dim, # key\n dim, # value\n num_heads=num_heads_fusion,\n dropout=fusion_drop)\n\n self.res_gate = nn.Sequential(\n nn.Linear(dim, dim, bias=False),\n nn.ReLU(),\n nn.Linear(dim, dim, bias=False),\n nn.Tanh()\n )\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, H, W, l, l_mask):\n \"\"\" Forward function.\n\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n\n # calculate attention mask for SW-MSA\n Hp = int(np.ceil(H / self.window_size)) * self.window_size\n Wp = int(np.ceil(W / self.window_size)) * self.window_size\n img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1\n h_slices = (slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None))\n w_slices = (slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None))\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask) # output of a Block has shape (B, H*W, dim)\n\n # PWAM fusion\n x_residual = self.fusion(x, l, l_mask)\n # apply a gate on the residual\n x = x + (self.res_gate(x_residual) * x_residual)\n\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x_residual, H, W, x_down, Wh, Ww\n else:\n return x_residual, H, W, x, H, W\n\n\nclass PWAM(nn.Module):\n def __init__(self, dim, v_in_channels, l_in_channels, key_channels, value_channels, num_heads=0, dropout=0.0):\n super(PWAM, self).__init__()\n # input x shape: (B, H*W, dim)\n self.vis_project = nn.Sequential(nn.Conv1d(dim, dim, 1, 1), # the init function sets bias to 0 if bias is True\n nn.GELU(),\n nn.Dropout(dropout)\n )\n\n self.image_lang_att = SpatialImageLanguageAttention(v_in_channels, # v_in\n l_in_channels, # l_in\n key_channels, # key\n value_channels, # value\n out_channels=value_channels, # out\n num_heads=num_heads)\n\n self.project_mm = nn.Sequential(nn.Conv1d(value_channels, value_channels, 1, 1),\n nn.GELU(),\n nn.Dropout(dropout)\n )\n\n def forward(self, x, l, l_mask):\n # input x shape: (B, H*W, dim)\n vis = self.vis_project(x.permute(0, 2, 1)) # (B, dim, H*W)\n\n lang = self.image_lang_att(x, l, l_mask) # (B, H*W, dim)\n\n lang = lang.permute(0, 2, 1) # (B, dim, H*W)\n\n mm = torch.mul(vis, lang)\n mm = self.project_mm(mm) # (B, dim, H*W)\n\n mm = mm.permute(0, 2, 1) # (B, H*W, dim)\n\n return mm\n\n\nclass SpatialImageLanguageAttention(nn.Module):\n def __init__(self, v_in_channels, l_in_channels, key_channels, value_channels, out_channels=None, num_heads=1):\n super(SpatialImageLanguageAttention, self).__init__()\n # x shape: (B, H*W, v_in_channels)\n # l input shape: (B, l_in_channels, N_l)\n # l_mask shape: (B, N_l, 1)\n self.v_in_channels = v_in_channels\n self.l_in_channels = l_in_channels\n self.out_channels = out_channels\n self.key_channels = key_channels\n self.value_channels = value_channels\n self.num_heads = num_heads\n if out_channels is None:\n self.out_channels = self.value_channels\n\n # Keys: language features: (B, l_in_channels, #words)\n # avoid any form of spatial normalization because a sentence contains many padding 0s\n self.f_key = nn.Sequential(\n nn.Conv1d(self.l_in_channels, self.key_channels, kernel_size=1, stride=1),\n )\n\n # Queries: visual features: (B, H*W, v_in_channels)\n self.f_query = nn.Sequential(\n nn.Conv1d(self.v_in_channels, self.key_channels, kernel_size=1, stride=1),\n nn.InstanceNorm1d(self.key_channels),\n )\n\n # Values: language features: (B, l_in_channels, #words)\n self.f_value = nn.Sequential(\n nn.Conv1d(self.l_in_channels, self.value_channels, kernel_size=1, stride=1),\n )\n\n # Out projection\n self.W = nn.Sequential(\n nn.Conv1d(self.value_channels, self.out_channels, kernel_size=1, stride=1),\n nn.InstanceNorm1d(self.out_channels),\n )\n\n def forward(self, x, l, l_mask):\n # x shape: (B, H*W, v_in_channels)\n # l input shape: (B, l_in_channels, N_l)\n # l_mask shape: (B, N_l, 1)\n B, HW = x.size(0), x.size(1)\n x = x.permute(0, 2, 1) # (B, key_channels, H*W)\n l_mask = l_mask.permute(0, 2, 1) # (B, N_l, 1) -> (B, 1, N_l)\n\n query = self.f_query(x) # (B, key_channels, H*W) if Conv1D\n query = query.permute(0, 2, 1) # (B, H*W, key_channels)\n key = self.f_key(l) # (B, key_channels, N_l)\n value = self.f_value(l) # (B, self.value_channels, N_l)\n key = key * l_mask # (B, key_channels, N_l)\n value = value * l_mask # (B, self.value_channels, N_l)\n n_l = value.size(-1)\n query = query.reshape(B, HW, self.num_heads, self.key_channels//self.num_heads).permute(0, 2, 1, 3)\n # (b, num_heads, H*W, self.key_channels//self.num_heads)\n key = key.reshape(B, self.num_heads, self.key_channels//self.num_heads, n_l)\n # (b, num_heads, self.key_channels//self.num_heads, n_l)\n value = value.reshape(B, self.num_heads, self.value_channels//self.num_heads, n_l)\n # # (b, num_heads, self.value_channels//self.num_heads, n_l)\n l_mask = l_mask.unsqueeze(1) # (b, 1, 1, n_l)\n\n sim_map = torch.matmul(query, key) # (B, self.num_heads, H*W, N_l)\n sim_map = (self.key_channels ** -.5) * sim_map # scaled dot product\n\n sim_map = sim_map + (1e4*l_mask - 1e4) # assign a very small number to padding positions\n sim_map = F.softmax(sim_map, dim=-1) # (B, num_heads, h*w, N_l)\n out = torch.matmul(sim_map, value.permute(0, 1, 3, 2)) # (B, num_heads, H*W, self.value_channels//num_heads)\n out = out.permute(0, 2, 1, 3).contiguous().reshape(B, HW, self.value_channels) # (B, H*W, value_channels)\n out = out.permute(0, 2, 1) # (B, value_channels, HW)\n out = self.W(out) # (B, value_channels, HW)\n out = out.permute(0, 2, 1) # (B, HW, value_channels)\n\n return out\n"
},
{
"path": "lib/mask_predictor.py",
"content": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom collections import OrderedDict\n\n\nclass SimpleDecoding(nn.Module):\n def __init__(self, c4_dims, factor=2):\n super(SimpleDecoding, self).__init__()\n\n hidden_size = c4_dims//factor\n c4_size = c4_dims\n c3_size = c4_dims//(factor**1)\n c2_size = c4_dims//(factor**2)\n c1_size = c4_dims//(factor**3)\n\n self.conv1_4 = nn.Conv2d(c4_size+c3_size, hidden_size, 3, padding=1, bias=False)\n self.bn1_4 = nn.BatchNorm2d(hidden_size)\n self.relu1_4 = nn.ReLU()\n self.conv2_4 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False)\n self.bn2_4 = nn.BatchNorm2d(hidden_size)\n self.relu2_4 = nn.ReLU()\n\n self.conv1_3 = nn.Conv2d(hidden_size + c2_size, hidden_size, 3, padding=1, bias=False)\n self.bn1_3 = nn.BatchNorm2d(hidden_size)\n self.relu1_3 = nn.ReLU()\n self.conv2_3 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False)\n self.bn2_3 = nn.BatchNorm2d(hidden_size)\n self.relu2_3 = nn.ReLU()\n\n self.conv1_2 = nn.Conv2d(hidden_size + c1_size, hidden_size, 3, padding=1, bias=False)\n self.bn1_2 = nn.BatchNorm2d(hidden_size)\n self.relu1_2 = nn.ReLU()\n self.conv2_2 = nn.Conv2d(hidden_size, hidden_size, 3, padding=1, bias=False)\n self.bn2_2 = nn.BatchNorm2d(hidden_size)\n self.relu2_2 = nn.ReLU()\n\n self.conv1_1 = nn.Conv2d(hidden_size, 2, 1)\n\n def forward(self, x_c4, x_c3, x_c2, x_c1):\n # fuse Y4 and Y3\n if x_c4.size(-2) < x_c3.size(-2) or x_c4.size(-1) < x_c3.size(-1):\n x_c4 = F.interpolate(input=x_c4, size=(x_c3.size(-2), x_c3.size(-1)), mode='bilinear', align_corners=True)\n x = torch.cat([x_c4, x_c3], dim=1)\n x = self.conv1_4(x)\n x = self.bn1_4(x)\n x = self.relu1_4(x)\n x = self.conv2_4(x)\n x = self.bn2_4(x)\n x = self.relu2_4(x)\n # fuse top-down features and Y2 features\n if x.size(-2) < x_c2.size(-2) or x.size(-1) < x_c2.size(-1):\n x = F.interpolate(input=x, size=(x_c2.size(-2), x_c2.size(-1)), mode='bilinear', align_corners=True)\n x = torch.cat([x, x_c2], dim=1)\n x = self.conv1_3(x)\n x = self.bn1_3(x)\n x = self.relu1_3(x)\n x = self.conv2_3(x)\n x = self.bn2_3(x)\n x = self.relu2_3(x)\n # fuse top-down features and Y1 features\n if x.size(-2) < x_c1.size(-2) or x.size(-1) < x_c1.size(-1):\n x = F.interpolate(input=x, size=(x_c1.size(-2), x_c1.size(-1)), mode='bilinear', align_corners=True)\n x = torch.cat([x, x_c1], dim=1)\n x = self.conv1_2(x)\n x = self.bn1_2(x)\n x = self.relu1_2(x)\n x = self.conv2_2(x)\n x = self.bn2_2(x)\n x = self.relu2_2(x)\n\n return self.conv1_1(x)\n"
},
{
"path": "lib/mmcv_custom/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom .checkpoint import load_checkpoint\n\n__all__ = ['load_checkpoint']\n"
},
{
"path": "lib/mmcv_custom/checkpoint.py",
"content": "# Copyright (c) Open-MMLab. All rights reserved.\nimport io\nimport os\nimport os.path as osp\nimport pkgutil\nimport time\nimport warnings\nfrom collections import OrderedDict\nfrom importlib import import_module\nfrom tempfile import TemporaryDirectory\n\nimport torch\nimport torchvision\nfrom torch.optim import Optimizer\nfrom torch.utils import model_zoo\nfrom torch.nn import functional as F\n\nimport mmcv\nfrom mmcv.fileio import FileClient\nfrom mmcv.fileio import load as load_file\nfrom mmcv.parallel import is_module_wrapper\nfrom mmcv.utils import mkdir_or_exist\nfrom mmcv.runner import get_dist_info\n\nENV_MMCV_HOME = 'MMCV_HOME'\nENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\nDEFAULT_CACHE_DIR = '~/.cache'\n\n\ndef _get_mmcv_home():\n mmcv_home = os.path.expanduser(\n os.getenv(\n ENV_MMCV_HOME,\n os.path.join(\n os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))\n\n mkdir_or_exist(mmcv_home)\n return mmcv_home\n\n\ndef load_state_dict(module, state_dict, strict=False, logger=None):\n \"\"\"Load state_dict to a module.\n\n This method is modified from :meth:`torch.nn.Module.load_state_dict`.\n Default value for ``strict`` is set to ``False`` and the message for\n param mismatch will NOT be shown if strict is False.\n\n Args:\n module (Module): Module that receives the state_dict.\n state_dict (OrderedDict): Weights.\n strict (bool): whether to strictly enforce that the keys\n in :attr:`state_dict` match the keys returned by this module's\n :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.\n logger (:obj:`logging.Logger`, optional): Logger to log the error\n message. If not specified, print function will be used.\n \"\"\"\n unexpected_keys = []\n all_missing_keys = []\n err_msg = []\n\n metadata = getattr(state_dict, '_metadata', None)\n state_dict = state_dict.copy()\n if metadata is not None:\n state_dict._metadata = metadata\n\n # use _load_from_state_dict to enable checkpoint version control\n def load(module, prefix=''):\n # recursively check parallel module in case that the model has a\n # complicated structure, e.g., nn.Module(nn.Module(DDP))\n if is_module_wrapper(module):\n module = module.module\n local_metadata = {} if metadata is None else metadata.get(\n prefix[:-1], {})\n module._load_from_state_dict(state_dict, prefix, local_metadata, True,\n all_missing_keys, unexpected_keys,\n err_msg)\n for name, child in module._modules.items():\n if child is not None:\n load(child, prefix + name + '.')\n\n load(module)\n load = None # break load->load reference cycle\n\n # ignore \"num_batches_tracked\" of BN layers\n missing_keys = [\n key for key in all_missing_keys if 'num_batches_tracked' not in key\n ]\n\n if unexpected_keys:\n err_msg.append('unexpected key in source '\n f'state_dict: {\", \".join(unexpected_keys)}\\n')\n if missing_keys:\n err_msg.append(\n f'missing keys in source state_dict: {\", \".join(missing_keys)}\\n')\n\n if strict:\n rank, _ = get_dist_info()\n if len(err_msg) > 0 and rank == 0:\n err_msg.insert(\n 0, 'The model and loaded state dict do not match exactly\\n')\n err_msg = '\\n'.join(err_msg)\n if strict:\n raise RuntimeError(err_msg)\n elif logger is not None:\n logger.warning(err_msg)\n else:\n print(err_msg)\n\n\ndef load_url_dist(url, model_dir=None):\n \"\"\"In distributed setting, this function only download checkpoint at local\n rank 0.\"\"\"\n rank, world_size = get_dist_info()\n rank = int(os.environ.get('LOCAL_RANK', rank))\n if rank == 0:\n checkpoint = model_zoo.load_url(url, model_dir=model_dir)\n if world_size > 1:\n torch.distributed.barrier()\n if rank > 0:\n checkpoint = model_zoo.load_url(url, model_dir=model_dir)\n return checkpoint\n\n\ndef load_pavimodel_dist(model_path, map_location=None):\n \"\"\"In distributed setting, this function only download checkpoint at local\n rank 0.\"\"\"\n try:\n from pavi import modelcloud\n except ImportError:\n raise ImportError(\n 'Please install pavi to load checkpoint from modelcloud.')\n rank, world_size = get_dist_info()\n rank = int(os.environ.get('LOCAL_RANK', rank))\n if rank == 0:\n model = modelcloud.get(model_path)\n with TemporaryDirectory() as tmp_dir:\n downloaded_file = osp.join(tmp_dir, model.name)\n model.download(downloaded_file)\n checkpoint = torch.load(downloaded_file, map_location=map_location)\n if world_size > 1:\n torch.distributed.barrier()\n if rank > 0:\n model = modelcloud.get(model_path)\n with TemporaryDirectory() as tmp_dir:\n downloaded_file = osp.join(tmp_dir, model.name)\n model.download(downloaded_file)\n checkpoint = torch.load(\n downloaded_file, map_location=map_location)\n return checkpoint\n\n\ndef load_fileclient_dist(filename, backend, map_location):\n \"\"\"In distributed setting, this function only download checkpoint at local\n rank 0.\"\"\"\n rank, world_size = get_dist_info()\n rank = int(os.environ.get('LOCAL_RANK', rank))\n allowed_backends = ['ceph']\n if backend not in allowed_backends:\n raise ValueError(f'Load from Backend {backend} is not supported.')\n if rank == 0:\n fileclient = FileClient(backend=backend)\n buffer = io.BytesIO(fileclient.get(filename))\n checkpoint = torch.load(buffer, map_location=map_location)\n if world_size > 1:\n torch.distributed.barrier()\n if rank > 0:\n fileclient = FileClient(backend=backend)\n buffer = io.BytesIO(fileclient.get(filename))\n checkpoint = torch.load(buffer, map_location=map_location)\n return checkpoint\n\n\ndef get_torchvision_models():\n model_urls = dict()\n for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):\n if ispkg:\n continue\n _zoo = import_module(f'torchvision.models.{name}')\n if hasattr(_zoo, 'model_urls'):\n _urls = getattr(_zoo, 'model_urls')\n model_urls.update(_urls)\n return model_urls\n\n\ndef get_external_models():\n mmcv_home = _get_mmcv_home()\n default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')\n default_urls = load_file(default_json_path)\n assert isinstance(default_urls, dict)\n external_json_path = osp.join(mmcv_home, 'open_mmlab.json')\n if osp.exists(external_json_path):\n external_urls = load_file(external_json_path)\n assert isinstance(external_urls, dict)\n default_urls.update(external_urls)\n\n return default_urls\n\n\ndef get_mmcls_models():\n mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')\n mmcls_urls = load_file(mmcls_json_path)\n\n return mmcls_urls\n\n\ndef get_deprecated_model_names():\n deprecate_json_path = osp.join(mmcv.__path__[0],\n 'model_zoo/deprecated.json')\n deprecate_urls = load_file(deprecate_json_path)\n assert isinstance(deprecate_urls, dict)\n\n return deprecate_urls\n\n\ndef _process_mmcls_checkpoint(checkpoint):\n state_dict = checkpoint['state_dict']\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n if k.startswith('backbone.'):\n new_state_dict[k[9:]] = v\n new_checkpoint = dict(state_dict=new_state_dict)\n\n return new_checkpoint\n\n\ndef _load_checkpoint(filename, map_location=None):\n \"\"\"Load checkpoint from somewhere (modelzoo, file, url).\n\n Args:\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str | None): Same as :func:`torch.load`. Default: None.\n\n Returns:\n dict | OrderedDict: The loaded checkpoint. It can be either an\n OrderedDict storing model weights or a dict containing other\n information, which depends on the checkpoint.\n \"\"\"\n if filename.startswith('modelzoo://'):\n warnings.warn('The URL scheme of \"modelzoo://\" is deprecated, please '\n 'use \"torchvision://\" instead')\n model_urls = get_torchvision_models()\n model_name = filename[11:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('torchvision://'):\n model_urls = get_torchvision_models()\n model_name = filename[14:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('open-mmlab://'):\n model_urls = get_external_models()\n model_name = filename[13:]\n deprecated_urls = get_deprecated_model_names()\n if model_name in deprecated_urls:\n warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '\n f'of open-mmlab://{deprecated_urls[model_name]}')\n model_name = deprecated_urls[model_name]\n model_url = model_urls[model_name]\n # check if is url\n if model_url.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(model_url)\n else:\n filename = osp.join(_get_mmcv_home(), model_url)\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n elif filename.startswith('mmcls://'):\n model_urls = get_mmcls_models()\n model_name = filename[8:]\n checkpoint = load_url_dist(model_urls[model_name])\n checkpoint = _process_mmcls_checkpoint(checkpoint)\n elif filename.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(filename)\n elif filename.startswith('pavi://'):\n model_path = filename[7:]\n checkpoint = load_pavimodel_dist(model_path, map_location=map_location)\n elif filename.startswith('s3://'):\n checkpoint = load_fileclient_dist(\n filename, backend='ceph', map_location=map_location)\n else:\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n return checkpoint\n\n\ndef load_checkpoint(model,\n filename,\n map_location='cpu',\n strict=False,\n logger=None):\n \"\"\"Load checkpoint from a file or URI.\n\n Args:\n model (Module): Module to load checkpoint.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str): Same as :func:`torch.load`.\n strict (bool): Whether to allow different params for the model and\n checkpoint.\n logger (:mod:`logging.Logger` or None): The logger for error message.\n\n Returns:\n dict or OrderedDict: The loaded checkpoint.\n \"\"\"\n checkpoint = _load_checkpoint(filename, map_location)\n # OrderedDict is a subclass of dict\n if not isinstance(checkpoint, dict):\n raise RuntimeError(\n f'No state_dict found in checkpoint file {filename}')\n # get state_dict from checkpoint\n if 'state_dict' in checkpoint:\n state_dict = checkpoint['state_dict']\n elif 'model' in checkpoint:\n state_dict = checkpoint['model']\n else:\n state_dict = checkpoint\n # strip prefix of state_dict\n if list(state_dict.keys())[0].startswith('module.'):\n state_dict = {k[7:]: v for k, v in state_dict.items()}\n # for upper net weights only\n if list(state_dict.keys())[0].startswith('backbone.'):\n print('Start stripping upper net pre-fix and loading backbone weights to our swin encoder')\n state_dict = {k.replace('backbone.', ''): v for k, v in state_dict.items() if k.startswith('backbone.')}\n # for MoBY, load model of online branch\n if sorted(list(state_dict.keys()))[0].startswith('encoder'):\n state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}\n\n # reshape absolute position embedding\n if state_dict.get('absolute_pos_embed') is not None:\n absolute_pos_embed = state_dict['absolute_pos_embed']\n N1, L, C1 = absolute_pos_embed.size()\n N2, C2, H, W = model.absolute_pos_embed.size()\n if N1 != N2 or C1 != C2 or L != H*W:\n logger.warning(\"Error in loading absolute_pos_embed, pass\")\n else:\n state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)\n\n # interpolate position bias table if needed\n relative_position_bias_table_keys = [k for k in state_dict.keys() if \"relative_position_bias_table\" in k]\n for table_key in relative_position_bias_table_keys:\n table_pretrained = state_dict[table_key]\n table_current = model.state_dict()[table_key]\n L1, nH1 = table_pretrained.size()\n L2, nH2 = table_current.size()\n if nH1 != nH2:\n logger.warning(f\"Error in loading {table_key}, pass\")\n else:\n if L1 != L2:\n S1 = int(L1 ** 0.5)\n S2 = int(L2 ** 0.5)\n table_pretrained_resized = F.interpolate(\n table_pretrained.permute(1, 0).view(1, nH1, S1, S1),\n size=(S2, S2), mode='bicubic')\n state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)\n\n # load state_dict\n load_state_dict(model, state_dict, strict, logger)\n return checkpoint\n\n\ndef weights_to_cpu(state_dict):\n \"\"\"Copy a model state_dict to cpu.\n\n Args:\n state_dict (OrderedDict): Model weights on GPU.\n\n Returns:\n OrderedDict: Model weights on GPU.\n \"\"\"\n state_dict_cpu = OrderedDict()\n for key, val in state_dict.items():\n state_dict_cpu[key] = val.cpu()\n return state_dict_cpu\n\n\ndef _save_to_state_dict(module, destination, prefix, keep_vars):\n \"\"\"Saves module state to `destination` dictionary.\n\n This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (dict): A dict where state will be stored.\n prefix (str): The prefix for parameters and buffers used in this\n module.\n \"\"\"\n for name, param in module._parameters.items():\n if param is not None:\n destination[prefix + name] = param if keep_vars else param.detach()\n for name, buf in module._buffers.items():\n # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d\n if buf is not None:\n destination[prefix + name] = buf if keep_vars else buf.detach()\n\n\ndef get_state_dict(module, destination=None, prefix='', keep_vars=False):\n \"\"\"Returns a dictionary containing a whole state of the module.\n\n Both parameters and persistent buffers (e.g. running averages) are\n included. Keys are corresponding parameter and buffer names.\n\n This method is modified from :meth:`torch.nn.Module.state_dict` to\n recursively check parallel module in case that the model has a complicated\n structure, e.g., nn.Module(nn.Module(DDP)).\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (OrderedDict): Returned dict for the state of the\n module.\n prefix (str): Prefix of the key.\n keep_vars (bool): Whether to keep the variable property of the\n parameters. Default: False.\n\n Returns:\n dict: A dictionary containing a whole state of the module.\n \"\"\"\n # recursively check parallel module in case that the model has a\n # complicated structure, e.g., nn.Module(nn.Module(DDP))\n if is_module_wrapper(module):\n module = module.module\n\n # below is the same as torch.nn.Module.state_dict()\n if destination is None:\n destination = OrderedDict()\n destination._metadata = OrderedDict()\n destination._metadata[prefix[:-1]] = local_metadata = dict(\n version=module._version)\n _save_to_state_dict(module, destination, prefix, keep_vars)\n for name, child in module._modules.items():\n if child is not None:\n get_state_dict(\n child, destination, prefix + name + '.', keep_vars=keep_vars)\n for hook in module._state_dict_hooks.values():\n hook_result = hook(module, destination, prefix, local_metadata)\n if hook_result is not None:\n destination = hook_result\n return destination\n\n\ndef save_checkpoint(model, filename, optimizer=None, meta=None):\n \"\"\"Save checkpoint to file.\n\n The checkpoint will have 3 fields: ``meta``, ``state_dict`` and\n ``optimizer``. By default ``meta`` will contain version and time info.\n\n Args:\n model (Module): Module whose params are to be saved.\n filename (str): Checkpoint filename.\n optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.\n meta (dict, optional): Metadata to be saved in checkpoint.\n \"\"\"\n if meta is None:\n meta = {}\n elif not isinstance(meta, dict):\n raise TypeError(f'meta must be a dict or None, but got {type(meta)}')\n meta.update(mmcv_version=mmcv.__version__, time=time.asctime())\n\n if is_module_wrapper(model):\n model = model.module\n\n if hasattr(model, 'CLASSES') and model.CLASSES is not None:\n # save class name to the meta\n meta.update(CLASSES=model.CLASSES)\n\n checkpoint = {\n 'meta': meta,\n 'state_dict': weights_to_cpu(get_state_dict(model))\n }\n # save optimizer state dict in the checkpoint\n if isinstance(optimizer, Optimizer):\n checkpoint['optimizer'] = optimizer.state_dict()\n elif isinstance(optimizer, dict):\n checkpoint['optimizer'] = {}\n for name, optim in optimizer.items():\n checkpoint['optimizer'][name] = optim.state_dict()\n\n if filename.startswith('pavi://'):\n try:\n from pavi import modelcloud\n from pavi.exception import NodeNotFoundError\n except ImportError:\n raise ImportError(\n 'Please install pavi to load checkpoint from modelcloud.')\n model_path = filename[7:]\n root = modelcloud.Folder()\n model_dir, model_name = osp.split(model_path)\n try:\n model = modelcloud.get(model_dir)\n except NodeNotFoundError:\n model = root.create_training_model(model_dir)\n with TemporaryDirectory() as tmp_dir:\n checkpoint_file = osp.join(tmp_dir, model_name)\n with open(checkpoint_file, 'wb') as f:\n torch.save(checkpoint, f)\n f.flush()\n model.create_file(checkpoint_file, name=model_name)\n else:\n mmcv.mkdir_or_exist(osp.dirname(filename))\n # immediately flush buffer\n with open(filename, 'wb') as f:\n torch.save(checkpoint, f)\n f.flush()\n"
},
{
"path": "lib/segmentation.py",
"content": "import torch\nimport torch.nn as nn\nfrom .mask_predictor import SimpleDecoding\nfrom .backbone import MultiModalSwinTransformer\nfrom ._utils import LAVT, LAVTOne\n\n__all__ = ['lavt', 'lavt_one']\n\n\n# LAVT\ndef _segm_lavt(pretrained, args):\n # initialize the SwinTransformer backbone with the specified version\n if args.swin_type == 'tiny':\n embed_dim = 96\n depths = [2, 2, 6, 2]\n num_heads = [3, 6, 12, 24]\n elif args.swin_type == 'small':\n embed_dim = 96\n depths = [2, 2, 18, 2]\n num_heads = [3, 6, 12, 24]\n elif args.swin_type == 'base':\n embed_dim = 128\n depths = [2, 2, 18, 2]\n num_heads = [4, 8, 16, 32]\n elif args.swin_type == 'large':\n embed_dim = 192\n depths = [2, 2, 18, 2]\n num_heads = [6, 12, 24, 48]\n else:\n assert False\n # args.window12 added for test.py because state_dict is loaded after model initialization\n if 'window12' in pretrained or args.window12:\n print('Window size 12!')\n window_size = 12\n else:\n window_size = 7\n\n if args.mha:\n mha = args.mha.split('-') # if non-empty, then ['a', 'b', 'c', 'd']\n mha = [int(a) for a in mha]\n else:\n mha = [1, 1, 1, 1]\n\n out_indices = (0, 1, 2, 3)\n backbone = MultiModalSwinTransformer(embed_dim=embed_dim, depths=depths, num_heads=num_heads,\n window_size=window_size,\n ape=False, drop_path_rate=0.3, patch_norm=True,\n out_indices=out_indices,\n use_checkpoint=False, num_heads_fusion=mha,\n fusion_drop=args.fusion_drop\n )\n if pretrained:\n print('Initializing Multi-modal Swin Transformer weights from ' + pretrained)\n backbone.init_weights(pretrained=pretrained)\n else:\n print('Randomly initialize Multi-modal Swin Transformer weights.')\n backbone.init_weights()\n\n model_map = [SimpleDecoding, LAVT]\n\n classifier = model_map[0](8*embed_dim)\n base_model = model_map[1]\n\n model = base_model(backbone, classifier)\n return model\n\n\ndef _load_model_lavt(pretrained, args):\n model = _segm_lavt(pretrained, args)\n return model\n\n\ndef lavt(pretrained='', args=None):\n return _load_model_lavt(pretrained, args)\n\n\n###############################################\n# LAVT One: put BERT inside the overall model #\n###############################################\ndef _segm_lavt_one(pretrained, args):\n # initialize the SwinTransformer backbone with the specified version\n if args.swin_type == 'tiny':\n embed_dim = 96\n depths = [2, 2, 6, 2]\n num_heads = [3, 6, 12, 24]\n elif args.swin_type == 'small':\n embed_dim = 96\n depths = [2, 2, 18, 2]\n num_heads = [3, 6, 12, 24]\n elif args.swin_type == 'base':\n embed_dim = 128\n depths = [2, 2, 18, 2]\n num_heads = [4, 8, 16, 32]\n elif args.swin_type == 'large':\n embed_dim = 192\n depths = [2, 2, 18, 2]\n num_heads = [6, 12, 24, 48]\n else:\n assert False\n # args.window12 added for test.py because state_dict is loaded after model initialization\n if 'window12' in pretrained or args.window12:\n print('Window size 12!')\n window_size = 12\n else:\n window_size = 7\n\n if args.mha:\n mha = args.mha.split('-') # if non-empty, then ['a', 'b', 'c', 'd']\n mha = [int(a) for a in mha]\n else:\n mha = [1, 1, 1, 1]\n\n out_indices = (0, 1, 2, 3)\n backbone = MultiModalSwinTransformer(embed_dim=embed_dim, depths=depths, num_heads=num_heads,\n window_size=window_size,\n ape=False, drop_path_rate=0.3, patch_norm=True,\n out_indices=out_indices,\n use_checkpoint=False, num_heads_fusion=mha,\n fusion_drop=args.fusion_drop\n )\n if pretrained:\n print('Initializing Multi-modal Swin Transformer weights from ' + pretrained)\n backbone.init_weights(pretrained=pretrained)\n else:\n print('Randomly initialize Multi-modal Swin Transformer weights.')\n backbone.init_weights()\n\n model_map = [SimpleDecoding, LAVTOne]\n\n classifier = model_map[0](8*embed_dim)\n base_model = model_map[1]\n\n model = base_model(backbone, classifier, args)\n return model\n\n\ndef _load_model_lavt_one(pretrained, args):\n model = _segm_lavt_one(pretrained, args)\n return model\n\n\ndef lavt_one(pretrained='', args=None):\n return _load_model_lavt_one(pretrained, args)\n"
},
{
"path": "refer/LICENSE",
"content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n"
},
{
"path": "refer/Makefile",
"content": "all:\n\t# install pycocotools/mask locally\n\t# copy from https://github.com/pdollar/coco.git\n\tpython setup.py build_ext --inplace\n\trm -rf build\n\n"
},
{
"path": "refer/README.md",
"content": "## Note\nThis API is able to load all 4 referring expression datasets, i.e., RefClef, RefCOCO, RefCOCO+ and RefCOCOg. \nThey are with different train/val/test split by UNC, Google and UC Berkeley respectively. We provide all kinds of splits here.\n\n\n![\"Mountain](\"http://bvisionweb1.cs.unc.edu/licheng/referit/refer_example.jpg\",) | \n
\n
\n\n## Citation\nIf you used the following three datasets RefClef, RefCOCO and RefCOCO+ that were collected by UNC, please consider cite our EMNLP2014 paper; if you want to compare with our recent results, please check our ECCV2016 paper.\n```bash\nKazemzadeh, Sahar, et al. \"ReferItGame: Referring to Objects in Photographs of Natural Scenes.\" EMNLP 2014.\nYu, Licheng, et al. \"Modeling Context in Referring Expressions.\" ECCV 2016.\n```\n\n## Setup\nRun \"make\" before using the code.\nIt will generate ``_mask.c`` and ``_mask.so`` in ``external/`` folder.\nThese mask-related codes are copied from mscoco [API](https://github.com/pdollar/coco).\n\n## Download\nDownload the cleaned data and extract them into \"data\" folder\n- 1) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip\n- 2) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip\n- 3) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip \n- 4) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip \n\n## Prepare Images:\nBesides, add \"mscoco\" into the ``data/images`` folder, which can be from [mscoco](http://mscoco.org/dataset/#overview)\nCOCO's images are used for RefCOCO, RefCOCO+ and refCOCOg.\nFor RefCLEF, please add ``saiapr_tc-12`` into ``data/images`` folder. We extracted the related 19997 images to the cleaned RefCLEF dataset, which is a subset of the original [imageCLEF](http://imageclef.org/SIAPRdata). Download the [subset](http://bvisionweb1.cs.unc.edu/licheng/referit/data/images/saiapr_tc-12.zip) and unzip it to ``data/images/saiapr_tc-12``.\n\n## How to use\nThe \"refer.py\" is able to load all 4 datasets with different kinds of data split by UNC, Google, UMD and UC Berkeley.\n**Note for RefCOCOg, we suggest use UMD's split which has train/val/test splits and there is no overlap of images between different split.**\n```bash\n# locate your own data_root, and choose the dataset_splitBy you want to use\nrefer = REFER(data_root, dataset='refclef', splitBy='unc')\nrefer = REFER(data_root, dataset='refclef', splitBy='berkeley') # 2 train and 1 test images missed\nrefer = REFER(data_root, dataset='refcoco', splitBy='unc')\nrefer = REFER(data_root, dataset='refcoco', splitBy='google')\nrefer = REFER(data_root, dataset='refcoco+', splitBy='unc')\nrefer = REFER(data_root, dataset='refcocog', splitBy='google') # test split not released yet\nrefer = REFER(data_root, dataset='refcocog', splitBy='umd') # Recommended, including train/val/test\n```\n\n\n\n"
},
{
"path": "refer/data/README.md",
"content": "This directory should contain the following data:\n```\n$DATA_PATH\n├── images\n│ ├── mscoco\n│ └── saiaprtc12\n├── refcoco\n│ ├── instances.json\n│ ├── refs(google).p\n│ └── refs(unc).p\n├── refcoco+\n│ ├── instances.json\n│ └── refs(unc).p\n├── refcocog\n│ ├── instances.json\n│ └── refs(google).p\n└── refclef\n \t├── instances.json\n \t├── refs(unc).p\n\t └── refs(berkeley).p\n```\n\nNote, each detections/xxx.json contains \n``{'dets': ['box': {x, y, w, h}, 'image_id', 'object_id', 'score']}``. The ``object_id`` and ``score`` might be missing, depending on what proposal/detection technique we are using.\n\n## Download\nDownload my cleaned data and extract them into this folder.\n- 1) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip\n- 2) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip\n- 3) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip \n- 4) http://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip \n\nBesides make a folder named as \"images\".\nAdd \"mscoco\" into \"images/\". \nDownload MSCOCO from [mscoco](http://mscoco.org/dataset/#overview)\n\nAdd \"saiapr_tc-12\" into \"images/\". I only extracted the related images as a subset of the original [imageCLEF](http://imageclef.org/SIAPRdata), i.e., 19997 images. Please download the subset from here (http://bvisionweb1.cs.unc.edu/licheng/referit/data/images/saiapr_tc-12.zip).\n"
},
{
"path": "refer/evaluation/__init__.py",
"content": "__author__ = 'licheng'\n\n\n"
},
{
"path": "refer/evaluation/bleu/LICENSE",
"content": "Copyright (c) 2015 Xinlei Chen, Hao Fang, Tsung-Yi Lin, and Ramakrishna Vedantam\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n"
},
{
"path": "refer/evaluation/bleu/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "refer/evaluation/bleu/bleu.py",
"content": "#!/usr/bin/env python\n# \n# File Name : bleu.py\n#\n# Description : Wrapper for BLEU scorer.\n#\n# Creation Date : 06-01-2015\n# Last Modified : Thu 19 Mar 2015 09:13:28 PM PDT\n# Authors : Hao Fang and Tsung-Yi Lin \n\nfrom bleu_scorer import BleuScorer\n\n\nclass Bleu:\n def __init__(self, n=4):\n # default compute Blue score up to 4\n self._n = n\n self._hypo_for_image = {}\n self.ref_for_image = {}\n\n def compute_score(self, gts, res):\n\n assert(gts.keys() == res.keys())\n imgIds = gts.keys()\n\n bleu_scorer = BleuScorer(n=self._n)\n for id in imgIds:\n hypo = res[id]\n ref = gts[id]\n\n # Sanity check.\n assert(type(hypo) is list)\n assert(len(hypo) == 1)\n assert(type(ref) is list)\n assert(len(ref) >= 1)\n\n bleu_scorer += (hypo[0], ref)\n\n #score, scores = bleu_scorer.compute_score(option='shortest')\n score, scores = bleu_scorer.compute_score(option='closest', verbose=1)\n #score, scores = bleu_scorer.compute_score(option='average', verbose=1)\n\n # return (bleu, bleu_info)\n return score, scores\n\n def method(self):\n return \"Bleu\"\n"
},
{
"path": "refer/evaluation/bleu/bleu_scorer.py",
"content": "#!/usr/bin/env python\n\n# bleu_scorer.py\n# David Chiang \n\n# Copyright (c) 2004-2006 University of Maryland. All rights\n# reserved. Do not redistribute without permission from the\n# author. Not for commercial use.\n\n# Modified by: \n# Hao Fang \n# Tsung-Yi Lin \n\n'''Provides:\ncook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().\ncook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().\n'''\n\nimport copy\nimport sys, math, re\nfrom collections import defaultdict\n\ndef precook(s, n=4, out=False):\n \"\"\"Takes a string as input and returns an object that can be given to\n either cook_refs or cook_test. This is optional: cook_refs and cook_test\n can take string arguments as well.\"\"\"\n words = s.split()\n counts = defaultdict(int)\n for k in xrange(1,n+1):\n for i in xrange(len(words)-k+1):\n ngram = tuple(words[i:i+k])\n counts[ngram] += 1\n return (len(words), counts)\n\ndef cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with \"average\"\n '''Takes a list of reference sentences for a single segment\n and returns an object that encapsulates everything that BLEU\n needs to know about them.'''\n\n reflen = []\n maxcounts = {}\n for ref in refs:\n rl, counts = precook(ref, n)\n reflen.append(rl)\n for (ngram,count) in counts.iteritems():\n maxcounts[ngram] = max(maxcounts.get(ngram,0), count)\n\n # Calculate effective reference sentence length.\n if eff == \"shortest\":\n reflen = min(reflen)\n elif eff == \"average\":\n reflen = float(sum(reflen))/len(reflen)\n\n ## lhuang: N.B.: leave reflen computaiton to the very end!!\n \n ## lhuang: N.B.: in case of \"closest\", keep a list of reflens!! (bad design)\n\n return (reflen, maxcounts)\n\ndef cook_test(test, (reflen, refmaxcounts), eff=None, n=4):\n '''Takes a test sentence and returns an object that\n encapsulates everything that BLEU needs to know about it.'''\n\n testlen, counts = precook(test, n, True)\n\n result = {}\n\n # Calculate effective reference sentence length.\n \n if eff == \"closest\":\n result[\"reflen\"] = min((abs(l-testlen), l) for l in reflen)[1]\n else: ## i.e., \"average\" or \"shortest\" or None\n result[\"reflen\"] = reflen\n\n result[\"testlen\"] = testlen\n\n result[\"guess\"] = [max(0,testlen-k+1) for k in xrange(1,n+1)]\n\n result['correct'] = [0]*n\n for (ngram, count) in counts.iteritems():\n result[\"correct\"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)\n\n return result\n\nclass BleuScorer(object):\n \"\"\"Bleu scorer.\n \"\"\"\n\n __slots__ = \"n\", \"crefs\", \"ctest\", \"_score\", \"_ratio\", \"_testlen\", \"_reflen\", \"special_reflen\"\n # special_reflen is used in oracle (proportional effective ref len for a node).\n\n def copy(self):\n ''' copy the refs.'''\n new = BleuScorer(n=self.n)\n new.ctest = copy.copy(self.ctest)\n new.crefs = copy.copy(self.crefs)\n new._score = None\n return new\n\n def __init__(self, test=None, refs=None, n=4, special_reflen=None):\n ''' singular instance '''\n\n self.n = n\n self.crefs = []\n self.ctest = []\n self.cook_append(test, refs)\n self.special_reflen = special_reflen\n\n def cook_append(self, test, refs):\n '''called by constructor and __iadd__ to avoid creating new instances.'''\n \n if refs is not None:\n self.crefs.append(cook_refs(refs))\n if test is not None:\n cooked_test = cook_test(test, self.crefs[-1])\n self.ctest.append(cooked_test) ## N.B.: -1\n else:\n self.ctest.append(None) # lens of crefs and ctest have to match\n\n self._score = None ## need to recompute\n\n def ratio(self, option=None):\n self.compute_score(option=option)\n return self._ratio\n\n def score_ratio(self, option=None):\n '''return (bleu, len_ratio) pair'''\n return (self.fscore(option=option), self.ratio(option=option))\n\n def score_ratio_str(self, option=None):\n return \"%.4f (%.2f)\" % self.score_ratio(option)\n\n def reflen(self, option=None):\n self.compute_score(option=option)\n return self._reflen\n\n def testlen(self, option=None):\n self.compute_score(option=option)\n return self._testlen \n\n def retest(self, new_test):\n if type(new_test) is str:\n new_test = [new_test]\n assert len(new_test) == len(self.crefs), new_test\n self.ctest = []\n for t, rs in zip(new_test, self.crefs):\n self.ctest.append(cook_test(t, rs))\n self._score = None\n\n return self\n\n def rescore(self, new_test):\n ''' replace test(s) with new test(s), and returns the new score.'''\n \n return self.retest(new_test).compute_score()\n\n def size(self):\n assert len(self.crefs) == len(self.ctest), \"refs/test mismatch! %d<>%d\" % (len(self.crefs), len(self.ctest))\n return len(self.crefs)\n\n def __iadd__(self, other):\n '''add an instance (e.g., from another sentence).'''\n\n if type(other) is tuple:\n ## avoid creating new BleuScorer instances\n self.cook_append(other[0], other[1])\n else:\n assert self.compatible(other), \"incompatible BLEUs.\"\n self.ctest.extend(other.ctest)\n self.crefs.extend(other.crefs)\n self._score = None ## need to recompute\n\n return self \n\n def compatible(self, other):\n return isinstance(other, BleuScorer) and self.n == other.n\n\n def single_reflen(self, option=\"average\"):\n return self._single_reflen(self.crefs[0][0], option)\n\n def _single_reflen(self, reflens, option=None, testlen=None):\n \n if option == \"shortest\":\n reflen = min(reflens)\n elif option == \"average\":\n reflen = float(sum(reflens))/len(reflens)\n elif option == \"closest\":\n reflen = min((abs(l-testlen), l) for l in reflens)[1]\n else:\n assert False, \"unsupported reflen option %s\" % option\n\n return reflen\n\n def recompute_score(self, option=None, verbose=0):\n self._score = None\n return self.compute_score(option, verbose)\n \n def compute_score(self, option=None, verbose=0):\n n = self.n\n small = 1e-9\n tiny = 1e-15 ## so that if guess is 0 still return 0\n bleu_list = [[] for _ in range(n)]\n\n if self._score is not None:\n return self._score\n\n if option is None:\n option = \"average\" if len(self.crefs) == 1 else \"closest\"\n\n self._testlen = 0\n self._reflen = 0\n totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}\n\n # for each sentence\n for comps in self.ctest: \n testlen = comps['testlen']\n self._testlen += testlen\n\n if self.special_reflen is None: ## need computation\n reflen = self._single_reflen(comps['reflen'], option, testlen)\n else:\n reflen = self.special_reflen\n\n self._reflen += reflen\n \n for key in ['guess','correct']:\n for k in xrange(n):\n totalcomps[key][k] += comps[key][k]\n\n # append per image bleu score\n bleu = 1.\n for k in xrange(n):\n bleu *= (float(comps['correct'][k]) + tiny) \\\n /(float(comps['guess'][k]) + small) \n bleu_list[k].append(bleu ** (1./(k+1)))\n ratio = (testlen + tiny) / (reflen + small) ## N.B.: avoid zero division\n if ratio < 1:\n for k in xrange(n):\n bleu_list[k][-1] *= math.exp(1 - 1/ratio)\n\n if verbose > 1:\n print comps, reflen\n\n totalcomps['reflen'] = self._reflen\n totalcomps['testlen'] = self._testlen\n\n bleus = []\n bleu = 1.\n for k in xrange(n):\n bleu *= float(totalcomps['correct'][k] + tiny) \\\n / (totalcomps['guess'][k] + small)\n bleus.append(bleu ** (1./(k+1)))\n ratio = (self._testlen + tiny) / (self._reflen + small) ## N.B.: avoid zero division\n if ratio < 1:\n for k in xrange(n):\n bleus[k] *= math.exp(1 - 1/ratio)\n\n if verbose > 0:\n print totalcomps\n print \"ratio:\", ratio\n\n self._score = bleus\n return self._score, bleu_list\n"
},
{
"path": "refer/evaluation/cider/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "refer/evaluation/cider/cider.py",
"content": "# Filename: cider.py\n#\n# Description: Describes the class to compute the CIDEr (Consensus-Based Image Description Evaluation) Metric \n# by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726)\n#\n# Creation Date: Sun Feb 8 14:16:54 2015\n#\n# Authors: Ramakrishna Vedantam and Tsung-Yi Lin \n\nfrom cider_scorer import CiderScorer\nimport pdb\n\nclass Cider:\n \"\"\"\n Main Class to compute the CIDEr metric \n\n \"\"\"\n def __init__(self, test=None, refs=None, n=4, sigma=6.0):\n # set cider to sum over 1 to 4-grams\n self._n = n\n # set the standard deviation parameter for gaussian penalty\n self._sigma = sigma\n\n def compute_score(self, gts, res):\n \"\"\"\n Main function to compute CIDEr score\n :param hypo_for_image (dict) : dictionary with key and value \n ref_for_image (dict) : dictionary with key and value \n :return: cider (float) : computed CIDEr score for the corpus \n \"\"\"\n\n assert(gts.keys() == res.keys())\n imgIds = gts.keys()\n\n cider_scorer = CiderScorer(n=self._n, sigma=self._sigma)\n\n for id in imgIds:\n hypo = res[id]\n ref = gts[id]\n\n # Sanity check.\n assert(type(hypo) is list)\n assert(len(hypo) == 1)\n assert(type(ref) is list)\n assert(len(ref) > 0)\n\n cider_scorer += (hypo[0], ref)\n\n (score, scores) = cider_scorer.compute_score()\n\n return score, scores\n\n def method(self):\n return \"CIDEr\""
},
{
"path": "refer/evaluation/cider/cider_scorer.py",
"content": "#!/usr/bin/env python\n# Tsung-Yi Lin \n# Ramakrishna Vedantam \n\nimport copy\nfrom collections import defaultdict\nimport numpy as np\nimport pdb\nimport math\n\ndef precook(s, n=4, out=False):\n \"\"\"\n Takes a string as input and returns an object that can be given to\n either cook_refs or cook_test. This is optional: cook_refs and cook_test\n can take string arguments as well.\n :param s: string : sentence to be converted into ngrams\n :param n: int : number of ngrams for which representation is calculated\n :return: term frequency vector for occuring ngrams\n \"\"\"\n words = s.split()\n counts = defaultdict(int)\n for k in xrange(1,n+1):\n for i in xrange(len(words)-k+1):\n ngram = tuple(words[i:i+k])\n counts[ngram] += 1\n return counts\n\ndef cook_refs(refs, n=4): ## lhuang: oracle will call with \"average\"\n '''Takes a list of reference sentences for a single segment\n and returns an object that encapsulates everything that BLEU\n needs to know about them.\n :param refs: list of string : reference sentences for some image\n :param n: int : number of ngrams for which (ngram) representation is calculated\n :return: result (list of dict)\n '''\n return [precook(ref, n) for ref in refs]\n\ndef cook_test(test, n=4):\n '''Takes a test sentence and returns an object that\n encapsulates everything that BLEU needs to know about it.\n :param test: list of string : hypothesis sentence for some image\n :param n: int : number of ngrams for which (ngram) representation is calculated\n :return: result (dict)\n '''\n return precook(test, n, True)\n\nclass CiderScorer(object):\n \"\"\"CIDEr scorer.\n \"\"\"\n\n def copy(self):\n ''' copy the refs.'''\n new = CiderScorer(n=self.n)\n new.ctest = copy.copy(self.ctest)\n new.crefs = copy.copy(self.crefs)\n return new\n\n def __init__(self, test=None, refs=None, n=4, sigma=6.0):\n ''' singular instance '''\n self.n = n\n self.sigma = sigma\n self.crefs = []\n self.ctest = []\n self.document_frequency = defaultdict(float)\n self.cook_append(test, refs)\n self.ref_len = None\n\n def cook_append(self, test, refs):\n '''called by constructor and __iadd__ to avoid creating new instances.'''\n\n if refs is not None:\n self.crefs.append(cook_refs(refs))\n if test is not None:\n self.ctest.append(cook_test(test)) ## N.B.: -1\n else:\n self.ctest.append(None) # lens of crefs and ctest have to match\n\n def size(self):\n assert len(self.crefs) == len(self.ctest), \"refs/test mismatch! %d<>%d\" % (len(self.crefs), len(self.ctest))\n return len(self.crefs)\n\n def __iadd__(self, other):\n '''add an instance (e.g., from another sentence).'''\n\n if type(other) is tuple:\n ## avoid creating new CiderScorer instances\n self.cook_append(other[0], other[1])\n else:\n self.ctest.extend(other.ctest)\n self.crefs.extend(other.crefs)\n\n return self\n def compute_doc_freq(self):\n '''\n Compute term frequency for reference data.\n This will be used to compute idf (inverse document frequency later)\n The term frequency is stored in the object\n :return: None\n '''\n for refs in self.crefs:\n # refs, k ref captions of one image\n for ngram in set([ngram for ref in refs for (ngram,count) in ref.iteritems()]):\n self.document_frequency[ngram] += 1\n # maxcounts[ngram] = max(maxcounts.get(ngram,0), count)\n\n def compute_cider(self):\n def counts2vec(cnts):\n \"\"\"\n Function maps counts of ngram to vector of tfidf weights.\n The function returns vec, an array of dictionary that store mapping of n-gram and tf-idf weights.\n The n-th entry of array denotes length of n-grams.\n :param cnts:\n :return: vec (array of dict), norm (array of float), length (int)\n \"\"\"\n vec = [defaultdict(float) for _ in range(self.n)]\n length = 0\n norm = [0.0 for _ in range(self.n)]\n for (ngram,term_freq) in cnts.iteritems():\n # give word count 1 if it doesn't appear in reference corpus\n df = np.log(max(1.0, self.document_frequency[ngram]))\n # ngram index\n n = len(ngram)-1\n # tf (term_freq) * idf (precomputed idf) for n-grams\n vec[n][ngram] = float(term_freq)*(self.ref_len - df)\n # compute norm for the vector. the norm will be used for computing similarity\n norm[n] += pow(vec[n][ngram], 2)\n\n if n == 1:\n length += term_freq\n norm = [np.sqrt(n) for n in norm]\n return vec, norm, length\n\n def sim(vec_hyp, vec_ref, norm_hyp, norm_ref, length_hyp, length_ref):\n '''\n Compute the cosine similarity of two vectors.\n :param vec_hyp: array of dictionary for vector corresponding to hypothesis\n :param vec_ref: array of dictionary for vector corresponding to reference\n :param norm_hyp: array of float for vector corresponding to hypothesis\n :param norm_ref: array of float for vector corresponding to reference\n :param length_hyp: int containing length of hypothesis\n :param length_ref: int containing length of reference\n :return: array of score for each n-grams cosine similarity\n '''\n delta = float(length_hyp - length_ref)\n # measure consine similarity\n val = np.array([0.0 for _ in range(self.n)])\n for n in range(self.n):\n # ngram\n for (ngram,count) in vec_hyp[n].iteritems():\n # vrama91 : added clipping\n val[n] += min(vec_hyp[n][ngram], vec_ref[n][ngram]) * vec_ref[n][ngram]\n\n if (norm_hyp[n] != 0) and (norm_ref[n] != 0):\n val[n] /= (norm_hyp[n]*norm_ref[n])\n\n assert(not math.isnan(val[n]))\n # vrama91: added a length based gaussian penalty\n val[n] *= np.e**(-(delta**2)/(2*self.sigma**2))\n return val\n\n # compute log reference length\n self.ref_len = np.log(float(len(self.crefs)))\n\n scores = []\n for test, refs in zip(self.ctest, self.crefs):\n # compute vector for test captions\n vec, norm, length = counts2vec(test)\n # compute vector for ref captions\n score = np.array([0.0 for _ in range(self.n)])\n for ref in refs:\n vec_ref, norm_ref, length_ref = counts2vec(ref)\n score += sim(vec, vec_ref, norm, norm_ref, length, length_ref)\n # change by vrama91 - mean of ngram scores, instead of sum\n score_avg = np.mean(score)\n # divide by number of references\n score_avg /= len(refs)\n # multiply score by 10\n score_avg *= 10.0\n # append score of an image to the score list\n scores.append(score_avg)\n return scores\n\n def compute_score(self, option=None, verbose=0):\n # compute idf\n self.compute_doc_freq()\n # assert to check document frequency\n assert(len(self.ctest) >= max(self.document_frequency.values()))\n # compute cider score\n score = self.compute_cider()\n # debug\n # print score\n return np.mean(np.array(score)), np.array(score)"
},
{
"path": "refer/evaluation/meteor/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "refer/evaluation/meteor/meteor.py",
"content": "#!/usr/bin/env python\n\n# Python wrapper for METEOR implementation, by Xinlei Chen\n# Acknowledge Michael Denkowski for the generous discussion and help \n\nimport os\nimport sys\nimport subprocess\nimport threading\n\n# Assumes meteor-1.5.jar is in the same directory as meteor.py. Change as needed.\nMETEOR_JAR = 'meteor-1.5.jar'\n# print METEOR_JAR\n\nclass Meteor:\n\n def __init__(self):\n self.meteor_cmd = ['java', '-jar', '-Xmx2G', METEOR_JAR, \\\n '-', '-', '-stdio', '-l', 'en', '-norm']\n self.meteor_p = subprocess.Popen(self.meteor_cmd, \\\n cwd=os.path.dirname(os.path.abspath(__file__)), \\\n stdin=subprocess.PIPE, \\\n stdout=subprocess.PIPE, \\\n stderr=subprocess.PIPE)\n # Used to guarantee thread safety\n self.lock = threading.Lock()\n\n def compute_score(self, gts, res):\n assert(gts.keys() == res.keys())\n imgIds = gts.keys()\n scores = []\n\n eval_line = 'EVAL'\n self.lock.acquire()\n for i in imgIds:\n assert(len(res[i]) == 1)\n stat = self._stat(res[i][0], gts[i])\n eval_line += ' ||| {}'.format(stat)\n\n self.meteor_p.stdin.write('{}\\n'.format(eval_line))\n for i in range(0,len(imgIds)):\n scores.append(float(self.meteor_p.stdout.readline().strip()))\n score = float(self.meteor_p.stdout.readline().strip())\n self.lock.release()\n\n return score, scores\n\n def method(self):\n return \"METEOR\"\n\n def _stat(self, hypothesis_str, reference_list):\n # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words\n hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')\n score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))\n self.meteor_p.stdin.write('{}\\n'.format(score_line))\n return self.meteor_p.stdout.readline().strip()\n\n def _score(self, hypothesis_str, reference_list):\n self.lock.acquire()\n # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words\n hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')\n score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))\n self.meteor_p.stdin.write('{}\\n'.format(score_line))\n stats = self.meteor_p.stdout.readline().strip()\n eval_line = 'EVAL ||| {}'.format(stats)\n # EVAL ||| stats \n self.meteor_p.stdin.write('{}\\n'.format(eval_line))\n score = float(self.meteor_p.stdout.readline().strip())\n self.lock.release()\n return score\n \n def __exit__(self):\n self.lock.acquire()\n self.meteor_p.stdin.close()\n self.meteor_p.wait()\n self.lock.release()\n"
},
{
"path": "refer/evaluation/readme.txt",
"content": "This folder contains modified coco-caption evaluation, which is downloaded from https://github.com/tylin/coco-caption.git\nand refEvaluation which is to be called by the refer algorithm.\n\nMore specifically, this folder contains:\n1. bleu/\n2. cider/\n3. meteor/\n4. rouge/\n5. tokenizer/\n6. __init__.py\n7. refEvaluation.py\n"
},
{
"path": "refer/evaluation/refEvaluation.py",
"content": "from tokenizer.ptbtokenizer import PTBTokenizer\nfrom bleu.bleu import Bleu\nfrom meteor.meteor import Meteor\nfrom rouge.rouge import Rouge\nfrom cider.cider import Cider\n\n\"\"\"\nInput: refer and Res = [{ref_id, sent}]\n\nThings of interest\nevalRefs - list of ['ref_id', 'CIDEr', 'Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4', 'ROUGE_L', 'METEOR']\neval - dict of {metric: score}\nrefToEval - dict of {ref_id: ['ref_id', 'CIDEr', 'Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4', 'ROUGE_L', 'METEOR']}\n\"\"\"\n\nclass RefEvaluation:\n def __init__ (self, refer, Res):\n \"\"\"\n :param refer: refer class of current dataset\n :param Res: [{'ref_id', 'sent'}]\n \"\"\"\n self.evalRefs = []\n self.eval = {}\n self.refToEval = {}\n self.refer = refer\n self.Res = Res\n\n def evaluate(self):\n\n evalRefIds = [ann['ref_id'] for ann in self.Res]\n\n refToGts = {}\n for ref_id in evalRefIds:\n ref = self.refer.Refs[ref_id]\n gt_sents = [sent['sent'].encode('ascii', 'ignore').decode('ascii') for sent in ref['sentences']] # up to 3 expressions\n refToGts[ref_id] = gt_sents\n refToRes = {ann['ref_id']: [ann['sent']] for ann in self.Res}\n\n print 'tokenization...'\n tokenizer = PTBTokenizer()\n self.refToRes = tokenizer.tokenize(refToRes)\n self.refToGts = tokenizer.tokenize(refToGts)\n\n # =================================================\n # Set up scorers\n # =================================================\n print 'setting up scorers...'\n scorers = [\n (Bleu(4), [\"Bleu_1\", \"Bleu_2\", \"Bleu_3\", \"Bleu_4\"]),\n (Meteor(),\"METEOR\"),\n (Rouge(), \"ROUGE_L\"),\n (Cider(), \"CIDEr\")\n ]\n\n # =================================================\n # Compute scores\n # =================================================\n for scorer, method in scorers:\n print 'computing %s score...'%(scorer.method())\n score, scores = scorer.compute_score(self.refToGts, self.refToRes)\n if type(method) == list:\n for sc, scs, m in zip(score, scores, method):\n self.setEval(sc, m)\n self.setRefToEvalRefs(scs, self.refToGts.keys(), m)\n print \"%s: %0.3f\"%(m, sc)\n else:\n self.setEval(score, method)\n self.setRefToEvalRefs(scores, self.refToGts.keys(), method)\n print \"%s: %0.3f\"%(method, score)\n self.setEvalRefs()\n\n def setEval(self, score, method):\n self.eval[method] = score\n\n def setRefToEvalRefs(self, scores, refIds, method):\n for refId, score in zip(refIds, scores):\n if not refId in self.refToEval:\n self.refToEval[refId] = {}\n self.refToEval[refId][\"ref_id\"] = refId\n self.refToEval[refId][method] = score\n\n def setEvalRefs(self):\n self.evalRefs = [eval for refId, eval in self.refToEval.items()]\n\n\nif __name__ == '__main__':\n\n import os.path as osp\n import sys\n ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))\n sys.path.insert(0, osp.join(ROOT_DIR, 'lib', 'datasets'))\n from refer import REFER\n\n # load refer of dataset\n dataset = 'refcoco'\n refer = REFER(dataset, splitBy = 'google')\n\n # mimic some Res\n val_refIds = refer.getRefIds(split='test')\n ref_id = 49767\n print \"GD: %s\" % refer.Refs[ref_id]['sentences']\n Res = [{'ref_id': ref_id, 'sent': 'left bottle'}]\n\n # evaluate some refer expressions\n refEval = RefEvaluation(refer, Res)\n refEval.evaluate()\n\n # print output evaluation scores\n for metric, score in refEval.eval.items():\n print '%s: %.3f'%(metric, score)\n\n # demo how to use evalImgs to retrieve low score result\n # evals = [eva for eva in refEval.evalRefs if eva['CIDEr']<30]\n # print 'ground truth sents'\n # refId = evals[0]['ref_id']\n # print 'refId: %s' % refId\n # print [sent['sent'] for sent in refer.Refs[refId]['sentences']]\n #\n # print 'generated sent (CIDEr score %0.1f)' % (evals[0]['CIDEr'])\n\n # print refEval.refToEval[8]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
},
{
"path": "refer/evaluation/rouge/__init__.py",
"content": "__author__ = 'vrama91'\n"
},
{
"path": "refer/evaluation/rouge/rouge.py",
"content": "#!/usr/bin/env python\n# \n# File Name : rouge.py\n#\n# Description : Computes ROUGE-L metric as described by Lin and Hovey (2004)\n#\n# Creation Date : 2015-01-07 06:03\n# Author : Ramakrishna Vedantam \n\nimport numpy as np\nimport pdb\n\ndef my_lcs(string, sub):\n \"\"\"\n Calculates longest common subsequence for a pair of tokenized strings\n :param string : list of str : tokens from a string split using whitespace\n :param sub : list of str : shorter string, also split using whitespace\n :returns: length (list of int): length of the longest common subsequence between the two strings\n\n Note: my_lcs only gives length of the longest common subsequence, not the actual LCS\n \"\"\"\n if(len(string)< len(sub)):\n sub, string = string, sub\n\n lengths = [[0 for i in range(0,len(sub)+1)] for j in range(0,len(string)+1)]\n\n for j in range(1,len(sub)+1):\n for i in range(1,len(string)+1):\n if(string[i-1] == sub[j-1]):\n lengths[i][j] = lengths[i-1][j-1] + 1\n else:\n lengths[i][j] = max(lengths[i-1][j] , lengths[i][j-1])\n\n return lengths[len(string)][len(sub)]\n\nclass Rouge():\n '''\n Class for computing ROUGE-L score for a set of candidate sentences for the MS COCO test set\n\n '''\n def __init__(self):\n # vrama91: updated the value below based on discussion with Hovey\n self.beta = 1.2\n\n def calc_score(self, candidate, refs):\n \"\"\"\n Compute ROUGE-L score given one candidate and references for an image\n :param candidate: str : candidate sentence to be evaluated\n :param refs: list of str : COCO reference sentences for the particular image to be evaluated\n :returns score: int (ROUGE-L score for the candidate evaluated against references)\n \"\"\"\n assert(len(candidate)==1)\t\n assert(len(refs)>0) \n prec = []\n rec = []\n\n # split into tokens\n token_c = candidate[0].split(\" \")\n \t\n for reference in refs:\n # split into tokens\n token_r = reference.split(\" \")\n # compute the longest common subsequence\n lcs = my_lcs(token_r, token_c)\n prec.append(lcs/float(len(token_c)))\n rec.append(lcs/float(len(token_r)))\n\n prec_max = max(prec)\n rec_max = max(rec)\n\n if(prec_max!=0 and rec_max !=0):\n score = ((1 + self.beta**2)*prec_max*rec_max)/float(rec_max + self.beta**2*prec_max)\n else:\n score = 0.0\n return score\n\n def compute_score(self, gts, res):\n \"\"\"\n Computes Rouge-L score given a set of reference and candidate sentences for the dataset\n Invoked by evaluate_captions.py \n :param hypo_for_image: dict : candidate / test sentences with \"image name\" key and \"tokenized sentences\" as values \n :param ref_for_image: dict : reference MS-COCO sentences with \"image name\" key and \"tokenized sentences\" as values\n :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)\n \"\"\"\n assert(gts.keys() == res.keys())\n imgIds = gts.keys()\n\n score = []\n for id in imgIds:\n hypo = res[id]\n ref = gts[id]\n\n score.append(self.calc_score(hypo, ref))\n\n # Sanity check.\n assert(type(hypo) is list)\n assert(len(hypo) == 1)\n assert(type(ref) is list)\n assert(len(ref) > 0)\n\n average_score = np.mean(np.array(score))\n return average_score, np.array(score)\n\n def method(self):\n return \"Rouge\"\n"
},
{
"path": "refer/evaluation/tokenizer/__init__.py",
"content": "__author__ = 'hfang'\n"
},
{
"path": "refer/evaluation/tokenizer/ptbtokenizer.py",
"content": "#!/usr/bin/env python\n# \n# File Name : ptbtokenizer.py\n#\n# Description : Do the PTB Tokenization and remove punctuations.\n#\n# Creation Date : 29-12-2014\n# Last Modified : Thu Mar 19 09:53:35 2015\n# Authors : Hao Fang and Tsung-Yi Lin \n\nimport os\nimport sys\nimport subprocess\nimport tempfile\nimport itertools\n\n# path to the stanford corenlp jar\nSTANFORD_CORENLP_3_4_1_JAR = 'stanford-corenlp-3.4.1.jar'\n\n# punctuations to be removed from the sentences\nPUNCTUATIONS = [\"''\", \"'\", \"``\", \"`\", \"-LRB-\", \"-RRB-\", \"-LCB-\", \"-RCB-\", \\\n \".\", \"?\", \"!\", \",\", \":\", \"-\", \"--\", \"...\", \";\"] \n\nclass PTBTokenizer:\n \"\"\"Python wrapper of Stanford PTBTokenizer\"\"\"\n\n def tokenize(self, captions_for_image):\n cmd = ['java', '-cp', STANFORD_CORENLP_3_4_1_JAR, \\\n 'edu.stanford.nlp.process.PTBTokenizer', \\\n '-preserveLines', '-lowerCase']\n\n # ======================================================\n # prepare data for PTB Tokenizer\n # ======================================================\n final_tokenized_captions_for_image = {}\n image_id = [k for k, v in captions_for_image.items() for _ in range(len(v))]\n sentences = '\\n'.join([c.replace('\\n', ' ') for k, v in captions_for_image.items() for c in v])\n\n # ======================================================\n # save sentences to temporary file\n # ======================================================\n path_to_jar_dirname=os.path.dirname(os.path.abspath(__file__))\n tmp_file = tempfile.NamedTemporaryFile(delete=False, dir=path_to_jar_dirname)\n tmp_file.write(sentences)\n tmp_file.close()\n\n # ======================================================\n # tokenize sentence\n # ======================================================\n cmd.append(os.path.basename(tmp_file.name))\n p_tokenizer = subprocess.Popen(cmd, cwd=path_to_jar_dirname, \\\n stdout=subprocess.PIPE)\n token_lines = p_tokenizer.communicate(input=sentences.rstrip())[0]\n lines = token_lines.split('\\n')\n # remove temp file\n os.remove(tmp_file.name)\n\n # ======================================================\n # create dictionary for tokenized captions\n # ======================================================\n for k, line in zip(image_id, lines):\n if not k in final_tokenized_captions_for_image:\n final_tokenized_captions_for_image[k] = []\n tokenized_caption = ' '.join([w for w in line.rstrip().split(' ') \\\n if w not in PUNCTUATIONS])\n final_tokenized_captions_for_image[k].append(tokenized_caption)\n\n return final_tokenized_captions_for_image\n"
},
{
"path": "refer/external/README.md",
"content": "The codes inside this folder are copied from pycocotools: https://github.com/pdollar/coco\r"
},
{
"path": "refer/external/__init__.py",
"content": "__author__ = 'tylin'\n"
},
{
"path": "refer/external/_mask.pyx",
"content": "# distutils: language = c\n# distutils: sources = external/maskApi.c\n\n#**************************************************************************\n# Microsoft COCO Toolbox. version 2.0\n# Data, paper, and tutorials available at: http://mscoco.org/\n# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n# Licensed under the Simplified BSD License [see coco/license.txt]\n#**************************************************************************\n\n__author__ = 'tsungyi'\n\n# import both Python-level and C-level symbols of Numpy\n# the API uses Numpy to interface C and Python\nimport numpy as np\ncimport numpy as np\nfrom libc.stdlib cimport malloc, free\n\n# intialized Numpy. must do.\nnp.import_array()\n\n# import numpy C function\n# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management\ncdef extern from \"numpy/arrayobject.h\":\n void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)\n\n# Declare the prototype of the C functions in MaskApi.h\ncdef extern from \"maskApi.h\":\n ctypedef unsigned int uint\n ctypedef unsigned long siz\n ctypedef unsigned char byte\n ctypedef double* BB\n ctypedef struct RLE:\n siz h,\n siz w,\n siz m,\n uint* cnts,\n void rlesInit( RLE **R, siz n )\n void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n )\n void rleDecode( const RLE *R, byte *mask, siz n )\n void rleMerge( const RLE *R, RLE *M, siz n, bint intersect )\n void rleArea( const RLE *R, siz n, uint *a )\n void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o )\n void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o )\n void rleToBbox( const RLE *R, BB bb, siz n )\n void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n )\n void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w )\n char* rleToString( const RLE *R )\n void rleFrString( RLE *R, char *s, siz h, siz w )\n\n# python class to wrap RLE array in C\n# the class handles the memory allocation and deallocation\ncdef class RLEs:\n cdef RLE *_R\n cdef siz _n\n\n def __cinit__(self, siz n =0):\n rlesInit(&self._R, n)\n self._n = n\n\n # free the RLE array here\n def __dealloc__(self):\n if self._R is not NULL:\n for i in range(self._n):\n free(self._R[i].cnts)\n free(self._R)\n def __getattr__(self, key):\n if key == 'n':\n return self._n\n raise AttributeError(key)\n\n# python class to wrap Mask array in C\n# the class handles the memory allocation and deallocation\ncdef class Masks:\n cdef byte *_mask\n cdef siz _h\n cdef siz _w\n cdef siz _n\n\n def __cinit__(self, h, w, n):\n self._mask = malloc(h*w*n* sizeof(byte))\n self._h = h\n self._w = w\n self._n = n\n # def __dealloc__(self):\n # the memory management of _mask has been passed to np.ndarray\n # it doesn't need to be freed here\n\n # called when passing into np.array() and return an np.ndarray in column-major order\n def __array__(self):\n cdef np.npy_intp shape[1]\n shape[0] = self._h*self._w*self._n\n # Create a 1D array, and reshape it to fortran/Matlab column-major array\n ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F')\n # The _mask allocated by Masks is now handled by ndarray\n PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA)\n return ndarray\n\n# internal conversion from Python RLEs object to compressed RLE format\ndef _toString(RLEs Rs):\n cdef siz n = Rs.n\n cdef bytes py_string\n cdef char* c_string\n objs = []\n for i in range(n):\n c_string = rleToString( &Rs._R[i] )\n py_string = c_string\n objs.append({\n 'size': [Rs._R[i].h, Rs._R[i].w],\n 'counts': py_string\n })\n free(c_string)\n return objs\n\n# internal conversion from compressed RLE format to Python RLEs object\ndef _frString(rleObjs):\n cdef siz n = len(rleObjs)\n Rs = RLEs(n)\n cdef bytes py_string\n cdef char* c_string\n for i, obj in enumerate(rleObjs):\n py_string = str(obj['counts'])\n c_string = py_string\n rleFrString( &Rs._R[i], c_string, obj['size'][0], obj['size'][1] )\n return Rs\n\n# encode mask to RLEs objects\n# list of RLE string can be generated by RLEs member function\ndef encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask):\n h, w, n = mask.shape[0], mask.shape[1], mask.shape[2]\n cdef RLEs Rs = RLEs(n)\n rleEncode(Rs._R,mask.data,h,w,n)\n objs = _toString(Rs)\n return objs\n\n# decode mask from compressed list of RLE string or RLEs object\ndef decode(rleObjs):\n cdef RLEs Rs = _frString(rleObjs)\n h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n\n masks = Masks(h, w, n)\n rleDecode( Rs._R, masks._mask, n );\n return np.array(masks)\n\ndef merge(rleObjs, bint intersect=0):\n cdef RLEs Rs = _frString(rleObjs)\n cdef RLEs R = RLEs(1)\n rleMerge(Rs._R, R._R, Rs._n, intersect)\n obj = _toString(R)[0]\n return obj\n\ndef area(rleObjs):\n cdef RLEs Rs = _frString(rleObjs)\n cdef uint* _a = malloc(Rs._n* sizeof(uint))\n rleArea(Rs._R, Rs._n, _a)\n cdef np.npy_intp shape[1]\n shape[0] = Rs._n\n a = np.array((Rs._n, ), dtype=np.uint8)\n a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a)\n PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA)\n return a\n\n# iou computation. support function overload (RLEs-RLEs and bbox-bbox).\ndef iou( dt, gt, pyiscrowd ):\n def _preproc(objs):\n if len(objs) == 0:\n return objs\n if type(objs) == np.ndarray:\n if len(objs.shape) == 1:\n objs = objs.reshape((objs[0], 1))\n # check if it's Nx4 bbox\n if not len(objs.shape) == 2 or not objs.shape[1] == 4:\n raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension')\n objs = objs.astype(np.double)\n elif type(objs) == list:\n # check if list is in box format and convert it to np.ndarray\n isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs]))\n isrle = np.all(np.array([type(obj) == dict for obj in objs]))\n if isbox:\n objs = np.array(objs, dtype=np.double)\n if len(objs.shape) == 1:\n objs = objs.reshape((1,objs.shape[0]))\n elif isrle:\n objs = _frString(objs)\n else:\n raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])')\n else:\n raise Exception('unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.')\n return objs\n def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):\n rleIou( dt._R, gt._R, m, n, iscrowd.data, _iou.data )\n def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):\n bbIou( dt.data, gt.data, m, n, iscrowd.data, _iou.data )\n def _len(obj):\n cdef siz N = 0\n if type(obj) == RLEs:\n N = obj.n\n elif len(obj)==0:\n pass\n elif type(obj) == np.ndarray:\n N = obj.shape[0]\n return N\n # convert iscrowd to numpy array\n cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8)\n # simple type checking\n cdef siz m, n\n dt = _preproc(dt)\n gt = _preproc(gt)\n m = _len(dt)\n n = _len(gt)\n if m == 0 or n == 0:\n return []\n if not type(dt) == type(gt):\n raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray')\n\n # define local variables\n cdef double* _iou = 0\n cdef np.npy_intp shape[1]\n # check type and assign iou function\n if type(dt) == RLEs:\n _iouFun = _rleIou\n elif type(dt) == np.ndarray:\n _iouFun = _bbIou\n else:\n raise Exception('input data type not allowed.')\n _iou = malloc(m*n* sizeof(double))\n iou = np.zeros((m*n, ), dtype=np.double)\n shape[0] = m*n\n iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou)\n PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA)\n _iouFun(dt, gt, iscrowd, m, n, iou)\n return iou.reshape((m,n), order='F')\n\ndef toBbox( rleObjs ):\n cdef RLEs Rs = _frString(rleObjs)\n cdef siz n = Rs.n\n cdef BB _bb = malloc(4*n* sizeof(double))\n rleToBbox( Rs._R, _bb, n )\n cdef np.npy_intp shape[1]\n shape[0] = 4*n\n bb = np.array((1,4*n), dtype=np.double)\n bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4))\n PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA)\n return bb\n\ndef frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ):\n cdef siz n = bb.shape[0]\n Rs = RLEs(n)\n rleFrBbox( Rs._R, bb.data, h, w, n )\n objs = _toString(Rs)\n return objs\n\ndef frPoly( poly, siz h, siz w ):\n cdef np.ndarray[np.double_t, ndim=1] np_poly\n n = len(poly)\n Rs = RLEs(n)\n for i, p in enumerate(poly):\n np_poly = np.array(p, dtype=np.double, order='F')\n rleFrPoly( &Rs._R[i], np_poly.data, len(np_poly)/2, h, w )\n objs = _toString(Rs)\n return objs\n\ndef frUncompressedRLE(ucRles, siz h, siz w):\n cdef np.ndarray[np.uint32_t, ndim=1] cnts\n cdef RLE R\n cdef uint *data\n n = len(ucRles)\n objs = []\n for i in range(n):\n Rs = RLEs(1)\n cnts = np.array(ucRles[i]['counts'], dtype=np.uint32)\n # time for malloc can be saved here but it's fine\n data = malloc(len(cnts)* sizeof(uint))\n for j in range(len(cnts)):\n data[j] = cnts[j]\n R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), data)\n Rs._R[0] = R\n objs.append(_toString(Rs)[0])\n return objs\n\ndef frPyObjects(pyobj, siz h, w):\n if type(pyobj) == np.ndarray:\n objs = frBbox(pyobj, h, w )\n elif type(pyobj) == list and len(pyobj[0]) == 4:\n objs = frBbox(pyobj, h, w )\n elif type(pyobj) == list and len(pyobj[0]) > 4:\n objs = frPoly(pyobj, h, w )\n elif type(pyobj) == list and type(pyobj[0]) == dict:\n objs = frUncompressedRLE(pyobj, h, w)\n else:\n raise Exception('input type is not supported.')\n return objs\n"
},
{
"path": "refer/external/mask.py",
"content": "__author__ = 'tsungyi'\n\nimport external._mask as _mask\n\n# Interface for manipulating masks stored in RLE format.\n#\n# RLE is a simple yet efficient format for storing binary masks. RLE\n# first divides a vector (or vectorized image) into a series of piecewise\n# constant regions and then for each piece simply stores the length of\n# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would\n# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]\n# (note that the odd counts are always the numbers of zeros). Instead of\n# storing the counts directly, additional compression is achieved with a\n# variable bitrate representation based on a common scheme called LEB128.\n#\n# Compression is greatest given large piecewise constant regions.\n# Specifically, the size of the RLE is proportional to the number of\n# *boundaries* in M (or for an image the number of boundaries in the y\n# direction). Assuming fairly simple shapes, the RLE representation is\n# O(sqrt(n)) where n is number of pixels in the object. Hence space usage\n# is substantially lower, especially for large simple objects (large n).\n#\n# Many common operations on masks can be computed directly using the RLE\n# (without need for decoding). This includes computations such as area,\n# union, intersection, etc. All of these operations are linear in the\n# size of the RLE, in other words they are O(sqrt(n)) where n is the area\n# of the object. Computing these operations on the original mask is O(n).\n# Thus, using the RLE can result in substantial computational savings.\n#\n# The following API functions are defined:\n# encode - Encode binary masks using RLE.\n# decode - Decode binary masks encoded via RLE.\n# merge - Compute union or intersection of encoded masks.\n# iou - Compute intersection over union between masks.\n# area - Compute area of encoded masks.\n# toBbox - Get bounding boxes surrounding encoded masks.\n# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.\n#\n# Usage:\n# Rs = encode( masks )\n# masks = decode( Rs )\n# R = merge( Rs, intersect=false )\n# o = iou( dt, gt, iscrowd )\n# a = area( Rs )\n# bbs = toBbox( Rs )\n# Rs = frPyObjects( [pyObjects], h, w )\n#\n# In the API the following formats are used:\n# Rs - [dict] Run-length encoding of binary masks\n# R - dict Run-length encoding of binary mask\n# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)\n# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore\n# bbs - [nx4] Bounding box(es) stored as [x y w h]\n# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)\n# dt,gt - May be either bounding boxes or encoded masks\n# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).\n#\n# Finally, a note about the intersection over union (iou) computation.\n# The standard iou of a ground truth (gt) and detected (dt) object is\n# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))\n# For \"crowd\" regions, we use a modified criteria. If a gt object is\n# marked as \"iscrowd\", we allow a dt to match any subregion of the gt.\n# Choosing gt' in the crowd gt that best matches the dt can be done using\n# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing\n# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)\n# For crowd gt regions we use this modified criteria above for the iou.\n#\n# To compile run \"python setup.py build_ext --inplace\"\n# Please do not contact us for help with compiling.\n#\n# Microsoft COCO Toolbox. version 2.0\n# Data, paper, and tutorials available at: http://mscoco.org/\n# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n# Licensed under the Simplified BSD License [see coco/license.txt]\n\nencode = _mask.encode\ndecode = _mask.decode\niou = _mask.iou\nmerge = _mask.merge\narea = _mask.area\ntoBbox = _mask.toBbox\nfrPyObjects = _mask.frPyObjects"
},
{
"path": "refer/external/maskApi.c",
"content": "/**************************************************************************\n* Microsoft COCO Toolbox. version 2.0\n* Data, paper, and tutorials available at: http://mscoco.org/\n* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n* Licensed under the Simplified BSD License [see coco/license.txt]\n**************************************************************************/\n#include \"maskApi.h\"\n#include \n#include \n\nuint umin( uint a, uint b ) { return (ab) ? a : b; }\n\nvoid rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) {\n R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m);\n siz j; if(cnts) for(j=0; jcnts[j]=cnts[j];\n}\n\nvoid rleFree( RLE *R ) {\n free(R->cnts); R->cnts=0;\n}\n\nvoid rlesInit( RLE **R, siz n ) {\n siz i; *R = (RLE*) malloc(sizeof(RLE)*n);\n for(i=0; i0 ) {\n c=umin(ca,cb); cc+=c; ct=0;\n ca-=c; if(!ca && a0) {\n crowd=iscrowd!=NULL && iscrowd[g];\n if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; }\n siz ka, kb, a, b; uint c, ca, cb, ct, i, u; int va, vb;\n ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0;\n cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1;\n while( ct>0 ) {\n c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0;\n ca-=c; if(!ca && athr) keep[j]=0;\n }\n }\n}\n\nvoid bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) {\n double h, w, i, u, ga, da; siz g, d; int crowd;\n for( g=0; gthr) keep[j]=0;\n }\n }\n}\n\nvoid rleToBbox( const RLE *R, BB bb, siz n ) {\n siz i; for( i=0; id?1:c=dy && xs>xe) || (dxye);\n if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; }\n s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy;\n if(dx>=dy) for( d=0; d<=dx; d++ ) {\n t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++;\n } else for( d=0; d<=dy; d++ ) {\n t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++;\n }\n }\n /* get points along y-boundary and downsample */\n free(x); free(y); k=m; m=0; double xd, yd;\n x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k);\n for( j=1; jw-1 ) continue;\n yd=(double)(v[j]h) yd=h; yd=ceil(yd);\n x[m]=(int) xd; y[m]=(int) yd; m++;\n }\n /* compute rle encoding given y-boundary points */\n k=m; a=malloc(sizeof(uint)*(k+1));\n for( j=0; j0) b[m++]=a[j++]; else {\n j++; if(jm, p=0; long x; int more;\n char *s=malloc(sizeof(char)*m*6);\n for( i=0; icnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1;\n while( more ) {\n char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0;\n if(more) c |= 0x20; c+=48; s[p++]=c;\n }\n }\n s[p]=0; return s;\n}\n\nvoid rleFrString( RLE *R, char *s, siz h, siz w ) {\n siz m=0, p=0, k; long x; int more; uint *cnts;\n while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n while( s[p] ) {\n x=0; k=0; more=1;\n while( more ) {\n char c=s[p]-48; x |= (c & 0x1f) << 5*k;\n more = c & 0x20; p++; k++;\n if(!more && (c & 0x10)) x |= -1 << 5*k;\n }\n if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n }\n rleInit(R,h,w,m,cnts); free(cnts);\n}\n"
},
{
"path": "refer/external/maskApi.h",
"content": "/**************************************************************************\n* Microsoft COCO Toolbox. version 2.0\n* Data, paper, and tutorials available at: http://mscoco.org/\n* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.\n* Licensed under the Simplified BSD License [see coco/license.txt]\n**************************************************************************/\n#pragma once\n\ntypedef unsigned int uint;\ntypedef unsigned long siz;\ntypedef unsigned char byte;\ntypedef double* BB;\ntypedef struct { siz h, w, m; uint *cnts; } RLE;\n\n/* Initialize/destroy RLE. */\nvoid rleInit( RLE *R, siz h, siz w, siz m, uint *cnts );\nvoid rleFree( RLE *R );\n\n/* Initialize/destroy RLE array. */\nvoid rlesInit( RLE **R, siz n );\nvoid rlesFree( RLE **R, siz n );\n\n/* Encode binary masks using RLE. */\nvoid rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n );\n\n/* Decode binary masks encoded via RLE. */\nvoid rleDecode( const RLE *R, byte *mask, siz n );\n\n/* Compute union or intersection of encoded masks. */\nvoid rleMerge( const RLE *R, RLE *M, siz n, int intersect );\n\n/* Compute area of encoded masks. */\nvoid rleArea( const RLE *R, siz n, uint *a );\n\n/* Compute intersection over union between masks. */\nvoid rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o );\n\n/* Compute non-maximum suppression between bounding masks */\nvoid rleNms( RLE *dt, siz n, uint *keep, double thr );\n\n/* Compute intersection over union between bounding boxes. */\nvoid bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o );\n\n/* Compute non-maximum suppression between bounding boxes */\nvoid bbNms( BB dt, siz n, uint *keep, double thr );\n\n/* Get bounding boxes surrounding encoded masks. */\nvoid rleToBbox( const RLE *R, BB bb, siz n );\n\n/* Convert bounding boxes to encoded masks. */\nvoid rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n );\n\n/* Convert polygon to encoded mask. */\nvoid rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w );\n\n/* Get compressed string representation of encoded mask. */\nchar* rleToString( const RLE *R );\n\n/* Convert from compressed string representation of encoded mask. */\nvoid rleFrString( RLE *R, char *s, siz h, siz w );\n"
},
{
"path": "refer/pyEvalDemo.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"from refer import REFER\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import os.path as osp\\n\",\n \"import json\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.patches import Rectangle\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"loading dataset refcoco into memory...\\n\",\n \"creating index...\\n\",\n \"index created.\\n\",\n \"DONE (t=9.47s)\\n\"\n ]\n }\n ],\n \"source\": [\n \"data_root = './data' # contains refclef, refcoco, refcoco+, refcocog and images\\n\",\n \"dataset = 'refcoco'\\n\",\n \"splitBy = 'unc'\\n\",\n \"refer = REFER(data_root, dataset, splitBy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Evaluate Refering Expressions by Language Metrics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sys.path.insert(0, './evaluation')\\n\",\n \"from refEvaluation import RefEvaluation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{u'sent': u'man in black', u'ref_id': 47}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Here's our example expression file\\n\",\n \"sample_expr_file = json.load(open('test/sample_expressions_testA.json', 'r'))\\n\",\n \"sample_exprs = sample_expr_file['predictions']\\n\",\n \"print sample_exprs[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tokenization...\\n\",\n \"setting up scorers...\\n\",\n \"computing Bleu score...\\n\",\n \"{'reflen': 5356, 'guess': [5009, 3034, 1477, 275], 'testlen': 5009, 'correct': [2576, 580, 112, 2]}\\n\",\n \"ratio: 0.935212845407\\n\",\n \"Bleu_1: 0.480\\n\",\n \"Bleu_2: 0.293\\n\",\n \"Bleu_3: 0.182\\n\",\n \"Bleu_4: 0.080\\n\",\n \"computing METEOR score...\\n\",\n \"METEOR: 0.172\\n\",\n \"computing Rouge score...\\n\",\n \"ROUGE_L: 0.414\\n\",\n \"computing CIDEr score...\\n\",\n \"CIDEr: 0.669\\n\"\n ]\n }\n ],\n \"source\": [\n \"refEval = RefEvaluation(refer, sample_exprs)\\n\",\n \"refEval.evaluate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2. Evaluate Referring Expressions by Duplicate Rate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"108/750 (14.40%) images have duplicate predicted sentences.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# evalue how many images contain duplicate expressions\\n\",\n \"pred_refToSent = {int(it['ref_id']): it['sent'] for it in sample_exprs}\\n\",\n \"pred_imgToSents = {}\\n\",\n \"for ref_id, pred_sent in pred_refToSent.items():\\n\",\n \" image_id = refer.Refs[ref_id]['image_id']\\n\",\n \" pred_imgToSents[image_id] = pred_imgToSents.get(image_id, []) + [pred_sent]\\n\",\n \"# count duplicate\\n\",\n \"duplicate = 0\\n\",\n \"for image_id, sents in pred_imgToSents.items():\\n\",\n \" if len(set(sents)) < len(sents):\\n\",\n \" duplicate += 1\\n\",\n \"ratio = duplicate*100.0 / len(pred_imgToSents)\\n\",\n \"print '%s/%s (%.2f%%) images have duplicate predicted sentences.' % (duplicate, len(pred_imgToSents), ratio)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# 3.Evaluate Referring Comprehension\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# IoU function\\n\",\n \"def computeIoU(box1, box2):\\n\",\n \" # each box is of [x1, y1, w, h]\\n\",\n \" inter_x1 = max(box1[0], box2[0])\\n\",\n \" inter_y1 = max(box1[1], box2[1])\\n\",\n \" inter_x2 = min(box1[0]+box1[2]-1, box2[0]+box2[2]-1)\\n\",\n \" inter_y2 = min(box1[1]+box1[3]-1, box2[1]+box2[3]-1)\\n\",\n \"\\n\",\n \" if inter_x1 < inter_x2 and inter_y1 < inter_y2:\\n\",\n \" inter = (inter_x2-inter_x1+1)*(inter_y2-inter_y1+1)\\n\",\n \" else:\\n\",\n \" inter = 0\\n\",\n \" union = box1[2]*box1[3] + box2[2]*box2[3] - inter\\n\",\n \" return float(inter)/union\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# randomly sample one ref\\n\",\n \"ref_ids = refer.getRefIds()\\n\",\n \"ref_id = ref_ids[np.random.randint(0, len(ref_ids))]\\n\",\n \"ref = refer.Refs[ref_id]\\n\",\n \"\\n\",\n \"# let's fake one bounding box by randomly picking one instance inside this image\\n\",\n \"image_id = ref['image_id']\\n\",\n \"anns = refer.imgToAnns[image_id]\\n\",\n \"ann = anns[np.random.randint(0, len(anns))]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1. person bending\\n\",\n \"2. man\\n\",\n \"3. the person bending over\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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9pynm9Vv98sqOpzjv2CkaU7v5/F7gN0e0/TW3ycxeJv0V6f+dknEVNC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\\nEeknS58+j0g/Wfr0eUT+H9bjcOnSi+aPAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# draw box of the ref using 'green'\\n\",\n \"plt.figure()\\n\",\n \"refer.showRef(ref, seg_box='box')\\n\",\n \"# draw box of the ann using 'red'\\n\",\n \"ax = plt.gca()\\n\",\n \"bbox = ann['bbox']\\n\",\n \"box_plot = Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor='red', linewidth=2)\\n\",\n \"ax.add_patch(box_plot)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"IoU=[0.09], wrong comprehension!\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Is the ann actually our ref?\\n\",\n \"# i.e., IoU >= 0.5?\\n\",\n \"ref_box = refer.refToAnn[ref_id]['bbox']\\n\",\n \"ann_box = ann['bbox']\\n\",\n \"IoU = computeIoU(ref_box, ann_box)\\n\",\n \"if IoU >= 0.5:\\n\",\n \" print 'IoU=[%.2f], correct comprehension!' % IoU\\n\",\n \"else:\\n\",\n \" print 'IoU=[%.2f], wrong comprehension!' % IoU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
},
{
"path": "refer/pyReferDemo.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"from refer import REFER\\n\",\n \"import numpy as np\\n\",\n \"import skimage.io as io\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load Refer Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"loading dataset refcoco into memory...\\n\",\n \"creating index...\\n\",\n \"index created.\\n\",\n \"DONE (t=9.88s)\\n\"\n ]\n }\n ],\n \"source\": [\n \"data_root = './data' # contains refclef, refcoco, refcoco+, refcocog and images\\n\",\n \"dataset = 'refcoco'\\n\",\n \"splitBy = 'unc'\\n\",\n \"refer = REFER(data_root, dataset, splitBy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Stats about the Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dataset [refcoco_unc] contains: \\n\",\n \"142210 expressions for 50000 refs in 19994 images.\\n\",\n \"\\n\",\n \"Among them:\\n\",\n \"42404 refs are in split [train].\\n\",\n \"3811 refs are in split [val].\\n\",\n \"3785 refs are in split [test].\\n\"\n ]\n }\n ],\n \"source\": [\n \"# print stats about the given dataset\\n\",\n \"print 'dataset [%s_%s] contains: ' % (dataset, splitBy)\\n\",\n \"ref_ids = refer.getRefIds()\\n\",\n \"image_ids = refer.getImgIds()\\n\",\n \"print '%s expressions for %s refs in %s images.' % (len(refer.Sents), len(ref_ids), len(image_ids))\\n\",\n \"\\n\",\n \"print '\\\\nAmong them:'\\n\",\n \"if dataset == 'refclef':\\n\",\n \" if splitBy == 'unc':\\n\",\n \" splits = ['train', 'val', 'testA', 'testB', 'testC']\\n\",\n \" else:\\n\",\n \" splits = ['train', 'val', 'test']\\n\",\n \"elif dataset == 'refcoco':\\n\",\n \" splits = ['train', 'val', 'test']\\n\",\n \"elif dataset == 'refcoco+':\\n\",\n \" splits = ['train', 'val', 'test']\\n\",\n \"elif dataset == 'refcocog':\\n\",\n \" splits = ['train', 'val'] # we don't have test split for refcocog right now.\\n\",\n \" \\n\",\n \"for split in splits:\\n\",\n \" ref_ids = refer.getRefIds(split=split)\\n\",\n \" print '%s refs are in split [%s].' % (len(ref_ids), split)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Show Refered Object and its Expressions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ref_id [22758] (ann_id [540661])\\n\",\n \"1. woman in front\\n\",\n \"2. lady smiling\\n\",\n \"3. woman\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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GlFCe//nu/gyRev8Bu//muc2hyy1lYErmB7NGext6CfJLxSZlzx0FYa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\\n2WXanrPs2Z5dpu05y57t2WXa/wVjO2xV5Ti03wAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# randomly sample one ref\\n\",\n \"ref_ids = refer.getRefIds()\\n\",\n \"ref_id = ref_ids[np.random.randint(0, len(ref_ids))]\\n\",\n \"ref = refer.Refs[ref_id]\\n\",\n \"print 'ref_id [%s] (ann_id [%s])' % (ref_id, refer.refToAnn[ref_id]['id'])\\n\",\n \"# show the segmentation of the referred object\\n\",\n \"plt.figure()\\n\",\n \"refer.showRef(ref, seg_box='seg')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1. woman in front\\n\",\n \"2. lady smiling\\n\",\n \"3. woman\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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hK3JvuoIEijmE6S8eE/+yf5/Ks3+Omf+qdc2Fljs6uIfMXeeMHycMkw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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# or show the bounding box of the referred object\\n\",\n \"refer.showRef(ref, seg_box='box')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sent_id[64727]: woman in front\\n\",\n \"sent_id[64728]: lady smiling\\n\",\n \"sent_id[64729]: woman\\n\"\n ]\n }\n ],\n \"source\": [\n \"# let's look at the details of each ref\\n\",\n \"for sent in ref['sentences']:\\n\",\n \" print 'sent_id[%s]: %s' % (sent['sent_id'], sent['sent'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
},
{
"path": "refer/refer.py",
"content": "\"\"\"\nThis interface provides access to four datasets:\n1) refclef\n2) refcoco\n3) refcoco+\n4) refcocog\nsplit by unc and google\n\nThe following API functions are defined:\nREFER - REFER api class\ngetRefIds - get ref ids that satisfy given filter conditions.\ngetAnnIds - get ann ids that satisfy given filter conditions.\ngetImgIds - get image ids that satisfy given filter conditions.\ngetCatIds - get category ids that satisfy given filter conditions.\nloadRefs - load refs with the specified ref ids.\nloadAnns - load anns with the specified ann ids.\nloadImgs - load images with the specified image ids.\nloadCats - load category names with the specified category ids.\ngetRefBox - get ref's bounding box [x, y, w, h] given the ref_id\nshowRef - show image, segmentation or box of the referred object with the ref\ngetMask - get mask and area of the referred object given ref\nshowMask - show mask of the referred object given ref\n\"\"\"\n\nimport sys\nimport os.path as osp\nimport json\nimport pickle as pickle\nimport time\nimport itertools\nimport skimage.io as io\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Polygon, Rectangle\nfrom pprint import pprint\nimport numpy as np \nfrom pycocotools import mask\n\n\nclass REFER:\n\n def __init__(self, data_root, dataset='refcoco', splitBy='unc'):\n # provide data_root folder which contains refclef, refcoco, refcoco+ and refcocog\n # also provide dataset name and splitBy information\n # e.g., dataset = 'refcoco', splitBy = 'unc'\n print('loading dataset %s into memory...' % dataset)\n if dataset == 'refcocog':\n print('Split by {}!'.format(splitBy))\n self.ROOT_DIR = osp.abspath(osp.dirname(__file__))\n self.DATA_DIR = osp.join(data_root, dataset)\n if dataset in ['refcoco', 'refcoco+', 'refcocog']:\n self.IMAGE_DIR = osp.join(data_root, 'images/mscoco/images/train2014')\n elif dataset == 'refclef':\n self.IMAGE_DIR = osp.join(data_root, 'images/saiapr_tc-12')\n else:\n print('No refer dataset is called [%s]' % dataset)\n sys.exit()\n\n # load refs from data/dataset/refs(dataset).json\n tic = time.time()\n ref_file = osp.join(self.DATA_DIR, 'refs(' + splitBy + ').p')\n self.data = {}\n self.data['dataset'] = dataset\n f = open(ref_file, 'r')\n self.data['refs'] = pickle.load(open(ref_file, 'rb'))\n\n # load annotations from data/dataset/instances.json\n instances_file = osp.join(self.DATA_DIR, 'instances.json')\n instances = json.load(open(instances_file, 'r'))\n self.data['images'] = instances['images']\n self.data['annotations'] = instances['annotations']\n self.data['categories'] = instances['categories']\n\n # create index\n self.createIndex()\n print('DONE (t=%.2fs)' % (time.time() - tic))\n\n def createIndex(self):\n # create sets of mapping\n # 1) Refs: \t \t{ref_id: ref}\n # 2) Anns: \t \t{ann_id: ann}\n # 3) Imgs:\t\t \t{image_id: image}\n # 4) Cats: \t \t{category_id: category_name}\n # 5) Sents: \t{sent_id: sent}\n # 6) imgToRefs: \t{image_id: refs}\n # 7) imgToAnns: \t{image_id: anns}\n # 8) refToAnn: \t{ref_id: ann}\n # 9) annToRef: \t{ann_id: ref}\n # 10) catToRefs: \t{category_id: refs}\n # 11) sentToRef: \t{sent_id: ref}\n # 12) sentToTokens: {sent_id: tokens}\n print('creating index...')\n # fetch info from instances\n Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {}\n for ann in self.data['annotations']:\n Anns[ann['id']] = ann\n imgToAnns[ann['image_id']] = imgToAnns.get(ann['image_id'], []) + [ann]\n for img in self.data['images']:\n Imgs[img['id']] = img\n for cat in self.data['categories']:\n Cats[cat['id']] = cat['name']\n\n # fetch info from refs\n Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {}\n Sents, sentToRef, sentToTokens = {}, {}, {}\n for ref in self.data['refs']:\n # ids\n ref_id = ref['ref_id']\n ann_id = ref['ann_id']\n category_id = ref['category_id']\n image_id = ref['image_id']\n\n # add mapping related to ref\n Refs[ref_id] = ref\n imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref]\n catToRefs[category_id] = catToRefs.get(category_id, []) + [ref]\n refToAnn[ref_id] = Anns[ann_id]\n annToRef[ann_id] = ref\n\n # add mapping of sent\n for sent in ref['sentences']:\n Sents[sent['sent_id']] = sent\n sentToRef[sent['sent_id']] = ref\n sentToTokens[sent['sent_id']] = sent['tokens']\n\n # create class members\n self.Refs = Refs\n self.Anns = Anns\n self.Imgs = Imgs\n self.Cats = Cats\n self.Sents = Sents\n self.imgToRefs = imgToRefs\n self.imgToAnns = imgToAnns\n self.refToAnn = refToAnn\n self.annToRef = annToRef\n self.catToRefs = catToRefs\n self.sentToRef = sentToRef\n self.sentToTokens = sentToTokens\n print('index created.')\n\n def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=''):\n image_ids = image_ids if type(image_ids) == list else [image_ids]\n cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]\n ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]\n\n if len(image_ids) == len(cat_ids) == len(ref_ids) == len(split) == 0:\n refs = self.data['refs']\n else:\n if not len(image_ids) == 0:\n refs = [self.imgToRefs[image_id] for image_id in image_ids]\n else:\n refs = self.data['refs']\n if not len(cat_ids) == 0:\n refs = [ref for ref in refs if ref['category_id'] in cat_ids]\n if not len(ref_ids) == 0:\n refs = [ref for ref in refs if ref['ref_id'] in ref_ids]\n if not len(split) == 0:\n if split in ['testA', 'testB', 'testC']:\n refs = [ref for ref in refs if split[-1] in ref['split']] # we also consider testAB, testBC, ...\n elif split in ['testAB', 'testBC', 'testAC']:\n refs = [ref for ref in refs if ref['split'] == split] # rarely used I guess...\n elif split == 'test':\n refs = [ref for ref in refs if 'test' in ref['split']]\n elif split == 'train' or split == 'val':\n refs = [ref for ref in refs if ref['split'] == split]\n else:\n print('No such split [%s]' % split)\n sys.exit()\n ref_ids = [ref['ref_id'] for ref in refs]\n return ref_ids\n\n def getAnnIds(self, image_ids=[], cat_ids=[], ref_ids=[]):\n image_ids = image_ids if type(image_ids) == list else [image_ids]\n cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]\n ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]\n\n if len(image_ids) == len(cat_ids) == len(ref_ids) == 0:\n ann_ids = [ann['id'] for ann in self.data['annotations']]\n else:\n if not len(image_ids) == 0:\n lists = [self.imgToAnns[image_id] for image_id in image_ids if\n image_id in self.imgToAnns] # list of [anns]\n anns = list(itertools.chain.from_iterable(lists))\n else:\n anns = self.data['annotations']\n if not len(cat_ids) == 0:\n anns = [ann for ann in anns if ann['category_id'] in cat_ids]\n ann_ids = [ann['id'] for ann in anns]\n if not len(ref_ids) == 0:\n ids = set(ann_ids).intersection(set([self.Refs[ref_id]['ann_id'] for ref_id in ref_ids]))\n return ann_ids\n\n def getImgIds(self, ref_ids=[]):\n ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]\n\n if not len(ref_ids) == 0:\n image_ids = list(set([self.Refs[ref_id]['image_id'] for ref_id in ref_ids]))\n else:\n image_ids = self.Imgs.keys()\n return image_ids\n\n def getCatIds(self):\n return self.Cats.keys()\n\n def loadRefs(self, ref_ids=[]):\n if type(ref_ids) == list:\n return [self.Refs[ref_id] for ref_id in ref_ids]\n elif type(ref_ids) == int:\n return [self.Refs[ref_ids]]\n\n def loadAnns(self, ann_ids=[]):\n if type(ann_ids) == list:\n return [self.Anns[ann_id] for ann_id in ann_ids]\n elif type(ann_ids) == int or type(ann_ids) == unicode:\n return [self.Anns[ann_ids]]\n\n def loadImgs(self, image_ids=[]):\n if type(image_ids) == list:\n return [self.Imgs[image_id] for image_id in image_ids]\n elif type(image_ids) == int:\n return [self.Imgs[image_ids]]\n\n def loadCats(self, cat_ids=[]):\n if type(cat_ids) == list:\n return [self.Cats[cat_id] for cat_id in cat_ids]\n elif type(cat_ids) == int:\n return [self.Cats[cat_ids]]\n\n def getRefBox(self, ref_id):\n ref = self.Refs[ref_id]\n ann = self.refToAnn[ref_id]\n return ann['bbox'] # [x, y, w, h]\n\n def showRef(self, ref, seg_box='seg'):\n ax = plt.gca()\n # show image\n image = self.Imgs[ref['image_id']]\n I = io.imread(osp.join(self.IMAGE_DIR, image['file_name']))\n ax.imshow(I)\n # show refer expression\n for sid, sent in enumerate(ref['sentences']):\n print('%s. %s' % (sid + 1, sent['sent']))\n # show segmentations\n if seg_box == 'seg':\n ann_id = ref['ann_id']\n ann = self.Anns[ann_id]\n polygons = []\n color = []\n c = 'none'\n if type(ann['segmentation'][0]) == list:\n # polygon used for refcoco*\n for seg in ann['segmentation']:\n poly = np.array(seg).reshape((len(seg) / 2, 2))\n polygons.append(Polygon(poly, True, alpha=0.4))\n color.append(c)\n p = PatchCollection(polygons, facecolors=color, edgecolors=(1, 1, 0, 0), linewidths=3, alpha=1)\n ax.add_collection(p) # thick yellow polygon\n p = PatchCollection(polygons, facecolors=color, edgecolors=(1, 0, 0, 0), linewidths=1, alpha=1)\n ax.add_collection(p) # thin red polygon\n else:\n # mask used for refclef\n rle = ann['segmentation']\n m = mask.decode(rle)\n img = np.ones((m.shape[0], m.shape[1], 3))\n color_mask = np.array([2.0, 166.0, 101.0]) / 255\n for i in range(3):\n img[:, :, i] = color_mask[i]\n ax.imshow(np.dstack((img, m * 0.5)))\n # show bounding-box\n elif seg_box == 'box':\n ann_id = ref['ann_id']\n ann = self.Anns[ann_id]\n bbox = self.getRefBox(ref['ref_id'])\n box_plot = Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor='green', linewidth=3)\n ax.add_patch(box_plot)\n\n def getMask(self, ref):\n # return mask, area and mask-center\n ann = self.refToAnn[ref['ref_id']]\n image = self.Imgs[ref['image_id']]\n\n if type(ann['segmentation'][0]) == list: # polygon\n rle = mask.frPyObjects(ann['segmentation'], image['height'], image['width'])\n else:\n rle = ann['segmentation']\n\n m = mask.decode(rle)\n m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)\n m = m.astype(np.uint8) # convert to np.uint8\n # compute area\n area = sum(mask.area(rle)) # should be close to ann['area']\n return {'mask': m, 'area': area}\n\n\n def showMask(self, ref):\n M = self.getMask(ref)\n msk = M['mask']\n ax = plt.gca()\n ax.imshow(msk)\n\n\nif __name__ == '__main__':\n refer = REFER(dataset='refcocog', splitBy='google')\n ref_ids = refer.getRefIds()\n\n ref_ids = refer.getRefIds(split='train')\n print('There are %s training referred objects.' % len(ref_ids))\n\n for ref_id in ref_ids:\n ref = refer.loadRefs(ref_id)[0]\n if len(ref['sentences']) < 2:\n continue\n print('The label is %s.' % refer.Cats[ref['category_id']])\n plt.figure()\n refer.showRef(ref, seg_box='box')\n plt.show()\n\n"
},
{
"path": "refer/setup.py",
"content": "from distutils.core import setup\nfrom Cython.Build import cythonize\nfrom distutils.extension import Extension\nimport numpy as np\n\next_modules = [\n Extension(\n 'external._mask',\n sources=['external/maskApi.c', 'external/_mask.pyx'],\n include_dirs = [np.get_include(), 'external'],\n extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'],\n )\n ]\n\nsetup(\n name='external',\n packages=['external'],\n package_dir = {'external': 'external'},\n version='2.0',\n ext_modules=cythonize(ext_modules)\n )\n"
},
{
"path": "refer/test/sample_expressions_testA.json",
"content": "{\"predictions\":[{\"sent\":\"man in black\",\"ref_id\":47},{\"sent\":\"person on right\",\"ref_id\":109},{\"sent\":\"woman in red\",\"ref_id\":110},{\"sent\":\"car behind bike\",\"ref_id\":111},{\"sent\":\"car on left\",\"ref_id\":112},{\"sent\":\"man in blue\",\"ref_id\":382},{\"sent\":\"man in white\",\"ref_id\":383},{\"sent\":\"left person\",\"ref_id\":519},{\"sent\":\"man on right\",\"ref_id\":520},{\"sent\":\"person in background\",\"ref_id\":525},{\"sent\":\"person on left\",\"ref_id\":526},{\"sent\":\"man in white\",\"ref_id\":527},{\"sent\":\"guy in white\",\"ref_id\":528},{\"sent\":\"guy in red\",\"ref_id\":537},{\"sent\":\"white shirt\",\"ref_id\":538},{\"sent\":\"player in white\",\"ref_id\":539},{\"sent\":\"red shirt\",\"ref_id\":557},{\"sent\":\"girl\",\"ref_id\":558},{\"sent\":\"baby\",\"ref_id\":588},{\"sent\":\"baby\",\"ref_id\":589},{\"sent\":\"woman in front\",\"ref_id\":640},{\"sent\":\"girl\",\"ref_id\":641},{\"sent\":\"right guy\",\"ref_id\":732},{\"sent\":\"man in white\",\"ref_id\":733},{\"sent\":\"middle guy\",\"ref_id\":734},{\"sent\":\"woman\",\"ref_id\":756},{\"sent\":\"man on right\",\"ref_id\":757},{\"sent\":\"woman\",\"ref_id\":814},{\"sent\":\"man in white\",\"ref_id\":815},{\"sent\":\"man in white shirt\",\"ref_id\":828},{\"sent\":\"woman on right\",\"ref_id\":829},{\"sent\":\"man in red\",\"ref_id\":931},{\"sent\":\"woman in pink\",\"ref_id\":932},{\"sent\":\"girl in pink\",\"ref_id\":933},{\"sent\":\"middle guy\",\"ref_id\":945},{\"sent\":\"second from right\",\"ref_id\":946},{\"sent\":\"left guy\",\"ref_id\":947},{\"sent\":\"white jacket\",\"ref_id\":954},{\"sent\":\"right guy\",\"ref_id\":955},{\"sent\":\"blue jacket\",\"ref_id\":956},{\"sent\":\"man in white shirt\",\"ref_id\":1023},{\"sent\":\"man\",\"ref_id\":1024},{\"sent\":\"man in back\",\"ref_id\":1052},{\"sent\":\"left guy\",\"ref_id\":1053},{\"sent\":\"woman on right\",\"ref_id\":1152},{\"sent\":\"woman on right\",\"ref_id\":1153},{\"sent\":\"left guy\",\"ref_id\":1154},{\"sent\":\"woman on right\",\"ref_id\":1333},{\"sent\":\"man in black shirt\",\"ref_id\":1334},{\"sent\":\"man\",\"ref_id\":1362},{\"sent\":\"man\",\"ref_id\":1363},{\"sent\":\"right guy\",\"ref_id\":1371},{\"sent\":\"left guy\",\"ref_id\":1372},{\"sent\":\"man in front\",\"ref_id\":1406},{\"sent\":\"man on left\",\"ref_id\":1407},{\"sent\":\"person on right\",\"ref_id\":1568},{\"sent\":\"person in front\",\"ref_id\":1569},{\"sent\":\"man in black\",\"ref_id\":1582},{\"sent\":\"man in front\",\"ref_id\":1583},{\"sent\":\"right skier\",\"ref_id\":1623},{\"sent\":\"person in front\",\"ref_id\":1624},{\"sent\":\"second from left\",\"ref_id\":1679},{\"sent\":\"man on left\",\"ref_id\":1680},{\"sent\":\"second from right\",\"ref_id\":1681},{\"sent\":\"left guy\",\"ref_id\":1682},{\"sent\":\"woman on right\",\"ref_id\":1683},{\"sent\":\"girl on right\",\"ref_id\":1684},{\"sent\":\"man on right\",\"ref_id\":1811},{\"sent\":\"man in front of man in white shirt\",\"ref_id\":1812},{\"sent\":\"woman in white shirt\",\"ref_id\":1861},{\"sent\":\"man in black\",\"ref_id\":1862},{\"sent\":\"groom\",\"ref_id\":1882},{\"sent\":\"bride\",\"ref_id\":1883},{\"sent\":\"middle guy\",\"ref_id\":1977},{\"sent\":\"left guy\",\"ref_id\":1978},{\"sent\":\"right guy\",\"ref_id\":1979},{\"sent\":\"second from left\",\"ref_id\":1980},{\"sent\":\"person on left\",\"ref_id\":1990},{\"sent\":\"left person\",\"ref_id\":1991},{\"sent\":\"player\",\"ref_id\":2001},{\"sent\":\"top left corner\",\"ref_id\":2002},{\"sent\":\"girl in white on left\",\"ref_id\":2129},{\"sent\":\"white shirt\",\"ref_id\":2130},{\"sent\":\"woman in white\",\"ref_id\":2131},{\"sent\":\"red jacket\",\"ref_id\":2173},{\"sent\":\"red\",\"ref_id\":2174},{\"sent\":\"catcher\",\"ref_id\":2256},{\"sent\":\"umpire\",\"ref_id\":2257},{\"sent\":\"baby\",\"ref_id\":2264},{\"sent\":\"man\",\"ref_id\":2265},{\"sent\":\"boy in blue\",\"ref_id\":2291},{\"sent\":\"boy in red\",\"ref_id\":2292},{\"sent\":\"man in black\",\"ref_id\":2375},{\"sent\":\"man in black\",\"ref_id\":2376},{\"sent\":\"blue jacket\",\"ref_id\":2721},{\"sent\":\"bottom left\",\"ref_id\":2722},{\"sent\":\"man\",\"ref_id\":2767},{\"sent\":\"man\",\"ref_id\":2768},{\"sent\":\"batter\",\"ref_id\":2805},{\"sent\":\"right guy\",\"ref_id\":2806},{\"sent\":\"batter\",\"ref_id\":2807},{\"sent\":\"woman in black\",\"ref_id\":2981},{\"sent\":\"woman in white\",\"ref_id\":2982},{\"sent\":\"left girl\",\"ref_id\":3247},{\"sent\":\"man in white\",\"ref_id\":3248},{\"sent\":\"man on left\",\"ref_id\":3257},{\"sent\":\"woman in middle\",\"ref_id\":3258},{\"sent\":\"woman on right\",\"ref_id\":3259},{\"sent\":\"man in middle\",\"ref_id\":3260},{\"sent\":\"guy on right\",\"ref_id\":3366},{\"sent\":\"left person\",\"ref_id\":3367},{\"sent\":\"girl in pink\",\"ref_id\":3768},{\"sent\":\"girl in pink\",\"ref_id\":3769},{\"sent\":\"right guy\",\"ref_id\":3772},{\"sent\":\"man\",\"ref_id\":3773},{\"sent\":\"man in blue shirt\",\"ref_id\":3805},{\"sent\":\"person in blue shirt\",\"ref_id\":3806},{\"sent\":\"man in black\",\"ref_id\":3807},{\"sent\":\"guy in red\",\"ref_id\":4002},{\"sent\":\"second horse from left\",\"ref_id\":4003},{\"sent\":\"guy in blue shirt\",\"ref_id\":4014},{\"sent\":\"man in blue shirt\",\"ref_id\":4015},{\"sent\":\"left person\",\"ref_id\":4016},{\"sent\":\"man in blue\",\"ref_id\":4017},{\"sent\":\"girl on right\",\"ref_id\":4089},{\"sent\":\"girl\",\"ref_id\":4090},{\"sent\":\"woman\",\"ref_id\":4101},{\"sent\":\"girl\",\"ref_id\":4102},{\"sent\":\"woman in black\",\"ref_id\":4143},{\"sent\":\"person sitting on left\",\"ref_id\":4144},{\"sent\":\"man in black\",\"ref_id\":4145},{\"sent\":\"white shirt\",\"ref_id\":4159},{\"sent\":\"man on right\",\"ref_id\":4160},{\"sent\":\"right girl\",\"ref_id\":4174},{\"sent\":\"left girl\",\"ref_id\":4175},{\"sent\":\"person on right\",\"ref_id\":4176},{\"sent\":\"girl on left\",\"ref_id\":4177},{\"sent\":\"woman in red\",\"ref_id\":4178},{\"sent\":\"bride\",\"ref_id\":4187},{\"sent\":\"right kid\",\"ref_id\":4188},{\"sent\":\"person on left\",\"ref_id\":4190},{\"sent\":\"woman in black\",\"ref_id\":4191},{\"sent\":\"woman in white\",\"ref_id\":4192},{\"sent\":\"left person\",\"ref_id\":4308},{\"sent\":\"person on left\",\"ref_id\":4309},{\"sent\":\"man on left\",\"ref_id\":4333},{\"sent\":\"man in blue shirt\",\"ref_id\":4334},{\"sent\":\"batter\",\"ref_id\":4345},{\"sent\":\"catcher\",\"ref_id\":4346},{\"sent\":\"umpire\",\"ref_id\":4347},{\"sent\":\"man on right\",\"ref_id\":4396},{\"sent\":\"right person\",\"ref_id\":4397},{\"sent\":\"woman in blue\",\"ref_id\":4461},{\"sent\":\"man in black\",\"ref_id\":4462},{\"sent\":\"man on left\",\"ref_id\":4463},{\"sent\":\"woman on right\",\"ref_id\":4485},{\"sent\":\"woman\",\"ref_id\":4486},{\"sent\":\"right horse\",\"ref_id\":4487},{\"sent\":\"right horse\",\"ref_id\":4488},{\"sent\":\"left horse\",\"ref_id\":4489},{\"sent\":\"woman\",\"ref_id\":4578},{\"sent\":\"woman\",\"ref_id\":4579},{\"sent\":\"left bottom corner\",\"ref_id\":4580},{\"sent\":\"woman in blue\",\"ref_id\":4581},{\"sent\":\"head bottom right\",\"ref_id\":4582},{\"sent\":\"woman\",\"ref_id\":4616},{\"sent\":\"man\",\"ref_id\":4617},{\"sent\":\"woman in black\",\"ref_id\":4711},{\"sent\":\"left horse\",\"ref_id\":4712},{\"sent\":\"umpire\",\"ref_id\":4765},{\"sent\":\"catcher\",\"ref_id\":4766},{\"sent\":\"man on right\",\"ref_id\":4868},{\"sent\":\"man on right\",\"ref_id\":4869},{\"sent\":\"man\",\"ref_id\":4870},{\"sent\":\"white shirt\",\"ref_id\":5012},{\"sent\":\"right guy\",\"ref_id\":5118},{\"sent\":\"man in white\",\"ref_id\":5119},{\"sent\":\"woman\",\"ref_id\":5149},{\"sent\":\"man\",\"ref_id\":5150},{\"sent\":\"right dog\",\"ref_id\":5170},{\"sent\":\"left dog\",\"ref_id\":5171},{\"sent\":\"woman\",\"ref_id\":5244},{\"sent\":\"woman\",\"ref_id\":5245},{\"sent\":\"woman in black\",\"ref_id\":5289},{\"sent\":\"woman in black\",\"ref_id\":5290},{\"sent\":\"man on right\",\"ref_id\":5291},{\"sent\":\"person on left\",\"ref_id\":5292},{\"sent\":\"woman in black\",\"ref_id\":5293},{\"sent\":\"person on right\",\"ref_id\":5309},{\"sent\":\"woman in red\",\"ref_id\":5310},{\"sent\":\"girl\",\"ref_id\":5311},{\"sent\":\"person on right\",\"ref_id\":5389},{\"sent\":\"man\",\"ref_id\":5390},{\"sent\":\"man\",\"ref_id\":5550},{\"sent\":\"woman on right\",\"ref_id\":5551},{\"sent\":\"man on left\",\"ref_id\":5552},{\"sent\":\"man in white\",\"ref_id\":5615},{\"sent\":\"woman in red\",\"ref_id\":5648},{\"sent\":\"woman in blue\",\"ref_id\":5649},{\"sent\":\"woman in black\",\"ref_id\":5650},{\"sent\":\"catcher\",\"ref_id\":5767},{\"sent\":\"batter\",\"ref_id\":5768},{\"sent\":\"umpire\",\"ref_id\":5769},{\"sent\":\"couch on left\",\"ref_id\":5776},{\"sent\":\"couch on right\",\"ref_id\":5777},{\"sent\":\"guy in red\",\"ref_id\":5782},{\"sent\":\"red shirt\",\"ref_id\":5783},{\"sent\":\"batter\",\"ref_id\":5784},{\"sent\":\"girl on left\",\"ref_id\":5811},{\"sent\":\"woman\",\"ref_id\":5812},{\"sent\":\"bowl of white stuff on left\",\"ref_id\":5923},{\"sent\":\"bowl of rice in the back\",\"ref_id\":5924},{\"sent\":\"top right glass\",\"ref_id\":5925},{\"sent\":\"top left corner\",\"ref_id\":5926},{\"sent\":\"skier in middle\",\"ref_id\":6042},{\"sent\":\"left skier\",\"ref_id\":6043},{\"sent\":\"guy in white shirt\",\"ref_id\":6073},{\"sent\":\"man\",\"ref_id\":6074},{\"sent\":\"man in black\",\"ref_id\":6081},{\"sent\":\"guy in white\",\"ref_id\":6082},{\"sent\":\"umpire\",\"ref_id\":6118},{\"sent\":\"batter\",\"ref_id\":6119},{\"sent\":\"right guy\",\"ref_id\":6210},{\"sent\":\"guy on right\",\"ref_id\":6211},{\"sent\":\"kid\",\"ref_id\":6237},{\"sent\":\"kid\",\"ref_id\":6238},{\"sent\":\"baby\",\"ref_id\":6239},{\"sent\":\"woman\",\"ref_id\":6257},{\"sent\":\"person on right\",\"ref_id\":6461},{\"sent\":\"woman\",\"ref_id\":6462},{\"sent\":\"hand on left\",\"ref_id\":6607},{\"sent\":\"left kid\",\"ref_id\":6698},{\"sent\":\"player in red\",\"ref_id\":6699},{\"sent\":\"player\",\"ref_id\":6799},{\"sent\":\"guy in white\",\"ref_id\":6800},{\"sent\":\"right person\",\"ref_id\":6809},{\"sent\":\"guy in red\",\"ref_id\":6810},{\"sent\":\"catcher\",\"ref_id\":6811},{\"sent\":\"catcher\",\"ref_id\":6812},{\"sent\":\"guy in white shirt\",\"ref_id\":6956},{\"sent\":\"girl in blue\",\"ref_id\":6957},{\"sent\":\"red shirt\",\"ref_id\":6958},{\"sent\":\"bottom left corner\",\"ref_id\":7045},{\"sent\":\"girl in 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},
{
"path": "refer/test/sample_expressions_testB.json",
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couch\",\"ref_id\":25504},{\"sent\":\"couch\",\"ref_id\":25505},{\"sent\":\"bear in front\",\"ref_id\":25659},{\"sent\":\"bear\",\"ref_id\":25660},{\"sent\":\"white bear\",\"ref_id\":25694},{\"sent\":\"bear\",\"ref_id\":25695},{\"sent\":\"red bus\",\"ref_id\":25717},{\"sent\":\"left bus\",\"ref_id\":25718},{\"sent\":\"red bus on right\",\"ref_id\":25719},{\"sent\":\"toilet in front\",\"ref_id\":25762},{\"sent\":\"sink on the right\",\"ref_id\":25763},{\"sent\":\"apple on left\",\"ref_id\":25788},{\"sent\":\"right slice\",\"ref_id\":25789},{\"sent\":\"glass on left\",\"ref_id\":25826},{\"sent\":\"glass on right\",\"ref_id\":25827},{\"sent\":\"right elephant\",\"ref_id\":25831},{\"sent\":\"elephant on right\",\"ref_id\":25832},{\"sent\":\"top right microwave\",\"ref_id\":25888},{\"sent\":\"left monitor\",\"ref_id\":25889},{\"sent\":\"broccoli on left\",\"ref_id\":26005},{\"sent\":\"broccoli on right\",\"ref_id\":26006},{\"sent\":\"broccoli in middle\",\"ref_id\":26007},{\"sent\":\"bottom 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right\",\"ref_id\":26619},{\"sent\":\"bear in front\",\"ref_id\":26825},{\"sent\":\"right bear\",\"ref_id\":26826},{\"sent\":\"left bus\",\"ref_id\":26844},{\"sent\":\"bus in front\",\"ref_id\":26845},{\"sent\":\"red light\",\"ref_id\":27005},{\"sent\":\"traffic light\",\"ref_id\":27006},{\"sent\":\"traffic light\",\"ref_id\":27007},{\"sent\":\"middle giraffe\",\"ref_id\":27130},{\"sent\":\"left woman\",\"ref_id\":27131},{\"sent\":\"right bear\",\"ref_id\":27214},{\"sent\":\"left bear\",\"ref_id\":27215},{\"sent\":\"right cat\",\"ref_id\":27232},{\"sent\":\"cat on left\",\"ref_id\":27233},{\"sent\":\"zebra in front\",\"ref_id\":27247},{\"sent\":\"right zebra\",\"ref_id\":27248},{\"sent\":\"left zebra\",\"ref_id\":27249},{\"sent\":\"right car\",\"ref_id\":27250},{\"sent\":\"white car\",\"ref_id\":27251},{\"sent\":\"top left microwave\",\"ref_id\":27288},{\"sent\":\"microwave on right\",\"ref_id\":27289},{\"sent\":\"toilet on 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corner\",\"ref_id\":38920},{\"sent\":\"right\",\"ref_id\":39016},{\"sent\":\"left vase\",\"ref_id\":39017},{\"sent\":\"right bottle\",\"ref_id\":39018},{\"sent\":\"left elephant\",\"ref_id\":39024},{\"sent\":\"elephant on right\",\"ref_id\":39025},{\"sent\":\"left elephant\",\"ref_id\":39026},{\"sent\":\"giraffe in middle\",\"ref_id\":39045},{\"sent\":\"left train\",\"ref_id\":39150},{\"sent\":\"front train\",\"ref_id\":39151},{\"sent\":\"cat on the left\",\"ref_id\":39159},{\"sent\":\"top cat\",\"ref_id\":39160},{\"sent\":\"left suitcase\",\"ref_id\":39206},{\"sent\":\"right\",\"ref_id\":39207},{\"sent\":\"giraffe in front\",\"ref_id\":39387},{\"sent\":\"giraffe in front\",\"ref_id\":39388},{\"sent\":\"white teddy bear on right\",\"ref_id\":39400},{\"sent\":\"teddy bear in middle\",\"ref_id\":39401},{\"sent\":\"bear on left\",\"ref_id\":39402},{\"sent\":\"bottom right bear\",\"ref_id\":39403},{\"sent\":\"bear on left\",\"ref_id\":39404},{\"sent\":\"bear on right\",\"ref_id\":39405},{\"sent\":\"left elephant\",\"ref_id\":39448},{\"sent\":\"elephant in front\",\"ref_id\":39449},{\"sent\":\"cow on left\",\"ref_id\":39456},{\"sent\":\"cow on right\",\"ref_id\":39457},{\"sent\":\"right sandwich\",\"ref_id\":39460},{\"sent\":\"sandwich on left\",\"ref_id\":39461},{\"sent\":\"left chair\",\"ref_id\":39765},{\"sent\":\"chair on left\",\"ref_id\":39766},{\"sent\":\"left cow\",\"ref_id\":39797},{\"sent\":\"cow on right\",\"ref_id\":39798},{\"sent\":\"green apple\",\"ref_id\":39815},{\"sent\":\"green apple\",\"ref_id\":39816},{\"sent\":\"green apple\",\"ref_id\":39817},{\"sent\":\"right zebra\",\"ref_id\":39850},{\"sent\":\"zebra in front\",\"ref_id\":39851},{\"sent\":\"right pizza\",\"ref_id\":39883},{\"sent\":\"pizza in front\",\"ref_id\":39884},{\"sent\":\"pizza on left\",\"ref_id\":39885},{\"sent\":\"right laptop\",\"ref_id\":39891},{\"sent\":\"left laptop\",\"ref_id\":39892},{\"sent\":\"left truck\",\"ref_id\":39963},{\"sent\":\"red 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cat\",\"ref_id\":40401},{\"sent\":\"the plant\",\"ref_id\":40456},{\"sent\":\"left bunch\",\"ref_id\":40457},{\"sent\":\"right dog\",\"ref_id\":40458},{\"sent\":\"dog on left\",\"ref_id\":40459},{\"sent\":\"left bike\",\"ref_id\":40479},{\"sent\":\"front bike\",\"ref_id\":40480},{\"sent\":\"bike on right\",\"ref_id\":40500},{\"sent\":\"bike in front\",\"ref_id\":40501},{\"sent\":\"bike on left\",\"ref_id\":40502},{\"sent\":\"top bowl\",\"ref_id\":40554},{\"sent\":\"right sandwich\",\"ref_id\":40555},{\"sent\":\"cow in front\",\"ref_id\":40571},{\"sent\":\"cow on right\",\"ref_id\":40572},{\"sent\":\"left meter\",\"ref_id\":40753},{\"sent\":\"left car\",\"ref_id\":40754},{\"sent\":\"bottom left orange\",\"ref_id\":40762},{\"sent\":\"orange\",\"ref_id\":40763},{\"sent\":\"left giraffe\",\"ref_id\":40804},{\"sent\":\"giraffe on right\",\"ref_id\":40805},{\"sent\":\"bottom left fruit\",\"ref_id\":40810},{\"sent\":\"banana slice in the middle\",\"ref_id\":40811},{\"sent\":\"banana on right\",\"ref_id\":40812},{\"sent\":\"toilet\",\"ref_id\":40909},{\"sent\":\"toilet\",\"ref_id\":40910},{\"sent\":\"second from right\",\"ref_id\":40945},{\"sent\":\"second row from right\",\"ref_id\":40946},{\"sent\":\"second from right\",\"ref_id\":40947},{\"sent\":\"second banana from left\",\"ref_id\":40948},{\"sent\":\"second row from left\",\"ref_id\":40949},{\"sent\":\"left elephant\",\"ref_id\":41094},{\"sent\":\"baby elephant\",\"ref_id\":41095},{\"sent\":\"right cow\",\"ref_id\":41136},{\"sent\":\"giraffe in front\",\"ref_id\":41137},{\"sent\":\"chair on right\",\"ref_id\":41148},{\"sent\":\"bed\",\"ref_id\":41167},{\"sent\":\"right bed\",\"ref_id\":41168},{\"sent\":\"left screen\",\"ref_id\":41173},{\"sent\":\"right monitor\",\"ref_id\":41174},{\"sent\":\"left white chair\",\"ref_id\":41197},{\"sent\":\"chair on right\",\"ref_id\":41198},{\"sent\":\"UNK\",\"ref_id\":41209},{\"sent\":\"blue UNK\",\"ref_id\":41351},{\"sent\":\"right giraffe\",\"ref_id\":41359},{\"sent\":\"giraffe on left\",\"ref_id\":41360},{\"sent\":\"right bed\",\"ref_id\":41531},{\"sent\":\"red bed\",\"ref_id\":41532},{\"sent\":\"left giraffe\",\"ref_id\":41551},{\"sent\":\"right giraffe\",\"ref_id\":41552},{\"sent\":\"sheep in back\",\"ref_id\":41743},{\"sent\":\"sheep in front\",\"ref_id\":41744},{\"sent\":\"broccoli on left\",\"ref_id\":41795},{\"sent\":\"broccoli on right\",\"ref_id\":41796},{\"sent\":\"broccoli on right\",\"ref_id\":41797},{\"sent\":\"broccoli on left\",\"ref_id\":41798},{\"sent\":\"broccoli on left\",\"ref_id\":41799},{\"sent\":\"bottom right food\",\"ref_id\":41805},{\"sent\":\"left bowl\",\"ref_id\":41806},{\"sent\":\"white bowl on right\",\"ref_id\":41807},{\"sent\":\"right most food\",\"ref_id\":41808},{\"sent\":\"bowl of food on left\",\"ref_id\":41809},{\"sent\":\"sheep in back\",\"ref_id\":41877},{\"sent\":\"right sheep\",\"ref_id\":41878},{\"sent\":\"cat on right\",\"ref_id\":41938},{\"sent\":\"cat on left\",\"ref_id\":41939},{\"sent\":\"left horse\",\"ref_id\":42136},{\"sent\":\"left horse\",\"ref_id\":42137},{\"sent\":\"bird\",\"ref_id\":42284},{\"sent\":\"bird\",\"ref_id\":42285},{\"sent\":\"left bird\",\"ref_id\":42296},{\"sent\":\"duck\",\"ref_id\":42297},{\"sent\":\"red surfboard\",\"ref_id\":42329},{\"sent\":\"white boat\",\"ref_id\":42330},{\"sent\":\"bottom left toilet\",\"ref_id\":42354},{\"sent\":\"white toilet\",\"ref_id\":42355},{\"sent\":\"toilet on left\",\"ref_id\":42356},{\"sent\":\"toilet on right\",\"ref_id\":42357},{\"sent\":\"toilet on left\",\"ref_id\":42358},{\"sent\":\"toilet on left\",\"ref_id\":42359},{\"sent\":\"bananas on left\",\"ref_id\":42428},{\"sent\":\"banana bunch\",\"ref_id\":42429},{\"sent\":\"bowl of food on right\",\"ref_id\":42658},{\"sent\":\"bowl of food\",\"ref_id\":42659},{\"sent\":\"bowl of food\",\"ref_id\":42660},{\"sent\":\"left donut\",\"ref_id\":42697},{\"sent\":\"bottom right donut\",\"ref_id\":42698},{\"sent\":\"train\",\"ref_id\":42839},{\"sent\":\"left bird\",\"ref_id\":42922},{\"sent\":\"bird on right\",\"ref_id\":42923},{\"sent\":\"zebra on left\",\"ref_id\":42928},{\"sent\":\"zebra on right\",\"ref_id\":42929},{\"sent\":\"zebra in back\",\"ref_id\":42930},{\"sent\":\"sandwich on top\",\"ref_id\":43160},{\"sent\":\"top left sandwich\",\"ref_id\":43161},{\"sent\":\"cake\",\"ref_id\":43210},{\"sent\":\"bottom left suitcase\",\"ref_id\":43211},{\"sent\":\"bird on the left\",\"ref_id\":43396},{\"sent\":\"plate of food\",\"ref_id\":43446},{\"sent\":\"top bowl\",\"ref_id\":43447},{\"sent\":\"plate with lettuce\",\"ref_id\":43448},{\"sent\":\"sandwich on right\",\"ref_id\":43449},{\"sent\":\"right sandwich\",\"ref_id\":43480},{\"sent\":\"sandwich on left\",\"ref_id\":43481},{\"sent\":\"toilet\",\"ref_id\":43581},{\"sent\":\"toilet\",\"ref_id\":43582},{\"sent\":\"cat on right\",\"ref_id\":43596},{\"sent\":\"black chair bottom right\",\"ref_id\":43597},{\"sent\":\"giraffe on right\",\"ref_id\":43598},{\"sent\":\"left giraffe\",\"ref_id\":43599},{\"sent\":\"right sandwich\",\"ref_id\":43700},{\"sent\":\"sandwich on left\",\"ref_id\":43701},{\"sent\":\"red truck\",\"ref_id\":43784},{\"sent\":\"truck\",\"ref_id\":43785},{\"sent\":\"banana on right\",\"ref_id\":43815},{\"sent\":\"right banana\",\"ref_id\":43816},{\"sent\":\"bottom right corner\",\"ref_id\":43817},{\"sent\":\"banana on right\",\"ref_id\":43818},{\"sent\":\"middle banana\",\"ref_id\":43819},{\"sent\":\"cow on left\",\"ref_id\":43940},{\"sent\":\"top cow\",\"ref_id\":43941},{\"sent\":\"black suitcase on right\",\"ref_id\":43987},{\"sent\":\"black suitcase on right\",\"ref_id\":43988},{\"sent\":\"red suitcase\",\"ref_id\":43989},{\"sent\":\"black suitcase in middle\",\"ref_id\":43990},{\"sent\":\"baby\",\"ref_id\":44146},{\"sent\":\"teddy bear\",\"ref_id\":44147},{\"sent\":\"bottom left food\",\"ref_id\":44160},{\"sent\":\"right plate\",\"ref_id\":44161},{\"sent\":\"bottom tray\",\"ref_id\":44162},{\"sent\":\"top plate\",\"ref_id\":44163},{\"sent\":\"right bed\",\"ref_id\":44228},{\"sent\":\"bed on left\",\"ref_id\":44229},{\"sent\":\"bottom left bowl\",\"ref_id\":44296},{\"sent\":\"bottom banana\",\"ref_id\":44297},{\"sent\":\"front giraffe\",\"ref_id\":44300},{\"sent\":\"giraffe on right\",\"ref_id\":44301},{\"sent\":\"plane in front\",\"ref_id\":44369},{\"sent\":\"plane on left\",\"ref_id\":44370},{\"sent\":\"plane in front\",\"ref_id\":44408},{\"sent\":\"plane in front\",\"ref_id\":44409},{\"sent\":\"top right cake\",\"ref_id\":44426},{\"sent\":\"left half of sandwich\",\"ref_id\":44427},{\"sent\":\"left sandwich\",\"ref_id\":44428},{\"sent\":\"right half of sandwich\",\"ref_id\":44429},{\"sent\":\"bottom left bread\",\"ref_id\":44430},{\"sent\":\"bottom left cake\",\"ref_id\":44431},{\"sent\":\"right half of sandwich\",\"ref_id\":44432},{\"sent\":\"white toilet\",\"ref_id\":44454},{\"sent\":\"left sheep\",\"ref_id\":44463},{\"sent\":\"sheep in front\",\"ref_id\":44464},{\"sent\":\"sandwich on right\",\"ref_id\":44467},{\"sent\":\"left sandwich\",\"ref_id\":44468},{\"sent\":\"right car\",\"ref_id\":44514},{\"sent\":\"car on left\",\"ref_id\":44515},{\"sent\":\"yellow cab\",\"ref_id\":44516},{\"sent\":\"right toilet\",\"ref_id\":44573},{\"sent\":\"toilet on left\",\"ref_id\":44574},{\"sent\":\"bottom right bear\",\"ref_id\":44661},{\"sent\":\"bear on left\",\"ref_id\":44662},{\"sent\":\"bear on right\",\"ref_id\":44663},{\"sent\":\"right meter\",\"ref_id\":44814},{\"sent\":\"left meter\",\"ref_id\":44815},{\"sent\":\"red sauce\",\"ref_id\":44856},{\"sent\":\"glass on left\",\"ref_id\":44857},{\"sent\":\"bear on right\",\"ref_id\":44884},{\"sent\":\"brown bear\",\"ref_id\":44885},{\"sent\":\"bear on left\",\"ref_id\":44886},{\"sent\":\"bear on right\",\"ref_id\":44887},{\"sent\":\"teddy bear on left\",\"ref_id\":44888},{\"sent\":\"left meter\",\"ref_id\":44941},{\"sent\":\"right meter\",\"ref_id\":44942},{\"sent\":\"UNK\",\"ref_id\":44989},{\"sent\":\"top right corner\",\"ref_id\":44990},{\"sent\":\"bed\",\"ref_id\":45056},{\"sent\":\"bottom left suitcase\",\"ref_id\":45057},{\"sent\":\"blue umbrella\",\"ref_id\":45127},{\"sent\":\"left blue vase\",\"ref_id\":45128},{\"sent\":\"blue umbrella\",\"ref_id\":45129},{\"sent\":\"left blue umbrella\",\"ref_id\":45130},{\"sent\":\"bottom right corner\",\"ref_id\":45131},{\"sent\":\"glass of the beer\",\"ref_id\":45256},{\"sent\":\"right side of table\",\"ref_id\":45257},{\"sent\":\"white bus\",\"ref_id\":45498},{\"sent\":\"bus in middle\",\"ref_id\":45499},{\"sent\":\"right bunch of bananas\",\"ref_id\":45503},{\"sent\":\"bananas on right\",\"ref_id\":45504},{\"sent\":\"bananas on the left\",\"ref_id\":45505},{\"sent\":\"elephant on right\",\"ref_id\":45533},{\"sent\":\"elephant on left\",\"ref_id\":45534},{\"sent\":\"elephant in front\",\"ref_id\":45535},{\"sent\":\"left zebra\",\"ref_id\":45645},{\"sent\":\"right zebra\",\"ref_id\":45646},{\"sent\":\"middle row\",\"ref_id\":45680},{\"sent\":\"bottom right cake\",\"ref_id\":45681},{\"sent\":\"white car in back\",\"ref_id\":45957},{\"sent\":\"train\",\"ref_id\":45958},{\"sent\":\"pizza\",\"ref_id\":45972},{\"sent\":\"pizza slice\",\"ref_id\":45973},{\"sent\":\"table in front\",\"ref_id\":45974},{\"sent\":\"top right corner\",\"ref_id\":45975},{\"sent\":\"sandwich on left\",\"ref_id\":45976},{\"sent\":\"right half of sandwich\",\"ref_id\":45977},{\"sent\":\"blue thing\",\"ref_id\":45984},{\"sent\":\"blue thing\",\"ref_id\":45985},{\"sent\":\"chair behind dog\",\"ref_id\":46007},{\"sent\":\"chair on right\",\"ref_id\":46008},{\"sent\":\"donut on right\",\"ref_id\":46423},{\"sent\":\"bottom row second from left\",\"ref_id\":46439},{\"sent\":\"bottom row second from left\",\"ref_id\":46440},{\"sent\":\"second row from bottom right\",\"ref_id\":46441},{\"sent\":\"right giraffe\",\"ref_id\":46476},{\"sent\":\"giraffe on left\",\"ref_id\":46477},{\"sent\":\"truck on right\",\"ref_id\":46501},{\"sent\":\"white truck\",\"ref_id\":46502},{\"sent\":\"white truck\",\"ref_id\":46503},{\"sent\":\"right elephant\",\"ref_id\":46569},{\"sent\":\"middle elephant\",\"ref_id\":46570},{\"sent\":\"right zebra\",\"ref_id\":46668},{\"sent\":\"zebra in front\",\"ref_id\":46669},{\"sent\":\"top pizza\",\"ref_id\":46684},{\"sent\":\"pizza\",\"ref_id\":46685},{\"sent\":\"car in front\",\"ref_id\":46724},{\"sent\":\"car in front\",\"ref_id\":46725},{\"sent\":\"sheep in front\",\"ref_id\":46744},{\"sent\":\"bottom right bowl\",\"ref_id\":46773},{\"sent\":\"bowl of food on left\",\"ref_id\":46774},{\"sent\":\"middle bowl\",\"ref_id\":46775},{\"sent\":\"left pizza\",\"ref_id\":46796},{\"sent\":\"pizza on right\",\"ref_id\":46797},{\"sent\":\"pizza on the right\",\"ref_id\":46798},{\"sent\":\"bear on left\",\"ref_id\":46817},{\"sent\":\"right bear\",\"ref_id\":46818},{\"sent\":\"right plant\",\"ref_id\":46965},{\"sent\":\"left UNK\",\"ref_id\":46966},{\"sent\":\"left suitcase\",\"ref_id\":46986},{\"sent\":\"right suitcase\",\"ref_id\":46987},{\"sent\":\"right suitcase\",\"ref_id\":46988},{\"sent\":\"right suitcase\",\"ref_id\":46989},{\"sent\":\"suitcase in middle\",\"ref_id\":46990},{\"sent\":\"top carrot\",\"ref_id\":47273},{\"sent\":\"top carrot\",\"ref_id\":47274},{\"sent\":\"top carrot\",\"ref_id\":47275},{\"sent\":\"top carrot\",\"ref_id\":47276},{\"sent\":\"bottom left carrot\",\"ref_id\":47277},{\"sent\":\"right giraffe\",\"ref_id\":47305},{\"sent\":\"left giraffe\",\"ref_id\":47306},{\"sent\":\"bowl of food\",\"ref_id\":47310},{\"sent\":\"bowl of rice\",\"ref_id\":47311},{\"sent\":\"top right bowl\",\"ref_id\":47312},{\"sent\":\"train on right\",\"ref_id\":47313},{\"sent\":\"train on right\",\"ref_id\":47314},{\"sent\":\"train on left\",\"ref_id\":47315},{\"sent\":\"chair on left\",\"ref_id\":47318},{\"sent\":\"right giraffe\",\"ref_id\":47366},{\"sent\":\"left zebra\",\"ref_id\":47367},{\"sent\":\"white cow\",\"ref_id\":47450},{\"sent\":\"big cow\",\"ref_id\":47451},{\"sent\":\"white sheep on right\",\"ref_id\":47452},{\"sent\":\"white sheep on right\",\"ref_id\":47453},{\"sent\":\"top right sheep\",\"ref_id\":47454},{\"sent\":\"white cow\",\"ref_id\":47455},{\"sent\":\"top left suitcase\",\"ref_id\":47529},{\"sent\":\"white boat on left\",\"ref_id\":47530},{\"sent\":\"white boat\",\"ref_id\":47531},{\"sent\":\"left giraffe\",\"ref_id\":47603},{\"sent\":\"right giraffe\",\"ref_id\":47604},{\"sent\":\"white plate\",\"ref_id\":47644},{\"sent\":\"top left food\",\"ref_id\":47645},{\"sent\":\"bear on right\",\"ref_id\":47740},{\"sent\":\"bear on right\",\"ref_id\":47741},{\"sent\":\"bear in middle\",\"ref_id\":47742},{\"sent\":\"bear on right\",\"ref_id\":47743},{\"sent\":\"bed on left\",\"ref_id\":47840},{\"sent\":\"bed\",\"ref_id\":47841},{\"sent\":\"black dog\",\"ref_id\":47875},{\"sent\":\"dog on right\",\"ref_id\":47876},{\"sent\":\"giraffe in front\",\"ref_id\":47931},{\"sent\":\"giraffe on left\",\"ref_id\":47932},{\"sent\":\"left person\",\"ref_id\":47957},{\"sent\":\"giraffe on left\",\"ref_id\":48055},{\"sent\":\"giraffe\",\"ref_id\":48056},{\"sent\":\"bowl of food\",\"ref_id\":48175},{\"sent\":\"right bowl\",\"ref_id\":48176},{\"sent\":\"zebra in front\",\"ref_id\":48302},{\"sent\":\"right zebra\",\"ref_id\":48303},{\"sent\":\"bottom left food\",\"ref_id\":48441},{\"sent\":\"left piece of food\",\"ref_id\":48442},{\"sent\":\"top right food\",\"ref_id\":48443},{\"sent\":\"right food\",\"ref_id\":48444},{\"sent\":\"left vase\",\"ref_id\":48545},{\"sent\":\"right vase\",\"ref_id\":48546},{\"sent\":\"second bike from right\",\"ref_id\":48585},{\"sent\":\"second from right\",\"ref_id\":48586},{\"sent\":\"second from left\",\"ref_id\":48587},{\"sent\":\"left bike\",\"ref_id\":48588},{\"sent\":\"second bike from right\",\"ref_id\":48589},{\"sent\":\"right half of sandwich\",\"ref_id\":48623},{\"sent\":\"left sandwich\",\"ref_id\":48624},{\"sent\":\"right sandwich\",\"ref_id\":48625},{\"sent\":\"bottom right chair\",\"ref_id\":48681},{\"sent\":\"right couch\",\"ref_id\":48682},{\"sent\":\"bottom left corner\",\"ref_id\":48683},{\"sent\":\"couch on right\",\"ref_id\":48684},{\"sent\":\"couch\",\"ref_id\":48685},{\"sent\":\"left couch\",\"ref_id\":48686},{\"sent\":\"left cake\",\"ref_id\":48861},{\"sent\":\"right cake\",\"ref_id\":48862},{\"sent\":\"left elephant\",\"ref_id\":48865},{\"sent\":\"baby elephant\",\"ref_id\":48866},{\"sent\":\"right elephant\",\"ref_id\":48888},{\"sent\":\"left elephant\",\"ref_id\":48889},{\"sent\":\"table in front\",\"ref_id\":49111},{\"sent\":\"right truck\",\"ref_id\":49165},{\"sent\":\"truck\",\"ref_id\":49166},{\"sent\":\"truck\",\"ref_id\":49167},{\"sent\":\"right truck\",\"ref_id\":49168},{\"sent\":\"right bike\",\"ref_id\":49248},{\"sent\":\"right bike\",\"ref_id\":49249},{\"sent\":\"right bike\",\"ref_id\":49250},{\"sent\":\"donut in middle\",\"ref_id\":49288},{\"sent\":\"donut on left\",\"ref_id\":49289},{\"sent\":\"donut in middle\",\"ref_id\":49290},{\"sent\":\"giraffe on left\",\"ref_id\":49291},{\"sent\":\"left giraffe\",\"ref_id\":49292},{\"sent\":\"giraffe in front\",\"ref_id\":49293},{\"sent\":\"left cup\",\"ref_id\":49377},{\"sent\":\"right cup\",\"ref_id\":49378},{\"sent\":\"left bottle\",\"ref_id\":49429},{\"sent\":\"bottle on right\",\"ref_id\":49430},{\"sent\":\"pizza\",\"ref_id\":49446},{\"sent\":\"pizza slice on right\",\"ref_id\":49447},{\"sent\":\"pizza slice on right\",\"ref_id\":49448},{\"sent\":\"broccoli on the right\",\"ref_id\":49455},{\"sent\":\"broccoli in the middle\",\"ref_id\":49456},{\"sent\":\"second board from right\",\"ref_id\":49502},{\"sent\":\"blue board\",\"ref_id\":49503},{\"sent\":\"right plant\",\"ref_id\":49583},{\"sent\":\"left plant\",\"ref_id\":49584},{\"sent\":\"black suitcase\",\"ref_id\":49672},{\"sent\":\"blue tie\",\"ref_id\":49673},{\"sent\":\"middle bus\",\"ref_id\":49701},{\"sent\":\"second bus from right\",\"ref_id\":49702},{\"sent\":\"right bus\",\"ref_id\":49703},{\"sent\":\"right suitcase\",\"ref_id\":49721},{\"sent\":\"right side of pizza\",\"ref_id\":49781},{\"sent\":\"right slice\",\"ref_id\":49782},{\"sent\":\"dog on right\",\"ref_id\":49818},{\"sent\":\"left dog\",\"ref_id\":49819},{\"sent\":\"left car\",\"ref_id\":49824},{\"sent\":\"white car\",\"ref_id\":49825},{\"sent\":\"right cup\",\"ref_id\":49949},{\"sent\":\"right cup\",\"ref_id\":49950},{\"sent\":\"zebra in back\",\"ref_id\":49986},{\"sent\":\"zebra on left\",\"ref_id\":49987},{\"sent\":\"car on left\",\"ref_id\":25},{\"sent\":\"car on left\",\"ref_id\":26},{\"sent\":\"top sandwich\",\"ref_id\":27},{\"sent\":\"top left donut\",\"ref_id\":28},{\"sent\":\"zebra on left\",\"ref_id\":45},{\"sent\":\"right zebra\",\"ref_id\":46},{\"sent\":\"chair in front of man\",\"ref_id\":164},{\"sent\":\"bottom right corner\",\"ref_id\":165},{\"sent\":\"left chair\",\"ref_id\":166},{\"sent\":\"top right corner\",\"ref_id\":232},{\"sent\":\"pizza in front\",\"ref_id\":233},{\"sent\":\"glass in back\",\"ref_id\":234},{\"sent\":\"left glass\",\"ref_id\":235},{\"sent\":\"yellow fruit on left\",\"ref_id\":259}]}"
},
{
"path": "requirements.txt",
"content": "requests\nfilelock\ntqdm\ntimm\nmmcv-full==1.3.12\nmmsegmentation==0.17.0\nftfy\nregex\nscipy\nscikit-image\npycocotools==2.0.2\nopencv-python==4.5.3.56\ntokenizers==0.8.1rc1\nh5py"
},
{
"path": "test.py",
"content": "import datetime\nimport os\nimport time\n\nimport torch\nimport torch.utils.data\nfrom torch import nn\n\nfrom bert.modeling_bert import BertModel\nimport torchvision\n\nfrom lib import segmentation\nimport transforms as T\nimport utils\n\nimport numpy as np\nfrom PIL import Image\nimport torch.nn.functional as F\n\n\ndef get_dataset(image_set, transform, args):\n from data.dataset_refer_bert import ReferDataset\n ds = ReferDataset(args,\n split=image_set,\n image_transforms=transform,\n target_transforms=None,\n eval_mode=True\n )\n num_classes = 2\n return ds, num_classes\n\n\ndef evaluate(model, data_loader, bert_model, device):\n model.eval()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n\n # evaluation variables\n cum_I, cum_U = 0, 0\n eval_seg_iou_list = [.5, .6, .7, .8, .9]\n seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)\n seg_total = 0\n mean_IoU = []\n header = 'Test:'\n\n with torch.no_grad():\n for data in metric_logger.log_every(data_loader, 100, header):\n image, target, sentences, attentions = data\n image, target, sentences, attentions = image.to(device), target.to(device), \\\n sentences.to(device), attentions.to(device)\n sentences = sentences.squeeze(1)\n attentions = attentions.squeeze(1)\n target = target.cpu().data.numpy()\n for j in range(sentences.size(-1)):\n if bert_model is not None:\n last_hidden_states = bert_model(sentences[:, :, j], attention_mask=attentions[:, :, j])[0]\n embedding = last_hidden_states.permute(0, 2, 1)\n output = model(image, embedding, l_mask=attentions[:, :, j].unsqueeze(-1))\n else:\n output = model(image, sentences[:, :, j], l_mask=attentions[:, :, j])\n\n output = output.cpu()\n output_mask = output.argmax(1).data.numpy()\n I, U = computeIoU(output_mask, target)\n if U == 0:\n this_iou = 0.0\n else:\n this_iou = I*1.0/U\n mean_IoU.append(this_iou)\n cum_I += I\n cum_U += U\n for n_eval_iou in range(len(eval_seg_iou_list)):\n eval_seg_iou = eval_seg_iou_list[n_eval_iou]\n seg_correct[n_eval_iou] += (this_iou >= eval_seg_iou)\n seg_total += 1\n\n del image, target, sentences, attentions, output, output_mask\n if bert_model is not None:\n del last_hidden_states, embedding\n\n mean_IoU = np.array(mean_IoU)\n mIoU = np.mean(mean_IoU)\n print('Final results:')\n print('Mean IoU is %.2f\\n' % (mIoU*100.))\n results_str = ''\n for n_eval_iou in range(len(eval_seg_iou_list)):\n results_str += ' precision@%s = %.2f\\n' % \\\n (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)\n results_str += ' overall IoU = %.2f\\n' % (cum_I * 100. / cum_U)\n print(results_str)\n\n\ndef get_transform(args):\n transforms = [T.Resize(args.img_size, args.img_size),\n T.ToTensor(),\n T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ]\n\n return T.Compose(transforms)\n\n\ndef computeIoU(pred_seg, gd_seg):\n I = np.sum(np.logical_and(pred_seg, gd_seg))\n U = np.sum(np.logical_or(pred_seg, gd_seg))\n\n return I, U\n\n\ndef main(args):\n device = torch.device(args.device)\n dataset_test, _ = get_dataset(args.split, get_transform(args=args), args)\n test_sampler = torch.utils.data.SequentialSampler(dataset_test)\n data_loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=1,\n sampler=test_sampler, num_workers=args.workers)\n print(args.model)\n single_model = segmentation.__dict__[args.model](pretrained='',args=args)\n checkpoint = torch.load(args.resume, map_location='cpu')\n single_model.load_state_dict(checkpoint['model'])\n model = single_model.to(device)\n\n if args.model != 'lavt_one':\n model_class = BertModel\n single_bert_model = model_class.from_pretrained(args.ck_bert)\n # work-around for a transformers bug; need to update to a newer version of transformers to remove these two lines\n if args.ddp_trained_weights:\n single_bert_model.pooler = None\n single_bert_model.load_state_dict(checkpoint['bert_model'])\n bert_model = single_bert_model.to(device)\n else:\n bert_model = None\n\n evaluate(model, data_loader_test, bert_model, device=device)\n\n\nif __name__ == \"__main__\":\n from args import get_parser\n parser = get_parser()\n args = parser.parse_args()\n print('Image size: {}'.format(str(args.img_size)))\n main(args)\n"
},
{
"path": "train.py",
"content": "import datetime\nimport os\nimport time\n\nimport torch\nimport torch.utils.data\nfrom torch import nn\n\nfrom functools import reduce\nimport operator\nfrom bert.modeling_bert import BertModel\n\nimport torchvision\nfrom lib import segmentation\n\nimport transforms as T\nimport utils\nimport numpy as np\n\nimport torch.nn.functional as F\n\nimport gc\nfrom collections import OrderedDict\n\n\ndef get_dataset(image_set, transform, args):\n from data.dataset_refer_bert import ReferDataset\n ds = ReferDataset(args,\n split=image_set,\n image_transforms=transform,\n target_transforms=None\n )\n num_classes = 2\n\n return ds, num_classes\n\n\n# IoU calculation for validation\ndef IoU(pred, gt):\n pred = pred.argmax(1)\n\n intersection = torch.sum(torch.mul(pred, gt))\n union = torch.sum(torch.add(pred, gt)) - intersection\n\n if intersection == 0 or union == 0:\n iou = 0\n else:\n iou = float(intersection) / float(union)\n\n return iou, intersection, union\n\n\ndef get_transform(args):\n transforms = [T.Resize(args.img_size, args.img_size),\n T.ToTensor(),\n T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ]\n\n return T.Compose(transforms)\n\n\ndef criterion(input, target):\n weight = torch.FloatTensor([0.9, 1.1]).cuda()\n return nn.functional.cross_entropy(input, target, weight=weight)\n\n\ndef evaluate(model, data_loader, bert_model):\n model.eval()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n header = 'Test:'\n total_its = 0\n acc_ious = 0\n\n # evaluation variables\n cum_I, cum_U = 0, 0\n eval_seg_iou_list = [.5, .6, .7, .8, .9]\n seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)\n seg_total = 0\n mean_IoU = []\n\n with torch.no_grad():\n for data in metric_logger.log_every(data_loader, 100, header):\n total_its += 1\n image, target, sentences, attentions = data\n image, target, sentences, attentions = image.cuda(non_blocking=True),\\\n target.cuda(non_blocking=True),\\\n sentences.cuda(non_blocking=True),\\\n attentions.cuda(non_blocking=True)\n\n sentences = sentences.squeeze(1)\n attentions = attentions.squeeze(1)\n\n if bert_model is not None:\n last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]\n embedding = last_hidden_states.permute(0, 2, 1) # (B, 768, N_l) to make Conv1d happy\n attentions = attentions.unsqueeze(dim=-1) # (B, N_l, 1)\n output = model(image, embedding, l_mask=attentions)\n else:\n output = model(image, sentences, l_mask=attentions)\n\n iou, I, U = IoU(output, target)\n acc_ious += iou\n mean_IoU.append(iou)\n cum_I += I\n cum_U += U\n for n_eval_iou in range(len(eval_seg_iou_list)):\n eval_seg_iou = eval_seg_iou_list[n_eval_iou]\n seg_correct[n_eval_iou] += (iou >= eval_seg_iou)\n seg_total += 1\n iou = acc_ious / total_its\n\n mean_IoU = np.array(mean_IoU)\n mIoU = np.mean(mean_IoU)\n print('Final results:')\n print('Mean IoU is %.2f\\n' % (mIoU * 100.))\n results_str = ''\n for n_eval_iou in range(len(eval_seg_iou_list)):\n results_str += ' precision@%s = %.2f\\n' % \\\n (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)\n results_str += ' overall IoU = %.2f\\n' % (cum_I * 100. / cum_U)\n print(results_str)\n\n return 100 * iou, 100 * cum_I / cum_U\n\n\ndef train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq,\n iterations, bert_model):\n model.train()\n metric_logger = utils.MetricLogger(delimiter=\" \")\n metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))\n header = 'Epoch: [{}]'.format(epoch)\n train_loss = 0\n total_its = 0\n\n for data in metric_logger.log_every(data_loader, print_freq, header):\n total_its += 1\n image, target, sentences, attentions = data\n image, target, sentences, attentions = image.cuda(non_blocking=True),\\\n target.cuda(non_blocking=True),\\\n sentences.cuda(non_blocking=True),\\\n attentions.cuda(non_blocking=True)\n\n sentences = sentences.squeeze(1)\n attentions = attentions.squeeze(1)\n\n if bert_model is not None:\n last_hidden_states = bert_model(sentences, attention_mask=attentions)[0] # (6, 10, 768)\n embedding = last_hidden_states.permute(0, 2, 1) # (B, 768, N_l) to make Conv1d happy\n attentions = attentions.unsqueeze(dim=-1) # (batch, N_l, 1)\n output = model(image, embedding, l_mask=attentions)\n else:\n output = model(image, sentences, l_mask=attentions)\n\n loss = criterion(output, target)\n optimizer.zero_grad() # set_to_none=True is only available in pytorch 1.6+\n loss.backward()\n optimizer.step()\n lr_scheduler.step()\n\n torch.cuda.synchronize()\n train_loss += loss.item()\n iterations += 1\n metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0][\"lr\"])\n\n del image, target, sentences, attentions, loss, output, data\n if bert_model is not None:\n del last_hidden_states, embedding\n\n gc.collect()\n torch.cuda.empty_cache()\n torch.cuda.synchronize()\n\n\ndef main(args):\n dataset, num_classes = get_dataset(\"train\",\n get_transform(args=args),\n args=args)\n dataset_test, _ = get_dataset(\"val\",\n get_transform(args=args),\n args=args)\n\n # batch sampler\n print(f\"local rank {args.local_rank} / global rank {utils.get_rank()} successfully built train dataset.\")\n num_tasks = utils.get_world_size()\n global_rank = utils.get_rank()\n train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank,\n shuffle=True)\n test_sampler = torch.utils.data.SequentialSampler(dataset_test)\n\n # data loader\n data_loader = torch.utils.data.DataLoader(\n dataset, batch_size=args.batch_size,\n sampler=train_sampler, num_workers=args.workers, pin_memory=args.pin_mem, drop_last=True)\n\n data_loader_test = torch.utils.data.DataLoader(\n dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers)\n\n # model initialization\n print(args.model)\n model = segmentation.__dict__[args.model](pretrained=args.pretrained_swin_weights,\n args=args)\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)\n model.cuda()\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)\n single_model = model.module\n\n if args.model != 'lavt_one':\n model_class = BertModel\n bert_model = model_class.from_pretrained(args.ck_bert)\n bert_model.pooler = None # a work-around for a bug in Transformers = 3.0.2 that appears for DistributedDataParallel\n bert_model.cuda()\n bert_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(bert_model)\n bert_model = torch.nn.parallel.DistributedDataParallel(bert_model, device_ids=[args.local_rank])\n single_bert_model = bert_model.module\n else:\n bert_model = None\n single_bert_model = None\n\n # resume training\n if args.resume:\n checkpoint = torch.load(args.resume, map_location='cpu')\n single_model.load_state_dict(checkpoint['model'])\n if args.model != 'lavt_one':\n single_bert_model.load_state_dict(checkpoint['bert_model'])\n\n # parameters to optimize\n backbone_no_decay = list()\n backbone_decay = list()\n for name, m in single_model.backbone.named_parameters():\n if 'norm' in name or 'absolute_pos_embed' in name or 'relative_position_bias_table' in name:\n backbone_no_decay.append(m)\n else:\n backbone_decay.append(m)\n\n if args.model != 'lavt_one':\n params_to_optimize = [\n {'params': backbone_no_decay, 'weight_decay': 0.0},\n {'params': backbone_decay},\n {\"params\": [p for p in single_model.classifier.parameters() if p.requires_grad]},\n # the following are the parameters of bert\n {\"params\": reduce(operator.concat,\n [[p for p in single_bert_model.encoder.layer[i].parameters()\n if p.requires_grad] for i in range(10)])},\n ]\n else:\n params_to_optimize = [\n {'params': backbone_no_decay, 'weight_decay': 0.0},\n {'params': backbone_decay},\n {\"params\": [p for p in single_model.classifier.parameters() if p.requires_grad]},\n # the following are the parameters of bert\n {\"params\": reduce(operator.concat,\n [[p for p in single_model.text_encoder.encoder.layer[i].parameters()\n if p.requires_grad] for i in range(10)])},\n ]\n\n # optimizer\n optimizer = torch.optim.AdamW(params_to_optimize,\n lr=args.lr,\n weight_decay=args.weight_decay,\n amsgrad=args.amsgrad\n )\n\n # learning rate scheduler\n lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,\n lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)\n\n # housekeeping\n start_time = time.time()\n iterations = 0\n best_oIoU = -0.1\n\n # resume training (optimizer, lr scheduler, and the epoch)\n if args.resume:\n optimizer.load_state_dict(checkpoint['optimizer'])\n lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n resume_epoch = checkpoint['epoch']\n else:\n resume_epoch = -999\n\n # training loops\n for epoch in range(max(0, resume_epoch+1), args.epochs):\n data_loader.sampler.set_epoch(epoch)\n train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq,\n iterations, bert_model)\n iou, overallIoU = evaluate(model, data_loader_test, bert_model)\n\n print('Average object IoU {}'.format(iou))\n print('Overall IoU {}'.format(overallIoU))\n save_checkpoint = (best_oIoU < overallIoU)\n if save_checkpoint:\n print('Better epoch: {}\\n'.format(epoch))\n if single_bert_model is not None:\n dict_to_save = {'model': single_model.state_dict(), 'bert_model': single_bert_model.state_dict(),\n 'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,\n 'lr_scheduler': lr_scheduler.state_dict()}\n else:\n dict_to_save = {'model': single_model.state_dict(),\n 'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,\n 'lr_scheduler': lr_scheduler.state_dict()}\n\n utils.save_on_master(dict_to_save, os.path.join(args.output_dir,\n 'model_best_{}.pth'.format(args.model_id)))\n best_oIoU = overallIoU\n\n # summarize\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('Training time {}'.format(total_time_str))\n\n\nif __name__ == \"__main__\":\n from args import get_parser\n parser = get_parser()\n args = parser.parse_args()\n # set up distributed learning\n utils.init_distributed_mode(args)\n print('Image size: {}'.format(str(args.img_size)))\n main(args)\n"
},
{
"path": "transforms.py",
"content": "import numpy as np\nfrom PIL import Image\nimport random\n\nimport torch\nfrom torchvision import transforms as T\nfrom torchvision.transforms import functional as F\n\n\ndef pad_if_smaller(img, size, fill=0):\n min_size = min(img.size)\n if min_size < size:\n ow, oh = img.size\n padh = size - oh if oh < size else 0\n padw = size - ow if ow < size else 0\n img = F.pad(img, (0, 0, padw, padh), fill=fill)\n return img\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, image, target):\n for t in self.transforms:\n image, target = t(image, target)\n return image, target\n\n\nclass Resize(object):\n def __init__(self, h, w):\n self.h = h\n self.w = w\n\n def __call__(self, image, target):\n image = F.resize(image, (self.h, self.w))\n # If size is a sequence like (h, w), the output size will be matched to this.\n # If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio\n target = F.resize(target, (self.h, self.w), interpolation=Image.NEAREST)\n return image, target\n\n\nclass RandomResize(object):\n def __init__(self, min_size, max_size=None):\n self.min_size = min_size\n if max_size is None:\n max_size = min_size\n self.max_size = max_size\n\n def __call__(self, image, target):\n size = random.randint(self.min_size, self.max_size) # Return a random integer N such that a <= N <= b. Alias for randrange(a, b+1)\n image = F.resize(image, size)\n # If size is a sequence like (h, w), the output size will be matched to this.\n # If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio\n target = F.resize(target, size, interpolation=Image.NEAREST)\n return image, target\n\n\nclass RandomHorizontalFlip(object):\n def __init__(self, flip_prob):\n self.flip_prob = flip_prob\n\n def __call__(self, image, target):\n if random.random() < self.flip_prob:\n image = F.hflip(image)\n target = F.hflip(target)\n return image, target\n\n\nclass RandomCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, image, target):\n image = pad_if_smaller(image, self.size)\n target = pad_if_smaller(target, self.size, fill=255)\n crop_params = T.RandomCrop.get_params(image, (self.size, self.size))\n image = F.crop(image, *crop_params)\n target = F.crop(target, *crop_params)\n return image, target\n\n\nclass CenterCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, image, target):\n image = F.center_crop(image, self.size)\n target = F.center_crop(target, self.size)\n return image, target\n\n\nclass ToTensor(object):\n def __call__(self, image, target):\n image = F.to_tensor(image)\n target = torch.as_tensor(np.asarray(target).copy(), dtype=torch.int64)\n return image, target\n\n\nclass RandomAffine(object):\n def __init__(self, angle, translate, scale, shear, resample=0, fillcolor=None):\n self.angle = angle\n self.translate = translate\n self.scale = scale\n self.shear = shear\n self.resample = resample\n self.fillcolor = fillcolor\n\n def __call__(self, image, target):\n affine_params = T.RandomAffine.get_params(self.angle, self.translate, self.scale, self.shear, image.size)\n image = F.affine(image, *affine_params)\n target = F.affine(target, *affine_params)\n return image, target\n\n\nclass Normalize(object):\n def __init__(self, mean, std):\n self.mean = mean\n self.std = std\n\n def __call__(self, image, target):\n image = F.normalize(image, mean=self.mean, std=self.std)\n return image, target\n\n"
},
{
"path": "utils.py",
"content": "from __future__ import print_function\nfrom collections import defaultdict, deque\nimport datetime\nimport math\nimport time\nimport torch\nimport torch.distributed as dist\nimport torch.backends.cudnn as cudnn\n\nimport errno\nimport os\n\nimport sys\n\n\nclass SmoothedValue(object):\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n\n def __init__(self, window_size=20, fmt=None):\n if fmt is None:\n fmt = \"{median:.4f} ({global_avg:.4f})\"\n self.deque = deque(maxlen=window_size)\n self.total = 0.0\n self.count = 0\n self.fmt = fmt\n\n def update(self, value, n=1):\n self.deque.append(value)\n self.count += n\n self.total += value * n\n\n def synchronize_between_processes(self):\n \"\"\"\n Warning: does not synchronize the deque!\n \"\"\"\n if not is_dist_avail_and_initialized():\n return\n t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')\n dist.barrier()\n dist.all_reduce(t)\n t = t.tolist()\n self.count = int(t[0])\n self.total = t[1]\n\n @property\n def median(self):\n d = torch.tensor(list(self.deque))\n return d.median().item()\n\n @property\n def avg(self):\n d = torch.tensor(list(self.deque), dtype=torch.float32)\n return d.mean().item()\n\n @property\n def global_avg(self):\n return self.total / self.count\n\n @property\n def max(self):\n return max(self.deque)\n\n @property\n def value(self):\n return self.deque[-1]\n\n def __str__(self):\n return self.fmt.format(\n median=self.median,\n avg=self.avg,\n global_avg=self.global_avg,\n max=self.max,\n value=self.value)\n\n\nclass MetricLogger(object):\n def __init__(self, delimiter=\"\\t\"):\n self.meters = defaultdict(SmoothedValue)\n self.delimiter = delimiter\n\n def update(self, **kwargs):\n for k, v in kwargs.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n assert isinstance(v, (float, int))\n self.meters[k].update(v)\n\n def __getattr__(self, attr):\n if attr in self.meters:\n return self.meters[attr]\n if attr in self.__dict__:\n return self.__dict__[attr]\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n type(self).__name__, attr))\n\n def __str__(self):\n loss_str = []\n for name, meter in self.meters.items():\n loss_str.append(\n \"{}: {}\".format(name, str(meter))\n )\n return self.delimiter.join(loss_str)\n\n def synchronize_between_processes(self):\n for meter in self.meters.values():\n meter.synchronize_between_processes()\n\n def add_meter(self, name, meter):\n self.meters[name] = meter\n\n def log_every(self, iterable, print_freq, header=None):\n i = 0\n if not header:\n header = ''\n start_time = time.time()\n end = time.time()\n iter_time = SmoothedValue(fmt='{avg:.4f}')\n data_time = SmoothedValue(fmt='{avg:.4f}')\n space_fmt = ':' + str(len(str(len(iterable)))) + 'd'\n log_msg = self.delimiter.join([\n header,\n '[{0' + space_fmt + '}/{1}]',\n 'eta: {eta}',\n '{meters}',\n 'time: {time}',\n 'data: {data}',\n 'max mem: {memory:.0f}'\n ])\n MB = 1024.0 * 1024.0\n for obj in iterable:\n data_time.update(time.time() - end)\n yield obj\n iter_time.update(time.time() - end)\n if i % print_freq == 0:\n eta_seconds = iter_time.global_avg * (len(iterable) - i)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n print(log_msg.format(\n i, len(iterable), eta=eta_string,\n meters=str(self),\n time=str(iter_time), data=str(data_time),\n memory=torch.cuda.max_memory_allocated() / MB))\n sys.stdout.flush()\n\n i += 1\n end = time.time()\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print('{} Total time: {}'.format(header, total_time_str))\n\n\ndef mkdir(path):\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\ndef setup_for_distributed(is_master):\n \"\"\"\n This function disables printing when not in master process\n \"\"\"\n import builtins as __builtin__\n builtin_print = __builtin__.print\n\n def print(*args, **kwargs):\n force = kwargs.pop('force', False)\n if is_master or force:\n builtin_print(*args, **kwargs)\n\n __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n\n\ndef get_world_size():\n if not is_dist_avail_and_initialized():\n return 1\n return dist.get_world_size()\n\n\ndef get_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return dist.get_rank()\n\n\ndef is_main_process():\n return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n if is_main_process():\n torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:\n rank = int(os.environ[\"RANK\"])\n world_size = int(os.environ['WORLD_SIZE'])\n print(f\"RANK and WORLD_SIZE in environment: {rank}/{world_size}\")\n else:\n rank = -1\n world_size = -1\n\n torch.cuda.set_device(args.local_rank)\n torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)\n torch.distributed.barrier()\n setup_for_distributed(is_main_process())\n\n if args.output_dir:\n mkdir(args.output_dir)\n if args.model_id:\n mkdir(os.path.join('./models/', args.model_id))\n"
}
]